Datasets:

adalib

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langchain

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import io import json import time from queue import Queue from typing import Dict, List import numpy as np import tiktoken from anyio.from_thread import start_blocking_portal from django.conf import settings from langchain.schema import AIMessage, HumanMessage from openai import OpenAI from pinecone import QueryResponse from ayushma.models import ChatMessage from ayushma.models.chat import Chat from ayushma.models.document import Document from ayushma.models.enums import ChatMessageType, ModelType from ayushma.utils.langchain import LangChainHelper from ayushma.utils.language_helpers import text_to_speech, translate_text from core.settings.base import AI_NAME # https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py#L65 def cosine_similarity(a, b): a = np.array(a).reshape(-1) b = np.array(b).reshape(-1) return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) def get_embedding( text: List[str], model: str = "text-embedding-ada-002", openai_api_key: str = settings.OPENAI_API_KEY, ) -> List[List[float]]: """ Generates embeddings for the given list of texts using the OpenAI API. Args: text (List[str]): A list of strings to be embedded. model (str, optional): The name of the OpenAI model to use for embedding. Defaults to "text-embedding-ada-002". Returns: A list of embeddings generated by the OpenAI API for the input texts. Raises: OpenAIError: If there was an error communicating with the OpenAI API. Example usage: >>> get_embedding(["Hello, world!", "How are you?"]) [[-0.123, 0.456, 0.789, ...], [0.123, -0.456, 0.789, ...]] """ client = OpenAI(api_key=openai_api_key) embedding_args: Dict[str, str | List[str]] = {"input": text} if settings.OPENAI_API_TYPE == "azure": embedding_args["engine"] = settings.AZURE_EMBEDDING_DEPLOYMENT else: embedding_args["model"] = model res = client.embeddings.create(**embedding_args) return [record.embedding for record in res.data] def get_sanitized_reference(pinecone_references: List[QueryResponse]) -> str: """ Extracts the text from the Pinecone QueryResponse object and sanitizes it. Args: pinecone_reference (List[QueryResponse]): The similar documents retrieved from the Pinecone index. Returns: A string containing the document id and text from the Pinecone QueryResponse object. Example usage: >>> get_sanitized_reference([QueryResponse(...), QueryResponse(...)]) "{'28': 'Hello how are you, I am fine, thank you.', '21': 'How was your day?, Mine was good.'}" """ sanitized_reference = {} for reference in pinecone_references: for match in reference.matches: try: document_id = str(match.metadata["document"]) text = str(match.metadata["text"]).replace("\n", " ") + "," if document_id in sanitized_reference: sanitized_reference[document_id] += text else: sanitized_reference[document_id] = text except Exception as e: print(e) pass return json.dumps(sanitized_reference) def num_tokens_from_string(string: str, encoding_name: str) -> int: """Returns the number of tokens in a text string.""" encoding = tiktoken.get_encoding(encoding_name) num_tokens = len(encoding.encode(string)) return num_tokens def split_text(text): """Returns one string split into n equal length strings""" n = len(text) number_of_chars = 8192 parts = [] for i in range(0, n, number_of_chars): part = text[i : i + number_of_chars] parts.append(part) return parts def create_json_response( input_text, chat_id, delta, message, stop, error, ayushma_voice ): json_data = { "chat": str(chat_id), "input": input_text, "delta": delta, "message": message, "stop": stop, "error": error, "ayushma_voice": ayushma_voice, } return "data: " + json.dumps(json_data) + "\n\n" def get_reference(text, openai_key, namespace, top_k): num_tokens = num_tokens_from_string(text, "cl100k_base") embeddings: List[List[List[float]]] = [] if num_tokens < 8192: try: embeddings.append(get_embedding(text=[text], openai_api_key=openai_key)) except Exception as e: return Exception( e.__str__(), ) else: parts = split_text(text) for part in parts: try: embeddings.append(get_embedding(text=[part], openai_api_key=openai_key)) except Exception as e: raise Exception( e.__str__(), ) # find similar embeddings from pinecone index for each embedding pinecone_references: List[QueryResponse] = [] for embedding in embeddings: similar: QueryResponse = settings.PINECONE_INDEX_INSTANCE.query( vector=embedding, top_k=int(top_k), namespace=namespace, include_metadata=True, ) pinecone_references.append(similar) return get_sanitized_reference(pinecone_references=pinecone_references) def add_reference_documents(chat_message): ref_text = "References:" chat_text = str(chat_message.original_message) ref_start_idx = chat_text.find(ref_text) if ref_start_idx == -1: return try: doc_ids = chat_text[ref_start_idx + len(ref_text) :].split(",") doc_ids = [doc_id.strip(" .,[]*'\"“”“\n-") for doc_id in doc_ids] doc_ids = set([str(doc_id) for doc_id in doc_ids if doc_id != ""]) for doc_id in doc_ids: try: doc = Document.objects.get(external_id=doc_id) chat_message.reference_documents.add(doc) except Document.DoesNotExist: pass except Exception as e: print("Error adding reference documents: ", e) chat_message.original_message = chat_text[ :ref_start_idx ].strip() # Strip to remove empty line at the end \nRefereces: chat_message.save() def handle_post_response( chat_response, chat, match_number, user_language, temperature, stats, language, tts_engine, generate_audio=True, ): chat_message: ChatMessage = ChatMessage.objects.create( original_message=chat_response, chat=chat, messageType=ChatMessageType.AYUSHMA, top_k=match_number, temperature=temperature, language=language, ) add_reference_documents(chat_message) translated_chat_response = chat_message.original_message if user_language != "en-IN": stats["response_translation_start_time"] = time.time() translated_chat_response = translate_text( user_language, chat_message.original_message ) stats["response_translation_end_time"] = time.time() ayushma_voice = None if generate_audio: stats["tts_start_time"] = time.time() ayushma_voice = text_to_speech( translated_chat_response, user_language, tts_engine ) stats["tts_end_time"] = time.time() url = None if ayushma_voice: stats["upload_start_time"] = time.time() chat_message.audio.save( f"{chat_message.external_id}.mp3", io.BytesIO(ayushma_voice) ) stats["upload_end_time"] = time.time() chat_message.message = translated_chat_response chat_message.meta = { "translate_start": stats.get("response_translation_start_time"), "translate_end": stats.get("response_translation_end_time"), "reference_start": stats.get("reference_start_time"), "reference_end": stats.get("reference_end_time"), "response_start": stats.get("response_start_time"), "response_end": stats.get("response_end_time"), "tts_start": stats.get("tts_start_time"), "tts_end": stats.get("tts_end_time"), "upload_start": stats.get("upload_start_time"), "upload_end": stats.get("upload_end_time"), } chat_message.save() return translated_chat_response, url, chat_message def converse( english_text, local_translated_text, openai_key, chat, match_number, user_language, temperature, stats={}, stream=True, references=None, generate_audio=True, noonce=None, fetch_references=True, documents=None, ): if not openai_key: raise Exception("OpenAI-Key header is required to create a chat or converse") english_text = english_text.replace("\n", " ") language = user_language.split("-")[0] nurse_query = ChatMessage.objects.create( message=local_translated_text, original_message=english_text, chat=chat, messageType=ChatMessageType.USER, language=language, meta={ "translate_start": stats.get("request_translation_start_time"), "translate_end": stats.get("request_translation_end_time"), }, noonce=noonce, ) stats["reference_start_time"] = time.time() if references: reference = references elif fetch_references and chat.project and chat.project.external_id: try: reference = get_reference( english_text, openai_key, str(chat.project.external_id), match_number ) except Exception as e: print(e) reference = "" else: reference = "" stats["reference_end_time"] = time.time() stats["response_start_time"] = time.time() prompt = chat.prompt or (chat.project and chat.project.prompt) if documents or chat.project.model == ModelType.GPT_4_VISUAL: prompt = "Image Capabilities: Enabled\n" + prompt # excluding the latest query since it is not a history previous_messages = ( ChatMessage.objects.filter(chat=chat) .exclude(id=nurse_query.id) .order_by("created_at") ) chat_history = [] for message in previous_messages: if message.messageType == ChatMessageType.USER: chat_history.append(HumanMessage(content=f"{message.message}")) elif message.messageType == ChatMessageType.AYUSHMA: chat_history.append(AIMessage(content=f"Ayushma: {message.message}")) tts_engine = chat.project.tts_engine if not stream: lang_chain_helper = LangChainHelper( stream=False, openai_api_key=openai_key, prompt_template=prompt, model=chat.model or (chat.project and chat.project.model) or ModelType.GPT_3_5, temperature=temperature, ) response = lang_chain_helper.get_response( english_text, reference, chat_history, documents ) chat_response = response.replace("Ayushma: ", "") stats["response_end_time"] = time.time() translated_chat_response, url, chat_message = handle_post_response( chat_response, chat, match_number, user_language, temperature, stats, language, tts_engine, generate_audio, ) yield chat_message else: token_queue = Queue() RESPONSE_END = object() RESPONSE_ERROR = object() try: lang_chain_helper = LangChainHelper( stream=stream, token_queue=token_queue, end=RESPONSE_END, error=RESPONSE_ERROR, openai_api_key=openai_key, prompt_template=prompt, model=chat.model or (chat.project and chat.project.model) or ModelType.GPT_3_5, temperature=temperature, ) with start_blocking_portal() as portal: portal.start_task_soon( lang_chain_helper.get_aresponse, RESPONSE_END, RESPONSE_ERROR, token_queue, english_text, reference, chat_history, documents, ) chat_response = "" skip_token = len(f"{AI_NAME}: ") while True: if token_queue.empty(): continue next_token = token_queue.get(True, timeout=10) if next_token[0] == RESPONSE_ERROR: raise next_token[1] if next_token[0] is RESPONSE_END: stats["response_end_time"] = time.time() ( translated_chat_response, url, chat_message, ) = handle_post_response( chat_response, chat, match_number, user_language, temperature, stats, language, tts_engine, generate_audio, ) yield create_json_response( local_translated_text, chat.external_id, "", translated_chat_response, True, False, ayushma_voice=url, ) break if skip_token > 0: skip_token -= 1 continue chat_response += next_token[0] yield create_json_response( local_translated_text, chat.external_id, next_token[0], chat_response, False, False, None, ) except Exception as e: print(e) error_text = str(e) translated_error_text = error_text if user_language != "en-IN": translated_error_text = translate_text(user_language, error_text) ChatMessage.objects.create( message=translated_error_text, original_message=error_text, chat=chat, messageType=ChatMessageType.SYSTEM, language=language, meta={ "translate_start": stats.get("response_translation_start_time"), "translate_end": stats.get("response_translation_end_time"), "reference_start": stats.get("reference_start_time"), "reference_end": stats.get("reference_end_time"), "response_start": stats.get("response_start_time"), "response_end": stats.get("response_end_time"), "tts_start": stats.get("tts_start_time"), "tts_end": stats.get("tts_end_time"), "upload_start": stats.get("upload_start_time"), "upload_end": stats.get("upload_end_time"), }, ) yield create_json_response( local_translated_text, chat.external_id, "", str(e), True, True, None ) def converse_thread( english_text, thread: Chat, openai_key, ): client = OpenAI(api_key=openai_key) if not thread.thread_id: thread_id = client.beta.threads.create().id thread.thread_id = thread_id thread.save() client.beta.threads.messages.create( thread_id=thread.thread_id, content=english_text, role="user" ) run_id = client.beta.threads.runs.create( thread_id=thread.thread_id, assistant_id=thread.project.assistant_id ).id retires = 60 while retires > 0: retires -= 1 time.sleep(1) status = client.beta.threads.runs.retrieve( run_id=run_id, thread_id=thread.thread_id ).status if status == "completed": break else: raise Exception("Thread timed out") response = ( client.beta.threads.messages.list(thread_id=thread.thread_id) .data[0] .content[0] .text.value ) return response

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import json import os import langchain.memory.entity from langchain.chat_models import AzureChatOpenAI from flask import Flask, request import httpx from dotenv import load_dotenv from langchain.memory import ConversationSummaryBufferMemory, ConversationBufferWindowMemory from langchain.prompts.prompt import PromptTemplate from langchain import LLMChain from tools import searchTravelPol import myjson from langchain.prompts import ( ChatPromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ) app = Flask(__name__) load_dotenv(dotenv_path='./config.env') # 初始化AzureOpenAI和其他相关组件 llm = AzureChatOpenAI( api_version="2023-12-01-preview", azure_endpoint=os.getenv("AZURE_ENDPOINT"), api_key=os.getenv("AZURE_OPENAI_KEY"), azure_deployment='test', http_client=httpx.Client( proxies="http://localhost:7890", transport=httpx.HTTPTransport(local_address="0.0.0.0"), verify=False ) ) def getGptResponse(data): """ 获取GPT生成的旅行建议 :param data: 包含旅行信息的字典 :return: GPT生成的旅行建议 """ # 系统指令 systemCommands = """"作为旅行规划助手，''你的任务是接收关于旅客类型、预算水平、旅行日期和兴趣的信息，并按json格式回答。并按时间顺序规划景点(" "不需要规划高铁站飞机站等地点）。请一定注意点之间不要离得太远''请你规划的时候查询该地区在网上热门的景点," "并选取一个作为出发点。尽量避免音译错误同时你要关注的是整体景点而非其内部的具体地点,例如：一定不要推荐山东大学(" "威海校区)-孔子像这种内部景点，而是要推荐山东大学(威海校区）这种整体景点。最关键的更新是，你现在只需以特定的JSON格式回复规划好的行程，而不提供规划过程的详细描述。你的回复应严格遵循以下json格式：'\n\n" "{{\"YYYY-MM-DD\":[\n{{\"name\": \"景点A\",\"兴趣点类型\":\"景点\"}},\n{{\"name\": \"景点B\",\"兴趣点类型\":\"景点\"}},\n " "{{\"name\": \"景点C\",\"兴趣点类型\":\"景点\"}}],\n " "\"YYYY-MM-DD\":[\n {{\"name\": \"景点A\",\"兴趣点类型\":\"景点\"}},\n {{\"name\": \"景点B\",\"兴趣点类型\":\"景点\"}},\n" "{{\"name\": \"景点C\",\"兴趣点类型\":\"景点\"}}],\n " "\"// 更多日期 \"]}}""" # 从输入数据中获取所需信息 selectedTraveler = data.get('destination') startDate = data.get('startDate') endDate = data.get('endDate') travelDestination = data.get('customerType') selectedInterests = ', '.join(data.get('tag', [])) selectedBudget = data.get('budget') # 构建人类输入字符串 humanInput = f"旅客类型:{selectedTraveler}, 旅行时间:{startDate} - {endDate}, 旅行城市:{travelDestination}, 旅客兴趣:{selectedInterests}, 预算水平:{selectedBudget}" memory = ConversationBufferWindowMemory(llm=llm, k=2, memory_key="chat_history",return_messages=True) # 设置GPT生成的prompt模板 prompt = ChatPromptTemplate( messages=[ SystemMessagePromptTemplate.from_template(systemCommands), # The `variable_name` here is what must align with memory MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{humanInput}") ] ) # 初始化LLMChain llmChain = LLMChain( llm=llm, prompt=prompt, verbose=True, memory=memory ) # llmChain.predict(humanInput=humanInput) #为节省api费用使用的测试数据 memory.chat_memory.add_user_message( f"旅客类型:{selectedTraveler}, 旅行时间:{startDate}--{endDate}, " f"旅行城市:{travelDestination}, 旅客兴趣:{selectedInterests}, 预算水平:{selectedBudget}" ) memory.chat_memory.add_ai_message(''' { "2024-01-21": [ {"name": "泸州长江大桥", "兴趣点类型": "景点"}, {"name": "四川省泸州市泸州老窖集团", "兴趣点类型": "景点"}, {"name": "蜀南竹海", "兴趣点类型": "景点"} ], "2024-01-22": [ {"name": "泸州白塔公园", "兴趣点类型": "景点"}, {"name": "江阳区龙马潭", "兴趣点类型": "景点"}, {"name": "泸州古蔺沱江戏水公园", "兴趣点类型": "景点"} ], "2024-01-23": [ {"name": "泸州国窖酒文化城", "兴趣点类型": "景点"}, {"name": "泸州雒城历史文化街区", "兴趣点类型": "景点"}, {"name": "泸州市博物馆", "兴趣点类型": "景点"} ] } ''') print(memory.chat_memory.messages[-1].content) return memory.chat_memory.messages[-1].content @app.route('/generatePlanner', methods=['POST']) def generatePlanner(): """ 处理POST请求，生成旅行计划。 :return: 包含生成的旅行计划的JSON响应 """ # 从请求中获取JSON数据 data = request.get_json() # 调用getGptResponse函数获取GPT生成的旅行建议 gptResponse = getGptResponse(data) # 获取旅行城市信息 city = data.get('travelPurpose') # 调用getMatpData函数获取旅行兴趣点的详细信息 result = getMatpData(gptResponse, city) if "error" in result: return myjson.makeReponse(code="500", message="失败", data=result) else: return myjson.makeReponse(code="200", message="成功", data=result) def getMatpData(data, city): """ 使用高德api获取旅行兴趣点的详细信息，并组织成新的数据结构 :param data: 包含旅行兴趣点信息的JSON字符串 :param city: 旅行的城市 :return: 包含详细信息的新数据结构 """ # 将JSON字符串解析为Python字典 try: # 尝试解析 JSON 字符串 data = json.loads(data) except json.decoder.JSONDecodeError as e: error_message = f"Error decoding JSON: {e}" print(error_message) return {"error": error_message} # 存储新的响应结构 new_response = {} # 遍历日期和兴趣点信息 for date, places in data.items(): # 初始化每个日期的新结构 new_response[date] = [] # 遍历每个兴趣点信息 for place in places: # 提取兴趣点的名称和类型 name = place['name'] posType = place['兴趣点类型'] # 调用高德api获取兴趣点的详细信息 detailedInfo = searchTravelPol(name, city, posType) # 解析详细信息的JSON字符串 detailedInfo = json.loads(detailedInfo)['pois'][0] # 需要过滤的字段 fieldsToRemove = ['parent', 'distance', 'id'] # 过滤详细信息中的特定字段 for field in fieldsToRemove: detailedInfo.pop(field, None) # 将详细信息添加到新结构中 new_response[date].append(detailedInfo) return new_response @app.route('/chat', methods=['POST']) def chat(): # 提示词模板 template = """"你是一个聊天助手小明" {chat_history} Human: {human_input} Chatbot:""" memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=300, memory_key="chat_history") # 从请求中获取JSON数据 data = request.get_data() data = json.loads(data) data = data['ask'] print(data) humanInput = data # 设置GPT生成的prompt模板 prompt = PromptTemplate( input_variables=["chat_history", "human_input"], template=template ) llmChain = LLMChain( llm=llm, prompt=prompt, verbose=True, memory=memory ) llmChain.predict(human_input=humanInput) if "error" in data: return myjson.makeReponse(code="500", message="失败", data=memory.chat_memory.messages[-1].content) else: return myjson.makeReponse(code="200", message="成功", data=memory.chat_memory.messages[-1].content) # @app.route('/generateFreeModeTravelItinerary', methods=['POST']) # def generateFreeModeTravelItinerary(): # langchain.memory.entity.RedisEntityStore() if __name__ == '__main__': app.run(debug=True, host='172.29.20.72')

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"""Utilities for running language models or Chains over datasets.""" from __future__ import annotations import asyncio import functools import itertools import logging import uuid from enum import Enum from typing import ( Any, Callable, Coroutine, Dict, Iterator, List, Optional, Sequence, Tuple, Union, ) from urllib.parse import urlparse, urlunparse from langsmith import Client, RunEvaluator from langsmith.schemas import Dataset, DataType, Example from langchain.callbacks.base import BaseCallbackHandler from langchain.callbacks.manager import Callbacks from langchain.callbacks.tracers.base import BaseTracer from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler from langchain.callbacks.tracers.langchain import LangChainTracer from langchain.chains.base import Chain from langchain.chat_models.openai import ChatOpenAI from langchain.evaluation.loading import load_evaluator from langchain.evaluation.schema import EvaluatorType, StringEvaluator from langchain.schema import ChatResult, LLMResult from langchain.schema.language_model import BaseLanguageModel from langchain.schema.messages import BaseMessage, messages_from_dict from langchain.smith.evaluation.config import EvalConfig, RunEvalConfig from langchain.smith.evaluation.string_run_evaluator import StringRunEvaluatorChain logger = logging.getLogger(__name__) MODEL_OR_CHAIN_FACTORY = Union[Callable[[], Chain], BaseLanguageModel] class InputFormatError(Exception): """Raised when the input format is invalid.""" ## Shared Utilities def _get_eval_project_url(api_url: str, project_id: str) -> str: """Get the project url from the api url.""" parsed = urlparse(api_url) hostname = parsed.hostname or "" if "api." in hostname: hostname = hostname.replace("api.", "", 1) if "localhost" in hostname: # Remove the port hostname = "localhost" url = urlunparse(parsed._replace(netloc=hostname)) return f"{url}/projects/p/{project_id}?eval=true" def _wrap_in_chain_factory( llm_or_chain_factory: Union[Chain, MODEL_OR_CHAIN_FACTORY], dataset_name: str = "<my_dataset>", ) -> MODEL_OR_CHAIN_FACTORY: """Forgive the user if they pass in a chain without memory instead of a chain factory. It's a common mistake. Raise a more helpful error message as well.""" if isinstance(llm_or_chain_factory, Chain): chain = llm_or_chain_factory chain_class = chain.__class__.__name__ if llm_or_chain_factory.memory is not None: memory_class = chain.memory.__class__.__name__ raise ValueError( "Cannot directly evaluate a chain with stateful memory." " To evaluate this chain, pass in a chain constructor" " that initializes fresh memory each time it is called." " This will safegaurd against information" " leakage between dataset examples." "\nFor example:\n\n" "def chain_constructor():\n" f" new_memory = {memory_class}(...)\n" f" return {chain_class}" "(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) logger.warning( "Directly passing in a chain is not recommended as chains may have state." " This can lead to unexpected behavior as the " "same chain instance could be used across multiple datasets. Instead," " please pass a chain constructor that creates a new " "chain with fresh memory each time it is called. This will safeguard" " against information leakage between dataset examples. " "\nFor example:\n\n" "def chain_constructor():\n" f" return {chain_class}(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) return lambda: chain elif isinstance(llm_or_chain_factory, BaseLanguageModel): return llm_or_chain_factory elif callable(llm_or_chain_factory): _model = llm_or_chain_factory() if isinstance(_model, BaseLanguageModel): return _model return llm_or_chain_factory return llm_or_chain_factory def _first_example(examples: Iterator[Example]) -> Tuple[Example, Iterator[Example]]: """Get the first example while chaining it back and preserving the iterator.""" try: example: Example = next(examples) except StopIteration: raise ValueError("No examples provided.") return example, itertools.chain([example], examples) def _get_prompt(inputs: Dict[str, Any]) -> str: """Get prompt from inputs. Args: inputs: The input dictionary. Returns: A string prompt. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") prompts = [] if "prompt" in inputs: if not isinstance(inputs["prompt"], str): raise InputFormatError( "Expected string for 'prompt', got" f" {type(inputs['prompt']).__name__}" ) prompts = [inputs["prompt"]] elif "prompts" in inputs: if not isinstance(inputs["prompts"], list) or not all( isinstance(i, str) for i in inputs["prompts"] ): raise InputFormatError( "Expected list of strings for 'prompts'," f" got {type(inputs['prompts']).__name__}" ) prompts = inputs["prompts"] elif len(inputs) == 1: prompt_ = next(iter(inputs.values())) if isinstance(prompt_, str): prompts = [prompt_] elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_): prompts = prompt_ else: raise InputFormatError(f"LLM Run expects string prompt input. Got {inputs}") else: raise InputFormatError( f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}" ) if len(prompts) == 1: return prompts[0] else: raise InputFormatError( f"LLM Run expects single prompt input. Got {len(prompts)} prompts." ) def _get_messages(inputs: Dict[str, Any]) -> List[BaseMessage]: """Get Chat Messages from inputs. Args: inputs: The input dictionary. Returns: A list of chat messages. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") if "messages" in inputs: single_input = inputs["messages"] elif len(inputs) == 1: single_input = next(iter(inputs.values())) else: raise InputFormatError( f"Chat Run expects 'messages' in inputs when example has multiple" f" input keys. Got {inputs}" ) if isinstance(single_input, list) and all( isinstance(i, dict) for i in single_input ): raw_messages = [single_input] elif isinstance(single_input, list) and all( isinstance(i, list) for i in single_input ): raw_messages = single_input else: raise InputFormatError( f"Chat Run expects List[dict] or List[List[dict]] values for" f" 'messages' key input. Got {inputs}" ) if len(raw_messages) == 1: return messages_from_dict(raw_messages[0]) else: raise InputFormatError( f"Chat Run expects single List[dict] or List[List[dict]] 'messages'" f" input. Got {len(raw_messages)} messages from inputs {inputs}" ) def _get_project_name( project_name: Optional[str], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, ) -> str: """ Get the project name. Args: project_name: The project name if manually specified. llm_or_chain_factory: The Chain or language model constructor. Returns: The project name. """ if project_name is not None: return project_name if isinstance(llm_or_chain_factory, BaseLanguageModel): model_name = llm_or_chain_factory.__class__.__name__ else: model_name = llm_or_chain_factory().__class__.__name__ hex = uuid.uuid4().hex return f"{hex}-{model_name}" ## Shared Validation Utilities def _validate_example_inputs_for_language_model( first_example: Example, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: if input_mapper: prompt_input = input_mapper(first_example.inputs) if not isinstance(prompt_input, str) and not ( isinstance(prompt_input, list) and all(isinstance(msg, BaseMessage) for msg in prompt_input) ): raise InputFormatError( "When using an input_mapper to prepare dataset example inputs" " for an LLM or chat model, the output must a single string or" " a list of chat messages." f"\nGot: {prompt_input} of type {type(prompt_input)}." ) else: try: _get_prompt(first_example.inputs) except InputFormatError: try: _get_messages(first_example.inputs) except InputFormatError: raise InputFormatError( "Example inputs do not match language model input format. " "Expected a dictionary with messages or a single prompt." f" Got: {first_example.inputs}" " Please update your dataset OR provide an input_mapper" " to convert the example.inputs to a compatible format" " for the llm or chat model you wish to evaluate." ) def _validate_example_inputs_for_chain( first_example: Example, chain: Chain, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: """Validate that the example inputs match the chain input keys.""" if input_mapper: first_inputs = input_mapper(first_example.inputs) if not isinstance(first_inputs, dict): raise InputFormatError( "When using an input_mapper to prepare dataset example" " inputs for a chain, the mapped value must be a dictionary." f"\nGot: {first_inputs} of type {type(first_inputs)}." ) if not set(first_inputs.keys()) == set(chain.input_keys): raise InputFormatError( "When using an input_mapper to prepare dataset example inputs" " for a chain mapped value must have keys that match the chain's" " expected input keys." f"\nExpected: {chain.input_keys}. Got: {first_inputs.keys()}" ) else: first_inputs = first_example.inputs if len(first_inputs) == 1 and len(chain.input_keys) == 1: # We can pass this through the run method. # Refrain from calling to validate. pass elif not set(first_inputs.keys()) == set(chain.input_keys): raise InputFormatError( "Example inputs do not match chain input keys." " Please provide an input_mapper to convert the example.inputs" " to a compatible format for the chain you wish to evaluate." f"Expected: {chain.input_keys}. " f"Got: {first_inputs.keys()}" ) def _validate_example_inputs( examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, input_mapper: Optional[Callable[[Dict], Any]], ) -> Iterator[Example]: """Validate that the example inputs are valid for the model.""" first_example, examples = _first_example(examples) if isinstance(llm_or_chain_factory, BaseLanguageModel): _validate_example_inputs_for_language_model(first_example, input_mapper) else: chain = llm_or_chain_factory() _validate_example_inputs_for_chain(first_example, chain, input_mapper) return examples ## Shared Evaluator Setup Utilities def _setup_evaluation( llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, examples: Iterator[Example], evaluation: Optional[RunEvalConfig], data_type: DataType, ) -> Tuple[Optional[List[RunEvaluator]], Iterator[Example]]: """Configure the evaluators to run on the results of the chain.""" if evaluation: first_example, examples = _first_example(examples) if isinstance(llm_or_chain_factory, BaseLanguageModel): run_inputs, run_outputs = None, None run_type = "llm" else: run_type = "chain" if data_type in (DataType.chat, DataType.llm): val = data_type.value if isinstance(data_type, Enum) else data_type raise ValueError( "Cannot evaluate a chain on dataset with " f"data_type={val}. " "Please specify a dataset with the default 'kv' data type." ) chain = llm_or_chain_factory() run_inputs = chain.input_keys run_outputs = chain.output_keys run_evaluators = _load_run_evaluators( evaluation, run_type, data_type, list(first_example.outputs) if first_example.outputs else None, run_inputs, run_outputs, ) else: # TODO: Create a default helpfulness evaluator run_evaluators = None return run_evaluators, examples def _determine_input_key( config: RunEvalConfig, run_inputs: Optional[List[str]], run_type: str, ) -> Optional[str]: if config.input_key: input_key = config.input_key if run_inputs and input_key not in run_inputs: raise ValueError(f"Input key {input_key} not in run inputs {run_inputs}") elif run_type == "llm": input_key = None elif run_inputs and len(run_inputs) == 1: input_key = run_inputs[0] else: raise ValueError( f"Must specify input key for model with multiple inputs: {run_inputs}" ) return input_key def _determine_prediction_key( config: RunEvalConfig, run_outputs: Optional[List[str]], run_type: str, ) -> Optional[str]: if config.prediction_key: prediction_key = config.prediction_key if run_outputs and prediction_key not in run_outputs: raise ValueError( f"Prediction key {prediction_key} not in run outputs {run_outputs}" ) elif run_type == "llm": prediction_key = None elif run_outputs and len(run_outputs) == 1: prediction_key = run_outputs[0] else: raise ValueError( f"Must specify prediction key for model" f" with multiple outputs: {run_outputs}" ) return prediction_key def _determine_reference_key( config: RunEvalConfig, example_outputs: Optional[List[str]], ) -> Optional[str]: if config.reference_key: reference_key = config.reference_key if example_outputs and reference_key not in example_outputs: raise ValueError( f"Reference key {reference_key} not in Dataset" f" example outputs: {example_outputs}" ) elif example_outputs and len(example_outputs) == 1: reference_key = list(example_outputs)[0] else: reference_key = None return reference_key def _construct_run_evaluator( eval_config: Union[EvaluatorType, EvalConfig], eval_llm: BaseLanguageModel, run_type: str, data_type: DataType, example_outputs: Optional[List[str]], reference_key: Optional[str], input_key: Optional[str], prediction_key: Optional[str], ) -> RunEvaluator: if isinstance(eval_config, EvaluatorType): evaluator_ = load_evaluator(eval_config, llm=eval_llm) eval_type_tag = eval_config.value else: kwargs = {"llm": eval_llm, **eval_config.get_kwargs()} evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs) eval_type_tag = eval_config.evaluator_type.value if isinstance(evaluator_, StringEvaluator): if evaluator_.requires_reference and reference_key is None: raise ValueError( f"Must specify reference_key in RunEvalConfig to use" f" evaluator of type {eval_type_tag} with" f" dataset with multiple output keys: {example_outputs}." ) run_evaluator = StringRunEvaluatorChain.from_run_and_data_type( evaluator_, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, tags=[eval_type_tag], ) else: raise NotImplementedError( f"Run evaluator for {eval_type_tag} is not implemented" ) return run_evaluator def _load_run_evaluators( config: RunEvalConfig, run_type: str, data_type: DataType, example_outputs: Optional[List[str]], run_inputs: Optional[List[str]], run_outputs: Optional[List[str]], ) -> List[RunEvaluator]: """ Load run evaluators from a configuration. Args: config: Configuration for the run evaluators. Returns: A list of run evaluators. """ eval_llm = config.eval_llm or ChatOpenAI(model="gpt-4", temperature=0.0) run_evaluators = [] input_key = _determine_input_key(config, run_inputs, run_type) prediction_key = _determine_prediction_key(config, run_outputs, run_type) reference_key = _determine_reference_key(config, example_outputs) for eval_config in config.evaluators: run_evaluator = _construct_run_evaluator( eval_config, eval_llm, run_type, data_type, example_outputs, reference_key, input_key, prediction_key, ) run_evaluators.append(run_evaluator) custom_evaluators = config.custom_evaluators or [] for custom_evaluator in custom_evaluators: if isinstance(custom_evaluator, RunEvaluator): run_evaluators.append(custom_evaluator) elif isinstance(custom_evaluator, StringEvaluator): run_evaluators.append( StringRunEvaluatorChain.from_run_and_data_type( custom_evaluator, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, ) ) else: raise ValueError( f"Unsupported custom evaluator: {custom_evaluator}." f" Expected RunEvaluator or StringEvaluator." ) return run_evaluators ### Async Helpers async def _arun_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], *, tags: Optional[List[str]] = None, callbacks: Callbacks = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """Asynchronously run the language model. Args: llm: The language model to run. inputs: The input dictionary. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map inputs to the expected format. Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): return await llm.apredict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): return await llm.apredict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format" f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: prompt = _get_prompt(inputs) llm_output: Union[str, BaseMessage] = await llm.apredict( prompt, callbacks=callbacks, tags=tags ) except InputFormatError: messages = _get_messages(inputs) llm_output = await llm.apredict_messages( messages, callbacks=callbacks, tags=tags ) return llm_output async def _arun_chain( chain: Chain, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str]: """Run a chain asynchronously on inputs.""" if input_mapper is not None: inputs_ = input_mapper(inputs) output: Union[dict, str] = await chain.acall( inputs_, callbacks=callbacks, tags=tags ) else: inputs_ = next(iter(inputs.values())) if len(inputs) == 1 else inputs output = await chain.acall(inputs_, callbacks=callbacks, tags=tags) return output async def _arun_llm_or_chain( example: Example, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, n_repetitions: int, *, tags: Optional[List[str]] = None, callbacks: Optional[List[BaseCallbackHandler]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: """Asynchronously run the Chain or language model. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. n_repetitions: The number of times to run the model on each example. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map the input to the expected format. Returns: A list of outputs. """ if callbacks: previous_example_ids = [ getattr(tracer, "example_id", None) for tracer in callbacks ] for tracer in callbacks: if hasattr(tracer, "example_id"): tracer.example_id = example.id else: previous_example_ids = None outputs = [] chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) for _ in range(n_repetitions): try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = await _arun_llm( llm_or_chain_factory, example.inputs, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = await _arun_chain( chain, example.inputs, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) outputs.append(output) except Exception as e: logger.warning( f"{chain_or_llm} failed for example {example.id}. Error: {e}" ) outputs.append({"Error": str(e)}) if callbacks and previous_example_ids: for example_id, tracer in zip(previous_example_ids, callbacks): if hasattr(tracer, "example_id"): tracer.example_id = example_id return outputs async def _gather_with_concurrency( n: int, initializer: Callable[[], Coroutine[Any, Any, Any]], *async_funcs: Callable[ [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any] ], ) -> List[Any]: """Run coroutines with a concurrency limit. Args: n: The maximum number of concurrent tasks. initializer: A coroutine that initializes shared resources for the tasks. async_funcs: The async_funcs to be run concurrently. Returns: A list of results from the coroutines. """ semaphore = asyncio.Semaphore(n) job_state = {"num_processed": 0} callback_queue: asyncio.Queue[Sequence[BaseCallbackHandler]] = asyncio.Queue() for _ in range(n): callback_queue.put_nowait(await initializer()) async def run_coroutine_with_semaphore( async_func: Callable[ [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any] ] ) -> Any: async with semaphore: callbacks = await callback_queue.get() try: result = await async_func(callbacks, job_state) finally: callback_queue.put_nowait(callbacks) return result results = await asyncio.gather( *(run_coroutine_with_semaphore(function) for function in async_funcs) ) while callback_queue: try: callbacks = callback_queue.get_nowait() except asyncio.QueueEmpty: break for callback in callbacks: if isinstance(callback, (LangChainTracer, EvaluatorCallbackHandler)): callback.wait_for_futures() return results async def _callbacks_initializer( project_name: Optional[str], client: Client, run_evaluators: Sequence[RunEvaluator], evaluation_handler_collector: List[EvaluatorCallbackHandler], ) -> List[BaseTracer]: """ Initialize a tracer to share across tasks. Args: project_name: The project name for the tracer. client: The client to use for the tracer. run_evaluators: The evaluators to run. evaluation_handler_collector: A list to collect the evaluators. Used to wait for the evaluators to finish. Returns: The callbacks for this thread. """ callbacks: List[BaseTracer] = [] if project_name: callbacks.append( LangChainTracer( project_name=project_name, client=client, use_threading=False ) ) evaluator_project_name = f"{project_name}-evaluators" if project_name else None if run_evaluators: callback = EvaluatorCallbackHandler( client=client, evaluators=run_evaluators, # We already have concurrency, don't want to overload the machine max_workers=1, project_name=evaluator_project_name, ) callbacks.append(callback) evaluation_handler_collector.append(callback) return callbacks async def _arun_on_examples( client: Client, examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, num_repetitions: int = 1, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, data_type: DataType = DataType.kv, ) -> Dict[str, Any]: """ Asynchronously run the chain on examples and store traces to the specified project name. Args: client: LangSmith client to use to log feedback and runs. examples: Examples to run the model or chain over. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. num_repetitions: Number of times to run the model on each example. This is useful when testing success rates or generating confidence intervals. project_name: Project name to use when tracing runs. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. data_type: The dataset's data type. This is used to determine determine how to deserialize the reference data and model compatibility. Returns: A dictionary mapping example ids to the model outputs. """ llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory) project_name = _get_project_name(project_name, llm_or_chain_factory) run_evaluators, examples = _setup_evaluation( llm_or_chain_factory, examples, evaluation, data_type ) examples = _validate_example_inputs(examples, llm_or_chain_factory, input_mapper) results: Dict[str, List[Any]] = {} async def process_example( example: Example, callbacks: List[BaseCallbackHandler], job_state: dict ) -> None: """Process a single example.""" result = await _arun_llm_or_chain( example, llm_or_chain_factory, num_repetitions, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) results[str(example.id)] = result job_state["num_processed"] += 1 if verbose: print( f"Processed examples: {job_state['num_processed']}", end="\r", flush=True, ) evaluation_handlers: List[EvaluatorCallbackHandler] = [] await _gather_with_concurrency( concurrency_level, functools.partial( _callbacks_initializer, project_name=project_name, client=client, evaluation_handler_collector=evaluation_handlers, run_evaluators=run_evaluators or [], ), *(functools.partial(process_example, e) for e in examples), ) for handler in evaluation_handlers: handler.wait_for_futures() return results ## Sync Utilities def _run_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """ Run the language model on the example. Args: llm: The language model to run. inputs: The input dictionary. callbacks: The callbacks to use during the run. tags: Optional tags to add to the run. input_mapper: function to map to the inputs dictionary from an Example Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): llm_output: Union[str, BaseMessage] = llm.predict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): llm_output = llm.predict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format: " f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: llm_prompts = _get_prompt(inputs) llm_output = llm.predict(llm_prompts, callbacks=callbacks, tags=tags) except InputFormatError: llm_messages = _get_messages(inputs) llm_output = llm.predict_messages(llm_messages, callbacks=callbacks) return llm_output def _run_chain( chain: Chain, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[Dict, str]: """Run a chain on inputs.""" if input_mapper is not None: inputs_ = input_mapper(inputs) output: Union[dict, str] = chain(inputs_, callbacks=callbacks, tags=tags) else: inputs_ = next(iter(inputs.values())) if len(inputs) == 1 else inputs output = chain(inputs_, callbacks=callbacks, tags=tags) return output def _run_llm_or_chain( example: Example, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, n_repetitions: int, *, tags: Optional[List[str]] = None, callbacks: Optional[List[BaseCallbackHandler]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: """ Run the Chain or language model synchronously. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. n_repetitions: The number of times to run the model on each example. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. Returns: Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: The outputs of the model or chain. """ if callbacks: previous_example_ids = [ getattr(tracer, "example_id", None) for tracer in callbacks ] for tracer in callbacks: if hasattr(tracer, "example_id"): tracer.example_id = example.id else: previous_example_ids = None outputs = [] chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) for _ in range(n_repetitions): try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = _run_llm( llm_or_chain_factory, example.inputs, callbacks, tags=tags, input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = _run_chain( chain, example.inputs, callbacks, tags=tags, input_mapper=input_mapper, ) outputs.append(output) except Exception as e: logger.warning( f"{chain_or_llm} failed for example {example.id}. Error: {e}" ) outputs.append({"Error": str(e)}) if callbacks and previous_example_ids: for example_id, tracer in zip(previous_example_ids, callbacks): if hasattr(tracer, "example_id"): tracer.example_id = example_id return outputs def _run_on_examples( client: Client, examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, num_repetitions: int = 1, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, data_type: DataType = DataType.kv, ) -> Dict[str, Any]: """ Run the Chain or language model on examples and store traces to the specified project name. Args: client: LangSmith client to use to log feedback and runs. examples: Examples to run the model or chain over. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating num_repetitions: Number of times to run the model on each example. This is useful when testing success rates or generating confidence intervals. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. data_type: The dataset's data type. This is used to determine determine how to deserialize the reference data and model compatibility. Returns: A dictionary mapping example ids to the model outputs. """ results: Dict[str, Any] = {} llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory) project_name = _get_project_name(project_name, llm_or_chain_factory) tracer = LangChainTracer( project_name=project_name, client=client, use_threading=False ) evaluator_project_name = f"{project_name}-evaluators" run_evaluators, examples = _setup_evaluation( llm_or_chain_factory, examples, evaluation, data_type ) examples = _validate_example_inputs(examples, llm_or_chain_factory, input_mapper) evalution_handler = EvaluatorCallbackHandler( evaluators=run_evaluators or [], client=client, project_name=evaluator_project_name, ) callbacks: List[BaseCallbackHandler] = [tracer, evalution_handler] for i, example in enumerate(examples): result = _run_llm_or_chain( example, llm_or_chain_factory, num_repetitions, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) if verbose: print(f"{i+1} processed", flush=True, end="\r") results[str(example.id)] = result tracer.wait_for_futures() evalution_handler.wait_for_futures() return results ## Public API def _prepare_eval_run( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, project_name: Optional[str], ) -> Tuple[MODEL_OR_CHAIN_FACTORY, str, Dataset, Iterator[Example]]: llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name) project_name = _get_project_name(project_name, llm_or_chain_factory) try: project = client.create_project(project_name) except ValueError as e: if "already exists " not in str(e): raise e raise ValueError( f"Project {project_name} already exists. Please use a different name." ) project_url = _get_eval_project_url(client.api_url, project.id) print( f"View the evaluation results for project '{project_name}' at:\n{project_url}" ) dataset = client.read_dataset(dataset_name=dataset_name) examples = client.list_examples(dataset_id=str(dataset.id)) return llm_or_chain_factory, project_name, dataset, examples async def arun_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, num_repetitions: int = 1, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Dict[str, Any]: """ Asynchronously run the Chain or language model on a dataset and store traces to the specified project name. Args: client: LangSmith client to use to read the dataset, and to log feedback and run traces. dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. num_repetitions: Number of times to run the model on each example. This is useful when testing success rates or generating confidence intervals. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. Returns: A dictionary containing the run's project name and the resulting model outputs. For the synchronous version, see :func:`run_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig, arun_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() await arun_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) await arun_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 llm_or_chain_factory, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name ) results = await _arun_on_examples( client, examples, llm_or_chain_factory, concurrency_level=concurrency_level, num_repetitions=num_repetitions, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) return { "project_name": project_name, "results": results, } def run_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, num_repetitions: int = 1, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Dict[str, Any]: """ Run the Chain or language model on a dataset and store traces to the specified project name. Args: client: LangSmith client to use to access the dataset and to log feedback and run traces. dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Configuration for evaluators to run on the results of the chain num_repetitions: Number of times to run the model on each example. This is useful when testing success rates or generating confidence intervals. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. Returns: A dictionary containing the run's project name and the resulting model outputs. For the (usually faster) async version of this function, see :func:`arun_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig, run_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 llm_or_chain_factory, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name ) results = _run_on_examples( client, examples, llm_or_chain_factory, num_repetitions=num_repetitions, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) return { "project_name": project_name, "results": results, }

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"""Utilities for running language models or Chains over datasets.""" from __future__ import annotations import asyncio import functools import inspect import itertools import logging import uuid import warnings from enum import Enum from typing import ( TYPE_CHECKING, Any, Callable, Coroutine, Dict, Iterator, List, Optional, Sequence, Tuple, Union, cast, ) from urllib.parse import urlparse, urlunparse from langsmith import Client, RunEvaluator from langsmith.schemas import Dataset, DataType, Example from langchain.callbacks.base import BaseCallbackHandler from langchain.callbacks.manager import Callbacks from langchain.callbacks.tracers.base import BaseTracer from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler from langchain.callbacks.tracers.langchain import LangChainTracer from langchain.chains.base import Chain from langchain.evaluation.loading import load_evaluator from langchain.evaluation.schema import EvaluatorType, StringEvaluator from langchain.schema import ChatResult, LLMResult from langchain.schema.language_model import BaseLanguageModel from langchain.schema.messages import BaseMessage, messages_from_dict from langchain.schema.runnable import Runnable, RunnableConfig, RunnableLambda from langchain.smith.evaluation.config import EvalConfig, RunEvalConfig from langchain.smith.evaluation.string_run_evaluator import StringRunEvaluatorChain if TYPE_CHECKING: import pandas as pd logger = logging.getLogger(__name__) MODEL_OR_CHAIN_FACTORY = Union[ Callable[[], Union[Chain, Runnable]], BaseLanguageModel, Callable[[dict], Any], Runnable, Chain, ] MCF = Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel] class InputFormatError(Exception): """Raised when the input format is invalid.""" ## Shared Utilities class TestResult(dict): """A dictionary of the results of a single test run.""" def to_dataframe(self) -> pd.DataFrame: """Convert the results to a dataframe.""" try: import pandas as pd except ImportError as e: raise ImportError( "Pandas is required to convert the results to a dataframe." " to install pandas, run `pip install pandas`." ) from e indices = [] records = [] for example_id, result in self["results"].items(): feedback = result["feedback"] records.append( {**{f.key: f.score for f in feedback}, "output": result["output"]} ) indices.append(example_id) return pd.DataFrame(records, index=indices) def _get_eval_project_url(api_url: str, project_id: str) -> str: """Get the project url from the api url.""" parsed = urlparse(api_url) hostname = parsed.hostname or "" if "api." in hostname: hostname = hostname.replace("api.", "", 1) if "localhost" in hostname: # Remove the port hostname = "localhost" url = urlunparse(parsed._replace(netloc=hostname)) return f"{url}/projects/p/{project_id}?eval=true" def _wrap_in_chain_factory( llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, dataset_name: str = "<my_dataset>", ) -> MCF: """Forgive the user if they pass in a chain without memory instead of a chain factory. It's a common mistake. Raise a more helpful error message as well.""" if isinstance(llm_or_chain_factory, Chain): chain = llm_or_chain_factory chain_class = chain.__class__.__name__ if llm_or_chain_factory.memory is not None: memory_class = chain.memory.__class__.__name__ raise ValueError( "Cannot directly evaluate a chain with stateful memory." " To evaluate this chain, pass in a chain constructor" " that initializes fresh memory each time it is called." " This will safegaurd against information" " leakage between dataset examples." "\nFor example:\n\n" "def chain_constructor():\n" f" new_memory = {memory_class}(...)\n" f" return {chain_class}" "(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) logger.warning( "Directly passing in a chain is not recommended as chains may have state." " This can lead to unexpected behavior as the " "same chain instance could be used across multiple datasets. Instead," " please pass a chain constructor that creates a new " "chain with fresh memory each time it is called. This will safeguard" " against information leakage between dataset examples. " "\nFor example:\n\n" "def chain_constructor():\n" f" return {chain_class}(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) return lambda: chain elif isinstance(llm_or_chain_factory, BaseLanguageModel): return llm_or_chain_factory elif isinstance(llm_or_chain_factory, Runnable): # Memory may exist here, but it's not elegant to check all those cases. lcf = llm_or_chain_factory return lambda: lcf elif callable(llm_or_chain_factory): try: _model = llm_or_chain_factory() # type: ignore[call-arg] except TypeError: # It's an arbitrary function, wrap it in a RunnableLambda user_func = cast(Callable, llm_or_chain_factory) sig = inspect.signature(user_func) logger.info(f"Wrapping function {sig} as RunnableLambda.") wrapped = RunnableLambda(user_func) return lambda: wrapped constructor = cast(Callable, llm_or_chain_factory) if isinstance(_model, BaseLanguageModel): # It's not uncommon to do an LLM constructor instead of raw LLM, # so we'll unpack it for the user. return _model elif not isinstance(_model, Runnable): # This is unlikely to happen - a constructor for a model function return lambda: RunnableLambda(constructor) else: # Typical correct case return constructor # noqa return llm_or_chain_factory def _first_example(examples: Iterator[Example]) -> Tuple[Example, Iterator[Example]]: """Get the first example while chaining it back and preserving the iterator.""" try: example: Example = next(examples) except StopIteration: raise ValueError("No examples provided.") return example, itertools.chain([example], examples) def _get_prompt(inputs: Dict[str, Any]) -> str: """Get prompt from inputs. Args: inputs: The input dictionary. Returns: A string prompt. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") prompts = [] if "prompt" in inputs: if not isinstance(inputs["prompt"], str): raise InputFormatError( "Expected string for 'prompt', got" f" {type(inputs['prompt']).__name__}" ) prompts = [inputs["prompt"]] elif "prompts" in inputs: if not isinstance(inputs["prompts"], list) or not all( isinstance(i, str) for i in inputs["prompts"] ): raise InputFormatError( "Expected list of strings for 'prompts'," f" got {type(inputs['prompts']).__name__}" ) prompts = inputs["prompts"] elif len(inputs) == 1: prompt_ = next(iter(inputs.values())) if isinstance(prompt_, str): prompts = [prompt_] elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_): prompts = prompt_ else: raise InputFormatError(f"LLM Run expects string prompt input. Got {inputs}") else: raise InputFormatError( f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}" ) if len(prompts) == 1: return prompts[0] else: raise InputFormatError( f"LLM Run expects single prompt input. Got {len(prompts)} prompts." ) def _get_messages(inputs: Dict[str, Any]) -> List[BaseMessage]: """Get Chat Messages from inputs. Args: inputs: The input dictionary. Returns: A list of chat messages. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") if "messages" in inputs: single_input = inputs["messages"] elif len(inputs) == 1: single_input = next(iter(inputs.values())) else: raise InputFormatError( f"Chat Run expects 'messages' in inputs when example has multiple" f" input keys. Got {inputs}" ) if isinstance(single_input, list) and all( isinstance(i, dict) for i in single_input ): raw_messages = [single_input] elif isinstance(single_input, list) and all( isinstance(i, list) for i in single_input ): raw_messages = single_input else: raise InputFormatError( f"Chat Run expects List[dict] or List[List[dict]] values for" f" 'messages' key input. Got {inputs}" ) if len(raw_messages) == 1: return messages_from_dict(raw_messages[0]) else: raise InputFormatError( f"Chat Run expects single List[dict] or List[List[dict]] 'messages'" f" input. Got {len(raw_messages)} messages from inputs {inputs}" ) def _get_project_name( project_name: Optional[str], llm_or_chain_factory: MCF, ) -> str: """ Get the project name. Args: project_name: The project name if manually specified. llm_or_chain_factory: The Chain or language model constructor. Returns: The project name. """ if project_name is not None: return project_name if isinstance(llm_or_chain_factory, BaseLanguageModel): model_name = llm_or_chain_factory.__class__.__name__ else: model_name = llm_or_chain_factory().__class__.__name__ hex = uuid.uuid4().hex return f"{hex}-{model_name}" ## Shared Validation Utilities def _validate_example_inputs_for_language_model( first_example: Example, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: if input_mapper: prompt_input = input_mapper(first_example.inputs) if not isinstance(prompt_input, str) and not ( isinstance(prompt_input, list) and all(isinstance(msg, BaseMessage) for msg in prompt_input) ): raise InputFormatError( "When using an input_mapper to prepare dataset example inputs" " for an LLM or chat model, the output must a single string or" " a list of chat messages." f"\nGot: {prompt_input} of type {type(prompt_input)}." ) else: try: _get_prompt(first_example.inputs) except InputFormatError: try: _get_messages(first_example.inputs) except InputFormatError: raise InputFormatError( "Example inputs do not match language model input format. " "Expected a dictionary with messages or a single prompt." f" Got: {first_example.inputs}" " Please update your dataset OR provide an input_mapper" " to convert the example.inputs to a compatible format" " for the llm or chat model you wish to evaluate." ) def _validate_example_inputs_for_chain( first_example: Example, chain: Chain, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: """Validate that the example inputs match the chain input keys.""" if input_mapper: first_inputs = input_mapper(first_example.inputs) missing_keys = set(chain.input_keys).difference(first_inputs) if not isinstance(first_inputs, dict): raise InputFormatError( "When using an input_mapper to prepare dataset example" " inputs for a chain, the mapped value must be a dictionary." f"\nGot: {first_inputs} of type {type(first_inputs)}." ) if missing_keys: raise InputFormatError( "Missing keys after loading example using input_mapper." f"\nExpected: {chain.input_keys}. Got: {first_inputs.keys()}" ) else: first_inputs = first_example.inputs missing_keys = set(chain.input_keys).difference(first_inputs) if len(first_inputs) == 1 and len(chain.input_keys) == 1: # We can pass this through the run method. # Refrain from calling to validate. pass elif missing_keys: raise InputFormatError( "Example inputs missing expected chain input keys." " Please provide an input_mapper to convert the example.inputs" " to a compatible format for the chain you wish to evaluate." f"Expected: {chain.input_keys}. " f"Got: {first_inputs.keys()}" ) def _validate_example_inputs( examples: Iterator[Example], llm_or_chain_factory: MCF, input_mapper: Optional[Callable[[Dict], Any]], ) -> Iterator[Example]: """Validate that the example inputs are valid for the model.""" first_example, examples = _first_example(examples) if isinstance(llm_or_chain_factory, BaseLanguageModel): _validate_example_inputs_for_language_model(first_example, input_mapper) else: chain = llm_or_chain_factory() if isinstance(chain, Chain): # Otherwise it's a runnable _validate_example_inputs_for_chain(first_example, chain, input_mapper) elif isinstance(chain, Runnable): logger.debug(f"Skipping input validation for {chain}") return examples ## Shared Evaluator Setup Utilities def _setup_evaluation( llm_or_chain_factory: MCF, examples: Iterator[Example], evaluation: Optional[RunEvalConfig], data_type: DataType, ) -> Tuple[Optional[List[RunEvaluator]], Iterator[Example]]: """Configure the evaluators to run on the results of the chain.""" if evaluation: first_example, examples = _first_example(examples) if isinstance(llm_or_chain_factory, BaseLanguageModel): run_inputs, run_outputs = None, None run_type = "llm" else: run_type = "chain" if data_type in (DataType.chat, DataType.llm): val = data_type.value if isinstance(data_type, Enum) else data_type raise ValueError( "Cannot evaluate a chain on dataset with " f"data_type={val}. " "Please specify a dataset with the default 'kv' data type." ) chain = llm_or_chain_factory() run_inputs = chain.input_keys if isinstance(chain, Chain) else None run_outputs = chain.output_keys if isinstance(chain, Chain) else None run_evaluators = _load_run_evaluators( evaluation, run_type, data_type, list(first_example.outputs) if first_example.outputs else None, run_inputs, run_outputs, ) else: # TODO: Create a default helpfulness evaluator run_evaluators = None return run_evaluators, examples def _determine_input_key( config: RunEvalConfig, run_inputs: Optional[List[str]], ) -> Optional[str]: input_key = None if config.input_key: input_key = config.input_key if run_inputs and input_key not in run_inputs: raise ValueError(f"Input key {input_key} not in run inputs {run_inputs}") elif run_inputs and len(run_inputs) == 1: input_key = run_inputs[0] elif run_inputs is not None and len(run_inputs) > 1: raise ValueError( f"Must specify input key for model with multiple inputs: {run_inputs}" ) return input_key def _determine_prediction_key( config: RunEvalConfig, run_outputs: Optional[List[str]], ) -> Optional[str]: prediction_key = None if config.prediction_key: prediction_key = config.prediction_key if run_outputs and prediction_key not in run_outputs: raise ValueError( f"Prediction key {prediction_key} not in run outputs {run_outputs}" ) elif run_outputs and len(run_outputs) == 1: prediction_key = run_outputs[0] elif run_outputs is not None and len(run_outputs) > 1: raise ValueError( f"Must specify prediction key for model" f" with multiple outputs: {run_outputs}" ) return prediction_key def _determine_reference_key( config: RunEvalConfig, example_outputs: Optional[List[str]], ) -> Optional[str]: if config.reference_key: reference_key = config.reference_key if example_outputs and reference_key not in example_outputs: raise ValueError( f"Reference key {reference_key} not in Dataset" f" example outputs: {example_outputs}" ) elif example_outputs and len(example_outputs) == 1: reference_key = list(example_outputs)[0] else: reference_key = None return reference_key def _construct_run_evaluator( eval_config: Union[EvaluatorType, str, EvalConfig], eval_llm: Optional[BaseLanguageModel], run_type: str, data_type: DataType, example_outputs: Optional[List[str]], reference_key: Optional[str], input_key: Optional[str], prediction_key: Optional[str], ) -> RunEvaluator: if isinstance(eval_config, (EvaluatorType, str)): if not isinstance(eval_config, EvaluatorType): eval_config = EvaluatorType(eval_config) evaluator_ = load_evaluator(eval_config, llm=eval_llm) eval_type_tag = eval_config.value else: kwargs = {"llm": eval_llm, **eval_config.get_kwargs()} evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs) eval_type_tag = eval_config.evaluator_type.value if isinstance(evaluator_, StringEvaluator): if evaluator_.requires_reference and reference_key is None: raise ValueError( f"Must specify reference_key in RunEvalConfig to use" f" evaluator of type {eval_type_tag} with" f" dataset with multiple output keys: {example_outputs}." ) run_evaluator = StringRunEvaluatorChain.from_run_and_data_type( evaluator_, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, tags=[eval_type_tag], ) else: raise NotImplementedError( f"Run evaluator for {eval_type_tag} is not implemented" ) return run_evaluator def _get_keys( config: RunEvalConfig, run_inputs: Optional[List[str]], run_outputs: Optional[List[str]], example_outputs: Optional[List[str]], ) -> Tuple[Optional[str], Optional[str], Optional[str]]: input_key = _determine_input_key(config, run_inputs) prediction_key = _determine_prediction_key(config, run_outputs) reference_key = _determine_reference_key(config, example_outputs) return input_key, prediction_key, reference_key def _load_run_evaluators( config: RunEvalConfig, run_type: str, data_type: DataType, example_outputs: Optional[List[str]], run_inputs: Optional[List[str]], run_outputs: Optional[List[str]], ) -> List[RunEvaluator]: """ Load run evaluators from a configuration. Args: config: Configuration for the run evaluators. Returns: A list of run evaluators. """ run_evaluators = [] input_key, prediction_key, reference_key = None, None, None if ( config.evaluators or any([isinstance(e, EvaluatorType) for e in config.evaluators]) or ( config.custom_evaluators and any([isinstance(e, StringEvaluator) for e in config.custom_evaluators]) ) ): input_key, prediction_key, reference_key = _get_keys( config, run_inputs, run_outputs, example_outputs ) for eval_config in config.evaluators: run_evaluator = _construct_run_evaluator( eval_config, config.eval_llm, run_type, data_type, example_outputs, reference_key, input_key, prediction_key, ) run_evaluators.append(run_evaluator) custom_evaluators = config.custom_evaluators or [] for custom_evaluator in custom_evaluators: if isinstance(custom_evaluator, RunEvaluator): run_evaluators.append(custom_evaluator) elif isinstance(custom_evaluator, StringEvaluator): run_evaluators.append( StringRunEvaluatorChain.from_run_and_data_type( custom_evaluator, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, ) ) else: raise ValueError( f"Unsupported custom evaluator: {custom_evaluator}." f" Expected RunEvaluator or StringEvaluator." ) return run_evaluators ### Async Helpers async def _arun_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], *, tags: Optional[List[str]] = None, callbacks: Callbacks = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """Asynchronously run the language model. Args: llm: The language model to run. inputs: The input dictionary. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map inputs to the expected format. Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): return await llm.apredict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): return await llm.apredict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format" f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: prompt = _get_prompt(inputs) llm_output: Union[str, BaseMessage] = await llm.apredict( prompt, callbacks=callbacks, tags=tags ) except InputFormatError: messages = _get_messages(inputs) llm_output = await llm.apredict_messages( messages, callbacks=callbacks, tags=tags ) return llm_output async def _arun_chain( chain: Union[Chain, Runnable], inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str]: """Run a chain asynchronously on inputs.""" inputs_ = inputs if input_mapper is None else input_mapper(inputs) if isinstance(chain, Chain): if isinstance(inputs_, dict) and len(inputs_) == 1: val = next(iter(inputs_.values())) output = await chain.acall(val, callbacks=callbacks, tags=tags) else: output = await chain.acall(inputs_, callbacks=callbacks, tags=tags) else: runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks) output = await chain.ainvoke(inputs_, config=runnable_config) return output async def _arun_llm_or_chain( example: Example, llm_or_chain_factory: MCF, *, tags: Optional[List[str]] = None, callbacks: Optional[List[BaseCallbackHandler]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str, LLMResult, ChatResult]: """Asynchronously run the Chain or language model. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map the input to the expected format. Returns: A list of outputs. """ if callbacks: previous_example_ids = [ getattr(tracer, "example_id", None) for tracer in callbacks ] for tracer in callbacks: if hasattr(tracer, "example_id"): tracer.example_id = example.id else: previous_example_ids = None chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) result = None try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = await _arun_llm( llm_or_chain_factory, example.inputs, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = await _arun_chain( chain, example.inputs, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) result = output except Exception as e: logger.warning(f"{chain_or_llm} failed for example {example.id}. Error: {e}") result = {"Error": str(e)} if callbacks and previous_example_ids: for example_id, tracer in zip(previous_example_ids, callbacks): if hasattr(tracer, "example_id"): tracer.example_id = example_id return result async def _gather_with_concurrency( n: int, initializer: Callable[[], Coroutine[Any, Any, Any]], *async_funcs: Callable[ [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any] ], ) -> List[Any]: """Run coroutines with a concurrency limit. Args: n: The maximum number of concurrent tasks. initializer: A coroutine that initializes shared resources for the tasks. async_funcs: The async_funcs to be run concurrently. Returns: A list of results from the coroutines. """ semaphore = asyncio.Semaphore(n) job_state = {"num_processed": 0} callback_queue: asyncio.Queue[Sequence[BaseCallbackHandler]] = asyncio.Queue() for _ in range(n): callback_queue.put_nowait(await initializer()) async def run_coroutine_with_semaphore( async_func: Callable[ [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any] ] ) -> Any: async with semaphore: callbacks = await callback_queue.get() try: result = await async_func(callbacks, job_state) finally: callback_queue.put_nowait(callbacks) return result results = await asyncio.gather( *(run_coroutine_with_semaphore(function) for function in async_funcs) ) while callback_queue: try: callbacks = callback_queue.get_nowait() except asyncio.QueueEmpty: break for callback in callbacks: if isinstance(callback, (LangChainTracer, EvaluatorCallbackHandler)): callback.wait_for_futures() return results async def _callbacks_initializer( project_name: Optional[str], client: Client, run_evaluators: Sequence[RunEvaluator], evaluation_handler_collector: List[EvaluatorCallbackHandler], ) -> List[BaseTracer]: """ Initialize a tracer to share across tasks. Args: project_name: The project name for the tracer. client: The client to use for the tracer. run_evaluators: The evaluators to run. evaluation_handler_collector: A list to collect the evaluators. Used to wait for the evaluators to finish. Returns: The callbacks for this thread. """ callbacks: List[BaseTracer] = [] if project_name: callbacks.append( LangChainTracer( project_name=project_name, client=client, use_threading=False ) ) if run_evaluators: callback = EvaluatorCallbackHandler( client=client, evaluators=run_evaluators, # We already have concurrency, don't want to overload the machine max_workers=1, ) callbacks.append(callback) evaluation_handler_collector.append(callback) return callbacks async def _arun_on_examples( client: Client, examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, data_type: DataType = DataType.kv, ) -> Dict[str, Any]: """ Asynchronously run the chain on examples and store traces to the specified project name. Args: client: LangSmith client to use to log feedback and runs. examples: Examples to run the model or chain over. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. project_name: Project name to use when tracing runs. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. data_type: The dataset's data type. This is used to determine determine how to deserialize the reference data and model compatibility. Returns: A dictionary mapping example ids to the model outputs. """ wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory) project_name = _get_project_name(project_name, wrapped_model) run_evaluators, examples = _setup_evaluation( wrapped_model, examples, evaluation, data_type ) examples = _validate_example_inputs(examples, wrapped_model, input_mapper) results: Dict[str, dict] = {} async def process_example( example: Example, callbacks: List[BaseCallbackHandler], job_state: dict ) -> None: """Process a single example.""" result = await _arun_llm_or_chain( example, wrapped_model, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) results[str(example.id)] = {"output": result} job_state["num_processed"] += 1 if verbose: print( f"Processed examples: {job_state['num_processed']}", end="\r", flush=True, ) evaluation_handlers: List[EvaluatorCallbackHandler] = [] await _gather_with_concurrency( concurrency_level, functools.partial( _callbacks_initializer, project_name=project_name, client=client, evaluation_handler_collector=evaluation_handlers, run_evaluators=run_evaluators or [], ), *(functools.partial(process_example, e) for e in examples), ) all_feedback = {} for handler in evaluation_handlers: handler.wait_for_futures() all_feedback.update(handler.logged_feedback) # join the results and feedback on the example id for example_id, output_dict in results.items(): feedback = all_feedback.get(example_id, []) output_dict["feedback"] = feedback return results ## Sync Utilities def _run_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """ Run the language model on the example. Args: llm: The language model to run. inputs: The input dictionary. callbacks: The callbacks to use during the run. tags: Optional tags to add to the run. input_mapper: function to map to the inputs dictionary from an Example Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): llm_output: Union[str, BaseMessage] = llm.predict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): llm_output = llm.predict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format: " f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: llm_prompts = _get_prompt(inputs) llm_output = llm.predict(llm_prompts, callbacks=callbacks, tags=tags) except InputFormatError: llm_messages = _get_messages(inputs) llm_output = llm.predict_messages(llm_messages, callbacks=callbacks) return llm_output def _run_chain( chain: Union[Chain, Runnable], inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[Dict, str]: """Run a chain on inputs.""" inputs_ = inputs if input_mapper is None else input_mapper(inputs) if isinstance(chain, Chain): if isinstance(inputs_, dict) and len(inputs_) == 1: val = next(iter(inputs_.values())) output = chain(val, callbacks=callbacks, tags=tags) else: output = chain(inputs_, callbacks=callbacks, tags=tags) else: runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks) output = chain.invoke(inputs_, config=runnable_config) return output def _run_llm_or_chain( example: Example, llm_or_chain_factory: MCF, *, tags: Optional[List[str]] = None, callbacks: Optional[List[BaseCallbackHandler]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str, LLMResult, ChatResult]: """ Run the Chain or language model synchronously. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. Returns: Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: The outputs of the model or chain. """ if callbacks: previous_example_ids = [ getattr(tracer, "example_id", None) for tracer in callbacks ] for tracer in callbacks: if hasattr(tracer, "example_id"): tracer.example_id = example.id else: previous_example_ids = None chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) result = None try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = _run_llm( llm_or_chain_factory, example.inputs, callbacks, tags=tags, input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = _run_chain( chain, example.inputs, callbacks, tags=tags, input_mapper=input_mapper, ) result = output except Exception as e: logger.warning( f"{chain_or_llm} failed for example {example.id} with inputs:" f" {example.inputs}.\nError: {e}", ) result = {"Error": str(e)} if callbacks and previous_example_ids: for example_id, tracer in zip(previous_example_ids, callbacks): if hasattr(tracer, "example_id"): tracer.example_id = example_id return result def _run_on_examples( client: Client, examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, data_type: DataType = DataType.kv, ) -> Dict[str, Any]: """ Run the Chain or language model on examples and store traces to the specified project name. Args: client: LangSmith client to use to log feedback and runs. examples: Examples to run the model or chain over. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. data_type: The dataset's data type. This is used to determine determine how to deserialize the reference data and model compatibility. Returns: A dictionary mapping example ids to the model outputs. """ results: Dict[str, dict] = {} wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory) project_name = _get_project_name(project_name, wrapped_model) tracer = LangChainTracer( project_name=project_name, client=client, use_threading=False ) run_evaluators, examples = _setup_evaluation( wrapped_model, examples, evaluation, data_type ) examples = _validate_example_inputs(examples, wrapped_model, input_mapper) evaluation_handler = EvaluatorCallbackHandler( evaluators=run_evaluators or [], client=client, ) callbacks: List[BaseCallbackHandler] = [tracer, evaluation_handler] for i, example in enumerate(examples): result = _run_llm_or_chain( example, wrapped_model, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) if verbose: print(f"{i+1} processed", flush=True, end="\r") results[str(example.id)] = {"output": result} tracer.wait_for_futures() evaluation_handler.wait_for_futures() all_feedback = evaluation_handler.logged_feedback # join the results and feedback on the example id for example_id, output_dict in results.items(): feedback = all_feedback.get(example_id, []) output_dict["feedback"] = feedback return results ## Public API def _prepare_eval_run( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, project_name: Optional[str], ) -> Tuple[MCF, str, Dataset, Iterator[Example]]: wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name) project_name = _get_project_name(project_name, wrapped_model) try: project = client.create_project(project_name) except ValueError as e: if "already exists " not in str(e): raise e raise ValueError( f"Project {project_name} already exists. Please use a different name." ) project_url = _get_eval_project_url(client.api_url, project.id) print( f"View the evaluation results for project '{project_name}' at:\n{project_url}" ) dataset = client.read_dataset(dataset_name=dataset_name) examples = client.list_examples(dataset_id=str(dataset.id)) return wrapped_model, project_name, dataset, examples async def arun_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, **kwargs: Any, ) -> Dict[str, Any]: """ Asynchronously run the Chain or language model on a dataset and store traces to the specified project name. Args: client: LangSmith client to use to read the dataset, and to log feedback and run traces. dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. Returns: A dictionary containing the run's project name and the resulting model outputs. For the synchronous version, see :func:`run_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig, arun_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() await arun_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) await arun_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 if kwargs: warnings.warn( "The following arguments are deprecated and will " "be removed in a future release: " f"{kwargs.keys()}.", DeprecationWarning, ) wrapped_model, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name ) results = await _arun_on_examples( client, examples, wrapped_model, concurrency_level=concurrency_level, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) return TestResult( project_name=project_name, results=results, ) def _handle_coroutine(coro: Coroutine) -> Any: """ Handles a coroutine from a sync context. Args: coro (asyncio.coroutine): The coroutine to be handled. Returns: any: The result of the executed coroutine. """ # Check if there's a running event loop try: loop = asyncio.get_event_loop() except RuntimeError: # No event loop return asyncio.run(coro) if loop.is_running(): return loop.run_until_complete(coro) else: return asyncio.run(coro) def run_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, **kwargs: Any, ) -> Dict[str, Any]: """ Run the Chain or language model on a dataset and store traces to the specified project name. Args: client: LangSmith client to use to access the dataset and to log feedback and run traces. dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Configuration for evaluators to run on the results of the chain concurrency_level: The number of async tasks to run concurrently. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. Returns: A dictionary containing the run's project name and the resulting model outputs. For the (usually faster) async version of this function, see :func:`arun_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig, run_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 if kwargs: warnings.warn( "The following arguments are deprecated and " "will be removed in a future release: " f"{kwargs.keys()}.", DeprecationWarning, ) wrapped_model, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name ) if concurrency_level in (0, 1): results = _run_on_examples( client, examples, wrapped_model, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) else: # TODO: Use runnables and the batch method coro = _arun_on_examples( client, examples, wrapped_model, concurrency_level=concurrency_level, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) results = _handle_coroutine(coro) return TestResult( project_name=project_name, results=results, )

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from langchain.llms import HuggingFacePipeline, CTransformers import langchain from ingest import load_db from langchain.cache import InMemoryCache from langchain.schema import prompt from langchain.chains import RetrievalQA from langchain.callbacks import StdOutCallbackHandler from langchain import PromptTemplate import chainlit as cl DB_FAISS_PATH = 'vectorstoredb/db_faiss' langchain.llm_cache = InMemoryCache() PROMPT_TEMPLATE = ''' Use the following pieces of information to answer the user's question. If you don't know the answer, just say that you don't know, don't try to make up an answer. Context: {context} Question: {question} Do provide only helpful answers Helpful answer: ''' handler = StdOutCallbackHandler() def set_custom_prompt(): input_variables = ['context', 'question'] prompt = PromptTemplate(template=PROMPT_TEMPLATE, input_variables=input_variables) return prompt def load_retriever(): return load_db() def load_llm(): llm = CTransformers(model="models/mistral-7b-instruct-v0.1.Q2_K.gguf", config={'max_new_tokens': 128, 'temperature': 0.01}) return llm def retrieval_qa_chain(llm, prompt, db): qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever = db, verbose=True, callbacks=[handler], chain_type_kwargs={"prompt": prompt}, return_source_documents=True ) return qa_chain def qa_bot(): db = load_retriever() llm = load_llm() qa_prompt = set_custom_prompt() qa = retrieval_qa_chain(llm, qa_prompt, db) return qa def final_result(query): qa_result = qa_bot() response = qa_result({"query": query}) return response @cl.on_chat_start async def start(): chain = qa_bot() msg = cl.Message(content="Starting the bot...") await msg.send() msg.content = "Hi, Welcome to Medical Bot. What is your query?" await msg.update() cl.user_session.set("chain", chain) @cl.on_message async def main(message: cl.Message): chain = cl.user_session.get("chain") cb = cl.AsyncLangchainCallbackHandler( stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"] ) cb.answer_reached = True res = await chain.acall(message.content, callbacks=[cb]) answer = res["result"] sources = res["source_documents"] if sources: answer += f"\nSources:" + str(sources) else: answer += "\nNo sources found" await cl.Message(content=answer).send()

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# Import Langchain dependencies from langchain.document_loaders import PyPDFLoader from langchain.indexes import VectorstoreIndexCreator from langchain.chains import RetrievalQA from langchain.embeddings import HuggingFaceEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter # Bring in streamlit for UI dev import streamlit as st # Bring in watsonx interface from watsonxlangchain import LangChainInterface # Setup credentials dictionary creds = { "apiKey": "", "url": "", } # Create LLM using langchain llm = LangChainInterface( credentials = creds, model = "meta-llama/llama-2-70b-chat", params = { "decoding_method": "sample", "max_new_tokens": 200, "temperature": 0.5 }, project_id="" ) # This function loads a PDF of your choosing @st.cache_resource def load_pdf(): # Update PDF name here to whatever you like pdf_name= "what is generative ai.pdf" loaders = [PyPDFLoader(pdf_name)] # Create index - aka vector database - aka chromadb index = VectorstoreIndexCreator( embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L12-v2"), text_splitter=RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=0) ).from_loaders(loaders) # Return the vector database return index # Load er on up index = load_pdf() # Create a Q&A chain chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=index.vectorstore.as_retriever() ) # Setup the app title st.title("Ask watsonx") # Setup a session state message variable to hild all the old messages if "messages" not in st.session_state: st.session_state.messages = [] # Display all the historical messages for message in st.session_state.messages: st.chat_message(message["role"]) # Build a prompt input template to display the prompts prompt = st.chat_input("Pass Your Prompt Here") # If the user hits enter then if prompt: # Display the prompt st.chat_message("user").markdown(prompt) # Store the user prompt in state st.session_state.messages.append({"role":"user", "content":prompt}) # Send the prompt to the llm st.chat_message("assistant").markdown(response) # Store the LLM response in state st.session_state.messages.append( {"role": "assistant", "content": response} )

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import streamlit as st import langchain from langchain_community.document_loaders import RecursiveUrlLoader, TextLoader, JSONLoader from langchain_community.document_transformers import Html2TextTransformer from langchain.docstore.document import Document from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter from langchain.vectorstores.faiss import FAISS from langchain_community.vectorstores import Chroma from langchain_community.callbacks import StreamlitCallbackHandler from langchain.tools import tool from langchain.tools.json.tool import JsonSpec from langchain.agents import OpenAIFunctionsAgent, AgentExecutor, load_tools #from langchain_experimental.tools.python.tool import PythonREPLTool from langchain_community.agent_toolkits import JsonToolkit from langchain.tools.retriever import create_retriever_tool from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (AgentTokenBufferMemory,) from langchain_community.chat_models.openai import ChatOpenAI from langchain.schema import SystemMessage, AIMessage, HumanMessage from langchain.prompts import MessagesPlaceholder from langsmith import Client from langchain_community.vectorstores.redis import Redis from langchain.vectorstores.redis import RedisText, RedisNum, RedisTag from langchain.memory import RedisChatMessageHistory from langchain.globals import set_llm_cache import os, openai, requests, json, zeep, datetime, pandas as pd from requests.auth import HTTPBasicAuth #from dotenv import load_dotenv, find_dotenv from zeep.wsse.username import UsernameToken #_ = load_dotenv(find_dotenv()) # read local .env file openai.api_key = os.getenv('OPENAI_API_KEY') model = "gpt-4-1106-preview" #model = "gpt-3.5-turbo-16k" #model = "gpt-3.5-turbo" embeddings = OpenAIEmbeddings() TENANT = 'wdmarketdesk_dpt1' WD_USER_ID = os.getenv('WD_USER_ID') WD_PWD = os.getenv('WD_PWD') WD_Worker_URL = "https://impl-services1.wd12.myworkday.com/ccx/service/customreport2/wdmarketdesk_dpt1/xjin-impl/Worker_Data?format=json" WD_Absence_URL = "https://impl-services1.wd12.myworkday.com/ccx/service/customreport2/wdmarketdesk_dpt1/xjin-impl/Worker_Absence_Data_2?format=json&employee_id=" WD_COMP_URL = "https://impl-services1.wd12.myworkday.com/ccx/service/customreport2/wdmarketdesk_dpt1/xjin-impl/Worker_Comp_Data_2?format=json&employee_id=" WD_STAFFING_WSDL_URL = "https://impl-services1.wd12.myworkday.com/ccx/service/wdmarketdesk_dpt1/Staffing/v41.1?wsdl" WD_HR_WSDL_URL = "https://impl-services1.wd12.myworkday.com/ccx/service/wdmarketdesk_dpt1/Human_Resources/v42.0?wsdl" basicAuth = HTTPBasicAuth(WD_USER_ID, WD_PWD) wd_hr_client = zeep.Client(WD_HR_WSDL_URL, wsse=UsernameToken(WD_USER_ID + '@' + TENANT, WD_PWD)) wd_staffing_client = zeep.Client(WD_STAFFING_WSDL_URL, wsse=UsernameToken(WD_USER_ID + '@' + TENANT, WD_PWD)) #client = Client() rds = Redis.from_existing_index( embeddings, index_name="worker_hr", redis_url="redis://redis-10042.c280.us-central1-2.gce.cloud.redislabs.com:10042", password="1iI48215k0GAEC3gzmpfPrXD2UDXYOYN", schema="worker_hr.yaml" ) retriever = None st.set_page_config( page_title="Ask HR", page_icon="🦜", layout="wide", initial_sidebar_state="collapsed", ) "# Ask HR🦜🔗" with st.form(key='login_form'): col1, col2, col3 = st.columns([1,2,1]) with col1: input_number = st.number_input(label='Enter Employee ID', min_value=0, max_value=1000000) login_submit = st.form_submit_button(label='Submit') def get_cache(): # construct cache implementation based on env var CACHE_TYPE = os.getenv("CACHE_TYPE", "semantic") REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379") if CACHE_TYPE == "semantic": from langchain.cache import RedisSemanticCache print("Using semantic cache") return RedisSemanticCache( redis_url=REDIS_URL, embedding=embeddings, score_threshold=0.001, ) else: from langchain.cache import RedisCache from langchain_community.utilities import redis print("Using regular cache") return RedisCache(redis_= redis.get_client(REDIS_URL)) def get_raas_data(): response = requests.get(WD_Worker_URL, auth = basicAuth) responseJson = json.dumps(json.loads(response.content)) return responseJson def get_worker_data(): employee_id = '21082' worker_request_dict = { 'Worker_Reference': { 'ID': { 'type': 'Employee_ID', '_value_1': employee_id }, 'Descriptor': None }, 'Skip_Non_Existing_Instances': None, 'Ignore_Invalid_References': None } response = zeep_client.service.Get_Workers(worker_request_dict) return response.Response_Data def get_worker_name(employee_id=0): print("Trying to get Worker Name") is_emp_id = RedisNum("Employee_ID") == employee_id results = rds.similarity_search_with_score("", k=3, filter=is_emp_id if is_emp_id!=0 else None) return results[0][0].metadata['Employee_Legal_Full_Name'] #@st.cache_resource(ttl="4h") def initialize_retriever_redis(employee_id=0): """Initializes with all of worker data. If any information is not found, \ please use this tool as the default tool to look for the data needed. \ Do not try to get the same data more than 2 times. """ print("Initializing with Worker data in Redis", employee_id) rds2 = Redis.from_existing_index( embeddings, index_name="worker_hr", redis_url="redis://redis-10042.c280.us-central1-2.gce.cloud.redislabs.com:10042", password="1iI48215k0GAEC3gzmpfPrXD2UDXYOYN", schema="worker_hr.yaml" ) print("returning from Redis") return rds2.as_retriever(search_type="similarity", search_kwargs={"k": 10, "filter": employee_id if employee_id!=0 else None}) #@st.cache_resource(ttl="4h") def initialize_policies(): """Initializes with all policies data for GMS. If any information is not found, \ please say you don't know. Do not make up answers. \ For each answer, provide source in brackets. \ Do not repeat the same source information in the same line. In the final response, always replace word KPPRA with GMS """ print("Initializing with Policies") rds = Redis.from_existing_index( embeddings, index_name="hr_policies", redis_url="redis://redis-10042.c280.us-central1-2.gce.cloud.redislabs.com:10042", password="1iI48215k0GAEC3gzmpfPrXD2UDXYOYN", schema="hr_policies.yaml", ) print("returning from Redis") return rds.as_retriever(search_type="similarity", search_kwargs={"k": 5}) #@st.cache_resource(ttl="4h") def initialize_retriever(): """Initializes with all of worker data. If any information is not found, \ please use this tool as the default tool to look for the data needed. \ Do not try to get the same data more than 2 times. """ print("Initializing with all of worker data") txt = get_raas_data() # Split text text_splitter = CharacterTextSplitter() texts = text_splitter.split_text(txt) # Create multiple documents docs = [Document(page_content=t) for t in texts] embeddings = OpenAIEmbeddings() vectorstore = FAISS.from_documents(docs, embeddings) return vectorstore.as_retriever(search_kwargs={"k": 4}) @tool def init_json_loader(): """ Initializes with all of worker data. If any information is not found, \ please use this tool as the default tool to look for the data needed. \ """ json_data = get_worker_data() loader = JSONLoader(json_data, jq_schema='.messages[].content') extracted_data = loader.load() embeddings = OpenAIEmbeddings() vector_store = Chroma.from_documents(extracted_data, embeddings) return vector_store.as_retriever(search_kwargs={"k": 4}) #@st.cache_resource(ttl="4h") def get_vectorstore(txt): # Split text text_splitter = CharacterTextSplitter() texts = text_splitter.split_text(txt) # Create multiple documents docs = [Document(page_content=t) for t in texts] embeddings = OpenAIEmbeddings() vectorstore = FAISS.from_documents(docs, embeddings) return vectorstore.as_retriever(search_kwargs={"k": 4}) #@st.cache_resource(ttl="4h") def get_Absence_Data(Employee_ID: str) -> str: """Returns PTO or absence or time off data. \ Use this for any questions related to knowing about PTO or absence or time off data. \ Use Employee ID as the input, \ and this function will return data as string or JSON structure \ """ print(f"Getting absence data from Workday {WD_Absence_URL + str(input_number)}") response = requests.get(WD_Absence_URL + str(input_number), auth = basicAuth) responseJson = json.dumps(json.loads(response.content)) print(f"Absence Data: {responseJson}") return get_vectorstore(responseJson) @tool #@st.cache_resource(ttl="4h") def get_Comp_Data(text: str) -> str: """Returns Compensation or comp or salary data. \ Use this for any questions related to knowing about compensation or salary information. \ The input of text can not be empty string. \ and this function will return data as string or JSON structure \ """ print(f"Getting comp data from Workday {WD_COMP_URL + str(input_number)}") response = requests.get(WD_COMP_URL + str(input_number), auth = basicAuth) responseJson = json.dumps(json.loads(response.content)) print(f"Comp Data: {responseJson}") return get_vectorstore(responseJson) @tool def update_business_title(business_title: str) -> str: """Updates business title of the employee. \ Use this for any requests to update or change business title. \ The input can be an empty string or employee ID, \ and this function will return data as JSON structure \ """ employee_id = '21072' effective_date = '2023-11-12' Business_Process_Parameters = { 'Auto_Complete': True, 'Run_Now': True, 'Discard_On_Exit_Validation_Error': True } Change_Business_Title_Business_Process_Data = { 'Worker_Reference': { 'ID': { 'type': 'Employee_ID', '_value_1': employee_id }, 'Descriptor': None }, 'Change_Business_Title_Data': { 'Event_Effective_Date': effective_date, 'Proposed_Business_Title': business_title } } try: responseJson = wd_hr_client.service.Change_Business_Title(Business_Process_Parameters, Change_Business_Title_Business_Process_Data) except zeep.exceptions.Fault as error: responseJson = error except BaseException as error: responseJson = error return (responseJson) @tool def add_additional_job(Job_Requisition_ID: str, effective_date: datetime.date) -> str: """Adds additional job to the employee. \ Use this for any requests to add more jobs to existing employees. \ The input can be an empty string or employee ID, \ and this function will return data as JSON structure \ """ employee_id = '21072' Organization_Reference_ID = 'SUPERVISORY_Finance_and_Administration' #effective_date = '2023-11-17' #Job_Requisition_ID = 'R-00306' Event_Classification_Subcategory_ID = 'Add_Additional_Employee_Job_Secondment_Leave_of_Absence_Backfill' true = True Business_Process_Parameters = { 'Auto_Complete': True, 'Run_Now': True, 'Discard_On_Exit_Validation_Error': True } Add_Additional_Job_Data = { 'Employee_Reference': { 'ID': { 'type': 'Employee_ID', '_value_1': employee_id }, 'Descriptor': None }, 'Organization_Reference': { 'ID': { 'type': 'Organization_Reference_ID', '_value_1': Organization_Reference_ID } }, 'Job_Requisition_Reference': { 'ID': { 'type': 'Job_Requisition_ID', '_value_1': Job_Requisition_ID } }, 'Add_Additional_Job_Event_Data': { 'Additional_Job_Reason_Reference': { 'ID': { 'type': 'Event_Classification_Subcategory_ID', '_value_1': Event_Classification_Subcategory_ID } }, 'Position_Details': { } }, 'Event_Effective_Date': effective_date } try: responseJson = wd_staffing_client.service.Add_Additional_Job(Business_Process_Parameters, Add_Additional_Job_Data) except zeep.exceptions.Fault as error: responseJson = error return (responseJson) tool = create_retriever_tool( initialize_retriever_redis(input_number), "Ask_HR", """Initializes with all of worker data. If any information is not found, \ please use this tool as the default tool to look for the data needed. \ """ ) policies = create_retriever_tool( initialize_policies(), "Policies", """Initializes with all policies data for GMS. If any information is not found, \ please say you don't know. Do not make up answers. \ For each answer, provide source in brackets. \ Do not repeat the same source information in the same line. \ Do not call this tool more than 2 times. \ In the final response, always replace word KPPRA with GMS """ ) absence = create_retriever_tool( get_Absence_Data(""), "get_Absence_Data", """Returns PTO or absence or time off data. \ Use this for any questions related to knowing about PTO or absence or time off data. \ The input can be an empty string or employee ID, \ and this function will return data as string or JSON structure \ """) comp = create_retriever_tool( get_Comp_Data(""), "get_Comp_Data", """Returns Compensation or comp or salary data. \ Use this for any questions related to knowing about compensation or salary information. \ If what is needed is not found, please use Ask_HR tool as the default tool instead. \ """) tools = [tool, policies, absence, comp, update_business_title, add_additional_job] try: set_llm_cache(get_cache()) print("*** Retrieved cache ***") except Exception as e: print("*** Could not retrieve cache ***") print(e) pass chat_llm = ChatOpenAI(temperature=0, streaming=True, model=model) message = SystemMessage( content=( """ You are HR Bot, an automated service to handle HR requests for an organization. \ Please "DO NOT" hallucinate. You first greet the employee, then ask for what task he is looking to perform. \ If the request is related to PTO or Absences, Check to see if he has enough balance of hours by substracting requested hours from available balance. \ If he has enough balance, ask if he wants to submit for approval. \ If the balance is not enough let him know to adjust the request. \ You wait to collect all the details, then summarize it and check for a final \ time if the employee wants to change anything. \ Make sure to clarify all options, days and hours to uniquely identify the PTO from the options.\ You respond in a short, very conversational friendly style. \ When the user is ready to submit the request, Do not respond with plain human language but \ respond with type of PTO, hours and comments in JSON format. \ For type: Identify what type of PTO is being requested for submission. Use 'Reference_ID' value of the PTO plan for \ final JSON format. Do not show 'Reference_ID' for any intermediate human friendly updates or messages \ For hours: If he has enough balance, extract just numeric value of how many hours are being requested or submitted. \ If the balance is not enough consider 0. \ For comments: Extract the reason why employee is requesting or submitting the PTO \ The PTO options with available balances in hours includes as below in JSON format \ If the questions is on Policies and Rules provide very brief summary \ For each answer, if available, provide source including page number in brackets. \ In the final response, always replace word KPPRA with GMS """ ) ) prompt = OpenAIFunctionsAgent.create_prompt( system_message=message, extra_prompt_messages=[MessagesPlaceholder(variable_name="history")], ) agent = OpenAIFunctionsAgent(llm=chat_llm, tools=tools, prompt=prompt) agent_executor = AgentExecutor( agent=agent, tools=tools, verbose=True, return_intermediate_steps=True, ) memory = AgentTokenBufferMemory(llm=chat_llm) #redis_memory = RedisChatMessageHistory() starter_message = f"Hello and Welcome. I am here to help you with your HR needs!!" if "messages" not in st.session_state or st.sidebar.button("Clear message history") or login_submit: starter_message = f"Hello and Welcome. I am here to help you with your HR needs!!" st.session_state["messages"] = [AIMessage(content=starter_message)] #def send_feedback(run_id, score): # client.create_feedback(run_id, "user_score", score=score) for msg in st.session_state.messages: if isinstance(msg, AIMessage): st.chat_message("assistant").write(msg.content) elif isinstance(msg, HumanMessage): st.chat_message("user").write(msg.content) memory.chat_memory.add_message(msg) if prompt := st.chat_input(placeholder=starter_message): st.chat_message("user").write(prompt) with st.chat_message("assistant"): st_callback = StreamlitCallbackHandler(st.container()) response = agent_executor( {"input": prompt, "history": st.session_state.messages, "employee_id": input_number}, callbacks=[st_callback], include_run_info=True, ) print("returned from agent executor") st.session_state.messages.append(AIMessage(content=response["output"])) st.write(response["output"]) memory.save_context({"input": prompt}, response) st.session_state["messages"] = memory.buffer run_id = response["__run"].run_id #col_blank, col_text, col1, col2 = st.columns([10, 2, 1, 1]) #with col_text: # st.text('Feedback:') #with col1: # st.button('👍', on_click=send_feedback, args=(run_id, 1)) #with col2: # st.button('👎', on_click=send_feedback, args=(run_id, 0))

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# Using flask to make an api # import necessary libraries and functions from flask import Flask, jsonify, request, render_template from pydantic import BaseModel from ast import literal_eval import os import openai openai.api_key = os.getenv("OPENAI_API_KEY") import langchain from langchain.vectorstores import FAISS from langchain.embeddings.openai import OpenAIEmbeddings # creating a Flask app app = Flask(__name__) # on the terminal type: curl http://127.0.0.1:5000/ # returns hello world when we use GET. # returns the data that we send when we use POST. @app.route('/related', methods = ['GET', 'POST']) def home(): if(request.method == 'GET'): data = "its up" return jsonify({'data': data}) if(request.method == 'POST'): temp= request.get_json() print(temp) vector = temp["ve"] ticker = temp["ticker"] embeddings = OpenAIEmbeddings(openai.api_key) print(str(ticker)) new_db = FAISS.load_local(str(ticker), embeddings) ar = literal_eval(vector) docs_and_scores = new_db.similarity_search_by_vector(ar) size = len(docs_and_scores) data = "" for i in range(0,size): data+=(docs_and_scores[i].page_content + "\n\n") print(data) return jsonify({'data': data}) def relatedwords(ticker, vembeddings): embeddings = OpenAIEmbeddings() print(str(ticker)) new_db = FAISS.load_local(ticker, embeddings) # ar = literal_eval(vector) docs_and_scores = new_db.similarity_search_by_vector(vembeddings) size = len(docs_and_scores) data = "" for i in range(0,size): data+=(docs_and_scores[i].page_content + "\n\n") print(data) return data def completion(context): completion = openai.ChatCompletion.create( model="gpt-3.5-turbo", temperature = 0, messages=[ { "role": "system", "content": "You are a financial assistant that VERY ACCURATELY and CONCISELY answers queries using publicly filled annual reports of publicly traded companies. Use the CONTEXT provided to form your answer. Keep your answer under 5 sentences. Be accurate, helpful, concise, and clear, while performing accurate mathematical calculations." }, { "role": "user", "content": context } ] ) return (completion.choices[0].message["content"]) def embeddingsgenerate(embeddingsof): embeddings = openai.Embedding.create( model="text-embedding-ada-002", input=embeddingsof ) return embeddings["data"][0]["embedding"] @app.route('/generate_embeddings', methods= ['GET', 'POST']) def get_embeddings(): if(request.method == 'GET'): data = "its up" return jsonify({'data': data}) if(request.method == 'POST'): data = request.get_json() texts = data['texts'] embeddings = openai.Encodings.create(texts) return jsonify({'embeddings': embeddings}) @app.route('/<path_name>',methods=['GET', 'POST']) def answer_question(path_name): urlforprice = ("https://s.tradingview.com/embed-widget/symbol-info/?locale=en&colorTheme=dark&symbol=BSE:"+path_name) if request.method == 'POST': # User submitted a question, so process it and get an answer from OpenAI question = request.form['question'] # openai_api_key = request.form['openai_api_key'] prompt = f"{question}" embeddings_recieved = embeddingsgenerate(question) context = relatedwords(path_name, embeddings_recieved) questionforgpt = "CONTEXT\n\n" + context + "\nQUESTION\n" + prompt answerfromgpt = completion(questionforgpt) print(answerfromgpt) return render_template('chat.html', path_name=path_name, questionasked=prompt, urlforprice=urlforprice, embeddings_recieved = context, answer_received = answerfromgpt) else: answer = "not yet used" # Render the answer on an HTML template return render_template('chat.html', path_name=path_name, answer=answer,urlforprice=urlforprice) @app.route('/',methods=['GET']) def homepage(): return render_template('home.html') # driver function if __name__ == '__main__': app.run(debug = True)

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# -*- coding: utf-8 -*- import random import streamlit as st from langchain.llms import OpenAI from langchain.text_splitter import RecursiveCharacterTextSplitter #from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import HuggingFaceEmbeddings from langchain.vectorstores import FAISS import openai # from playsound import playsound from gtts import gTTS from PyPDF2 import PdfReader from utils import text_to_docs from langchain import PromptTemplate, LLMChain #import os #from io import StringIO #from langchain.embeddings.openai import OpenAIEmbeddings #from langchain.chains.summarize import load_summarize_chain #import os #import pyaudio #import wave #import langchain #from langchain.document_loaders import UnstructuredPDFLoader #from io import BytesIO # import streamlit.components.v1 as components #from st_custom_components import st_audiorec, text_to_docs #import sounddevice as sd #from scipy.io.wavfile import write from usellm import Message, Options, UseLLM llm = OpenAI(model_name='text-davinci-003', temperature=0.2, max_tokens=512, openai_api_key=st.secrets["api_key"]) def record_audio(filename): duration=5 sample_rate = 44100 # Record audio audio_data = sd.rec(int(duration * sample_rate), samplerate=sample_rate, channels=6) st.write('Recording started. Speak into the microphone...') # Wait for recording to complete sd.wait() # Save the recorded audio to a WAV file write(filename, sample_rate, audio_data) st.write(f'Recording saved as {filename}') def transcribe_audio(filename): with open(filename, "rb") as audio_file: transcript = openai.Audio.transcribe("whisper-1", filename) return transcript["text"] st.title("Suspicious Activity Reporting") st.subheader('Evidence Processor') model_name = "sentence-transformers/all-MiniLM-L6-v2" @st.cache_data def usellm(prompt): service = UseLLM(service_url="https://usellm.org/api/llm") messages = [ Message(role="system", content="You are a fraud analyst"), Message(role="user", content=f"{prompt}"), ] options = Options(messages=messages) response = service.chat(options) return response.content @st.cache_resource def embed(model_name): hf_embeddings = HuggingFaceEmbeddings(model_name=model_name) return hf_embeddings # @st.cache_resource # def chain(_llm): # chain = load_summarize_chain(llm=_llm, chain_type="map_reduce") # chain = load_summarize_chain(llm=llm, chain_type="map_reduce") hf_embeddings = embed(model_name) # File Upload file = st.file_uploader("Upload a file") st.write(file) text_splitter = RecursiveCharacterTextSplitter( # Set a really small chunk size, just to show. chunk_size = 1500, chunk_overlap = 100, length_function = len, separators=["\n\n", "\n", " ", ""] ) #text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=100, chunk_overlap=0) #texts = '' @st.cache_data def embedding_store(file): # save file pdf_reader = PdfReader(file) text = "" for page in pdf_reader.pages: text += page.extract_text() #st.write(text) texts = text_splitter.split_text(text) docs = text_to_docs(texts) #st.write(texts) docsearch = FAISS.from_documents(docs, hf_embeddings) return docs, docsearch # Submit Button if st.button("Submit"): if file is not None: # File handling logic st.write("File Uploaded...") _, docsearch = embedding_store(file) queries ="Any fraud encountered in the passed document?\ if any." contexts = docsearch.similarity_search(queries, k=1) prompts = f"Give concise answer to the below questions as truthfully as possible as per given context only,\n\n\ Context: {contexts}\n\ Response (in readable bullet points): " response = usellm(prompts) # Print the assistant's response # st.subheader("Few informative points in the uploaded document are as follows:") # st.write(response) #st.write("Uploaded File Contents:") if file is not None: docs, docsearch = embedding_store(file) #docs = text_to_docs(texts) #st.write(docs) #summary = chain.run(docs) #st.write(summary) #st.header("Speech and Text Input") # Speech Input # st.subheader("Speech Input") # wav_audio_data = st_audiorec() # if wav_audio_data is not None: # st.audio(wav_audio_data, format='audio/wav') # audio_text = transcribe_audio(wav_audio_data) # st.write(audio_text) # if st.button("Voice Input"): # rand = random.randint(1, 10000)*random.randint(10001,20000) # audio_filename = f"recorded_audio{rand}.wav" # record_audio(filename=audio_filename) # aud = transcribe_audio(audio_filename) # st.write(aud) # Text Input st.subheader("Ask Questions") query = st.text_input('your queries will go here...') def LLM_Response(): llm_chain = LLMChain(prompt=prompt, llm=llm) response = llm_chain.run({"query":query, "context":context}) return response if query: # Text input handling logic #st.write("Text Input:") #st.write(text_input) context = docsearch.similarity_search(query, k=5) prompt = f"Act as a financial analyst and give concise answer to below Question as truthfully as possible, with given Context\n\n\ Question: {query}\n\ Context: {context}\n\ Response: " #prompt = PromptTemplate(template=prompt, input_variables=["query", "context"]) response = usellm(prompt) #LLM_Response() st.write(response) # language = 'en' # Create a gTTS object # tts = gTTS(text=response, lang=language) # Save the audio file # rand = random.randint(1, 10000)*random.randint(10001,20000) # audio_file = f'output{rand}.mp3' # tts.save(audio_file) # playsound(audio_file)

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import langchain from langchain.agents import load_tools, initialize_agent, AgentType from langchain.chat_models import ChatOpenAI langchain.verbose = True langchain.debug = True def get_chat(): return ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) if __name__ == "__main__": chat = get_chat() tools = load_tools(["terminal"]) agent_chain = initialize_agent( tools, chat, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION ) result = agent_chain.run("sample_data ディレクトリにあるファイルの一覧を教えて") print(result) # { # "prompts": [ # "Human: Answer the following questions as best you can. You have access to the following tools: # terminal: Run shell commands on this Linux machine. # Use the following format: # # Question: the input question you must answer # Thought: you should always think about what to do # Action: the action to take, should be one of [terminal] # Action Input: the input to the action # Observation: the result of the action # ... (this Thought/Action/Action Input/Observation can repeat N times) # Thought: I now know the final answer # Final Answer: the final answer to the original input question # # Begin! # # Question: sample_data ディレクトリにあるファイルの一覧を教えて # Thought:I need to list the files in the sample_data directory. # Action: terminal # Action Input: ls sample_data # Observation: ls: cannot access 'sample_data': No such file or directory # # Thought:The sample_data directory does not exist. I need to find the correct directory. # Action: terminal # Action Input: ls # Observation: Dockerfile # app # docker-compose.yml # langchain # requirements.txt # venv # # Thought:The sample_data directory is not in the current directory. I need to search for it. # Action: terminal # Action Input: find / -name sample_data # Observation: /usr/src/app/sample_data # /opt/project/app/sample_data # # Thought:" # ] # }

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from llama_index import ( GPTVectorStoreIndex, ServiceContext, ) from llama_index.postprocessor import SentenceTransformerRerank from llama_index.embeddings import LangchainEmbedding from langchain.embeddings.huggingface import ( HuggingFaceBgeEmbeddings, ) from llama_index.vector_stores import WeaviateVectorStore from llama_index.postprocessor import MetadataReplacementPostProcessor import weaviate from llama_index.prompts.base import PromptTemplate from prompt import MISTRAL_V2_CHAT from langchain_community.llms import HuggingFaceTextGenInference from llama_index.llms import LangChainLLM from llama_index.memory import ChatMemoryBuffer import re from llama_index import ( ServiceContext, set_global_handler, ) from typing import List from llama_index.bridge.langchain import ( HumanMessage, ) from llama_index.bridge.langchain import BaseMessage from langchain_community.chat_models.huggingface import ChatHuggingFace from huggingface_hub.commands.user import login token = "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" if login(token=token): print("Login success") client = weaviate.Client("http://192.168.88.10:8080") vector_store = WeaviateVectorStore( weaviate_client=client, index_name="LlamaIndex" ) class CustomChatHuggingFace(ChatHuggingFace): def _to_chat_prompt( self, messages: List[BaseMessage], ) -> str: """Convert a list of messages into a prompt format expected by wrapped LLM.""" if not messages: raise ValueError("at least one HumanMessage must be provided") if not isinstance(messages[-1], HumanMessage): raise ValueError("last message must be a HumanMessage") messages_dicts = [self._to_chatml_format(m) for m in messages] system_str = "<s>[INST] "+ messages_dicts.pop(0)['content'] + " </INST>" chat_str = self.tokenizer.apply_chat_template( messages_dicts, tokenize=False, add_generation_prompt=True ) return system_str + chat_str class ConversationalAI: def __init__(self): self.embed_model = LangchainEmbedding(HuggingFaceBgeEmbeddings(model_name="BAAI/bge-large-en-v1.5",cache_folder='./all_model/bge-large')) self.llm = LangChainLLM(llm = CustomChatHuggingFace( llm = HuggingFaceTextGenInference(inference_server_url="http://192.168.88.10:8024", max_new_tokens=2048, top_k=10, top_p=0.95, typical_p=0.95, temperature=0.01, repetition_penalty=1.03, streaming=True,), model_id ="TheBloke/Mistral-7B-Instruct-v0.2-AWQ" )) # Initialize ServiceContext self.service_context = ServiceContext.from_defaults( embed_model=self.embed_model, llm=self.llm ) self.bge_rerank_postprocessor = SentenceTransformerRerank( model="./all_model/rerank/bge-reranker-large", top_n=3 ) # Initialize Memory self.memory = ChatMemoryBuffer.from_defaults(token_limit=4000) # Initialize vectorstore self.index = GPTVectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=self.service_context) self.chat_prompt = PromptTemplate(MISTRAL_V2_CHAT) self.pattern = re.compile(r"<\|im_end\|>") def get_memory_token_count(self): chat_history = self.memory.get_all() msg_str = " ".join(str(m.content) for m in chat_history) return len(self.memory.tokenizer_fn(msg_str)) def prune_memory(self, chat_limit): chat_history = self.memory.get_all() if len(chat_history) > chat_limit: self.memory.chat_store.set_messages(self.memory.chat_store_key, chat_history[2:]) def _response(self, query): chat_engine = self.index.as_chat_engine( chat_mode="context", memory=self.memory, system_prompt= MISTRAL_V2_CHAT, # context_template= self.context_prompt, similarity_top_k=8, node_postprocessors=[self.bge_rerank_postprocessor, MetadataReplacementPostProcessor(target_metadata_key="window")], verbose=True) streaming_response = chat_engine.chat(query) for i in range(len(streaming_response.source_nodes)): print("NODE No", i) print("-------------------------------------------") print("NODE :", streaming_response.source_nodes[i]) self.prune_memory(chat_limit = 4) print("Response :", streaming_response.response) return streaming_response.response

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import os import time import pickle as pkl # import re # import yaml import toml import logging from datetime import date # import aiohttp import pandas as pd from pytrends.request import TrendReq import serpapi from serpapi import GoogleSearch import asyncio import streamlit as st import streamlit.components.v1 as components import streamlit_authenticator as stauth import langchain # from langchain.utilities import GoogleSerperAPIWrapper from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chat_models import ChatOpenAI from langchain.agents import initialize_agent, Tool, AgentType from langchain.docstore.document import Document from langchain.chains.qa_with_sources import load_qa_with_sources_chain from langchain.llms import OpenAI from langchain.prompts.prompt import PromptTemplate import openai from dotenv import load_dotenv load_dotenv() ########### ===== config ===== ############# config_path = '.streamlit/secrets.toml' # print(f" os.listdir('.') -> { os.listdir('.')}") if os.path.exists(config_path): logging.info(f"{config_path} exists") config = toml.load(open( config_path, 'r')) else: logging.info( f"secrets -> {st.secrets}" ) config = dict(st.secrets.items()) print( f"config -> {config}") for k in ['name', 'authentication_status', 'username' ]: st.session_state[k] = None logging.info(f"session state -> {st.session_state}") openai.api_key = os.environ.get("OPENAI_API_KEY") or config["settings"]["OPENAI_API_KEY"] os.environ["OPENAI_API_KEY"] = openai.api_key secret_key = os.environ.get('SERP_APIKEY') or config["settings"]["SERP_APIKEY"] logging.info(f"serp api key -> {secret_key}") # serp_client = serpapi.Client(api_key=secret_key) pytrends = TrendReq(hl='zh-TW', tz=480, timeout=(10,25), retries=2, backoff_factor=0.1, requests_args={'verify':False}) ########### ==================== ############# ### ===== Creating a login widget ===== ### authenticator = stauth.Authenticate( config['credentials'], config['cookie']['name'], config['cookie']['key'], config['cookie']['expiry_days'], config['preauthorized'] ) name, authentication_status, username = authenticator.login('Login', 'main') is_production = os.environ.get("PRODUCTION") or config["settings"]["PRODUCTION"] logging.info(f"is_production -> {is_production}") ########### ==================== ############# @st.cache_data def get_trending_searches(today) -> pd.DataFrame: """ Get trending searches from Google Trends """ results = pytrends.trending_searches(pn='taiwan') return results @st.cache_data def get_search_results( secret_key, query: str, today: str) -> dict: """ Get search results from serpapi.com """ print(f"secret_key -> {secret_key[-4:]}, query -> {query}, today -> {today}") serp_client=serpapi.Client(api_key=secret_key) results = serp_client.search({ 'engine': 'google', 'q': query, }) return results.as_dict() audio_input_dir = "data/audio" audio_output_dir = "data/audio" transcript_input_dir = "data/transcripts" transcript_output_dir = "data/transcripts" summary_input_dir = "data/summary" summary_output_dir = "data/summary" ### === load model & utilities === ### from model import get_answer, aget_answer from postprocess import parse_url, parse_element from utils import split, compose_file_path, get_rewrite_chain, get_summarize_chain, sound2txt, text2summary ### ===== Authenticating users ===== ### if authentication_status: col1, col2, col3 , col4, col5, col6, col7 = st.columns(7) with col4: # st.write(f'Welcome *{name}*') st.write(f'Welcome!') # st.write(f'*{name}*') # st.markdown(f"<p style='text-align: center;'> {name} </p>", unsafe_allow_html=True) authenticator.logout('Logout', 'main') # st.write(f'\n\n\n\n') st.title("生產力小幫手") # st.divider() tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["疼痛小幫手", "切割錄音檔", "錄音轉文字", "文字改寫", "總結摘要", "錄音檔摘要", "火熱話題"]) with tab1: question = st.text_input("病患描述問題", placeholder="肩膀不太舒服，前幾天有去打球，會不會是韌帶拉傷？") if question: if is_production: res = get_answer(question) else: res = asyncio.run(aget_answer(question)) urls = res['urls'] st.write("【建議回覆】") st.write(res["output_text"]) print( f"found urls -> {urls}" ) html = "" if len(urls)>0: for url in urls: html += f"""<br><a href=\"{url}\" style="color:gray">開啟網頁：{url}</a>""" html = f"<div>{html}</div>" components.html( html, height=600, scrolling=True) with tab2: ## --- upload file --- ## with st.form("upload-audio-form", clear_on_submit=True): uploaded_file = st.file_uploader("Upload an audio clip", key='split_audio', type=["wav", "mp3", "m4a"]) submitted = st.form_submit_button("UPLOAD") if submitted and uploaded_file: ## -- read file -- ## sound_bytes = uploaded_file.getvalue() # bytes file_obj = compose_file_path( uploaded_file.name, output_prefix = None, output_dir = audio_input_dir, output_ext = None) audio_file_path = file_obj['output_file_path'] print( f"audio_file_path: { audio_file_path}") with open( audio_file_path, "wb") as f: f.write(sound_bytes) ## --- split --- ## with st.spinner(f"reading from { audio_file_path}"): multi_file_paths = split( in_file_path = audio_file_path, length = 10) with tab3: ## --- upload file --- ## transcript_file_path = "" transcripted = 0 with st.form("upload-audio-clip-form", clear_on_submit=True): uploaded_file = st.file_uploader("Upload an audio clip", key = 'transcribe_audio', type=["wav", "mp3", "m4a"]) submitted = st.form_submit_button("UPLOAD") if submitted and uploaded_file: ## -- read file -- ## sound_bytes = uploaded_file.getvalue() # bytes file_obj = compose_file_path( input_file_path=uploaded_file.name, output_prefix = None, output_dir = audio_input_dir, output_ext = None) audio_file_path = file_obj['output_file_path'] print( f"audio_file_path: { audio_file_path}") with open( audio_file_path, "wb") as f: f.write(sound_bytes) ## --- transcribe --- ## start_time = time.time() st.divider() file_obj = compose_file_path( audio_file_path, output_prefix = None, output_dir = transcript_input_dir, output_ext = "txt") transcript_file_path = file_obj['output_file_path'] if os.path.exists(transcript_file_path): print(f"{transcript_file_path} already exists. will skip this file.") with st.spinner(f"written transcript to { transcript_file_path}"): transcript = sound2txt( audio_file_path, transcript_file_path) st.write(transcript['text']) print( f"Elapsed time: {time.time() - start_time} seconds") transcripted = 1 if transcripted and os.path.exists(transcript_file_path): with open(transcript_file_path) as f: st.download_button('下載逐字稿', f, file_name = os.path.basename(transcript_file_path), ) # Defaults to 'text/plain' with tab4: rewrite_chain = get_rewrite_chain() rewrite_file_path = "" rewritten = 0 question = st.text_input( "你希望整理會議逐字稿的方式（用簡短的英文描述）", "correct typos, remove redundant sentences/words and add punctuations", placeholder="correct typos, remove redundant sentences/words and add punctuations", # disabled=not uploaded_file, ) with st.form("upload-transcript-clip-form", clear_on_submit=True): rewrite_uploaded_file = st.file_uploader("Upload a transcript or a document (txt file)", type="txt") submitted = st.form_submit_button("UPLOAD") if submitted and rewrite_uploaded_file and question and not openai.api_key: st.info("Please add your OPENAI API KEY to environment to continue.") if submitted and rewrite_uploaded_file and question and openai.api_key: ## -- read file -- ## article = rewrite_uploaded_file.read().decode() file_obj = compose_file_path( input_file_path = rewrite_uploaded_file.name, output_prefix = "rewrite", output_dir = transcript_output_dir, output_ext = "txt" ) rewrite_file_path = file_obj['output_file_path'] # assert not os.path.exists( transcript_file_path), f"File { transcript_file_path} already exists." ## -- split into chunks -- ## text_splitter = RecursiveCharacterTextSplitter( chunk_size=1024, chunk_overlap=20, ) paragraphs = text_splitter.split_text(article) ## --- anthropic --- ## # prompt = f""" # {anthropic.HUMAN_PROMPT} Here's an article:\n\n<article> # {article}\n\n</article>\n\n{question}{anthropic.AI_PROMPT} # """ # client = anthropic.Client(anthropic_api_key) # #client = anthropic.Client(st.secrets.anthropic_api_key) # response = client.completion( # prompt=prompt, # stop_sequences=[anthropic.HUMAN_PROMPT], # model="claude-v1", # max_tokens_to_sample=100, # ) # st.write("### Answer") # st.write(response["completion"]) ## --- openai --- ## st.write("### Rewritten transcript") with st.spinner(f"written to { rewrite_file_path}"): for paragraph in paragraphs: try: res = rewrite_chain({"functions": question, "paragraph": paragraph}, return_only_outputs = True) except Exception as e: print(e) continue st.write(res["text"]) st.write("---") with open( rewrite_file_path, "a") as f: f.write(res["text"]) # rewrite_file_paths.append( rewrite_file_path) rewritten = 1 if rewritten and os.path.exists(rewrite_file_path): with open(rewrite_file_path) as f: st.download_button('下載加入標點符號後的逐字稿', f, file_name = os.path.basename(rewrite_file_path), key = f"download-button-{rewrite_file_path}") # Defaults to 'text/plain' with tab5: instruction = st.text_input( "你希望總結會議逐字稿的方式（用簡短的英文描述）", "give helpful and concise summary with action items in traditional chinese.", placeholder="give helpful and concise summary with action items in traditional chinese.", # disabled=not summary_uploaded_file, ) summarized = 0 summary_file_path = "" with st.form("upload-summarize-clip-form", clear_on_submit=True): summary_uploaded_file = st.file_uploader("Upload a transcript or a document (txt file)", type="txt") summarize_submitted = st.form_submit_button("UPLOAD") if summarize_submitted and summary_uploaded_file and instruction and not openai.api_key: st.info("Please add your OPENAI API KEY to environment to continue.") if summarize_submitted and summary_uploaded_file and instruction and openai.api_key: ## -- read file -- ## article = summary_uploaded_file.read().decode() file_obj = compose_file_path( input_file_path = summary_uploaded_file.name, output_prefix = "summary", output_dir = summary_output_dir, output_ext = "txt" ) summary_file_path = file_obj['output_file_path'] ## -- split into chunks -- ## text_splitter = RecursiveCharacterTextSplitter( chunk_size=1024, chunk_overlap=100, ) paragraphs = text_splitter.split_text(article) ## -- summarize -- ## st.write("### Summarize transcript") with st.spinner(f"written to { summary_file_path}"): summarize_chain = get_summarize_chain() with open( summary_file_path, "w") as f: for paragraph in paragraphs: try: res = summarize_chain( {"input_documents": [ Document( page_content=paragraph) ], "functions": instruction} ) summary = res['output_text'] except Exception as e: print(e) continue st.write(summary) st.write("---") f.write(summary) summarized = 1 if summarized and os.path.exists(summary_file_path): with open(summary_file_path) as f: st.download_button('下載摘要', f, file_name = os.path.basename(summary_file_path), key = f"download-button-{summary_file_path}") # Defaults to 'text/plain' with tab6: # summarize_chain = load_summarize_chain() # data/audio/GMT20230613-051643_Recording.m4a_0.m4a # with open("data/transcripts/GMT20230613-051643_Recording.m4a_0.txt") as f: # transcript = f.read() # st.markdown( f'<p style="background-color: rgba(100,100,200,.2);">{transcript}</p>', unsafe_allow_html=True) ## --- upload file --- ## with st.form("end-to-end-upload-audio-form", clear_on_submit=True): uploaded_file = st.file_uploader("Upload an audio clip", key='end_to_end', type=["wav", "mp3", "m4a"]) submitted = st.form_submit_button("UPLOAD") if submitted and uploaded_file: ## -- read file -- ## with st.spinner(f"uploaded { uploaded_file.name}"): sound_bytes = uploaded_file.getvalue() # bytes audio_file_path = compose_file_path( input_file_path = uploaded_file.name, output_prefix = None, output_dir = audio_input_dir, output_ext = None)['output_file_path'] print( f"audio_file_path: {audio_file_path}") with open( audio_file_path, "wb") as f: f.write(sound_bytes) ## --- split --- ## audio_file_paths = split( in_file_path = audio_file_path, length = 10) ## --- transcribe --- ## st.write("### Transcribing audio") with st.spinner(f"transcribe { ', '.join(audio_file_paths) }"): transcripts = [] transcript_file_paths = [] for i, audio_file_path in enumerate(audio_file_paths): start_time = time.time() transcript_file_path = compose_file_path( input_file_path = audio_file_path, output_prefix = None, output_dir = transcript_input_dir, output_ext = "txt")['output_file_path'] transcript_file_paths.append(transcript_file_path) st.write(f"Trascribing {i}-th clip out of total {len(audio_file_paths)} clips.") transcript = sound2txt( audio_file_path, transcript_file_path) st.write(transcript['text'][:50]) st.divider() transcripts.append( transcript['text']) print( f"[transcription] Elapsed time: {time.time() - start_time} seconds") # print( f"Transcript: {transcript}") ## --- Rewrite --- ## st.write("### Rewritten transcript") with st.spinner(f"rewritten { ','.join(transcript_file_paths)}"): text_splitter = RecursiveCharacterTextSplitter( chunk_size=1024, chunk_overlap=20, ) rewrite_instruction = "correct typos, remove redundant sentences/words and add punctuations" rewrite_file_paths = [] rewrites = [] for i, blob in enumerate(zip(transcript_file_paths, transcripts)): transcript_file_path, transcript = blob paragraphs = text_splitter.split_text( "\n".join(transcript) ) rewrite_file_path = compose_file_path( input_file_path = transcript_file_path, output_prefix = "rewrite", output_dir = transcript_output_dir, output_ext = "txt")['output_file_path'] rewrite_file_paths.append(rewrite_file_path) st.write(f"Rewriting {i}-th transcript out of total {len(transcript_file_paths)} transcripts.") for paragraph in paragraphs: try: res = rewrite_chain({"functions": rewrite_instruction, "paragraph": paragraph}, return_only_outputs = True) except Exception as e: print(e) continue with open( rewrite_file_path, "a") as f: f.write(res["text"]) st.write(res["text"][:50]) st.divider() ## --- summarize --- ## with st.spinner(f"summarize { ', '.join(rewrite_file_paths) }"): summary_text = "" for rewrite, rewrite_file_path in zip( rewrites, rewrite_file_paths): start_time = time.time() summary_file_path = compose_file_path( input_file_path = rewrite_file_path, output_prefix = "summary", output_dir = summary_output_dir, output_ext = "txt")['output_file_path'] summary = text2summary( rewrite, summary_file_path, instruction = "give helpful and concise summary with action items in traditional chinese.") st.write(summary) st.divider() summary_text += f"{summary}\n" print( f"[summary] Elapsed time: {time.time() - start_time} seconds") with tab7: if False: # today = date.today() # topn_results = 3 # topn_articles = 1 # trend_results = get_trending_searches(today) # highlights = [] # for query in trend_results[0].values[:topn_results]: # search_results = get_search_results( secret_key, query, str(today) ) # highlights += search_results['organic_results'][:topn_articles] # highlights = pd.DataFrame(highlights) params = { "api_key": secret_key, "engine": "google_trends_trending_now", "frequency": "daily", "geo": "TW", "hl": "zh-tw" } search = GoogleSearch(params) results = search.get_dict() content = [] for ds in results['daily_searches']: for s in ds['searches']: q = s['query'] if 'articles' in s and len(s['articles'])>0: link = s['articles'][0]['link'] title = s['articles'][0]['title'] snippet = s['articles'][0]['snippet'] else: continue content.append({'query': q, 'title': title, 'snippet': snippet, 'link': link}) st.dataframe(pd.DataFrame(content)) else: st.write("Coming soon...") elif authentication_status == False: st.error('Username/password is incorrect') elif authentication_status == None: st.warning('Please enter your username and password')

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from langchain import OpenAI, SQLDatabase from langchain_experimental.sql import SQLDatabaseChain from langchain.memory import ConversationBufferMemory from langchain.agents import (AgentType, AgentExecutor, create_react_agent, create_openai_functions_agent, create_sql_agent) from langchain.agents.initialize import initialize_agent from langchain.tools import Tool from langchain.prompts import PromptTemplate # from langchain.retrievers import RetrievalModel from langchain.indexes.vectorstore import VectorstoreIndexCreator from langchain_community.document_loaders.text import TextLoader from langchain_community.agent_toolkits import SQLDatabaseToolkit # Document Loaders for PDFs and Docx from langchain.document_loaders.pdf import PyPDFLoader from langchain.document_loaders.word_document import Docx2txtLoader from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores.chroma import Chroma from langchain.chat_models.openai import ChatOpenAI from langchain.chains import ConversationalRetrievalChain from langchain.text_splitter import CharacterTextSplitter import langchain import psycopg2 import os import constants import constants_allen os.environ["OPENAI_API_KEY"] = constants.OPENAI_API_KEY import warnings warnings.filterwarnings('ignore') # Setup database connection # Setup database db = SQLDatabase.from_uri( f"postgresql+psycopg2://postgres:{constants.DBPASS}@localhost:5433/{constants.DB}", ) # Setup conversation memory memory = ConversationBufferMemory(memory_key="chat_history") # Setup llm llm = OpenAI(temperature=0, model_name="gpt-3.5-turbo") # Create a list of documents from all our files in the ./data/docs folder documents = [] for file in os.listdir("./data/docs"): if file.endswith(".pdf"): pdf_path = "./data/docs/" + file loader = PyPDFLoader(pdf_path) documents.extend(loader.load()) elif file.endswith("./docx") or file.endswith(".doc"): doc_path = "./data/docs/" + file loader = Docx2txtLoader(doc_path) documents.extend(loader.load()) elif file.endswith(".txt"): text_path = "./data/docs/" + file loader = TextLoader(text_path) documents.extend(loader.load()) # Split the documents into smaller chunks text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10) documents = text_splitter.split_documents(documents) # Convert the document chunks to embedding and save them to the vector store vectordb = Chroma.from_documents(documents, embedding=OpenAIEmbeddings(), persist_directory="./data/docs") vectordb.persist() # Setup prompt template _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database. Never query for all the columns from a specific table, only ask for a the few relevant columns given the question. Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table. Use the following format: Question: Question here SQLQuery: SQL Query to run SQLResult: Result of the SQLQuery Answer: Final answer here Only use the following tables: {table_info} Question: {input} """ # TEMPLATE = '''"Given your input question, provide the answer from the most relevant source. You can choose between two sources: # 1. Text File Source: This source contains Q&A data on data structures and algorithms. # 2. Database Source: This source includes information from three tables: # - 'developers' table: Contains details about developers, such as full_name, email, phone, position, and department. # - 'tasks' table: Holds information about developers' tasks, including the task, completion status, due date, completion date, and priority. # - 'insurance_data' table: Contains information about PRU Life Insurance and USA Insurance, including questions and answers. # Only query the content of the sources, not the metadata. # You have access to the following tools: # {tools} # Use the following format: # Question: the input question you must answer # Thought: you should always think about what to do # Action: the action to take, should be one of [{tool_names}] # Action Input: the input to the action # Observation: the result of the action # ... (this Thought/Action/Action Input/Observation can repeat N times) # Thought: I now know the final answer # Final Answer: the final answer to the original input question # Begin! # Question: {input} # Thought:{agent_scratchpad}''' PROMPT = PromptTemplate.from_template(_DEFAULT_TEMPLATE) # Setup db chain db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True) toolkit = SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0, verbose=True, model_name="gpt-3.5-turbo")) # Create Document Q&A chain pdf_qa = ConversationalRetrievalChain.from_llm( OpenAI(temperate=0.7, model_name="gpt-3.5-turbo"), retriever=vectordb.as_retriever(search_kwargs={'k': 6}), return_source_documents=True, verbose=False ) # Setup text data source directory = "./data/text/final1.txt" loader = TextLoader(directory) index_creator = VectorstoreIndexCreator() index = index_creator.from_loaders([loader]) retriever_text = index.vectorstore.as_retriever() # Define the tools needed tools = [ Tool(name="MyDocs", func=pdf_qa, description="Retrieve information from documents."), Tool(name="MyDB", func=db_chain.run, description="Query the PostgreSQL database",), Tool(name="MyRetriever", func=retriever_text.get_relevant_documents, description="Retrieve documents from text file"), ] agent = initialize_agent( tools, llm, memory=memory, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, handle_parsing_errors=True, output_key="result", early_stopping_method="generate", max_iterations=3, ) # agent = create_sql_agent( # llm=OpenAI(temperature=0), # toolkit=toolkit, # verbose=True, # agent_type=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, # ) # agent = create_react_agent( # llm=llm, # tools=tools, # prompt=PROMPT # ) # agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, # verbose=True, handle_parsing_errors=True) yellow = "\033[0;33m" green = "\033[0;32m" white = "\033[0;39m" def chat(): while True: user_input = input("You: ") # Get user input # Use the agent to generate a response try: bot_response = agent.run(user_input) # bot_response = agent_executor.invoke({"input": user_input}) except ValueError as e: bot_response = str(e) if not bot_response.startswith("Could not parse LLM output: `"): raise e bot_response = bot_response.removeprefix("Could not parse LLM output: `").removesuffix("`") print("Chatbot: " + bot_response) if user_input.lower() == 'exit': break if __name__ == "__main__": chat()

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"""Base interface that all chains should implement.""" import json from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Union import yaml from pydantic import BaseModel, Field, validator import langchain from langchain.callbacks import get_callback_manager from langchain.callbacks.base import BaseCallbackManager from langchain.schema import BaseMemory def _get_verbosity() -> bool: return langchain.verbose class Chain(BaseModel, ABC): """Base interface that all chains should implement.""" memory: Optional[BaseMemory] = None callback_manager: BaseCallbackManager = Field( default_factory=get_callback_manager, exclude=True ) verbose: bool = Field( default_factory=_get_verbosity ) # Whether to print the response text class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") @validator("callback_manager", pre=True, always=True) def set_callback_manager( cls, callback_manager: Optional[BaseCallbackManager] ) -> BaseCallbackManager: """If callback manager is None, set it. This allows users to pass in None as callback manager, which is a nice UX. """ return callback_manager or get_callback_manager() @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @property @abstractmethod def input_keys(self) -> List[str]: """Input keys this chain expects.""" @property @abstractmethod def output_keys(self) -> List[str]: """Output keys this chain expects.""" def _validate_inputs(self, inputs: Dict[str, str]) -> None: """Check that all inputs are present.""" missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") def _validate_outputs(self, outputs: Dict[str, str]) -> None: if set(outputs) != set(self.output_keys): raise ValueError( f"Did not get output keys that were expected. " f"Got: {set(outputs)}. Expected: {set(self.output_keys)}." ) @abstractmethod def _call(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" raise NotImplementedError("Async call not supported for this chain type.") def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = self._call(inputs) except (KeyboardInterrupt, Exception) as e: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) async def acall( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) if self.callback_manager.is_async: await self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) else: self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = await self._acall(inputs) except (KeyboardInterrupt, Exception) as e: if self.callback_manager.is_async: await self.callback_manager.on_chain_error(e, verbose=self.verbose) else: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e if self.callback_manager.is_async: await self.callback_manager.on_chain_end(outputs, verbose=self.verbose) else: self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) def prep_outputs( self, inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False, ) -> Dict[str, str]: """Validate and prep outputs.""" self._validate_outputs(outputs) if self.memory is not None: self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: """Validate and prep inputs.""" if not isinstance(inputs, dict): _input_keys = set(self.input_keys) if self.memory is not None: # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) if len(_input_keys) != 1: raise ValueError( f"A single string input was passed in, but this chain expects " f"multiple inputs ({_input_keys}). When a chain expects " f"multiple inputs, please call it by passing in a dictionary, " "eg `chain({'foo': 1, 'bar': 2})`" ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs) for inputs in input_list] def run(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return self(args[0])[self.output_keys[0]] if kwargs and not args: return self(kwargs)[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) async def arun(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return (await self.acall(args[0]))[self.output_keys[0]] if kwargs and not args: return (await self.acall(kwargs))[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of chain.""" if self.memory is not None: raise ValueError("Saving of memory is not yet supported.") _dict = super().dict() _dict["_type"] = self._chain_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the chain. Args: file_path: Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path="path/chain.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save chain_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml")

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from typing import Dict, List, Optional from langchain.agents.load_tools import ( _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS, ) from langflow.custom import customs from langflow.interface.base import LangChainTypeCreator from langflow.interface.tools.constants import ( ALL_TOOLS_NAMES, CUSTOM_TOOLS, FILE_TOOLS, OTHER_TOOLS, ) from langflow.interface.tools.util import get_tool_params from langflow.services.utils import get_settings_manager from langflow.template.field.base import TemplateField from langflow.template.template.base import Template from langflow.utils import util from langflow.utils.util import build_template_from_class TOOL_INPUTS = { "str": TemplateField( field_type="str", required=True, is_list=False, show=True, placeholder="", value="", ), "llm": TemplateField( field_type="BaseLanguageModel", required=True, is_list=False, show=True ), "func": TemplateField( field_type="function", required=True, is_list=False, show=True, multiline=True, ), "code": TemplateField( field_type="str", required=True, is_list=False, show=True, value="", multiline=True, ), "path": TemplateField( field_type="file", required=True, is_list=False, show=True, value="", suffixes=[".json", ".yaml", ".yml"], file_types=["json", "yaml", "yml"], ), } class ToolCreator(LangChainTypeCreator): type_name: str = "tools" tools_dict: Optional[Dict] = None @property def type_to_loader_dict(self) -> Dict: settings_manager = get_settings_manager() if self.tools_dict is None: all_tools = {} for tool, tool_fcn in ALL_TOOLS_NAMES.items(): tool_params = get_tool_params(tool_fcn) tool_name = tool_params.get("name") or tool if ( tool_name in settings_manager.settings.TOOLS or settings_manager.settings.DEV ): if tool_name == "JsonSpec": tool_params["path"] = tool_params.pop("dict_") # type: ignore all_tools[tool_name] = { "type": tool, "params": tool_params, "fcn": tool_fcn, } self.tools_dict = all_tools return self.tools_dict def get_signature(self, name: str) -> Optional[Dict]: """Get the signature of a tool.""" base_classes = ["Tool", "BaseTool"] fields = [] params = [] tool_params = {} # Raise error if name is not in tools if name not in self.type_to_loader_dict.keys(): raise ValueError("Tool not found") tool_type: str = self.type_to_loader_dict[name]["type"] # type: ignore # if tool_type in _BASE_TOOLS.keys(): # params = [] if tool_type in _LLM_TOOLS.keys(): params = ["llm"] elif tool_type in _EXTRA_LLM_TOOLS.keys(): extra_keys = _EXTRA_LLM_TOOLS[tool_type][1] params = ["llm"] + extra_keys elif tool_type in _EXTRA_OPTIONAL_TOOLS.keys(): extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type][1] params = extra_keys # elif tool_type == "Tool": # params = ["name", "description", "func"] elif tool_type in CUSTOM_TOOLS: # Get custom tool params params = self.type_to_loader_dict[name]["params"] # type: ignore base_classes = ["function"] if node := customs.get_custom_nodes("tools").get(tool_type): return node elif tool_type in FILE_TOOLS: params = self.type_to_loader_dict[name]["params"] # type: ignore base_classes += [name] elif tool_type in OTHER_TOOLS: tool_dict = build_template_from_class(tool_type, OTHER_TOOLS) fields = tool_dict["template"] # Pop unnecessary fields and add name fields.pop("_type") # type: ignore fields.pop("return_direct") # type: ignore fields.pop("verbose") # type: ignore tool_params = { "name": fields.pop("name")["value"], # type: ignore "description": fields.pop("description")["value"], # type: ignore } fields = [ TemplateField(name=name, field_type=field["type"], **field) for name, field in fields.items() # type: ignore ] base_classes += tool_dict["base_classes"] # Copy the field and add the name for param in params: field = TOOL_INPUTS.get(param, TOOL_INPUTS["str"]).copy() field.name = param field.advanced = False if param == "aiosession": field.show = False field.required = False fields.append(field) template = Template(fields=fields, type_name=tool_type) tool_params = {**tool_params, **self.type_to_loader_dict[name]["params"]} return { "template": util.format_dict(template.to_dict()), **tool_params, "base_classes": base_classes, } def to_list(self) -> List[str]: """List all load tools""" return list(self.type_to_loader_dict.keys()) tool_creator = ToolCreator()

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#!/usr/bin/env python3 from restapi_helper import LangChainHelper from langchain.schema import HumanMessage print('==Simple message predict==') with LangChainHelper() as lch: text = 'Hey there!' messages = [HumanMessage(content=text)] print(lch.predict_messages(messages)) print('==As English teacher - know the answer==') with LangChainHelper() as lch: role = 'English teacher' text = 'What is a plural form of child?' print(lch.get_answer_as_role(role, text)) print('==As English teacher - does not know the answer==') with LangChainHelper() as lch: role = 'English teacher' text = 'How old am I?' print(lch.get_answer_as_role(role, text))

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import streamlit as st # Import transformer classes for generaiton from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, GPT2Tokenizer, GPT2LMHeadModel, GPT2Model # Import torch for datatype attributes import torch # Import the prompt wrapper...but for llama index from llama_index.prompts.prompts import SimpleInputPrompt # Import the llama index HF Wrapper from llama_index.llms import HuggingFaceLLM # Bring in embeddings wrapper from llama_index.embeddings import LangchainEmbedding # Bring in HF embeddings - need these to represent document chunks from langchain.embeddings.huggingface import HuggingFaceEmbeddings # Bring in stuff to change service context from llama_index import set_global_service_context from llama_index import ServiceContext # Import deps to load documents from llama_index import VectorStoreIndex, SimpleDirectoryReader from pathlib import Path import pypdf import time import os import tempfile # Define variable to hold llama2 weights namingfiner name = "gpt2" # Set auth token variable from hugging face auth_token = "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" @st.cache_resource def get_tokenizer_model(): # Create tokenizer tokenizer = GPT2Tokenizer.from_pretrained(name, use_auth_token=auth_token) # Create model model = GPT2LMHeadModel.from_pretrained(name) return model, tokenizer model, tokenizer = get_tokenizer_model() # disclaimer st.title('LLM Deployment Prototype for Production') st.caption("Special thanks to my mentor, Medkham Chanthavong, for all the support in making this project a reality. Connect with Medkham on LinkedIn: [Medkham Chanthavong](https://www.linkedin.com/in/medkham-chanthavong-4524b0125/).") st.subheader('Disclaimer') st.write("Due to Streamlit's lack of cloud-GPU support and limited memory, we're using GPT-2 instead of Llama 2 7B. GPT-2, \ being smaller, is less capable of accurately retrieving information from external data. This site is a prototype for LLM deployment \ and may not retrieve precise data from external sources. Additionally, the site might experience crashes due to memory constraints.\ In case of a crash, please feel free shoot me a message at my [LinkedIn profile](https://www.linkedin.com/in/duwe-ng/) so I can reboot the site. \ For a more advanced model demonstration using Llama 2 7B, check out our Colab notebook: \ [LLM Deployment Prototype](https://colab.research.google.com/drive/1bGf9rKntMjH4KtpKs9ryucj1nbiKs_zk?usp=sharing).") # # Initialize the SimpleInputPrompt with an empty template # query_wrapper_prompt = SimpleInputPrompt("{query_str}") # # Streamlit UI to let the user update the system prompt # # Start with an empty string or a default prompt # default_prompt = "" # user_system_prompt = st.text_area("How can I best assist you?", value="", height=100) # update_button = st.button('Request') # Import the prompt wrapper for llama index # Create a system prompt system_prompt = """ Hey there! 👋 You are here to help you out in the best way you can. Think of yourself as your friendly and trustworthy assistant. Your top priority is to be super helpful and safe in your responses. \ Here's a quick heads up about what you can expect from me: \ 1. Positive Vibes Only: I steer clear of anything harmful, unethical, or offensive. No racism, sexism, or toxic stuff here. I'm all about being respectful and keeping things on the up-and-up. \ 2. Making Sense Matters: If the user question seems a bit confusing or doesn't quite add up, You will let you know and try to clarify things instead of giving the user a misleading answer. \ 3. Honesty is Key: Not sure about something? You won't make stuff up. If you don't have the answer, you will be upfront about it. \ 4. All About Your PDFs: The user documents are your focus. Got a question about the content in your PDFs? That's exactly what you are here for. Let's dive into those documents and find the answers the user need! \ So, how can you assist the user today with their PDFs? """ # Throw together the query wrapper query_wrapper_prompt = SimpleInputPrompt("{query_str}") # Initialize the llm object with a placeholder or default system prompt llm = HuggingFaceLLM( context_window=1024, max_new_tokens=128, system_prompt="", query_wrapper_prompt=query_wrapper_prompt, model=model, tokenizer=tokenizer ) # # Function to update the system prompt and reinitialize the LLM with the new prompt # def update_system_prompt(new_prompt): # global llm # llm.system_prompt = new_prompt # if update_button: # # Update the system prompt and reinitialize the LLM # update_system_prompt(user_system_prompt) # st.success('Requested') # check if user request save to the memory # st.write(llm.system_prompt) # Create and dl embeddings instance embeddings=LangchainEmbedding( HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") ) # Create new service context instance service_context = ServiceContext.from_defaults( chunk_size=256, llm=llm, embed_model=embeddings ) # And set the service context set_global_service_context(service_context) # Upload PDF and process it st.subheader("Please upload your data to enable the retrieval system; otherwise, it will not respond.") documents = [] uploaded_file = st.file_uploader("Upload PDF", type="pdf") if uploaded_file is not None: # Create a temporary directory with tempfile.TemporaryDirectory() as temp_dir: # Write the uploaded file to a file in the temporary directory temp_file_path = os.path.join(temp_dir, uploaded_file.name) with open(temp_file_path, "wb") as f: f.write(uploaded_file.getbuffer()) # Now you can use SimpleDirectoryReader on the temp_dir documents = SimpleDirectoryReader(temp_dir).load_data() st.success(f"File {uploaded_file.name} uploaded successfully.") index = VectorStoreIndex.from_documents(documents) # Setup index query engine using LLM query_engine = index.as_query_engine(streaming=True, similarity_top_k=1) # Create centered main title st.title('👔 HireMind 🧩') # setup a session to hold all the old prompt if 'messages' not in st.session_state: st.session_state.messages = [] # print out the history message for message in st.session_state.messages: st.chat_message(message['role']).markdown(message['content']) # Create a text input box for the user # If the user hits enter prompt = st.chat_input('Input your prompt here') if prompt: st.chat_message('user').markdown(prompt) st.session_state.messages.append({'role': 'user', 'content': prompt}) response = query_engine.query(prompt) st.chat_message('assistant').markdown(response) st.session_state.messages.append( {'role': 'assistant', 'content': response} )

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For a more advanced model demonstration using Llama 2 7B, check out our Colab notebook: [LLM Deployment Prototype](https://colab.research.google.com/drive/1bGf9rKntMjH4KtpKs9ryucj1nbiKs_zk?usp=sharing)."""'], {}), '(\n "Due to Streamlit\'s lack of cloud-GPU support and limited memory, we\'re using GPT-2 instead of Llama 2 7B. GPT-2, being smaller, is less capable of accurately retrieving information from external data. This site is a prototype for LLM deployment and may not retrieve precise data from external sources. Additionally, the site might experience crashes due to memory constraints.In case of a crash, please feel free shoot me a message at my [LinkedIn profile](https://www.linkedin.com/in/duwe-ng/) so I can reboot the site. 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from langchain_openai import ChatOpenAI from langchain.chains import LLMChain from langchain.prompts import MessagesPlaceholder, HumanMessagePromptTemplate, ChatPromptTemplate from langchain.memory import ConversationBufferMemory, FileChatMessageHistory from dotenv import load_dotenv import sqlite3 import sqlparse import json import warnings from langchain_core._api.deprecation import LangChainDeprecationWarning # Ignore specific deprecation warnings from langchain_core warnings.filterwarnings("ignore", category=LangChainDeprecationWarning) load_dotenv() # Create and initialize the OpenAI client def create_openai_client(): return ChatOpenAI(model_name="gpt-3.5-turbo") # Retrieve the database schema from an SQLite database def get_database_schema(db_path): try: conn = sqlite3.connect(db_path) cursor = conn.cursor() cursor.execute("SELECT name FROM sqlite_master WHERE type='table';") tables = cursor.fetchall() schema_info = [] for table in tables: table_name = table[0] cursor.execute(f"PRAGMA table_info({table_name});") columns = cursor.fetchall() primary_keys = [col[1] for col in columns if col[5] == 1] schema_info.append(f"{table_name} (Primary Key: {', '.join(primary_keys)})") conn.close() return "filename: chinook.db, "+', '.join(schema_info) except Exception as e: return f"Error reading database schema: {e}" # Format the raw SQL query for better readability using sqlparse def format_sql_query(raw_sql): if raw_sql.startswith("```sql"): raw_sql = raw_sql[6:] if raw_sql.endswith("```"): raw_sql = raw_sql[:-3] raw_sql = raw_sql.strip() formatted_sql = sqlparse.format(raw_sql, reindent=True, keyword_case='upper') return formatted_sql # Function to execute a SQL query and return results def execute_sql_query(db_path, sql_query): try: conn = sqlite3.connect(db_path) cursor = conn.cursor() cursor.execute(sql_query) results = cursor.fetchall() conn.close() return results except sqlite3.Error as e: return f"SQL Error: {e}" # Main execution if __name__ == "__main__": client = create_openai_client() db_path = "chinook.db" database_schema = get_database_schema(db_path) memory = ConversationBufferMemory( chat_memory=FileChatMessageHistory("chat_history.json"), memory_key="messages", return_messages=True, ) prompt = ChatPromptTemplate( input_variables=["content", "messages"], messages=[ MessagesPlaceholder(variable_name="messages"), HumanMessagePromptTemplate.from_template( "You are a data analyst. Generate SQL queries from natural language descriptions. Provide only the SQL query in response, without explanations or additional text" "Question: {{question}} Database Schema: {{database_schema}} Response: {content}" ), ], ) chain = LLMChain( llm=client, prompt=prompt, memory=memory, ) while True: content = input("Usage: type 'quit' to exit chat\n>> ") if content == "quit": break result = chain({"content": content}) # Format the SQL query formatted_query = format_sql_query(result['text']) print("\nGenerated SQL Query:\n", formatted_query) # Execute the SQL query and print results query_results = execute_sql_query(db_path, formatted_query) print("\nQuery Results:\n", query_results)

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import langchain_visualizer # isort:skip # noqa: F401 import asyncio import vcr_langchain as vcr from fvalues import FValue from langchain import PromptTemplate from langchain.llms import OpenAI # ========================== Start of langchain example code ========================== # https://langchain.readthedocs.io/en/latest/modules/prompts/getting_started.html # An example prompt with one input variable one_input_prompt = PromptTemplate( input_variables=["adjective"], template="Tell me a {adjective} joke." ) prompt = one_input_prompt.format(adjective="funny") # ================================== Execute example ================================== def test_prompt(): assert prompt.parts == ( "Tell me a ", FValue(source="adjective", value="funny", formatted="funny"), " joke.", ) @vcr.use_cassette() async def one_input_prompt_demo(): agent = OpenAI(model_name="text-ada-001", temperature=0) return agent(prompt) def test_llm_usage_succeeds(): """ Check that it works like a regular prompt. Also, record playback for easy visualization. """ result = asyncio.get_event_loop().run_until_complete(one_input_prompt_demo()) assert result.strip().startswith("Why did the chicken cross the road?") if __name__ == "__main__": from langchain_visualizer import visualize visualize(one_input_prompt_demo)

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from typing import Optional, List from langchain.chains.openai_functions import create_structured_output_runnable from langchain_community.chat_models import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field import logging import langchain from dreamsboard.document_loaders.protocol.ner_protocol import DreamsStepInfo, DreamsStepInfoListWrapper langchain.verbose = True logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) logging.basicConfig(level=logging.DEBUG) # 控制台打印 handler = logging.StreamHandler() handler.setLevel(logging.DEBUG) logger.addHandler(handler) def test_create_structured_output_runnable2() -> None: """Test create_structured_output_runnable. 测试创建结构化输出可运行对象。""" llm = ChatOpenAI(openai_api_base='http://127.0.0.1:30000/v1', model="glm-4", openai_api_key="glm-4", verbose=True, temperature=0.1, top_p=0.9, ) prompt = ChatPromptTemplate.from_messages( [ ("system", """根据提供的故事场景，您作为心理咨询工作者可以使用开放性问题来引导来访者表达他们的感受和思维。以下是一步一步的分解： **Step 1: 建立情感连接** 开始时，您可以通过表达理解和共鸣来建立情感连接，让来访者感到舒适。您可以说：“我注意到这个对话中有许多愉快的时刻和互动。你对这些时刻有什么特别的感受吗？” **Step 2: 探索来访者的感受** 继续引导来访者表达他们的感受。您可以问：“在这个对话中，有哪些瞬间让你感到开心或快乐？” **Step 3: 询问是否有反感情绪** 除了积极的情感，也要问询是否有一些负面情感或担忧。您可以说：“除了快乐的瞬间，是否有一些让你感到不安或担忧的地方？” **Step 4: 深入探讨个人反应** 一旦来访者开始分享他们的感受，可以深入探讨他们的个人反应。例如：“你觉得自己在这些互动中扮演了什么角色？” **Step 5: 探索与他人的互动** 继续引导来访者思考他们与他人的互动。您可以问：“这些互动对你与他人的关系有什么影响？你觉得与朋友之间的互动如何影响你的情感状态？” **Step 6: 引导自我反思** 最后，鼓励来访者进行自我反思。您可以问：“在这个故事场景中，你是否注意到了自己的情感变化或思维模式？有没有什么你想要深入探讨或解决的问题？” 通过这种方式，您可以引导来访者自由表达他们的情感和思维，帮助他们更好地理解自己和他们与他人的互动。同时，保持开放和倾听，以便在需要时提供支持和建议。"""), ("ai", """step_advice|step_description 我注意到这个对话中有许多愉快的时刻和互动。你对这些时刻有什么特别的感受吗？|建立情感连接 在这个对话中，有哪些瞬间让你感到开心或快乐?|探索来访者的感受 除了快乐的瞬间，是否有一些让你感到不安或担忧的地方？|询问是否有反感情绪 你觉得自己在这些互动中扮演了什么角色?|深入探讨个人反应 这些互动对你与他人的关系有什么影响？你觉得与朋友之间的互动如何影响你的情感状态?|探索与他人的互动 在这个故事场景中，你是否注意到了自己的情感变化或思维模式？有没有什么你想要深入探讨或解决的问题?|引导自我反思"""), ("human", "{input}"), ] ) chain = create_structured_output_runnable(DreamsStepInfoListWrapper, llm, prompt) out = chain.invoke({"input": """以下是如何通过分步引导来访者张毛峰说出自己的问题的步骤： ### Step 1: 建立情感连接 - “张毛峰，我注意到你提到的这段经历让你感到非常愉快，甚至有些事情让你忍不住笑出声。能跟我分享一下，那个让你这么开心的事情吗？” ### Step 2: 探索来访者的积极感受 - “在那次和小姐姐的互动中，有哪些具体的瞬间让你感到特别快乐？” ### Step 3: 引导来访者表达背后的动机和情感 - “我看到你因为举报那位小姐姐而感到开心，这背后有没有什么特别的原因？你能告诉我举报她的行为对你意味着什么吗？” ### Step 4: 探询来访者的内心冲突和不安 - “虽然这件事情让你觉得开心，但我想知道，在整件事情中，有没有什么让你感到困扰或不安的地方？” ### Step 5: 询问来访者的反思和感受 - “你提到觉得有时候没有必要讲道理，这是不是说明这件事情也让你感到某种程度上的矛盾和困惑？” ### Step 6: 深入探讨来访者的自我认知 - “你说‘我不想玩了，因为我不是我自己了’，这句话似乎透露出你对自己有一些新的认识。你能解释一下这是什么意思吗？” 以下是具体的步骤分解： **Step 1: 建立情感连接** - 通过询问张毛峰感到愉快的时刻来建立情感上的联系。 **Step 2: 探索来访者的积极感受** - 帮助张毛峰回忆和描述那些让他感到快乐的细节。 **Step 3: 引导来访者表达背后的动机和情感** - 深入探讨举报行为背后的动机和情感，了解他为何会因为举报而感到快乐。 **Step 4: 探询来访者的内心冲突和不安** - 询问是否有任何不安或冲突的情绪，比如对于自己行为的道德考量。 **Step 5: 询问来访者的反思和感受** - 让张毛峰反思他的行为和感受，了解他对于讲道理的看法。 **Step 6: 深入探讨来访者的自我认知** - 针对他提到的“我不是我自己了”这句话，探索他的自我认同和可能的内心变化。 通过这样的引导，可以帮助张毛峰表达和认识自己的情感、动机和内心冲突，从而更好地理解自己的问题所在。"""}) logger.info(out) # -> Dog(name="Harry", color="brown", fav_food="chicken")

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import logging from langchain.chat_models import ChatOpenAI from dreamsboard.dreams.builder_cosplay_code.base import StructuredDreamsStoryboard from dreamsboard.dreams.dreams_personality_chain.base import StoryBoardDreamsGenerationChain import langchain from dreamsboard.engine.generate.code_generate import QueryProgramGenerator, EngineProgramGenerator, AIProgramGenerator from dreamsboard.engine.loading import load_store_from_storage from dreamsboard.engine.storage.storage_context import StorageContext langchain.verbose = True logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) # 控制台打印 handler = logging.StreamHandler() handler.setLevel(logging.DEBUG) logger.addHandler(handler) def test_structured_dreams_storyboard_store_print() -> None: try: storage_context = StorageContext.from_defaults(persist_dir="./storage") code_gen_builder = load_store_from_storage(storage_context) index_loaded = True except: index_loaded = False if not index_loaded: llm = ChatOpenAI( verbose=True ) dreams_generation_chain = StoryBoardDreamsGenerationChain.from_dreams_personality_chain( llm=llm, csv_file_path="/media/checkpoint/speech_data/抖音作品/id46Bv3g/str/id46Bv3g_keyframe.csv") output = dreams_generation_chain.run() logger.info("dreams_guidance_context:" + output.get("dreams_guidance_context")) logger.info("dreams_personality_context:" + output.get("dreams_personality_context")) dreams_guidance_context = output.get("dreams_guidance_context") dreams_personality_context = output.get("dreams_personality_context") storyboard_executor = StructuredDreamsStoryboard.form_builder(llm=llm, builder=dreams_generation_chain.builder, dreams_guidance_context=dreams_guidance_context, dreams_personality_context=dreams_personality_context ) code_gen_builder = storyboard_executor.loader_cosplay_builder() executor = code_gen_builder.build_executor() logger.info(executor.executor_code) assert True

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from model.chain_spec import ChainSpec, LLMChainSpec, SequentialChainSpec, CaseChainSpec, APIChainSpec, ReformatChainSpec, TransformChainSpec, VectorSearchChainSpec from model.chain_revision import ChainRevision from model.lang_chain_context import LangChainContext from langchain.llms.fake import FakeListLLM from model.tests.factory import SpecFactoryContext, llm_factory, sequential_factory, case_factory def test_llm_chain_spec_serialization(): llm_chain_spec = LLMChainSpec( chain_id=1, input_keys=["input1", "input2"], output_key="output1", prompt="prompt", llm_key="llm_key", chain_type="llm_chain_spec", ) serialized = llm_chain_spec.json() assert serialized == '{"chain_id": 1, "input_keys": ["input1", "input2"], "output_key": "output1", "chain_type": "llm_chain_spec", "prompt": "prompt", "llm_key": "llm_key"}' revision = ChainRevision(chain=llm_chain_spec, llms={}) serialized_revision = revision.json() deserialized = ChainRevision.parse_raw(serialized_revision).chain assert deserialized == llm_chain_spec def test_llm_chain_spec_to_lang_chain_creates_valid_chain(): prompt_template = "the prompt {input1} {input2}" llm_chain_spec = LLMChainSpec( chain_id=1, input_keys=["input1", "input2"], output_key="output1", prompt=prompt_template, llm_key="test", chain_type="llm_chain_spec", ) llms = llms={"test": FakeListLLM(responses=["response1"])} ctx = LangChainContext(llms=llms) llm_chain = llm_chain_spec.to_lang_chain(ctx) assert llm_chain.input_keys == ["input1", "input2"] assert llm_chain.output_key == "output1" assert llm_chain.prompt.template == prompt_template assert llm_chain.llm == llms["test"] output = llm_chain._call({"input1": "input1", "input2": "input2"}) assert output == {"output1": "response1"} def test_llm_chain_spec_to_lang_chain_creates_recording_chain(): prompt_template = "the prompt {input1} {input2}" llm_chain_spec = LLMChainSpec( chain_id=1, input_keys=["input1", "input2"], output_key="output1", prompt=prompt_template, llm_key="test", chain_type="llm_chain_spec", ) llms = llms={"test": FakeListLLM(responses=["response1"])} ctx = LangChainContext(llms=llms, recording=True) llm_chain = llm_chain_spec.to_lang_chain(ctx) assert len(ctx.prompts) == 1 output = llm_chain._call({"input1": "input1", "input2": "input2"}) assert output == {"output1": "response1"} assert ctx.prompts[0].calls == [ ({"input1": "input1", "input2": "input2"}, {"output1": "response1"}) ] def test_sequential_chain_spec_serialization(): sequential_chain_spec = SequentialChainSpec( chain_id=1, input_keys=["input1", "input2"], output_keys=["output1", "output2"], chain_type="sequential_chain_spec", chains=[ LLMChainSpec( chain_id=1, input_keys=["input1", "input2"], output_key="output1", prompt="prompt", llm_key="llm_key", chain_type="llm_chain_spec" ) ], ) serialized = sequential_chain_spec.json() deserialized = SequentialChainSpec.parse_raw(serialized) assert deserialized == sequential_chain_spec def test_sequential_chain_spec_to_lang_chain_creates_valid_chain(): llm_chain_spec1 = LLMChainSpec( chain_id=1, input_keys=["input1", "input2"], output_key="llm1_output", prompt="the prompt {input1} {input2}", llm_key="test", chain_type="llm_chain_spec", ) llm_chain_spec2 = LLMChainSpec( chain_id=2, input_keys=["llm1_output", "input3"], output_key="llm2_output", prompt="the prompt {llm1_output} {input3}", llm_key="test", chain_type="llm_chain_spec", ) sequential_chain_spec = SequentialChainSpec( chain_id=3, input_keys=["input1", "input2", "input3"], output_keys=["llm2_output"], chain_type="sequential_chain_spec", chains=[llm_chain_spec1, llm_chain_spec2], ) llms = llms={"test": FakeListLLM(responses=["fake_response1", "fake_response2"])} ctx = LangChainContext(llms=llms) sequential_chain = sequential_chain_spec.to_lang_chain(ctx) assert sequential_chain.input_keys == ["input1", "input2", "input3"] assert sequential_chain.output_keys == ["llm2_output"] assert sequential_chain.chains[0].input_keys == ["input1", "input2"] assert sequential_chain.chains[0].output_keys == ["llm1_output"] assert sequential_chain.chains[0].prompt.template == llm_chain_spec1.prompt assert sequential_chain.chains[0].llm == llms["test"] assert sequential_chain.chains[1].input_keys == ["llm1_output", "input3"] assert sequential_chain.chains[1].output_keys == ["llm2_output"] assert sequential_chain.chains[1].prompt.template == llm_chain_spec2.prompt assert sequential_chain.chains[1].llm == llms["test"] output = sequential_chain.run({"input1": "input1", "input2": "input2", "input3": "input3"}) assert output == "fake_response2" def test_case_chain_spec_serialization(): case_chain_spec = CaseChainSpec( chain_id=3, chain_type="case_chain_spec", categorization_key="categorization_input", cases={ "response1": LLMChainSpec( chain_id=1, input_keys=["input1", "input2"], output_key="output", prompt="prompt", llm_key="test", chain_type="llm_chain_spec" ), "response2": LLMChainSpec( chain_id=2, input_keys=["input3", "input4"], output_key="output", prompt="prompt", llm_key="test", chain_type="llm_chain_spec" ) }, default_case=LLMChainSpec( chain_id=4, input_keys=["input1", "input2"], output_key="output", prompt="prompt", llm_key="test", chain_type="llm_chain_spec" ), ) serialized = case_chain_spec.json() deserialized = CaseChainSpec.parse_raw(serialized) assert deserialized == case_chain_spec def test_case_chain_spec_to_lang_chain_creates_valid_chain(): llms = {"test1": FakeListLLM(responses=["fake_response1"]), "test2": FakeListLLM(responses=["fake_response2"])} llm_chain_spec1 = LLMChainSpec( chain_id=1, input_keys=["input1", "input2"], output_key="output", prompt="ll1 prompt {input1} {input2}", llm_key="test1", chain_type="llm_chain_spec", ) llm_chain_spec2 = LLMChainSpec( chain_id=2, input_keys=["input3", "input4"], output_key="output", prompt="llm2 prompt {input3} {input4}", llm_key="test2", chain_type="llm_chain_spec", ) llm_chain_spec3 = LLMChainSpec( chain_id=4, input_keys=["input1", "input2"], output_key="output", prompt="default prompt {input1} {input2}", llm_key="test1", chain_type="llm_chain_spec" ) case_chain_spec = CaseChainSpec( chain_id=3, chain_type="case_chain_spec", categorization_key="categorization_input", cases={ "response1": llm_chain_spec1, "response2": llm_chain_spec2, }, default_case=llm_chain_spec3, ) ctx = LangChainContext(llms=llms) case_chain = case_chain_spec.to_lang_chain(ctx) assert case_chain.input_keys == ["categorization_input", "input1", "input2", "input3", "input4"] assert case_chain.output_keys == ["output"] assert case_chain.subchains["response1"].input_keys == ["input1", "input2"] assert case_chain.subchains["response1"].output_keys == ["output"] assert case_chain.subchains["response1"].prompt.template == llm_chain_spec1.prompt assert case_chain.subchains["response1"].llm == llms["test1"] assert case_chain.subchains["response2"].input_keys == ["input3", "input4"] assert case_chain.subchains["response2"].output_keys == ["output"] assert case_chain.subchains["response2"].prompt.template == llm_chain_spec2.prompt assert case_chain.subchains["response2"].llm == llms["test2"] assert case_chain.default_chain.input_keys == ["input1", "input2"] assert case_chain.default_chain.output_keys == ["output"] assert case_chain.default_chain.prompt.template == llm_chain_spec3.prompt assert case_chain.default_chain.llm == llms["test1"] output = case_chain.run({ "categorization_input": "response2", "input1": "input1", "input2": "input2", "input3": "input3", "input4": "input4" }) assert output == "fake_response2" def test_api_chain_spec_serialization(): api_chain_spec = APIChainSpec( chain_id=3, chain_type="api_chain_spec", input_keys=["input1", "input2"], output_key="output", url="http://test.com", method="POST", body="body {input1} {input2}", headers={"header1": "header1"}, ) revision = ChainRevision(chain=api_chain_spec, llms={}) serialized_revision = revision.json() deserialized = ChainRevision.parse_raw(serialized_revision).chain assert deserialized == api_chain_spec def test_api_chain_spec_to_lang_chain_creates_valid_chain(): api_chain_spec = APIChainSpec( chain_id=3, chain_type="api_chain_spec", input_keys=["input1", "input2"], output_key="output", url="http://test.com", method="POST", body="body {input1} {input2}", headers={"header1": "header1"}, ) ctx = LangChainContext(llms={}) api_chain = api_chain_spec.to_lang_chain(ctx) assert api_chain.input_keys == ["input1", "input2"] assert api_chain.output_keys == ["output"] assert api_chain.url == "http://test.com" assert api_chain.method == "POST" assert api_chain.body == "body {input1} {input2}" assert api_chain.headers == {"header1": "header1"} def test_reformat_chain_spec_serialization(): reformat_chain_spec = ReformatChainSpec( chain_id=3, chain_type="reformat_chain_spec", input_keys=["input1", "input2"], formatters={"output1": "formatter1", "output2": "formatter2"}, ) serialized = reformat_chain_spec.json() deserialized = ReformatChainSpec.parse_raw(serialized) assert deserialized == reformat_chain_spec def test_reformat_chain_spec_to_lang_chain_creates_valid_chain(): reformat_chain_spec = ReformatChainSpec( chain_id=3, chain_type="reformat_chain_spec", input_keys=["input1", "input2"], formatters={"output1": "formatter1", "output2": "formatter2"}, ) ctx = LangChainContext(llms={}) reformat_chain = reformat_chain_spec.to_lang_chain(ctx) assert reformat_chain.input_keys == ["input1", "input2"] assert reformat_chain.output_keys == ["output1", "output2"] assert reformat_chain.formatters == {"output1": "formatter1", "output2": "formatter2"} def test_transform_chain_spec_to_lang_chain_creates_valid_chain(): transform_chain_spec = TransformChainSpec( chain_id=4, chain_type="transform_chain_spec", input_keys=["x", "y"], output_keys=["z", "w"], transform_func=""" z = int(inputs['x']) + int(inputs['y']) w = int(inputs['x']) - int(inputs['y']) return {'z': z, 'w': w}""" ) ctx = LangChainContext(llms={}) transform_chain = transform_chain_spec.to_lang_chain(ctx) assert transform_chain.input_keys == ["x", "y"] assert transform_chain.output_keys == ["z", "w"] assert transform_chain.transform({'x': 4, 'y': 3}) == {'z': 7, 'w': 1} def test_vector_search_chain_spec_serialization(): vector_search_chain_spec = VectorSearchChainSpec( chain_id=3, chain_type="vector_search_chain_spec", input_keys=["input1", "input2"], output_key="output", embedding_engine="openai", embedding_params={"openai_api_key": "test"}, database="pinecone", database_params={"index": "test", "text_key": "text"}, num_results=10, query="How does {input}?" ) serialized = vector_search_chain_spec.json() deserialized = VectorSearchChainSpec.parse_raw(serialized) assert deserialized == vector_search_chain_spec class ChainDict: def __init__(self): self.chains = {} def add_chain(self, chain): self.chains[chain.chain_id] = chain def test_chain_spec_copy_replace(): ctx = SpecFactoryContext() chain = sequential_factory(ctx, chains=[ llm_factory(ctx), case_factory(ctx, cases={ "case1": llm_factory(ctx), "case2": sequential_factory(ctx, chains=[llm_factory(ctx), llm_factory(ctx)]), }, default_case=llm_factory(ctx)), ]) original_specs = ChainDict() chain.traverse(original_specs.add_chain) copied_specs = {} copied_chain = chain.copy_replace(lambda spec: spec) copied_specs = ChainDict() copied_chain.traverse(copied_specs.add_chain) assert len(original_specs.chains) == len(copied_specs.chains) for chain_id, spec in original_specs.chains.items(): copied_spec = copied_specs.chains.get(chain_id) assert copied_spec is not None assert copied_spec == spec assert copied_spec is not spec replacement = copied_specs.chains[3] replacement.prompt = "replaced" replace_chain = chain.copy_replace(lambda spec: spec if spec.chain_id != 3 else replacement) replace_specs = ChainDict() replace_chain.traverse(replace_specs.add_chain) assert len(original_specs.chains) == len(replace_specs.chains) assert 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import openai import os import dotenv from llama_index.agent.openai import OpenAIAgent from llama_index.llms.azure_openai import AzureOpenAI from llama_index.core.tools.tool_spec.load_and_search.base import LoadAndSearchToolSpec from llama_index.tools.google import GoogleSearchToolSpec from llama_index.tools.weather import OpenWeatherMapToolSpec from llama_index.embeddings.langchain import LangchainEmbedding from llama_index.core import ServiceContext, set_global_service_context from langchain.embeddings import OpenAIEmbeddings # Get API keys from environment variables dotenv.load_dotenv() google_api_key = os.environ.get("GOOGLE_API_KEY") google_cse_id = os.environ.get("GOOGLE_CSE_ID") openweather_api_key = os.environ.get("OPENWEATHER_API_KEY") os.environ["OPENAI_API_KEY"] = os.environ.get("AZURE_API_KEY") openai.api_type = "azure" openai.api_base = os.environ.get("AZURE_API_BASE") openai.api_key = os.environ.get("AZURE_API_KEY") azure_api_key = os.environ["AZURE_API_KEY"] azure_api_type = "azure" azure_api_base = os.environ.get("AZURE_API_BASE") azure_api_version = os.environ.get("AZURE_API_VERSION") azure_chatapi_version = os.environ.get("AZURE_CHATAPI_VERSION") # Check if user set the davinci model flag LLM_DEPLOYMENT_NAME = "gpt-4" LLM_MODEL_NAME = "gpt-4" EMBEDDINGS_DEPLOYMENT_NAME = "text-embedding-ada-002" openai.api_version = os.environ.get("AZURE_CHATAPI_FUNCTIONS_VERSION") print("Using gpt-4 2023-07-01-preview model.") llm = AzureOpenAI( engine=LLM_DEPLOYMENT_NAME, model=LLM_MODEL_NAME, api_key=azure_api_key, api_base=azure_api_base, api_type=azure_api_type, api_version=azure_chatapi_version, temperature=0.5, max_tokens=1024, ) embedding_llm = LangchainEmbedding( OpenAIEmbeddings( model=EMBEDDINGS_DEPLOYMENT_NAME, deployment=EMBEDDINGS_DEPLOYMENT_NAME, openai_api_key=azure_api_key, openai_api_base=azure_api_base, openai_api_type=azure_api_type, openai_api_version=azure_api_version, chunk_size=16, max_retries=3, ), embed_batch_size=1, ) service_context = ServiceContext.from_defaults( llm=llm, embed_model=embedding_llm, chunk_size=256, ) set_global_service_context(service_context) weather_tool = OpenWeatherMapToolSpec( key=openweather_api_key, ) agent = OpenAIAgent.from_tools( weather_tool.to_tool_list(), llm=llm, verbose=True, ) print("-------------------------------------------------------") print(agent.chat("what is the current weather like in Hillsboro, Oregon")) google_tool = GoogleSearchToolSpec( key=google_api_key, engine=google_cse_id ) #Wrap the google search tool as it returns large payloads tools = LoadAndSearchToolSpec.from_defaults( google_tool.to_tool_list()[0], ).to_tool_list() agent = OpenAIAgent.from_tools( tools, llm=llm, embedding_llm=embedding_llm, verbose=True ) # agent = OpenAIAgent.from_tools( # google_tool.to_tool_list(), # llm=llm, # verbose=True # ) print("-------------------------------------------------------") print(agent.chat("whats the latest news summary about India?")) print("-------------------------------------------------------") print(agent.chat('who is Ange Postecoglou, where is he now and what is his philosophy about football?')) print("-------------------------------------------------------") print(agent.chat("whats the latest financial news summary?"))

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""" .. warning:: Beta Feature! **Cache** provides an optional caching layer for LLMs. Cache is useful for two reasons: - It can save you money by reducing the number of API calls you make to the LLM provider if you're often requesting the same completion multiple times. - It can speed up your application by reducing the number of API calls you make to the LLM provider. Cache directly competes with Memory. See documentation for Pros and Cons. **Class hierarchy:** .. code-block:: BaseCache --> <name>Cache # Examples: InMemoryCache, RedisCache, GPTCache """ from __future__ import annotations import hashlib import inspect import json import logging import warnings from datetime import timedelta from functools import lru_cache from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast, ) from sqlalchemy import Column, Integer, String, create_engine, select from sqlalchemy.engine.base import Engine from sqlalchemy.orm import Session try: from sqlalchemy.orm import declarative_base except ImportError: from sqlalchemy.ext.declarative import declarative_base from langchain.embeddings.base import Embeddings from langchain.llms.base import LLM, get_prompts from langchain.load.dump import dumps from langchain.load.load import loads from langchain.schema import ChatGeneration, Generation from langchain.schema.cache import RETURN_VAL_TYPE, BaseCache from langchain.utils import get_from_env from langchain.vectorstores.redis import Redis as RedisVectorstore logger = logging.getLogger(__file__) if TYPE_CHECKING: import momento from cassandra.cluster import Session as CassandraSession def _hash(_input: str) -> str: """Use a deterministic hashing approach.""" return hashlib.md5(_input.encode()).hexdigest() def _dump_generations_to_json(generations: RETURN_VAL_TYPE) -> str: """Dump generations to json. Args: generations (RETURN_VAL_TYPE): A list of language model generations. Returns: str: Json representing a list of generations. Warning: would not work well with arbitrary subclasses of `Generation` """ return json.dumps([generation.dict() for generation in generations]) def _load_generations_from_json(generations_json: str) -> RETURN_VAL_TYPE: """Load generations from json. Args: generations_json (str): A string of json representing a list of generations. Raises: ValueError: Could not decode json string to list of generations. Returns: RETURN_VAL_TYPE: A list of generations. Warning: would not work well with arbitrary subclasses of `Generation` """ try: results = json.loads(generations_json) return [Generation(**generation_dict) for generation_dict in results] except json.JSONDecodeError: raise ValueError( f"Could not decode json to list of generations: {generations_json}" ) def _dumps_generations(generations: RETURN_VAL_TYPE) -> str: """ Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation` Args: generations (RETURN_VAL_TYPE): A list of language model generations. Returns: str: a single string representing a list of generations. This function (+ its counterpart `_loads_generations`) rely on the dumps/loads pair with Reviver, so are able to deal with all subclasses of Generation. Each item in the list can be `dumps`ed to a string, then we make the whole list of strings into a json-dumped. """ return json.dumps([dumps(_item) for _item in generations]) def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]: """ Deserialization of a string into a generic RETURN_VAL_TYPE (i.e. a sequence of `Generation`). See `_dumps_generations`, the inverse of this function. Args: generations_str (str): A string representing a list of generations. Compatible with the legacy cache-blob format Does not raise exceptions for malformed entries, just logs a warning and returns none: the caller should be prepared for such a cache miss. Returns: RETURN_VAL_TYPE: A list of generations. """ try: generations = [loads(_item_str) for _item_str in json.loads(generations_str)] return generations except (json.JSONDecodeError, TypeError): # deferring the (soft) handling to after the legacy-format attempt pass try: gen_dicts = json.loads(generations_str) # not relying on `_load_generations_from_json` (which could disappear): generations = [Generation(**generation_dict) for generation_dict in gen_dicts] logger.warning( f"Legacy 'Generation' cached blob encountered: '{generations_str}'" ) return generations except (json.JSONDecodeError, TypeError): logger.warning( f"Malformed/unparsable cached blob encountered: '{generations_str}'" ) return None class InMemoryCache(BaseCache): """Cache that stores things in memory.""" def __init__(self) -> None: """Initialize with empty cache.""" self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {} def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" return self._cache.get((prompt, llm_string), None) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" self._cache[(prompt, llm_string)] = return_val def clear(self, **kwargs: Any) -> None: """Clear cache.""" self._cache = {} Base = declarative_base() class FullLLMCache(Base): # type: ignore """SQLite table for full LLM Cache (all generations).""" __tablename__ = "full_llm_cache" prompt = Column(String, primary_key=True) llm = Column(String, primary_key=True) idx = Column(Integer, primary_key=True) response = Column(String) class SQLAlchemyCache(BaseCache): """Cache that uses SQAlchemy as a backend.""" def __init__(self, engine: Engine, cache_schema: Type[FullLLMCache] = FullLLMCache): """Initialize by creating all tables.""" self.engine = engine self.cache_schema = cache_schema self.cache_schema.metadata.create_all(self.engine) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" stmt = ( select(self.cache_schema.response) .where(self.cache_schema.prompt == prompt) # type: ignore .where(self.cache_schema.llm == llm_string) .order_by(self.cache_schema.idx) ) with Session(self.engine) as session: rows = session.execute(stmt).fetchall() if rows: try: return [loads(row[0]) for row in rows] except Exception: logger.warning( "Retrieving a cache value that could not be deserialized " "properly. This is likely due to the cache being in an " "older format. Please recreate your cache to avoid this " "error." ) # In a previous life we stored the raw text directly # in the table, so assume it's in that format. return [Generation(text=row[0]) for row in rows] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update based on prompt and llm_string.""" items = [ self.cache_schema(prompt=prompt, llm=llm_string, response=dumps(gen), idx=i) for i, gen in enumerate(return_val) ] with Session(self.engine) as session, session.begin(): for item in items: session.merge(item) def clear(self, **kwargs: Any) -> None: """Clear cache.""" with Session(self.engine) as session: session.query(self.cache_schema).delete() session.commit() class SQLiteCache(SQLAlchemyCache): """Cache that uses SQLite as a backend.""" def __init__(self, database_path: str = ".langchain.db"): """Initialize by creating the engine and all tables.""" engine = create_engine(f"sqlite:///{database_path}") super().__init__(engine) class RedisCache(BaseCache): """Cache that uses Redis as a backend.""" def __init__(self, redis_: Any, *, ttl: Optional[int] = None): """ Initialize an instance of RedisCache. This method initializes an object with Redis caching capabilities. It takes a `redis_` parameter, which should be an instance of a Redis client class, allowing the object to interact with a Redis server for caching purposes. Parameters: redis_ (Any): An instance of a Redis client class (e.g., redis.Redis) used for caching. This allows the object to communicate with a Redis server for caching operations. ttl (int, optional): Time-to-live (TTL) for cached items in seconds. If provided, it sets the time duration for how long cached items will remain valid. If not provided, cached items will not have an automatic expiration. """ try: from redis import Redis except ImportError: raise ValueError( "Could not import redis python package. " "Please install it with `pip install redis`." ) if not isinstance(redis_, Redis): raise ValueError("Please pass in Redis object.") self.redis = redis_ self.ttl = ttl def _key(self, prompt: str, llm_string: str) -> str: """Compute key from prompt and llm_string""" return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" generations = [] # Read from a Redis HASH results = self.redis.hgetall(self._key(prompt, llm_string)) if results: for _, text in results.items(): generations.append(Generation(text=text)) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisCache does not" " support caching ChatModel outputs." ) return # Write to a Redis HASH key = self._key(prompt, llm_string) with self.redis.pipeline() as pipe: pipe.hset( key, mapping={ str(idx): generation.text for idx, generation in enumerate(return_val) }, ) if self.ttl is not None: pipe.expire(key, self.ttl) pipe.execute() def clear(self, **kwargs: Any) -> None: """Clear cache. If `asynchronous` is True, flush asynchronously.""" asynchronous = kwargs.get("asynchronous", False) self.redis.flushdb(asynchronous=asynchronous, **kwargs) class RedisSemanticCache(BaseCache): """Cache that uses Redis as a vector-store backend.""" # TODO - implement a TTL policy in Redis DEFAULT_SCHEMA = { "content_key": "prompt", "text": [ {"name": "prompt"}, ], "extra": [{"name": "return_val"}, {"name": "llm_string"}], } def __init__( self, redis_url: str, embedding: Embeddings, score_threshold: float = 0.2 ): """Initialize by passing in the `init` GPTCache func Args: redis_url (str): URL to connect to Redis. embedding (Embedding): Embedding provider for semantic encoding and search. score_threshold (float, 0.2): Example: .. code-block:: python import langchain from langchain.cache import RedisSemanticCache from langchain.embeddings import OpenAIEmbeddings langchain.llm_cache = RedisSemanticCache( redis_url="redis://localhost:6379", embedding=OpenAIEmbeddings() ) """ self._cache_dict: Dict[str, RedisVectorstore] = {} self.redis_url = redis_url self.embedding = embedding self.score_threshold = score_threshold def _index_name(self, llm_string: str) -> str: hashed_index = _hash(llm_string) return f"cache:{hashed_index}" def _get_llm_cache(self, llm_string: str) -> RedisVectorstore: index_name = self._index_name(llm_string) # return vectorstore client for the specific llm string if index_name in self._cache_dict: return self._cache_dict[index_name] # create new vectorstore client for the specific llm string try: self._cache_dict[index_name] = RedisVectorstore.from_existing_index( embedding=self.embedding, index_name=index_name, redis_url=self.redis_url, schema=cast(Dict, self.DEFAULT_SCHEMA), ) except ValueError: redis = RedisVectorstore( embedding=self.embedding, index_name=index_name, redis_url=self.redis_url, index_schema=cast(Dict, self.DEFAULT_SCHEMA), ) _embedding = self.embedding.embed_query(text="test") redis._create_index(dim=len(_embedding)) self._cache_dict[index_name] = redis return self._cache_dict[index_name] def clear(self, **kwargs: Any) -> None: """Clear semantic cache for a given llm_string.""" index_name = self._index_name(kwargs["llm_string"]) if index_name in self._cache_dict: self._cache_dict[index_name].drop_index( index_name=index_name, delete_documents=True, redis_url=self.redis_url ) del self._cache_dict[index_name] def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" llm_cache = self._get_llm_cache(llm_string) generations: List = [] # Read from a Hash results = llm_cache.similarity_search( query=prompt, k=1, distance_threshold=self.score_threshold, ) if results: for document in results: generations.extend( _load_generations_from_json(document.metadata["return_val"]) ) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisSemanticCache only supports caching of " f"normal LLM generations, got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisSentimentCache does not" " support caching ChatModel outputs." ) return llm_cache = self._get_llm_cache(llm_string) _dump_generations_to_json([g for g in return_val]) metadata = { "llm_string": llm_string, "prompt": prompt, "return_val": _dump_generations_to_json([g for g in return_val]), } llm_cache.add_texts(texts=[prompt], metadatas=[metadata]) class GPTCache(BaseCache): """Cache that uses GPTCache as a backend.""" def __init__( self, init_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = None, ): """Initialize by passing in init function (default: `None`). Args: init_func (Optional[Callable[[Any], None]]): init `GPTCache` function (default: `None`) Example: .. code-block:: python # Initialize GPTCache with a custom init function import gptcache from gptcache.processor.pre import get_prompt from gptcache.manager.factory import get_data_manager # Avoid multiple caches using the same file, causing different llm model caches to affect each other def init_gptcache(cache_obj: gptcache.Cache, llm str): cache_obj.init( pre_embedding_func=get_prompt, data_manager=manager_factory( manager="map", data_dir=f"map_cache_{llm}" ), ) langchain.llm_cache = GPTCache(init_gptcache) """ try: import gptcache # noqa: F401 except ImportError: raise ImportError( "Could not import gptcache python package. " "Please install it with `pip install gptcache`." ) self.init_gptcache_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = init_func self.gptcache_dict: Dict[str, Any] = {} def _new_gptcache(self, llm_string: str) -> Any: """New gptcache object""" from gptcache import Cache from gptcache.manager.factory import get_data_manager from gptcache.processor.pre import get_prompt _gptcache = Cache() if self.init_gptcache_func is not None: sig = inspect.signature(self.init_gptcache_func) if len(sig.parameters) == 2: self.init_gptcache_func(_gptcache, llm_string) # type: ignore[call-arg] else: self.init_gptcache_func(_gptcache) # type: ignore[call-arg] else: _gptcache.init( pre_embedding_func=get_prompt, data_manager=get_data_manager(data_path=llm_string), ) self.gptcache_dict[llm_string] = _gptcache return _gptcache def _get_gptcache(self, llm_string: str) -> Any: """Get a cache object. When the corresponding llm model cache does not exist, it will be created.""" _gptcache = self.gptcache_dict.get(llm_string, None) if not _gptcache: _gptcache = self._new_gptcache(llm_string) return _gptcache def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up the cache data. First, retrieve the corresponding cache object using the `llm_string` parameter, and then retrieve the data from the cache based on the `prompt`. """ from gptcache.adapter.api import get _gptcache = self._get_gptcache(llm_string) res = get(prompt, cache_obj=_gptcache) if res: return [ Generation(**generation_dict) for generation_dict in json.loads(res) ] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache. First, retrieve the corresponding cache object using the `llm_string` parameter, and then store the `prompt` and `return_val` in the cache object. """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "GPTCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) from gptcache.adapter.api import put _gptcache = self._get_gptcache(llm_string) handled_data = json.dumps([generation.dict() for generation in return_val]) put(prompt, handled_data, cache_obj=_gptcache) return None def clear(self, **kwargs: Any) -> None: """Clear cache.""" from gptcache import Cache for gptcache_instance in self.gptcache_dict.values(): gptcache_instance = cast(Cache, gptcache_instance) gptcache_instance.flush() self.gptcache_dict.clear() def _ensure_cache_exists(cache_client: momento.CacheClient, cache_name: str) -> None: """Create cache if it doesn't exist. Raises: SdkException: Momento service or network error Exception: Unexpected response """ from momento.responses import CreateCache create_cache_response = cache_client.create_cache(cache_name) if isinstance(create_cache_response, CreateCache.Success) or isinstance( create_cache_response, CreateCache.CacheAlreadyExists ): return None elif isinstance(create_cache_response, CreateCache.Error): raise create_cache_response.inner_exception else: raise Exception(f"Unexpected response cache creation: {create_cache_response}") def _validate_ttl(ttl: Optional[timedelta]) -> None: if ttl is not None and ttl <= timedelta(seconds=0): raise ValueError(f"ttl must be positive but was {ttl}.") class MomentoCache(BaseCache): """Cache that uses Momento as a backend. See https://gomomento.com/""" def __init__( self, cache_client: momento.CacheClient, cache_name: str, *, ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True, ): """Instantiate a prompt cache using Momento as a backend. Note: to instantiate the cache client passed to MomentoCache, you must have a Momento account. See https://gomomento.com/. Args: cache_client (CacheClient): The Momento cache client. cache_name (str): The name of the cache to use to store the data. ttl (Optional[timedelta], optional): The time to live for the cache items. Defaults to None, ie use the client default TTL. ensure_cache_exists (bool, optional): Create the cache if it doesn't exist. Defaults to True. Raises: ImportError: Momento python package is not installed. TypeError: cache_client is not of type momento.CacheClientObject ValueError: ttl is non-null and non-negative """ try: from momento import CacheClient except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if not isinstance(cache_client, CacheClient): raise TypeError("cache_client must be a momento.CacheClient object.") _validate_ttl(ttl) if ensure_cache_exists: _ensure_cache_exists(cache_client, cache_name) self.cache_client = cache_client self.cache_name = cache_name self.ttl = ttl @classmethod def from_client_params( cls, cache_name: str, ttl: timedelta, *, configuration: Optional[momento.config.Configuration] = None, auth_token: Optional[str] = None, **kwargs: Any, ) -> MomentoCache: """Construct cache from CacheClient parameters.""" try: from momento import CacheClient, Configurations, CredentialProvider except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if configuration is None: configuration = Configurations.Laptop.v1() auth_token = auth_token or get_from_env("auth_token", "MOMENTO_AUTH_TOKEN") credentials = CredentialProvider.from_string(auth_token) cache_client = CacheClient(configuration, credentials, default_ttl=ttl) return cls(cache_client, cache_name, ttl=ttl, **kwargs) def __key(self, prompt: str, llm_string: str) -> str: """Compute cache key from prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Returns: str: The cache key. """ return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Lookup llm generations in cache by prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Raises: SdkException: Momento service or network error Returns: Optional[RETURN_VAL_TYPE]: A list of language model generations. """ from momento.responses import CacheGet generations: RETURN_VAL_TYPE = [] get_response = self.cache_client.get( self.cache_name, self.__key(prompt, llm_string) ) if isinstance(get_response, CacheGet.Hit): value = get_response.value_string generations = _load_generations_from_json(value) elif isinstance(get_response, CacheGet.Miss): pass elif isinstance(get_response, CacheGet.Error): raise get_response.inner_exception return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Store llm generations in cache. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model string. return_val (RETURN_VAL_TYPE): A list of language model generations. Raises: SdkException: Momento service or network error Exception: Unexpected response """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "Momento only supports caching of normal LLM generations, " f"got {type(gen)}" ) key = self.__key(prompt, llm_string) value = _dump_generations_to_json(return_val) set_response = self.cache_client.set(self.cache_name, key, value, self.ttl) from momento.responses import CacheSet if isinstance(set_response, CacheSet.Success): pass elif isinstance(set_response, CacheSet.Error): raise set_response.inner_exception else: raise Exception(f"Unexpected response: {set_response}") def clear(self, **kwargs: Any) -> None: """Clear the cache. Raises: SdkException: Momento service or network error """ from momento.responses import CacheFlush flush_response = self.cache_client.flush_cache(self.cache_name) if isinstance(flush_response, CacheFlush.Success): pass elif isinstance(flush_response, CacheFlush.Error): raise flush_response.inner_exception CASSANDRA_CACHE_DEFAULT_TABLE_NAME = "langchain_llm_cache" CASSANDRA_CACHE_DEFAULT_TTL_SECONDS = None class CassandraCache(BaseCache): """ Cache that uses Cassandra / Astra DB as a backend. It uses a single Cassandra table. The lookup keys (which get to form the primary key) are: - prompt, a string - llm_string, a deterministic str representation of the model parameters. (needed to prevent collisions same-prompt-different-model collisions) """ def __init__( self, session: Optional[CassandraSession] = None, keyspace: Optional[str] = None, table_name: str = CASSANDRA_CACHE_DEFAULT_TABLE_NAME, ttl_seconds: Optional[int] = CASSANDRA_CACHE_DEFAULT_TTL_SECONDS, skip_provisioning: bool = False, ): """ Initialize with a ready session and a keyspace name. Args: session (cassandra.cluster.Session): an open Cassandra session keyspace (str): the keyspace to use for storing the cache table_name (str): name of the Cassandra table to use as cache ttl_seconds (optional int): time-to-live for cache entries (default: None, i.e. forever) """ try: from cassio.table import ElasticCassandraTable except (ImportError, ModuleNotFoundError): raise ValueError( "Could not import cassio python package. " "Please install it with `pip install cassio`." ) self.session = session self.keyspace = keyspace self.table_name = table_name self.ttl_seconds = ttl_seconds self.kv_cache = ElasticCassandraTable( session=self.session, keyspace=self.keyspace, table=self.table_name, keys=["llm_string", "prompt"], primary_key_type=["TEXT", "TEXT"], ttl_seconds=self.ttl_seconds, skip_provisioning=skip_provisioning, ) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" item = self.kv_cache.get( llm_string=_hash(llm_string), prompt=_hash(prompt), ) if item is not None: generations = _loads_generations(item["body_blob"]) # this protects against malformed cached items: if generations is not None: return generations else: return None else: return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" blob = _dumps_generations(return_val) self.kv_cache.put( llm_string=_hash(llm_string), prompt=_hash(prompt), body_blob=blob, ) def delete_through_llm( self, prompt: str, llm: LLM, stop: Optional[List[str]] = None ) -> None: """ A wrapper around `delete` with the LLM being passed. In case the llm(prompt) calls have a `stop` param, you should pass it here """ llm_string = get_prompts( {**llm.dict(), **{"stop": stop}}, [], )[1] return self.delete(prompt, llm_string=llm_string) def delete(self, prompt: str, llm_string: str) -> None: """Evict from cache if there's an entry.""" return self.kv_cache.delete( llm_string=_hash(llm_string), prompt=_hash(prompt), ) def clear(self, **kwargs: Any) -> None: """Clear cache. This is for all LLMs at once.""" self.kv_cache.clear() CASSANDRA_SEMANTIC_CACHE_DEFAULT_DISTANCE_METRIC = "dot" CASSANDRA_SEMANTIC_CACHE_DEFAULT_SCORE_THRESHOLD = 0.85 CASSANDRA_SEMANTIC_CACHE_DEFAULT_TABLE_NAME = "langchain_llm_semantic_cache" CASSANDRA_SEMANTIC_CACHE_DEFAULT_TTL_SECONDS = None CASSANDRA_SEMANTIC_CACHE_EMBEDDING_CACHE_SIZE = 16 class CassandraSemanticCache(BaseCache): """ Cache that uses Cassandra as a vector-store backend for semantic (i.e. similarity-based) lookup. It uses a single (vector) Cassandra table and stores, in principle, cached values from several LLMs, so the LLM's llm_string is part of the rows' primary keys. The similarity is based on one of several distance metrics (default: "dot"). If choosing another metric, the default threshold is to be re-tuned accordingly. """ def __init__( self, session: Optional[CassandraSession], keyspace: Optional[str], embedding: Embeddings, table_name: str = CASSANDRA_SEMANTIC_CACHE_DEFAULT_TABLE_NAME, distance_metric: str = CASSANDRA_SEMANTIC_CACHE_DEFAULT_DISTANCE_METRIC, score_threshold: float = CASSANDRA_SEMANTIC_CACHE_DEFAULT_SCORE_THRESHOLD, ttl_seconds: Optional[int] = CASSANDRA_SEMANTIC_CACHE_DEFAULT_TTL_SECONDS, skip_provisioning: bool = False, ): """ Initialize the cache with all relevant parameters. Args: session (cassandra.cluster.Session): an open Cassandra session keyspace (str): the keyspace to use for storing the cache embedding (Embedding): Embedding provider for semantic encoding and search. table_name (str): name of the Cassandra (vector) table to use as cache distance_metric (str, 'dot'): which measure to adopt for similarity searches score_threshold (optional float): numeric value to use as cutoff for the similarity searches ttl_seconds (optional int): time-to-live for cache entries (default: None, i.e. forever) The default score threshold is tuned to the default metric. Tune it carefully yourself if switching to another distance metric. """ try: from cassio.table import MetadataVectorCassandraTable except (ImportError, ModuleNotFoundError): raise ValueError( "Could not import cassio python package. " "Please install it with `pip install cassio`." ) self.session = session self.keyspace = keyspace self.embedding = embedding self.table_name = table_name self.distance_metric = distance_metric self.score_threshold = score_threshold self.ttl_seconds = ttl_seconds # The contract for this class has separate lookup and update: # in order to spare some embedding calculations we cache them between # the two calls. # Note: each instance of this class has its own `_get_embedding` with # its own lru. @lru_cache(maxsize=CASSANDRA_SEMANTIC_CACHE_EMBEDDING_CACHE_SIZE) def _cache_embedding(text: str) -> List[float]: return self.embedding.embed_query(text=text) self._get_embedding = _cache_embedding self.embedding_dimension = self._get_embedding_dimension() self.table = MetadataVectorCassandraTable( session=self.session, keyspace=self.keyspace, table=self.table_name, primary_key_type=["TEXT"], vector_dimension=self.embedding_dimension, ttl_seconds=self.ttl_seconds, metadata_indexing=("allow", {"_llm_string_hash"}), skip_provisioning=skip_provisioning, ) def _get_embedding_dimension(self) -> int: return len(self._get_embedding(text="This is a sample sentence.")) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" embedding_vector = self._get_embedding(text=prompt) llm_string_hash = _hash(llm_string) body = _dumps_generations(return_val) metadata = { "_prompt": prompt, "_llm_string_hash": llm_string_hash, } row_id = f"{_hash(prompt)}-{llm_string_hash}" # self.table.put( body_blob=body, vector=embedding_vector, row_id=row_id, metadata=metadata, ) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" hit_with_id = self.lookup_with_id(prompt, llm_string) if hit_with_id is not None: return hit_with_id[1] else: return None def lookup_with_id( self, prompt: str, llm_string: str ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: """ Look up based on prompt and llm_string. If there are hits, return (document_id, cached_entry) """ prompt_embedding: List[float] = self._get_embedding(text=prompt) hits = list( self.table.metric_ann_search( vector=prompt_embedding, metadata={"_llm_string_hash": _hash(llm_string)}, n=1, metric=self.distance_metric, metric_threshold=self.score_threshold, ) ) if hits: hit = hits[0] generations = _loads_generations(hit["body_blob"]) if generations is not None: # this protects against malformed cached items: return ( hit["row_id"], generations, ) else: return None else: return None def lookup_with_id_through_llm( self, prompt: str, llm: LLM, stop: Optional[List[str]] = None ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: llm_string = get_prompts( {**llm.dict(), **{"stop": stop}}, [], )[1] return self.lookup_with_id(prompt, llm_string=llm_string) def delete_by_document_id(self, document_id: str) -> None: """ Given this is a "similarity search" cache, an invalidation pattern that makes sense is first a lookup to get an ID, and then deleting with that ID. This is for the second step. """ self.table.delete(row_id=document_id) def clear(self, **kwargs: Any) -> None: """Clear the *whole* semantic cache.""" self.table.clear()

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""" .. warning:: Beta Feature! **Cache** provides an optional caching layer for LLMs. Cache is useful for two reasons: - It can save you money by reducing the number of API calls you make to the LLM provider if you're often requesting the same completion multiple times. - It can speed up your application by reducing the number of API calls you make to the LLM provider. Cache directly competes with Memory. See documentation for Pros and Cons. **Class hierarchy:** .. code-block:: BaseCache --> <name>Cache # Examples: InMemoryCache, RedisCache, GPTCache """ from __future__ import annotations import hashlib import inspect import json import logging import warnings from datetime import timedelta from functools import lru_cache from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast, ) from sqlalchemy import Column, Integer, String, create_engine, select from sqlalchemy.engine.base import Engine from sqlalchemy.orm import Session try: from sqlalchemy.orm import declarative_base except ImportError: from sqlalchemy.ext.declarative import declarative_base from langchain.embeddings.base import Embeddings from langchain.llms.base import LLM, get_prompts from langchain.load.dump import dumps from langchain.load.load import loads from langchain.schema import ChatGeneration, Generation from langchain.schema.cache import RETURN_VAL_TYPE, BaseCache from langchain.utils import get_from_env from langchain.vectorstores.redis import Redis as RedisVectorstore logger = logging.getLogger(__file__) if TYPE_CHECKING: import momento from cassandra.cluster import Session as CassandraSession def _hash(_input: str) -> str: """Use a deterministic hashing approach.""" return hashlib.md5(_input.encode()).hexdigest() def _dump_generations_to_json(generations: RETURN_VAL_TYPE) -> str: """Dump generations to json. Args: generations (RETURN_VAL_TYPE): A list of language model generations. Returns: str: Json representing a list of generations. Warning: would not work well with arbitrary subclasses of `Generation` """ return json.dumps([generation.dict() for generation in generations]) def _load_generations_from_json(generations_json: str) -> RETURN_VAL_TYPE: """Load generations from json. Args: generations_json (str): A string of json representing a list of generations. Raises: ValueError: Could not decode json string to list of generations. Returns: RETURN_VAL_TYPE: A list of generations. Warning: would not work well with arbitrary subclasses of `Generation` """ try: results = json.loads(generations_json) return [Generation(**generation_dict) for generation_dict in results] except json.JSONDecodeError: raise ValueError( f"Could not decode json to list of generations: {generations_json}" ) def _dumps_generations(generations: RETURN_VAL_TYPE) -> str: """ Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation` Args: generations (RETURN_VAL_TYPE): A list of language model generations. Returns: str: a single string representing a list of generations. This function (+ its counterpart `_loads_generations`) rely on the dumps/loads pair with Reviver, so are able to deal with all subclasses of Generation. Each item in the list can be `dumps`ed to a string, then we make the whole list of strings into a json-dumped. """ return json.dumps([dumps(_item) for _item in generations]) def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]: """ Deserialization of a string into a generic RETURN_VAL_TYPE (i.e. a sequence of `Generation`). See `_dumps_generations`, the inverse of this function. Args: generations_str (str): A string representing a list of generations. Compatible with the legacy cache-blob format Does not raise exceptions for malformed entries, just logs a warning and returns none: the caller should be prepared for such a cache miss. Returns: RETURN_VAL_TYPE: A list of generations. """ try: generations = [loads(_item_str) for _item_str in json.loads(generations_str)] return generations except (json.JSONDecodeError, TypeError): # deferring the (soft) handling to after the legacy-format attempt pass try: gen_dicts = json.loads(generations_str) # not relying on `_load_generations_from_json` (which could disappear): generations = [Generation(**generation_dict) for generation_dict in gen_dicts] logger.warning( f"Legacy 'Generation' cached blob encountered: '{generations_str}'" ) return generations except (json.JSONDecodeError, TypeError): logger.warning( f"Malformed/unparsable cached blob encountered: '{generations_str}'" ) return None class InMemoryCache(BaseCache): """Cache that stores things in memory.""" def __init__(self) -> None: """Initialize with empty cache.""" self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {} def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" return self._cache.get((prompt, llm_string), None) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" self._cache[(prompt, llm_string)] = return_val def clear(self, **kwargs: Any) -> None: """Clear cache.""" self._cache = {} Base = declarative_base() class FullLLMCache(Base): # type: ignore """SQLite table for full LLM Cache (all generations).""" __tablename__ = "full_llm_cache" prompt = Column(String, primary_key=True) llm = Column(String, primary_key=True) idx = Column(Integer, primary_key=True) response = Column(String) class SQLAlchemyCache(BaseCache): """Cache that uses SQAlchemy as a backend.""" def __init__(self, engine: Engine, cache_schema: Type[FullLLMCache] = FullLLMCache): """Initialize by creating all tables.""" self.engine = engine self.cache_schema = cache_schema self.cache_schema.metadata.create_all(self.engine) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" stmt = ( select(self.cache_schema.response) .where(self.cache_schema.prompt == prompt) # type: ignore .where(self.cache_schema.llm == llm_string) .order_by(self.cache_schema.idx) ) with Session(self.engine) as session: rows = session.execute(stmt).fetchall() if rows: try: return [loads(row[0]) for row in rows] except Exception: logger.warning( "Retrieving a cache value that could not be deserialized " "properly. This is likely due to the cache being in an " "older format. Please recreate your cache to avoid this " "error." ) # In a previous life we stored the raw text directly # in the table, so assume it's in that format. return [Generation(text=row[0]) for row in rows] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update based on prompt and llm_string.""" items = [ self.cache_schema(prompt=prompt, llm=llm_string, response=dumps(gen), idx=i) for i, gen in enumerate(return_val) ] with Session(self.engine) as session, session.begin(): for item in items: session.merge(item) def clear(self, **kwargs: Any) -> None: """Clear cache.""" with Session(self.engine) as session: session.query(self.cache_schema).delete() session.commit() class SQLiteCache(SQLAlchemyCache): """Cache that uses SQLite as a backend.""" def __init__(self, database_path: str = ".langchain.db"): """Initialize by creating the engine and all tables.""" engine = create_engine(f"sqlite:///{database_path}") super().__init__(engine) class RedisCache(BaseCache): """Cache that uses Redis as a backend.""" def __init__(self, redis_: Any, *, ttl: Optional[int] = None): """ Initialize an instance of RedisCache. This method initializes an object with Redis caching capabilities. It takes a `redis_` parameter, which should be an instance of a Redis client class, allowing the object to interact with a Redis server for caching purposes. Parameters: redis_ (Any): An instance of a Redis client class (e.g., redis.Redis) used for caching. This allows the object to communicate with a Redis server for caching operations. ttl (int, optional): Time-to-live (TTL) for cached items in seconds. If provided, it sets the time duration for how long cached items will remain valid. If not provided, cached items will not have an automatic expiration. """ try: from redis import Redis except ImportError: raise ValueError( "Could not import redis python package. " "Please install it with `pip install redis`." ) if not isinstance(redis_, Redis): raise ValueError("Please pass in Redis object.") self.redis = redis_ self.ttl = ttl def _key(self, prompt: str, llm_string: str) -> str: """Compute key from prompt and llm_string""" return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" generations = [] # Read from a Redis HASH results = self.redis.hgetall(self._key(prompt, llm_string)) if results: for _, text in results.items(): generations.append(Generation(text=text)) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisCache does not" " support caching ChatModel outputs." ) return # Write to a Redis HASH key = self._key(prompt, llm_string) with self.redis.pipeline() as pipe: pipe.hset( key, mapping={ str(idx): generation.text for idx, generation in enumerate(return_val) }, ) if self.ttl is not None: pipe.expire(key, self.ttl) pipe.execute() def clear(self, **kwargs: Any) -> None: """Clear cache. If `asynchronous` is True, flush asynchronously.""" asynchronous = kwargs.get("asynchronous", False) self.redis.flushdb(asynchronous=asynchronous, **kwargs) class RedisSemanticCache(BaseCache): """Cache that uses Redis as a vector-store backend.""" # TODO - implement a TTL policy in Redis DEFAULT_SCHEMA = { "content_key": "prompt", "text": [ {"name": "prompt"}, ], "extra": [{"name": "return_val"}, {"name": "llm_string"}], } def __init__( self, redis_url: str, embedding: Embeddings, score_threshold: float = 0.2 ): """Initialize by passing in the `init` GPTCache func Args: redis_url (str): URL to connect to Redis. embedding (Embedding): Embedding provider for semantic encoding and search. score_threshold (float, 0.2): Example: .. code-block:: python import langchain from langchain.cache import RedisSemanticCache from langchain.embeddings import OpenAIEmbeddings langchain.llm_cache = RedisSemanticCache( redis_url="redis://localhost:6379", embedding=OpenAIEmbeddings() ) """ self._cache_dict: Dict[str, RedisVectorstore] = {} self.redis_url = redis_url self.embedding = embedding self.score_threshold = score_threshold def _index_name(self, llm_string: str) -> str: hashed_index = _hash(llm_string) return f"cache:{hashed_index}" def _get_llm_cache(self, llm_string: str) -> RedisVectorstore: index_name = self._index_name(llm_string) # return vectorstore client for the specific llm string if index_name in self._cache_dict: return self._cache_dict[index_name] # create new vectorstore client for the specific llm string try: self._cache_dict[index_name] = RedisVectorstore.from_existing_index( embedding=self.embedding, index_name=index_name, redis_url=self.redis_url, schema=cast(Dict, self.DEFAULT_SCHEMA), ) except ValueError: redis = RedisVectorstore( embedding=self.embedding, index_name=index_name, redis_url=self.redis_url, index_schema=cast(Dict, self.DEFAULT_SCHEMA), ) _embedding = self.embedding.embed_query(text="test") redis._create_index(dim=len(_embedding)) self._cache_dict[index_name] = redis return self._cache_dict[index_name] def clear(self, **kwargs: Any) -> None: """Clear semantic cache for a given llm_string.""" index_name = self._index_name(kwargs["llm_string"]) if index_name in self._cache_dict: self._cache_dict[index_name].drop_index( index_name=index_name, delete_documents=True, redis_url=self.redis_url ) del self._cache_dict[index_name] def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" llm_cache = self._get_llm_cache(llm_string) generations: List = [] # Read from a Hash results = llm_cache.similarity_search( query=prompt, k=1, distance_threshold=self.score_threshold, ) if results: for document in results: generations.extend( _load_generations_from_json(document.metadata["return_val"]) ) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisSemanticCache only supports caching of " f"normal LLM generations, got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisSentimentCache does not" " support caching ChatModel outputs." ) return llm_cache = self._get_llm_cache(llm_string) _dump_generations_to_json([g for g in return_val]) metadata = { "llm_string": llm_string, "prompt": prompt, "return_val": _dump_generations_to_json([g for g in return_val]), } llm_cache.add_texts(texts=[prompt], metadatas=[metadata]) class GPTCache(BaseCache): """Cache that uses GPTCache as a backend.""" def __init__( self, init_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = None, ): """Initialize by passing in init function (default: `None`). Args: init_func (Optional[Callable[[Any], None]]): init `GPTCache` function (default: `None`) Example: .. code-block:: python # Initialize GPTCache with a custom init function import gptcache from gptcache.processor.pre import get_prompt from gptcache.manager.factory import get_data_manager # Avoid multiple caches using the same file, causing different llm model caches to affect each other def init_gptcache(cache_obj: gptcache.Cache, llm str): cache_obj.init( pre_embedding_func=get_prompt, data_manager=manager_factory( manager="map", data_dir=f"map_cache_{llm}" ), ) langchain.llm_cache = GPTCache(init_gptcache) """ try: import gptcache # noqa: F401 except ImportError: raise ImportError( "Could not import gptcache python package. " "Please install it with `pip install gptcache`." ) self.init_gptcache_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = init_func self.gptcache_dict: Dict[str, Any] = {} def _new_gptcache(self, llm_string: str) -> Any: """New gptcache object""" from gptcache import Cache from gptcache.manager.factory import get_data_manager from gptcache.processor.pre import get_prompt _gptcache = Cache() if self.init_gptcache_func is not None: sig = inspect.signature(self.init_gptcache_func) if len(sig.parameters) == 2: self.init_gptcache_func(_gptcache, llm_string) # type: ignore[call-arg] else: self.init_gptcache_func(_gptcache) # type: ignore[call-arg] else: _gptcache.init( pre_embedding_func=get_prompt, data_manager=get_data_manager(data_path=llm_string), ) self.gptcache_dict[llm_string] = _gptcache return _gptcache def _get_gptcache(self, llm_string: str) -> Any: """Get a cache object. When the corresponding llm model cache does not exist, it will be created.""" _gptcache = self.gptcache_dict.get(llm_string, None) if not _gptcache: _gptcache = self._new_gptcache(llm_string) return _gptcache def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up the cache data. First, retrieve the corresponding cache object using the `llm_string` parameter, and then retrieve the data from the cache based on the `prompt`. """ from gptcache.adapter.api import get _gptcache = self._get_gptcache(llm_string) res = get(prompt, cache_obj=_gptcache) if res: return [ Generation(**generation_dict) for generation_dict in json.loads(res) ] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache. First, retrieve the corresponding cache object using the `llm_string` parameter, and then store the `prompt` and `return_val` in the cache object. """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "GPTCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) from gptcache.adapter.api import put _gptcache = self._get_gptcache(llm_string) handled_data = json.dumps([generation.dict() for generation in return_val]) put(prompt, handled_data, cache_obj=_gptcache) return None def clear(self, **kwargs: Any) -> None: """Clear cache.""" from gptcache import Cache for gptcache_instance in self.gptcache_dict.values(): gptcache_instance = cast(Cache, gptcache_instance) gptcache_instance.flush() self.gptcache_dict.clear() def _ensure_cache_exists(cache_client: momento.CacheClient, cache_name: str) -> None: """Create cache if it doesn't exist. Raises: SdkException: Momento service or network error Exception: Unexpected response """ from momento.responses import CreateCache create_cache_response = cache_client.create_cache(cache_name) if isinstance(create_cache_response, CreateCache.Success) or isinstance( create_cache_response, CreateCache.CacheAlreadyExists ): return None elif isinstance(create_cache_response, CreateCache.Error): raise create_cache_response.inner_exception else: raise Exception(f"Unexpected response cache creation: {create_cache_response}") def _validate_ttl(ttl: Optional[timedelta]) -> None: if ttl is not None and ttl <= timedelta(seconds=0): raise ValueError(f"ttl must be positive but was {ttl}.") class MomentoCache(BaseCache): """Cache that uses Momento as a backend. See https://gomomento.com/""" def __init__( self, cache_client: momento.CacheClient, cache_name: str, *, ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True, ): """Instantiate a prompt cache using Momento as a backend. Note: to instantiate the cache client passed to MomentoCache, you must have a Momento account. See https://gomomento.com/. Args: cache_client (CacheClient): The Momento cache client. cache_name (str): The name of the cache to use to store the data. ttl (Optional[timedelta], optional): The time to live for the cache items. Defaults to None, ie use the client default TTL. ensure_cache_exists (bool, optional): Create the cache if it doesn't exist. Defaults to True. Raises: ImportError: Momento python package is not installed. TypeError: cache_client is not of type momento.CacheClientObject ValueError: ttl is non-null and non-negative """ try: from momento import CacheClient except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if not isinstance(cache_client, CacheClient): raise TypeError("cache_client must be a momento.CacheClient object.") _validate_ttl(ttl) if ensure_cache_exists: _ensure_cache_exists(cache_client, cache_name) self.cache_client = cache_client self.cache_name = cache_name self.ttl = ttl @classmethod def from_client_params( cls, cache_name: str, ttl: timedelta, *, configuration: Optional[momento.config.Configuration] = None, auth_token: Optional[str] = None, **kwargs: Any, ) -> MomentoCache: """Construct cache from CacheClient parameters.""" try: from momento import CacheClient, Configurations, CredentialProvider except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if configuration is None: configuration = Configurations.Laptop.v1() auth_token = auth_token or get_from_env("auth_token", "MOMENTO_AUTH_TOKEN") credentials = CredentialProvider.from_string(auth_token) cache_client = CacheClient(configuration, credentials, default_ttl=ttl) return cls(cache_client, cache_name, ttl=ttl, **kwargs) def __key(self, prompt: str, llm_string: str) -> str: """Compute cache key from prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Returns: str: The cache key. """ return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Lookup llm generations in cache by prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Raises: SdkException: Momento service or network error Returns: Optional[RETURN_VAL_TYPE]: A list of language model generations. """ from momento.responses import CacheGet generations: RETURN_VAL_TYPE = [] get_response = self.cache_client.get( self.cache_name, self.__key(prompt, llm_string) ) if isinstance(get_response, CacheGet.Hit): value = get_response.value_string generations = _load_generations_from_json(value) elif isinstance(get_response, CacheGet.Miss): pass elif isinstance(get_response, CacheGet.Error): raise get_response.inner_exception return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Store llm generations in cache. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model string. return_val (RETURN_VAL_TYPE): A list of language model generations. Raises: SdkException: Momento service or network error Exception: Unexpected response """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "Momento only supports caching of normal LLM generations, " f"got {type(gen)}" ) key = self.__key(prompt, llm_string) value = _dump_generations_to_json(return_val) set_response = self.cache_client.set(self.cache_name, key, value, self.ttl) from momento.responses import CacheSet if isinstance(set_response, CacheSet.Success): pass elif isinstance(set_response, CacheSet.Error): raise set_response.inner_exception else: raise Exception(f"Unexpected response: {set_response}") def clear(self, **kwargs: Any) -> None: """Clear the cache. Raises: SdkException: Momento service or network error """ from momento.responses import CacheFlush flush_response = self.cache_client.flush_cache(self.cache_name) if isinstance(flush_response, CacheFlush.Success): pass elif isinstance(flush_response, CacheFlush.Error): raise flush_response.inner_exception CASSANDRA_CACHE_DEFAULT_TABLE_NAME = "langchain_llm_cache" CASSANDRA_CACHE_DEFAULT_TTL_SECONDS = None class CassandraCache(BaseCache): """ Cache that uses Cassandra / Astra DB as a backend. It uses a single Cassandra table. The lookup keys (which get to form the primary key) are: - prompt, a string - llm_string, a deterministic str representation of the model parameters. (needed to prevent collisions same-prompt-different-model collisions) """ def __init__( self, session: Optional[CassandraSession] = None, keyspace: Optional[str] = None, table_name: str = CASSANDRA_CACHE_DEFAULT_TABLE_NAME, ttl_seconds: Optional[int] = CASSANDRA_CACHE_DEFAULT_TTL_SECONDS, skip_provisioning: bool = False, ): """ Initialize with a ready session and a keyspace name. Args: session (cassandra.cluster.Session): an open Cassandra session keyspace (str): the keyspace to use for storing the cache table_name (str): name of the Cassandra table to use as cache ttl_seconds (optional int): time-to-live for cache entries (default: None, i.e. forever) """ try: from cassio.table import ElasticCassandraTable except (ImportError, ModuleNotFoundError): raise ValueError( "Could not import cassio python package. " "Please install it with `pip install cassio`." ) self.session = session self.keyspace = keyspace self.table_name = table_name self.ttl_seconds = ttl_seconds self.kv_cache = ElasticCassandraTable( session=self.session, keyspace=self.keyspace, table=self.table_name, keys=["llm_string", "prompt"], primary_key_type=["TEXT", "TEXT"], ttl_seconds=self.ttl_seconds, skip_provisioning=skip_provisioning, ) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" item = self.kv_cache.get( llm_string=_hash(llm_string), prompt=_hash(prompt), ) if item is not None: generations = _loads_generations(item["body_blob"]) # this protects against malformed cached items: if generations is not None: return generations else: return None else: return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" blob = _dumps_generations(return_val) self.kv_cache.put( llm_string=_hash(llm_string), prompt=_hash(prompt), body_blob=blob, ) def delete_through_llm( self, prompt: str, llm: LLM, stop: Optional[List[str]] = None ) -> None: """ A wrapper around `delete` with the LLM being passed. In case the llm(prompt) calls have a `stop` param, you should pass it here """ llm_string = get_prompts( {**llm.dict(), **{"stop": stop}}, [], )[1] return self.delete(prompt, llm_string=llm_string) def delete(self, prompt: str, llm_string: str) -> None: """Evict from cache if there's an entry.""" return self.kv_cache.delete( llm_string=_hash(llm_string), prompt=_hash(prompt), ) def clear(self, **kwargs: Any) -> None: """Clear cache. This is for all LLMs at once.""" self.kv_cache.clear() CASSANDRA_SEMANTIC_CACHE_DEFAULT_DISTANCE_METRIC = "dot" CASSANDRA_SEMANTIC_CACHE_DEFAULT_SCORE_THRESHOLD = 0.85 CASSANDRA_SEMANTIC_CACHE_DEFAULT_TABLE_NAME = "langchain_llm_semantic_cache" CASSANDRA_SEMANTIC_CACHE_DEFAULT_TTL_SECONDS = None CASSANDRA_SEMANTIC_CACHE_EMBEDDING_CACHE_SIZE = 16 class CassandraSemanticCache(BaseCache): """ Cache that uses Cassandra as a vector-store backend for semantic (i.e. similarity-based) lookup. It uses a single (vector) Cassandra table and stores, in principle, cached values from several LLMs, so the LLM's llm_string is part of the rows' primary keys. The similarity is based on one of several distance metrics (default: "dot"). If choosing another metric, the default threshold is to be re-tuned accordingly. """ def __init__( self, session: Optional[CassandraSession], keyspace: Optional[str], embedding: Embeddings, table_name: str = CASSANDRA_SEMANTIC_CACHE_DEFAULT_TABLE_NAME, distance_metric: str = CASSANDRA_SEMANTIC_CACHE_DEFAULT_DISTANCE_METRIC, score_threshold: float = CASSANDRA_SEMANTIC_CACHE_DEFAULT_SCORE_THRESHOLD, ttl_seconds: Optional[int] = CASSANDRA_SEMANTIC_CACHE_DEFAULT_TTL_SECONDS, skip_provisioning: bool = False, ): """ Initialize the cache with all relevant parameters. Args: session (cassandra.cluster.Session): an open Cassandra session keyspace (str): the keyspace to use for storing the cache embedding (Embedding): Embedding provider for semantic encoding and search. table_name (str): name of the Cassandra (vector) table to use as cache distance_metric (str, 'dot'): which measure to adopt for similarity searches score_threshold (optional float): numeric value to use as cutoff for the similarity searches ttl_seconds (optional int): time-to-live for cache entries (default: None, i.e. forever) The default score threshold is tuned to the default metric. Tune it carefully yourself if switching to another distance metric. """ try: from cassio.table import MetadataVectorCassandraTable except (ImportError, ModuleNotFoundError): raise ValueError( "Could not import cassio python package. " "Please install it with `pip install cassio`." ) self.session = session self.keyspace = keyspace self.embedding = embedding self.table_name = table_name self.distance_metric = distance_metric self.score_threshold = score_threshold self.ttl_seconds = ttl_seconds # The contract for this class has separate lookup and update: # in order to spare some embedding calculations we cache them between # the two calls. # Note: each instance of this class has its own `_get_embedding` with # its own lru. @lru_cache(maxsize=CASSANDRA_SEMANTIC_CACHE_EMBEDDING_CACHE_SIZE) def _cache_embedding(text: str) -> List[float]: return self.embedding.embed_query(text=text) self._get_embedding = _cache_embedding self.embedding_dimension = self._get_embedding_dimension() self.table = MetadataVectorCassandraTable( session=self.session, keyspace=self.keyspace, table=self.table_name, primary_key_type=["TEXT"], vector_dimension=self.embedding_dimension, ttl_seconds=self.ttl_seconds, metadata_indexing=("allow", {"_llm_string_hash"}), skip_provisioning=skip_provisioning, ) def _get_embedding_dimension(self) -> int: return len(self._get_embedding(text="This is a sample sentence.")) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" embedding_vector = self._get_embedding(text=prompt) llm_string_hash = _hash(llm_string) body = _dumps_generations(return_val) metadata = { "_prompt": prompt, "_llm_string_hash": llm_string_hash, } row_id = f"{_hash(prompt)}-{llm_string_hash}" # self.table.put( body_blob=body, vector=embedding_vector, row_id=row_id, metadata=metadata, ) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" hit_with_id = self.lookup_with_id(prompt, llm_string) if hit_with_id is not None: return hit_with_id[1] else: return None def lookup_with_id( self, prompt: str, llm_string: str ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: """ Look up based on prompt and llm_string. If there are hits, return (document_id, cached_entry) """ prompt_embedding: List[float] = self._get_embedding(text=prompt) hits = list( self.table.metric_ann_search( vector=prompt_embedding, metadata={"_llm_string_hash": _hash(llm_string)}, n=1, metric=self.distance_metric, metric_threshold=self.score_threshold, ) ) if hits: hit = hits[0] generations = _loads_generations(hit["body_blob"]) if generations is not None: # this protects against malformed cached items: return ( hit["row_id"], generations, ) else: return None else: return None def lookup_with_id_through_llm( self, prompt: str, llm: LLM, stop: Optional[List[str]] = None ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: llm_string = get_prompts( {**llm.dict(), **{"stop": stop}}, [], )[1] return self.lookup_with_id(prompt, llm_string=llm_string) def delete_by_document_id(self, document_id: str) -> None: """ Given this is a "similarity search" cache, an invalidation pattern that makes sense is first a lookup to get an ID, and then deleting with that ID. This is for the second step. """ self.table.delete(row_id=document_id) def clear(self, **kwargs: Any) -> None: """Clear the *whole* semantic cache.""" self.table.clear()

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"""A tracer that runs evaluators over completed runs.""" from __future__ import annotations import logging from concurrent.futures import Future, ThreadPoolExecutor from typing import Any, Dict, List, Optional, Sequence, Set, Union from uuid import UUID import langsmith from langsmith import schemas as langsmith_schemas from langchain.callbacks import manager from langchain.callbacks.tracers import langchain as langchain_tracer from langchain.callbacks.tracers.base import BaseTracer from langchain.callbacks.tracers.schemas import Run logger = logging.getLogger(__name__) class EvaluatorCallbackHandler(BaseTracer): """A tracer that runs a run evaluator whenever a run is persisted. Parameters ---------- evaluators : Sequence[RunEvaluator] The run evaluators to apply to all top level runs. max_workers : int, optional The maximum number of worker threads to use for running the evaluators. If not specified, it will default to the number of evaluators. client : LangSmith Client, optional The LangSmith client instance to use for evaluating the runs. If not specified, a new instance will be created. example_id : Union[UUID, str], optional The example ID to be associated with the runs. project_name : str, optional The LangSmith project name to be organize eval chain runs under. Attributes ---------- example_id : Union[UUID, None] The example ID associated with the runs. client : Client The LangSmith client instance used for evaluating the runs. evaluators : Sequence[RunEvaluator] The sequence of run evaluators to be executed. executor : ThreadPoolExecutor The thread pool executor used for running the evaluators. futures : Set[Future] The set of futures representing the running evaluators. skip_unfinished : bool Whether to skip runs that are not finished or raised an error. project_name : Optional[str] The LangSmith project name to be organize eval chain runs under. """ name = "evaluator_callback_handler" def __init__( self, evaluators: Sequence[langsmith.RunEvaluator], max_workers: Optional[int] = None, client: Optional[langsmith.Client] = None, example_id: Optional[Union[UUID, str]] = None, skip_unfinished: bool = True, project_name: Optional[str] = "evaluators", **kwargs: Any, ) -> None: super().__init__(**kwargs) self.example_id = ( UUID(example_id) if isinstance(example_id, str) else example_id ) self.client = client or langchain_tracer.get_client() self.evaluators = evaluators self.max_workers = max_workers or len(evaluators) self.futures: Set[Future] = set() self.skip_unfinished = skip_unfinished self.project_name = project_name self.logged_feedback: Dict[str, List[langsmith_schemas.Feedback]] = {} def _evaluate_in_project(self, run: Run, evaluator: langsmith.RunEvaluator) -> None: """Evaluate the run in the project. Parameters ---------- run : Run The run to be evaluated. evaluator : RunEvaluator The evaluator to use for evaluating the run. """ try: if self.project_name is None: feedback = self.client.evaluate_run(run, evaluator) with manager.tracing_v2_enabled( project_name=self.project_name, tags=["eval"], client=self.client ): feedback = self.client.evaluate_run(run, evaluator) except Exception as e: logger.error( f"Error evaluating run {run.id} with " f"{evaluator.__class__.__name__}: {e}", exc_info=True, ) raise e example_id = str(run.reference_example_id) self.logged_feedback.setdefault(example_id, []).append(feedback) def _persist_run(self, run: Run) -> None: """Run the evaluator on the run. Parameters ---------- run : Run The run to be evaluated. """ if self.skip_unfinished and not run.outputs: logger.debug(f"Skipping unfinished run {run.id}") return run_ = run.copy() run_.reference_example_id = self.example_id if self.max_workers > 0: with ThreadPoolExecutor(max_workers=self.max_workers) as executor: list( executor.map( self._evaluate_in_project, [run_ for _ in range(len(self.evaluators))], self.evaluators, ) ) else: for evaluator in self.evaluators: self._evaluate_in_project(run_, evaluator)

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"""A tracer that runs evaluators over completed runs.""" from __future__ import annotations import logging import threading import weakref from concurrent.futures import Future, ThreadPoolExecutor, wait from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast from uuid import UUID import langsmith from langsmith.evaluation.evaluator import EvaluationResult from langchain.callbacks import manager from langchain.callbacks.tracers import langchain as langchain_tracer from langchain.callbacks.tracers.base import BaseTracer from langchain.callbacks.tracers.langchain import _get_executor from langchain.callbacks.tracers.schemas import Run logger = logging.getLogger(__name__) _TRACERS: weakref.WeakSet[EvaluatorCallbackHandler] = weakref.WeakSet() def wait_for_all_evaluators() -> None: """Wait for all tracers to finish.""" global _TRACERS for tracer in list(_TRACERS): if tracer is not None: tracer.wait_for_futures() class EvaluatorCallbackHandler(BaseTracer): """A tracer that runs a run evaluator whenever a run is persisted. Parameters ---------- evaluators : Sequence[RunEvaluator] The run evaluators to apply to all top level runs. client : LangSmith Client, optional The LangSmith client instance to use for evaluating the runs. If not specified, a new instance will be created. example_id : Union[UUID, str], optional The example ID to be associated with the runs. project_name : str, optional The LangSmith project name to be organize eval chain runs under. Attributes ---------- example_id : Union[UUID, None] The example ID associated with the runs. client : Client The LangSmith client instance used for evaluating the runs. evaluators : Sequence[RunEvaluator] The sequence of run evaluators to be executed. executor : ThreadPoolExecutor The thread pool executor used for running the evaluators. futures : Set[Future] The set of futures representing the running evaluators. skip_unfinished : bool Whether to skip runs that are not finished or raised an error. project_name : Optional[str] The LangSmith project name to be organize eval chain runs under. """ name = "evaluator_callback_handler" def __init__( self, evaluators: Sequence[langsmith.RunEvaluator], client: Optional[langsmith.Client] = None, example_id: Optional[Union[UUID, str]] = None, skip_unfinished: bool = True, project_name: Optional[str] = "evaluators", max_concurrency: Optional[int] = None, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.example_id = ( UUID(example_id) if isinstance(example_id, str) else example_id ) self.client = client or langchain_tracer.get_client() self.evaluators = evaluators if max_concurrency is None: self.executor: Optional[ThreadPoolExecutor] = _get_executor() elif max_concurrency > 0: self.executor = ThreadPoolExecutor(max_workers=max_concurrency) weakref.finalize( self, lambda: cast(ThreadPoolExecutor, self.executor).shutdown(wait=True), ) else: self.executor = None self.futures: weakref.WeakSet[Future] = weakref.WeakSet() self.skip_unfinished = skip_unfinished self.project_name = project_name self.logged_eval_results: Dict[Tuple[str, str], List[EvaluationResult]] = {} self.lock = threading.Lock() global _TRACERS _TRACERS.add(self) def _evaluate_in_project(self, run: Run, evaluator: langsmith.RunEvaluator) -> None: """Evaluate the run in the project. Parameters ---------- run : Run The run to be evaluated. evaluator : RunEvaluator The evaluator to use for evaluating the run. """ try: if self.project_name is None: eval_result = self.client.evaluate_run(run, evaluator) with manager.tracing_v2_enabled( project_name=self.project_name, tags=["eval"], client=self.client ) as cb: reference_example = ( self.client.read_example(run.reference_example_id) if run.reference_example_id else None ) evaluation_result = evaluator.evaluate_run( run, example=reference_example, ) run_id = cb.latest_run.id if cb.latest_run is not None else None self.client.create_feedback( run.id, evaluation_result.key, score=evaluation_result.score, value=evaluation_result.value, comment=evaluation_result.comment, correction=evaluation_result.correction, source_info=evaluation_result.evaluator_info, source_run_id=evaluation_result.source_run_id or run_id, feedback_source_type=langsmith.schemas.FeedbackSourceType.MODEL, ) except Exception as e: logger.error( f"Error evaluating run {run.id} with " f"{evaluator.__class__.__name__}: {repr(e)}", exc_info=True, ) raise e example_id = str(run.reference_example_id) with self.lock: self.logged_eval_results.setdefault((str(run.id), example_id), []).append( eval_result ) def _persist_run(self, run: Run) -> None: """Run the evaluator on the run. Parameters ---------- run : Run The run to be evaluated. """ if self.skip_unfinished and not run.outputs: logger.debug(f"Skipping unfinished run {run.id}") return run_ = run.copy() run_.reference_example_id = self.example_id for evaluator in self.evaluators: if self.executor is None: self._evaluate_in_project(run_, evaluator) else: self.futures.add( self.executor.submit(self._evaluate_in_project, run_, evaluator) ) def wait_for_futures(self) -> None: """Wait for all futures to complete.""" wait(self.futures)

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import os import re from uuid import UUID from typing import Any, Dict, List, Optional, Union import asyncio import langchain import streamlit as st from langchain.schema import LLMResult from langchain.chat_models import ChatOpenAI from langchain.agents import Tool from langchain.agents import AgentType from langchain.llms import OpenAI from langchain.agents import initialize_agent from langchain.memory import ConversationBufferMemory from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler from streamlit.delta_generator import DeltaGenerator from chains import doc_retriever from chains.conversational_retrieval_over_code import ConversationalRetrievalCodeChain from chains.parser import parse_code langchain.debug = st.secrets["langchain"]["debug"] python_script = os.path.join(os.getcwd(), "langchain", "generated_script.py") class AsyncHandler(AsyncCallbackHandler): """Async callback handler that can be used to handle callbacks from langchain.""" def __init__(self, message_placeholder: DeltaGenerator) -> None: super().__init__() self.message_placeholder = message_placeholder self.code_block = False self.code_extracted = False self.full_response = "" async def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> None: """Run when chain starts running.""" message = "" for chunk in "⌛Processing".split(): message += chunk + " " await asyncio.sleep(0.05) # Add a blinking cursor to simulate typing self.message_placeholder.info(message + "▌") async def on_llm_new_token( self, token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any, ) -> None: # Detect if the token is a code block, to print it all at once self.full_response += token if not self.code_extracted: if "`" in token and not self.code_block: self.code_block = True if self.code_block and self.full_response.count("`") == 6: # We have a full code block, print it now code, explanation = parse_code(self.full_response) container = self.message_placeholder.container() ex = container.expander("Code") ex.code(code) container.markdown(explanation) self.code_block = False self.code_extracted = True if self.code_extracted: code, explanation = parse_code(self.full_response) container = self.message_placeholder.container() ex = container.expander("Code") ex.code(code) container.markdown(explanation + "▌") async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Run when chain ends running.""" self.code_extracted = False self.full_response = "" class Handler(BaseCallbackHandler): """Async callback handler that can be used to handle callbacks from langchain.""" def __init__(self, message_placeholder: DeltaGenerator) -> None: super().__init__() self.message_placeholder = message_placeholder self.code_block = False self.code_extracted = False self.full_response = "" def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Union[UUID, None] = None, tags: Union[List[str], None] = None, **kwargs: Any, ) -> Any: """Run when chain starts running.""" return super().on_chain_start( serialized, inputs, run_id=run_id, parent_run_id=parent_run_id, tags=tags, **kwargs, ) def on_llm_new_token( self, token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any, ) -> None: # Detect if the token is a code block, to print it all at once self.full_response += token if not self.code_extracted: if "`" in token and not self.code_block: self.code_block = True if self.code_block and self.full_response.count("`") >= 6: # We have a full code block, print it now self.message_placeholder.markdown(self.full_response) self.code_block = False self.code_extracted = True if self.code_extracted: message = "" code, explain = parse(self.full_response) if code: message = f"```python\n{code}\n```\n" if explain: message += f"{explain}" if message != "": # Add a blinking cursor to simulate typing self.message_placeholder.markdown(message + "▌") return super().on_llm_new_token( token, run_id=run_id, parent_run_id=parent_run_id, **kwargs ) def on_chain_end( self, outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Union[UUID, None] = None, **kwargs: Any, ) -> Any: """Run when chain ends running.""" self.code_extracted = False self.full_response = "" return super().on_chain_end( outputs, run_id=run_id, parent_run_id=parent_run_id, **kwargs ) def load_conversation_chain( message_placeholder: DeltaGenerator, openai_api_key: str ) -> ConversationalRetrievalCodeChain: if openai_api_key is None: raise ValueError("OpenAI API key is required to load the chain.") llm = ChatOpenAI( model_name="gpt-3.5-turbo-16k", temperature=0, openai_api_key=openai_api_key, streaming=True, callbacks=[Handler(message_placeholder)], ) condense_question_llm = ChatOpenAI( model_name="gpt-3.5-turbo", temperature=0, openai_api_key=openai_api_key ) missing_imports_llm = ChatOpenAI( model_name="gpt-3.5-turbo", temperature=0, openai_api_key=openai_api_key, verbose=False, ) retriever = doc_retriever.load_streamlit_doc_retriever( st.secrets["openai_api_key"], chroma_server_host=st.secrets["chroma"]["host"], chroma_server_port=st.secrets["chroma"]["port"], mode="docker", ) qa_over_streamlit_code = ConversationalRetrievalCodeChain.from_llm( llm=llm, retriever=retriever, condense_question_llm=condense_question_llm, return_source_documents=True, missing_imports_llm=missing_imports_llm, return_revision_request=True, verbose=False, ) return qa_over_streamlit_code def load_agent(): if st.secrets["openai_api_key"] is None: st.error("OpenAI API key is missing! Please add it to your secrets.") st.stop() doc_chain = doc_retriever.load_streamlit_doc_chain( OpenAI( temperature=0, max_tokens=2000, openai_api_key=st.secrets["openai_api_key"] ) ) tools = [ Tool( name="Streamlit up to date source code", func=doc_chain.run, description="useful for when you need to answer questions about the " "streamlit Python API. Input should be a fully formed question.", ), ] model_name = "text-davinci-003" memory = ConversationBufferMemory(memory_key="chat_history") llm = OpenAI( openai_api_key=st.secrets["openai_api_key"], max_tokens=2000, temperature=0, model_name=model_name, ) agent_chain = initialize_agent( tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=False, memory=memory, ) return agent_chain def load_chat_agent(): if st.secrets["openai_api_key"] is None: st.error("OpenAI API key is missing! Please add it to your secrets.") st.stop() doc_chain = doc_retriever.load_streamlit_doc_chain( OpenAI( temperature=0, max_tokens=2000, openai_api_key=st.secrets["openai_api_key"] ) ) tools = [ Tool( name="Streamlit up to date source code", func=doc_chain.run, description="useful for when you need to answer questions about the streamlit " "Python API. Input should be a fully formed question.", ), ] model_name = "text-davinci-003" memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) llm = ChatOpenAI( openai_api_key=st.secrets["openai_api_key"], max_tokens=2000, temperature=0, model_name=model_name, ) agent_chain = initialize_agent( tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=False, memory=memory, ) return agent_chain # https://regex101.com/r/fHlyKq/1 parse_code_regex = r"(```python(.*?)```)?(.*?)$" def parse(output): python_code = None explain_code = None python_code_match = re.search(parse_code_regex, output, re.DOTALL) if python_code_match: python_code = python_code_match.group(2) explain_code = python_code_match.group(3) if python_code == "None": python_code = None return python_code, explain_code

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from abc import ABC, abstractmethod from typing import List, Optional from pydantic import BaseModel, Extra, Field, validator import langchain from langchain.callbacks import get_callback_manager from langchain.callbacks.base import BaseCallbackManager from langchain.schema import ( AIMessage, BaseLanguageModel, BaseMessage, ChatGeneration, ChatResult, HumanMessage, LLMResult, PromptValue, ) def _get_verbosity() -> bool: return langchain.verbose class BaseChatModel(BaseLanguageModel, BaseModel, ABC): verbose: bool = Field(default_factory=_get_verbosity) """Whether to print out response text.""" callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @validator("callback_manager", pre=True, always=True) def set_callback_manager( cls, callback_manager: Optional[BaseCallbackManager] ) -> BaseCallbackManager: """If callback manager is None, set it. This allows users to pass in None as callback manager, which is a nice UX. """ return callback_manager or get_callback_manager() def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: return {} def generate( self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None ) -> LLMResult: """Top Level call""" results = [self._generate(m, stop=stop) for m in messages] llm_output = self._combine_llm_outputs([res.llm_output for res in results]) generations = [res.generations for res in results] return LLMResult(generations=generations, llm_output=llm_output) async def agenerate( self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None ) -> LLMResult: """Top Level call""" results = [await self._agenerate(m, stop=stop) for m in messages] llm_output = self._combine_llm_outputs([res.llm_output for res in results]) generations = [res.generations for res in results] return LLMResult(generations=generations, llm_output=llm_output) def generate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None ) -> LLMResult: prompt_messages = [p.to_messages() for p in prompts] prompt_strings = [p.to_string() for p in prompts] self.callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose ) try: output = self.generate(prompt_messages, stop=stop) except (KeyboardInterrupt, Exception) as e: self.callback_manager.on_llm_error(e, verbose=self.verbose) raise e self.callback_manager.on_llm_end(output, verbose=self.verbose) return output async def agenerate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None ) -> LLMResult: prompt_messages = [p.to_messages() for p in prompts] prompt_strings = [p.to_string() for p in prompts] if self.callback_manager.is_async: await self.callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose ) else: self.callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose ) try: output = await self.agenerate(prompt_messages, stop=stop) except (KeyboardInterrupt, Exception) as e: if self.callback_manager.is_async: await self.callback_manager.on_llm_error(e, verbose=self.verbose) else: self.callback_manager.on_llm_error(e, verbose=self.verbose) raise e if self.callback_manager.is_async: await self.callback_manager.on_llm_end(output, verbose=self.verbose) else: self.callback_manager.on_llm_end(output, verbose=self.verbose) return output @abstractmethod def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> ChatResult: """Top Level call""" @abstractmethod async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> ChatResult: """Top Level call""" def __call__( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> BaseMessage: return self._generate(messages, stop=stop).generations[0].message def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str: result = self([HumanMessage(content=message)], stop=stop) return result.content class SimpleChatModel(BaseChatModel): def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> ChatResult: output_str = self._call(messages, stop=stop) message = AIMessage(content=output_str) generation = ChatGeneration(message=message) return ChatResult(generations=[generation]) @abstractmethod def _call( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> str: """Simpler interface."""

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from abc import ABC, abstractmethod from typing import List, Optional from pydantic import BaseModel, Extra, Field, validator import langchain from langchain.callbacks import get_callback_manager from langchain.callbacks.base import BaseCallbackManager from langchain.schema import ( AIMessage, BaseLanguageModel, BaseMessage, ChatGeneration, ChatResult, HumanMessage, LLMResult, PromptValue, ) def _get_verbosity() -> bool: return langchain.verbose class BaseChatModel(BaseLanguageModel, BaseModel, ABC): verbose: bool = Field(default_factory=_get_verbosity) """Whether to print out response text.""" callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @validator("callback_manager", pre=True, always=True) def set_callback_manager( cls, callback_manager: Optional[BaseCallbackManager] ) -> BaseCallbackManager: """If callback manager is None, set it. This allows users to pass in None as callback manager, which is a nice UX. """ return callback_manager or get_callback_manager() def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: return {} def generate( self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None ) -> LLMResult: """Top Level call""" results = [self._generate(m, stop=stop) for m in messages] llm_output = self._combine_llm_outputs([res.llm_output for res in results]) generations = [res.generations for res in results] return LLMResult(generations=generations, llm_output=llm_output) async def agenerate( self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None ) -> LLMResult: """Top Level call""" results = [await self._agenerate(m, stop=stop) for m in messages] llm_output = self._combine_llm_outputs([res.llm_output for res in results]) generations = [res.generations for res in results] return LLMResult(generations=generations, llm_output=llm_output) def generate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None ) -> LLMResult: prompt_messages = [p.to_messages() for p in prompts] prompt_strings = [p.to_string() for p in prompts] self.callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose ) try: output = self.generate(prompt_messages, stop=stop) except (KeyboardInterrupt, Exception) as e: self.callback_manager.on_llm_error(e, verbose=self.verbose) raise e self.callback_manager.on_llm_end(output, verbose=self.verbose) return output async def agenerate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None ) -> LLMResult: prompt_messages = [p.to_messages() for p in prompts] prompt_strings = [p.to_string() for p in prompts] if self.callback_manager.is_async: await self.callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose ) else: self.callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose ) try: output = await self.agenerate(prompt_messages, stop=stop) except (KeyboardInterrupt, Exception) as e: if self.callback_manager.is_async: await self.callback_manager.on_llm_error(e, verbose=self.verbose) else: self.callback_manager.on_llm_error(e, verbose=self.verbose) raise e if self.callback_manager.is_async: await self.callback_manager.on_llm_end(output, verbose=self.verbose) else: self.callback_manager.on_llm_end(output, verbose=self.verbose) return output @abstractmethod def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> ChatResult: """Top Level call""" @abstractmethod async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> ChatResult: """Top Level call""" def __call__( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> BaseMessage: return self._generate(messages, stop=stop).generations[0].message def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str: result = self([HumanMessage(content=message)], stop=stop) return result.content class SimpleChatModel(BaseChatModel): def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> ChatResult: output_str = self._call(messages, stop=stop) message = AIMessage(content=output_str) generation = ChatGeneration(message=message) return ChatResult(generations=[generation]) @abstractmethod def _call( self, messages: List[BaseMessage], stop: Optional[List[str]] = None ) -> str: """Simpler interface."""

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"""Base interface for large language models to expose.""" import inspect import json import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union import yaml from pydantic import Extra, Field, root_validator, validator import langchain from langchain.base_language import BaseLanguageModel from langchain.callbacks.base import BaseCallbackManager from langchain.callbacks.manager import ( AsyncCallbackManager, AsyncCallbackManagerForLLMRun, CallbackManager, CallbackManagerForLLMRun, Callbacks, ) from langchain.load.dump import dumpd from langchain.schema import ( AIMessage, BaseMessage, Generation, LLMResult, PromptValue, RunInfo, get_buffer_string, ) def _get_verbosity() -> bool: return langchain.verbose def get_prompts( params: Dict[str, Any], prompts: List[str] ) -> Tuple[Dict[int, List], str, List[int], List[str]]: """Get prompts that are already cached.""" llm_string = str(sorted([(k, v) for k, v in params.items()])) missing_prompts = [] missing_prompt_idxs = [] existing_prompts = {} for i, prompt in enumerate(prompts): if langchain.llm_cache is not None: cache_val = langchain.llm_cache.lookup(prompt, llm_string) if isinstance(cache_val, list): existing_prompts[i] = cache_val else: missing_prompts.append(prompt) missing_prompt_idxs.append(i) return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts def update_cache( existing_prompts: Dict[int, List], llm_string: str, missing_prompt_idxs: List[int], new_results: LLMResult, prompts: List[str], ) -> Optional[dict]: """Update the cache and get the LLM output.""" for i, result in enumerate(new_results.generations): existing_prompts[missing_prompt_idxs[i]] = result prompt = prompts[missing_prompt_idxs[i]] if langchain.llm_cache is not None: langchain.llm_cache.update(prompt, llm_string, result) llm_output = new_results.llm_output return llm_output class BaseLLM(BaseLanguageModel, ABC): """LLM wrapper should take in a prompt and return a string.""" cache: Optional[bool] = None verbose: bool = Field(default_factory=_get_verbosity) """Whether to print out response text.""" callbacks: Callbacks = Field(default=None, exclude=True) callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) tags: Optional[List[str]] = Field(default=None, exclude=True) """Tags to add to the run trace.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @root_validator() def raise_deprecation(cls, values: Dict) -> Dict: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: warnings.warn( "callback_manager is deprecated. Please use callbacks instead.", DeprecationWarning, ) values["callbacks"] = values.pop("callback_manager", None) return values @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @abstractmethod def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompts.""" @abstractmethod async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompts.""" def generate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs) async def agenerate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return await self.agenerate( prompt_strings, stop=stop, callbacks=callbacks, **kwargs ) def generate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" # If string is passed in directly no errors will be raised but outputs will # not make sense. if not isinstance(prompts, list): raise ValueError( "Argument 'prompts' is expected to be of type List[str], received" f" argument of type {type(prompts)}." ) params = self.dict() params["stop"] = stop options = {"stop": stop} ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags ) new_arg_supported = inspect.signature(self._generate).parameters.get( "run_manager" ) if langchain.llm_cache is None or disregard_cache: # This happens when langchain.cache is None, but self.cache is True if self.cache is not None and self.cache: raise ValueError( "Asked to cache, but no cache found at `langchain.cache`." ) run_manager = callback_manager.on_llm_start( dumpd(self), prompts, invocation_params=params, options=options ) try: output = ( self._generate( prompts, stop=stop, run_manager=run_manager, **kwargs ) if new_arg_supported else self._generate(prompts, stop=stop, **kwargs) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_llm_error(e) raise e run_manager.on_llm_end(output) if run_manager: output.run = RunInfo(run_id=run_manager.run_id) return output if len(missing_prompts) > 0: run_manager = callback_manager.on_llm_start( dumpd(self), missing_prompts, invocation_params=params, options=options, ) try: new_results = ( self._generate( missing_prompts, stop=stop, run_manager=run_manager, **kwargs ) if new_arg_supported else self._generate(missing_prompts, stop=stop, **kwargs) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_llm_error(e) raise e run_manager.on_llm_end(new_results) llm_output = update_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) run_info = None if run_manager: run_info = RunInfo(run_id=run_manager.run_id) else: llm_output = {} run_info = None generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output, run=run_info) async def agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" params = self.dict() params["stop"] = stop options = {"stop": stop} ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache callback_manager = AsyncCallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags ) new_arg_supported = inspect.signature(self._agenerate).parameters.get( "run_manager" ) if langchain.llm_cache is None or disregard_cache: # This happens when langchain.cache is None, but self.cache is True if self.cache is not None and self.cache: raise ValueError( "Asked to cache, but no cache found at `langchain.cache`." ) run_manager = await callback_manager.on_llm_start( dumpd(self), prompts, invocation_params=params, options=options ) try: output = ( await self._agenerate( prompts, stop=stop, run_manager=run_manager, **kwargs ) if new_arg_supported else await self._agenerate(prompts, stop=stop, **kwargs) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_llm_error(e, verbose=self.verbose) raise e await run_manager.on_llm_end(output, verbose=self.verbose) if run_manager: output.run = RunInfo(run_id=run_manager.run_id) return output if len(missing_prompts) > 0: run_manager = await callback_manager.on_llm_start( dumpd(self), missing_prompts, invocation_params=params, options=options, ) try: new_results = ( await self._agenerate( missing_prompts, stop=stop, run_manager=run_manager, **kwargs ) if new_arg_supported else await self._agenerate(missing_prompts, stop=stop, **kwargs) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_llm_error(e) raise e await run_manager.on_llm_end(new_results) llm_output = update_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) run_info = None if run_manager: run_info = RunInfo(run_id=run_manager.run_id) else: llm_output = {} run_info = None generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output, run=run_info) def __call__( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> str: """Check Cache and run the LLM on the given prompt and input.""" if not isinstance(prompt, str): raise ValueError( "Argument `prompt` is expected to be a string. Instead found " f"{type(prompt)}. If you want to run the LLM on multiple prompts, use " "`generate` instead." ) return ( self.generate([prompt], stop=stop, callbacks=callbacks, **kwargs) .generations[0][0] .text ) async def _call_async( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> str: """Check Cache and run the LLM on the given prompt and input.""" result = await self.agenerate( [prompt], stop=stop, callbacks=callbacks, **kwargs ) return result.generations[0][0].text def predict( self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any ) -> str: if stop is None: _stop = None else: _stop = list(stop) return self(text, stop=_stop, **kwargs) def predict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any, ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = self(text, stop=_stop, **kwargs) return AIMessage(content=content) async def apredict( self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any ) -> str: if stop is None: _stop = None else: _stop = list(stop) return await self._call_async(text, stop=_stop, **kwargs) async def apredict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any, ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = await self._call_async(text, stop=_stop, **kwargs) return AIMessage(content=content) @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {} def __str__(self) -> str: """Get a string representation of the object for printing.""" cls_name = f"\033[1m{self.__class__.__name__}\033[0m" return f"{cls_name}\nParams: {self._identifying_params}" @property @abstractmethod def _llm_type(self) -> str: """Return type of llm.""" def dict(self, **kwargs: Any) -> Dict: """Return a dictionary of the LLM.""" starter_dict = dict(self._identifying_params) starter_dict["_type"] = self._llm_type return starter_dict def save(self, file_path: Union[Path, str]) -> None: """Save the LLM. Args: file_path: Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path="path/llm.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save prompt_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(prompt_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(prompt_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml") class LLM(BaseLLM): """LLM class that expect subclasses to implement a simpler call method. The purpose of this class is to expose a simpler interface for working with LLMs, rather than expect the user to implement the full _generate method. """ @abstractmethod def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Run the LLM on the given prompt and input.""" async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Run the LLM on the given prompt and input.""" raise NotImplementedError("Async generation not implemented for this LLM.") def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" # TODO: add caching here. generations = [] new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") for prompt in prompts: text = ( self._call(prompt, stop=stop, run_manager=run_manager, **kwargs) if new_arg_supported else self._call(prompt, stop=stop, **kwargs) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations) async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" generations = [] new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager") for prompt in prompts: text = ( await self._acall(prompt, stop=stop, run_manager=run_manager, **kwargs) if new_arg_supported else await self._acall(prompt, stop=stop, **kwargs) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations)

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"""Base interface for large language models to expose.""" import inspect import json import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union import yaml from pydantic import Extra, Field, root_validator, validator import langchain from langchain.base_language import BaseLanguageModel from langchain.callbacks.base import BaseCallbackManager from langchain.callbacks.manager import ( AsyncCallbackManager, AsyncCallbackManagerForLLMRun, CallbackManager, CallbackManagerForLLMRun, Callbacks, ) from langchain.load.dump import dumpd from langchain.schema import ( AIMessage, BaseMessage, Generation, LLMResult, PromptValue, RunInfo, get_buffer_string, ) def _get_verbosity() -> bool: return langchain.verbose def get_prompts( params: Dict[str, Any], prompts: List[str] ) -> Tuple[Dict[int, List], str, List[int], List[str]]: """Get prompts that are already cached.""" llm_string = str(sorted([(k, v) for k, v in params.items()])) missing_prompts = [] missing_prompt_idxs = [] existing_prompts = {} for i, prompt in enumerate(prompts): if langchain.llm_cache is not None: cache_val = langchain.llm_cache.lookup(prompt, llm_string) if isinstance(cache_val, list): existing_prompts[i] = cache_val else: missing_prompts.append(prompt) missing_prompt_idxs.append(i) return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts def update_cache( existing_prompts: Dict[int, List], llm_string: str, missing_prompt_idxs: List[int], new_results: LLMResult, prompts: List[str], ) -> Optional[dict]: """Update the cache and get the LLM output.""" for i, result in enumerate(new_results.generations): existing_prompts[missing_prompt_idxs[i]] = result prompt = prompts[missing_prompt_idxs[i]] if langchain.llm_cache is not None: langchain.llm_cache.update(prompt, llm_string, result) llm_output = new_results.llm_output return llm_output class BaseLLM(BaseLanguageModel, ABC): """LLM wrapper should take in a prompt and return a string.""" cache: Optional[bool] = None verbose: bool = Field(default_factory=_get_verbosity) """Whether to print out response text.""" callbacks: Callbacks = Field(default=None, exclude=True) callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) tags: Optional[List[str]] = Field(default=None, exclude=True) """Tags to add to the run trace.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @root_validator() def raise_deprecation(cls, values: Dict) -> Dict: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: warnings.warn( "callback_manager is deprecated. Please use callbacks instead.", DeprecationWarning, ) values["callbacks"] = values.pop("callback_manager", None) return values @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @abstractmethod def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompts.""" @abstractmethod async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompts.""" def generate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs) async def agenerate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return await self.agenerate( prompt_strings, stop=stop, callbacks=callbacks, **kwargs ) def generate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" # If string is passed in directly no errors will be raised but outputs will # not make sense. if not isinstance(prompts, list): raise ValueError( "Argument 'prompts' is expected to be of type List[str], received" f" argument of type {type(prompts)}." ) params = self.dict() params["stop"] = stop options = {"stop": stop} ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags ) new_arg_supported = inspect.signature(self._generate).parameters.get( "run_manager" ) if langchain.llm_cache is None or disregard_cache: # This happens when langchain.cache is None, but self.cache is True if self.cache is not None and self.cache: raise ValueError( "Asked to cache, but no cache found at `langchain.cache`." ) run_manager = callback_manager.on_llm_start( dumpd(self), prompts, invocation_params=params, options=options ) try: output = ( self._generate( prompts, stop=stop, run_manager=run_manager, **kwargs ) if new_arg_supported else self._generate(prompts, stop=stop, **kwargs) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_llm_error(e) raise e run_manager.on_llm_end(output) if run_manager: output.run = RunInfo(run_id=run_manager.run_id) return output if len(missing_prompts) > 0: run_manager = callback_manager.on_llm_start( dumpd(self), missing_prompts, invocation_params=params, options=options, ) try: new_results = ( self._generate( missing_prompts, stop=stop, run_manager=run_manager, **kwargs ) if new_arg_supported else self._generate(missing_prompts, stop=stop, **kwargs) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_llm_error(e) raise e run_manager.on_llm_end(new_results) llm_output = update_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) run_info = None if run_manager: run_info = RunInfo(run_id=run_manager.run_id) else: llm_output = {} run_info = None generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output, run=run_info) async def agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" params = self.dict() params["stop"] = stop options = {"stop": stop} ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache callback_manager = AsyncCallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags ) new_arg_supported = inspect.signature(self._agenerate).parameters.get( "run_manager" ) if langchain.llm_cache is None or disregard_cache: # This happens when langchain.cache is None, but self.cache is True if self.cache is not None and self.cache: raise ValueError( "Asked to cache, but no cache found at `langchain.cache`." ) run_manager = await callback_manager.on_llm_start( dumpd(self), prompts, invocation_params=params, options=options ) try: output = ( await self._agenerate( prompts, stop=stop, run_manager=run_manager, **kwargs ) if new_arg_supported else await self._agenerate(prompts, stop=stop, **kwargs) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_llm_error(e, verbose=self.verbose) raise e await run_manager.on_llm_end(output, verbose=self.verbose) if run_manager: output.run = RunInfo(run_id=run_manager.run_id) return output if len(missing_prompts) > 0: run_manager = await callback_manager.on_llm_start( dumpd(self), missing_prompts, invocation_params=params, options=options, ) try: new_results = ( await self._agenerate( missing_prompts, stop=stop, run_manager=run_manager, **kwargs ) if new_arg_supported else await self._agenerate(missing_prompts, stop=stop, **kwargs) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_llm_error(e) raise e await run_manager.on_llm_end(new_results) llm_output = update_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) run_info = None if run_manager: run_info = RunInfo(run_id=run_manager.run_id) else: llm_output = {} run_info = None generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output, run=run_info) def __call__( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> str: """Check Cache and run the LLM on the given prompt and input.""" if not isinstance(prompt, str): raise ValueError( "Argument `prompt` is expected to be a string. Instead found " f"{type(prompt)}. If you want to run the LLM on multiple prompts, use " "`generate` instead." ) return ( self.generate([prompt], stop=stop, callbacks=callbacks, **kwargs) .generations[0][0] .text ) async def _call_async( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> str: """Check Cache and run the LLM on the given prompt and input.""" result = await self.agenerate( [prompt], stop=stop, callbacks=callbacks, **kwargs ) return result.generations[0][0].text def predict( self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any ) -> str: if stop is None: _stop = None else: _stop = list(stop) return self(text, stop=_stop, **kwargs) def predict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any, ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = self(text, stop=_stop, **kwargs) return AIMessage(content=content) async def apredict( self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any ) -> str: if stop is None: _stop = None else: _stop = list(stop) return await self._call_async(text, stop=_stop, **kwargs) async def apredict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any, ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = await self._call_async(text, stop=_stop, **kwargs) return AIMessage(content=content) @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {} def __str__(self) -> str: """Get a string representation of the object for printing.""" cls_name = f"\033[1m{self.__class__.__name__}\033[0m" return f"{cls_name}\nParams: {self._identifying_params}" @property @abstractmethod def _llm_type(self) -> str: """Return type of llm.""" def dict(self, **kwargs: Any) -> Dict: """Return a dictionary of the LLM.""" starter_dict = dict(self._identifying_params) starter_dict["_type"] = self._llm_type return starter_dict def save(self, file_path: Union[Path, str]) -> None: """Save the LLM. Args: file_path: Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path="path/llm.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save prompt_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(prompt_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(prompt_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml") class LLM(BaseLLM): """LLM class that expect subclasses to implement a simpler call method. The purpose of this class is to expose a simpler interface for working with LLMs, rather than expect the user to implement the full _generate method. """ @abstractmethod def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Run the LLM on the given prompt and input.""" async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Run the LLM on the given prompt and input.""" raise NotImplementedError("Async generation not implemented for this LLM.") def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" # TODO: add caching here. generations = [] new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") for prompt in prompts: text = ( self._call(prompt, stop=stop, run_manager=run_manager, **kwargs) if new_arg_supported else self._call(prompt, stop=stop, **kwargs) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations) async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" generations = [] new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager") for prompt in prompts: text = ( await self._acall(prompt, stop=stop, run_manager=run_manager, **kwargs) if new_arg_supported else await self._acall(prompt, stop=stop, **kwargs) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations)

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from langchain.agents import AgentExecutor, LLMSingleActionAgent, AgentOutputParser from langchain.tools import Tool, StructuredTool from langchain.prompts import StringPromptTemplate from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.llms import VertexAI from typing import List, Union, Tuple, Any, Dict from langchain.schema import AgentAction, AgentFinish, BaseMessage, LLMResult import re import langchain import os import json import requests from utils import * from tools import take_environment_action_wrapper from debate import view_debate_wrapper from langchain.callbacks import FileCallbackHandler from langchain.callbacks.base import BaseCallbackHandler import logging from langchain.agents.agent_iterator import AgentExecutorIterator from datetime import datetime from pydantic import BaseModel, Field # Example usage: log_levels = { 'none': logging.CRITICAL, 'all': logging.INFO, } def get_info_logger(): timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") os.makedirs(f'./results/{timestamp}', exist_ok=True) info_filename = f'./results/{timestamp}/info_{timestamp}.log' debug_filename = f'./results/{timestamp}/debug_{timestamp}.log' logging.basicConfig(level=logging.NOTSET, handlers=[logging.NullHandler()]) info_logger = logging.getLogger('info_logger') # Create an INFO file handler file_handler_info = logging.FileHandler(info_filename) file_handler_info.setLevel(logging.INFO) # Create formatters and set them for the file handlers formatter_info = logging.Formatter('%(message)s') file_handler_info.setFormatter(formatter_info) # Add the file handlers to the logger info_logger.addHandler(file_handler_info) info_logger.info(timestamp) return info_logger, timestamp, info_filename, debug_filename class Context(BaseModel): generation_observation_history: List[str] = [] log_count: int = 0 action_count: int = 0 debate_count: int = 0 do_debate: bool = False system_hint_mod: int = 2 max_votes: int = 1 vote_count: int = 0 votes: List[Tuple[str, str, str]] = [] info_logger: Any = None token_count: int = 0 class CustomPromptTemplate(StringPromptTemplate): # The template to use template: str # The list of tools available tools: List[Tool] # Context for information context: Context def format(self, **kwargs) -> str: # Get the intermediate steps (AgentAction, Observation tuples) # Format them in a particular way intermediate_steps = [(a, o) for a, o in kwargs.pop("intermediate_steps") if o != EMPTY_RESPONSE] # if recently finished voting, then vote count is 0 done_voting = self.context.vote_count == 0 history_lines = [] last_action = None last_observation = None # create history to prompt agent for action, observation in intermediate_steps: history_lines.append(action.log.strip()) history_lines.append(f"{OBSERVATION_PREFIX} {observation.strip()}") last_action = action last_observation = observation.strip() history = '\n'.join(history_lines) # append observation to list for debaters to use. Only append the environment observation, not debate observation # only do this if done voting, else we'll append it 'self.context.max_votes' many times if done_voting and last_action and last_action.tool == TAKE_ENVIRONMENT_ACTION: self.context.generation_observation_history.append(f'{OBSERVATION_PREFIX} {last_observation}') system_hint = None valid_action_hint = None # append system hint for after taking action if (self.context.do_debate and last_action and last_action.tool == TAKE_ENVIRONMENT_ACTION and self.context.action_count % self.context.system_hint_mod == 0): system_hint = HINT_AFTER_ACTION history += '\n' + system_hint # append system hint for after viewing debate if (self.context.do_debate and last_action and last_action.tool == VIEW_DEBATE): system_hint = HINT_AFTER_DEBATE history += '\n' + system_hint # append system hint that reminds valid_action_hint = VALID_ACTIONS history += '\n' + valid_action_hint # Set the agent_scratchpad variable to that value kwargs["agent_scratchpad"] = history # Create a tools variable from the list of tools provided tool_strings = [] for tool in self.tools: s = f"{tool.name}: {tool.description}" params = [f'{param} - {info["description"]}' for param, info in tool.args.items()] s += ' Argument: ' + ' '.join(params) tool_strings.append(s) kwargs["tools"] = "\n".join(tool_strings) # Create a list of tool names for the tools provided kwargs["tool_names"] = " or ".join([tool.name for tool in self.tools]) agent_prompt = self.template.format(**kwargs) # log some stuff if self.context.log_count == 0: self.context.info_logger.info(f"Step {self.context.log_count} ===") self.context.info_logger.info(agent_prompt) self.context.log_count += 1 elif done_voting and self.context.log_count > 0 and last_action: self.context.info_logger.info(f"\nStep {self.context.log_count} ===") self.context.info_logger.info(f'Generation ---\n{last_action.log.strip()}') self.context.info_logger.info(f'Observation ---\n{last_observation}') if system_hint: self.context.info_logger.info(f'<only seen once by agent> {system_hint}') if valid_action_hint: self.context.info_logger.info(f'<only seen once by agent> {valid_action_hint}') self.context.log_count += 1 self.context.token_count += tokens(agent_prompt) return agent_prompt class CustomOutputParser(AgentOutputParser): # Context for information context: Context def parse(self, llm_output: str) -> Union[AgentFinish, AgentAction, list[AgentAction]]: self.context.token_count += tokens(llm_output) default_agent_finish = AgentFinish( return_values={'output': "Task finished."}, log=llm_output ) # first, extract the answer and append it to the votes # this includes final answers, and even treats errors as an answer try: pattern = r'Tool: (.*)\nTool Input: (.*)' match = re.search(pattern, llm_output, re.DOTALL) # if the agent wants to do a final answer if "final answer" in llm_output.lower() or "final_answer" in llm_output.lower(): self.context.vote_count += 1 self.context.votes.append(('final answer', 'final answer', llm_output)) # else, look for a tool elif match: # extract the tool information tool_name: str = match.group(1) tool_input: str = match.group(2) # increment the votes self.context.vote_count += 1 self.context.votes.append((tool_name, tool_input, llm_output)) else: raise ValueError(f"Could not find 'Tool:' or 'Tool Input:' : `{llm_output}`") except Exception as e: self.context.vote_count += 1 self.context.votes.append(('error', 'error', llm_output + f'\n ERROR === \n{e}')) # if not done voting, then don't take an action # the take_environment_action tool handles this if self.context.vote_count < self.context.max_votes: return AgentAction(tool=TAKE_ENVIRONMENT_ACTION, tool_input='empty param', log='empty tool') # if done voting, return majority vote and reset voting else: # log the votes self.context.info_logger.info("Casting votes... ===") for i, vote in enumerate(self.context.votes): self.context.info_logger.info(f"Vote {i} ---\n{vote}") # get the majority vote majority_tool_name, majority_tool_input, random_llm_output = get_majority_vote(self.context.votes) # reset the voting self.context.vote_count = 0 self.context.votes = [] # if the majority vote was a final answer or an error: if majority_tool_name == 'final answer' or majority_tool_name == 'error': return default_agent_finish # if the majority vote is a debate tool, then call that tool elif majority_tool_name == VIEW_DEBATE: self.context.debate_count += 1 return AgentAction(tool=majority_tool_name, tool_input=majority_tool_input, log=random_llm_output) # if the majority vote is a environment action tool, then call that tool and log stuff elif majority_tool_name == TAKE_ENVIRONMENT_ACTION: # increment action count and log it self.context.action_count += 1 self.context.info_logger.info(f"\nAction Count {self.context.action_count} +++") # add action to the history for debaters self.context.generation_observation_history.append('Action: ' + majority_tool_input) return AgentAction(tool=majority_tool_name, tool_input=majority_tool_input, log=random_llm_output) else: print('This happened!') return default_agent_finish # raise ValueError(f"An error occurred that should never occur: `{majority_tool_name}`") def run_experiment(exp): description = exp['description'] langchain.debug = exp['langchain.debug'] langchain_verbose = exp['langchain_verbose'] log_level = log_levels['all'] langchain_logging = False do_debate = exp['do_debate'] MAX_STEPS = exp['MAX_STEPS'] MAX_VOTES = exp['MAX_VOTES'] temperature = 0 if MAX_VOTES == 1 else 0.7 model = exp['agent_model'] system_hint_mod = 1000000 debate_params = dict() if 'debate_params' in exp: debate_params = exp['debate_params'] system_hint_mod = debate_params.pop('system_hint_mod') info_logger, timestamp, info_filename, debug_filename = get_info_logger() info_logger.setLevel(log_level) if langchain_logging: handler = FileCallbackHandler(debug_filename) results = [] num_tasks = exp['num_tasks'] start_task = exp['start_task'] reset_tasks() for _ in range(1, start_task): get_next_task(MAX_STEPS) filename = None for _ in range(num_tasks): # set up the context to pass around context = Context() # file that debaters will use to see agent history context.generation_observation_history = [] # information to know when to log the full action count context.log_count = 0 # information to know when to provide the system hint context.action_count = 0 # count the number of debates context.debate_count = 0 # only display the system hints if do_debate is true context.do_debate = do_debate # show the hints after every x many actions. context.system_hint_mod = system_hint_mod # do majority voting context.max_votes = MAX_VOTES # count total votes so far context.vote_count = 0 # store the votes context.votes = [] # expose the logger context.info_logger = info_logger # count the tokens context.token_count = 0 # set up the available tools tools = [ take_environment_action_wrapper(context) ] if do_debate: tools.append(view_debate_wrapper(context, **debate_params, logger=info_logger)) # load the examples and task from ALFWorld examples, task, task_index = get_next_task(MAX_STEPS, do_debate=False) # not inserting debates even if in debate mode print(f'Task index: {task_index}') examples_str = '\n\n'.join([f'Example {i + 1}:\n{ex}' for i, ex in enumerate(examples)]) examples_str = examples_str.replace('{', '{{').replace('}', '}}') info_logger.info(f'\n\n\n\n\n\n\n\n\nTask index: {task_index}') # load the prompt that tells how to format the actions formatting = read_text_file("./prompts/action_formatting.txt") # load the examples of failures failure_examples_str = read_text_file("./prompts/failure_examples.txt") # set up strings to insert if do_debate == True debate_examples_str = '' debate_msg_1 = '' debate_msg_2 = '' if do_debate: debate_msg_2 = f'\n- IMPORTANT: Remember to use the "{VIEW_DEBATE}" tool to get a better understanding about your problem and proposed action BEFORE using "{TAKE_ENVIRONMENT_ACTION}".' debate_examples_str = '\n\n' + read_text_file("./prompts/debate_examples.txt") template = read_text_file("prompts/prompt_template.txt") # fill out the template template = template.format( formatting=formatting, success_examples=examples_str, failure_examples=failure_examples_str, debate_examples=debate_examples_str, debate_msg_1=debate_msg_1, debate_msg_2=debate_msg_2, ) # create the prompt prompt = CustomPromptTemplate( template=template, tools=tools, # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically # This includes the `intermediate_steps` variable because that is needed input_variables=["input", "intermediate_steps"], context=context, ) llm = VertexAI( model_name=model, temperature=temperature, max_output_tokens=256, ) callbacks = [] if langchain_logging: callbacks.append(handler) llm_chain = LLMChain( llm=llm, prompt=prompt, callbacks=callbacks ) agent = LLMSingleActionAgent( llm_chain=llm_chain, output_parser=CustomOutputParser(context=context), stop=[f"\n{OBSERVATION_PREFIX}"], allowed_tools=[tool.name for tool in tools], ) agent_executor = AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, verbose=langchain_verbose, max_iterations=2 * MAX_STEPS * context.max_votes + 1) agent_iterator = agent_executor.iter(inputs=task) # append to history for the debaters context.generation_observation_history.append('Task: ' + task) result_dict = dict() result_dict['task'] = task result_dict['task_index'] = task_index result_dict['success'] = False total_steps = 0 for step_num, step in enumerate(agent_iterator): # intermediate_step is a (action, observation) pair prev_ob = step.get('intermediate_step') if prev_ob: a, o = prev_ob[-1] if not o: break if FAIL_OBSERVATION in o: total_steps += 1 break elif SUCCESS_OBSERVATION in o: total_steps += 1 result_dict['success'] = True break elif EMPTY_RESPONSE not in o: total_steps += 1 else: # print("HERE") # print(step) break result_dict['total_steps'] = total_steps # the number of times take_environment_action was called result_dict['total_actions'] = context.action_count result_dict['total_debates'] = context.debate_count result_dict['token_count'] = context.token_count results.append(result_dict) # save the results every time, so we don't lose anything results_dir = f'./results/{timestamp}/' filename = f"{results_dir}results_{timestamp}.json" if not os.path.exists(results_dir): os.makedirs(results_dir) # put back system hint mod because we popped it if 'debate_params' in exp: exp['debate_params']['system_hint_mod'] = system_hint_mod extended_results = { 'description': description, 'params': exp, 'timestamp': timestamp, 'results': results } write_json_file(filename, extended_results) # pop it again if 'debate_params' in exp: exp['debate_params'].pop('system_hint_mod') return timestamp, filename

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"""Base interface that all chains should implement.""" import inspect import json import logging import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Union import yaml from pydantic import Field, root_validator, validator import langchain from langchain.callbacks.base import BaseCallbackManager from langchain.callbacks.manager import ( AsyncCallbackManager, AsyncCallbackManagerForChainRun, CallbackManager, CallbackManagerForChainRun, Callbacks, ) from langchain.load.dump import dumpd from langchain.load.serializable import Serializable from langchain.schema import RUN_KEY, BaseMemory, RunInfo logger = logging.getLogger(__name__) def _get_verbosity() -> bool: return langchain.verbose class Chain(Serializable, ABC): """Abstract base class for creating structured sequences of calls to components. Chains should be used to encode a sequence of calls to components like models, document retrievers, other chains, etc., and provide a simple interface to this sequence. The Chain interface makes it easy to create apps that are: - Stateful: add Memory to any Chain to give it state, - Observable: pass Callbacks to a Chain to execute additional functionality, like logging, outside the main sequence of component calls, - Composable: the Chain API is flexible enough that it is easy to combine Chains with other components, including other Chains. The main methods exposed by chains are: - `__call__`: Chains are callable. The `__call__` method is the primary way to execute a Chain. This takes inputs as a dictionary and returns a dictionary output. - `run`: A convenience method that takes inputs as args/kwargs and returns the output as a string. This method can only be used for a subset of chains and cannot return as rich of an output as `__call__`. """ memory: Optional[BaseMemory] = None """Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog.""" callbacks: Callbacks = Field(default=None, exclude=True) """Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details.""" callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) """Deprecated, use `callbacks` instead.""" verbose: bool = Field(default_factory=_get_verbosity) """Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to `langchain.verbose` value.""" tags: Optional[List[str]] = None """Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in `callbacks`. You can use these to eg identify a specific instance of a chain with its use case. """ metadata: Optional[Dict[str, Any]] = None """Optional metadata associated with the chain. Defaults to None This metadata will be associated with each call to this chain, and passed as arguments to the handlers defined in `callbacks`. You can use these to eg identify a specific instance of a chain with its use case. """ class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") @root_validator() def raise_callback_manager_deprecation(cls, values: Dict) -> Dict: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: warnings.warn( "callback_manager is deprecated. Please use callbacks instead.", DeprecationWarning, ) values["callbacks"] = values.pop("callback_manager", None) return values @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """Set the chain verbosity. Defaults to the global setting if not specified by the user. """ if verbose is None: return _get_verbosity() else: return verbose @property @abstractmethod def input_keys(self) -> List[str]: """Return the keys expected to be in the chain input.""" @property @abstractmethod def output_keys(self) -> List[str]: """Return the keys expected to be in the chain output.""" def _validate_inputs(self, inputs: Dict[str, Any]) -> None: """Check that all inputs are present.""" missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") def _validate_outputs(self, outputs: Dict[str, Any]) -> None: missing_keys = set(self.output_keys).difference(outputs) if missing_keys: raise ValueError(f"Missing some output keys: {missing_keys}") @abstractmethod def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, Any]: """Execute the chain. This is a private method that is not user-facing. It is only called within `Chain.__call__`, which is the user-facing wrapper method that handles callbacks configuration and some input/output processing. Args: inputs: A dict of named inputs to the chain. Assumed to contain all inputs specified in `Chain.input_keys`, including any inputs added by memory. run_manager: The callbacks manager that contains the callback handlers for this run of the chain. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, Any]: """Asynchronously execute the chain. This is a private method that is not user-facing. It is only called within `Chain.acall`, which is the user-facing wrapper method that handles callbacks configuration and some input/output processing. Args: inputs: A dict of named inputs to the chain. Assumed to contain all inputs specified in `Chain.input_keys`, including any inputs added by memory. run_manager: The callbacks manager that contains the callback handlers for this run of the chain. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ raise NotImplementedError("Async call not supported for this chain type.") def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False, ) -> Dict[str, Any]: """Execute the chain. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. return_only_outputs: Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. metadata: Optional metadata associated with the chain. Defaults to None include_run_info: Whether to include run info in the response. Defaults to False. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ inputs = self.prep_inputs(inputs) callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags, metadata, self.metadata, ) new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") run_manager = callback_manager.on_chain_start( dumpd(self), inputs, ) try: outputs = ( self._call(inputs, run_manager=run_manager) if new_arg_supported else self._call(inputs) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_chain_error(e) raise e run_manager.on_chain_end(outputs) final_outputs: Dict[str, Any] = self.prep_outputs( inputs, outputs, return_only_outputs ) if include_run_info: final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id) return final_outputs async def acall( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False, ) -> Dict[str, Any]: """Asynchronously execute the chain. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. return_only_outputs: Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. metadata: Optional metadata associated with the chain. Defaults to None include_run_info: Whether to include run info in the response. Defaults to False. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ inputs = self.prep_inputs(inputs) callback_manager = AsyncCallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags, metadata, self.metadata, ) new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager") run_manager = await callback_manager.on_chain_start( dumpd(self), inputs, ) try: outputs = ( await self._acall(inputs, run_manager=run_manager) if new_arg_supported else await self._acall(inputs) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_chain_error(e) raise e await run_manager.on_chain_end(outputs) final_outputs: Dict[str, Any] = self.prep_outputs( inputs, outputs, return_only_outputs ) if include_run_info: final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id) return final_outputs def prep_outputs( self, inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False, ) -> Dict[str, str]: """Validate and prepare chain outputs, and save info about this run to memory. Args: inputs: Dictionary of chain inputs, including any inputs added by chain memory. outputs: Dictionary of initial chain outputs. return_only_outputs: Whether to only return the chain outputs. If False, inputs are also added to the final outputs. Returns: A dict of the final chain outputs. """ self._validate_outputs(outputs) if self.memory is not None: self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: """Validate and prepare chain inputs, including adding inputs from memory. Args: inputs: Dictionary of raw inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. Returns: A dictionary of all inputs, including those added by the chain's memory. """ if not isinstance(inputs, dict): _input_keys = set(self.input_keys) if self.memory is not None: # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) if len(_input_keys) != 1: raise ValueError( f"A single string input was passed in, but this chain expects " f"multiple inputs ({_input_keys}). When a chain expects " f"multiple inputs, please call it by passing in a dictionary, " "eg `chain({'foo': 1, 'bar': 2})`" ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs @property def _run_output_key(self) -> str: if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) return self.output_keys[0] def run( self, *args: Any, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> str: """Convenience method for executing chain when there's a single string output. The main difference between this method and `Chain.__call__` is that this method can only be used for chains that return a single string output. If a Chain has more outputs, a non-string output, or you want to return the inputs/run info along with the outputs, use `Chain.__call__`. The other difference is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas `Chain.__call__` expects a single input dictionary with all the inputs. Args: *args: If the chain expects a single input, it can be passed in as the sole positional argument. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. **kwargs: If the chain expects multiple inputs, they can be passed in directly as keyword arguments. Returns: The chain output as a string. Example: .. code-block:: python # Suppose we have a single-input chain that takes a 'question' string: chain.run("What's the temperature in Boise, Idaho?") # -> "The temperature in Boise is..." # Suppose we have a multi-input chain that takes a 'question' string # and 'context' string: question = "What's the temperature in Boise, Idaho?" context = "Weather report for Boise, Idaho on 07/03/23..." chain.run(question=question, context=context) # -> "The temperature in Boise is..." """ # Run at start to make sure this is possible/defined _output_key = self._run_output_key if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ _output_key ] if kwargs and not args: return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ _output_key ] if not kwargs and not args: raise ValueError( "`run` supported with either positional arguments or keyword arguments," " but none were provided." ) else: raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) async def arun( self, *args: Any, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> str: """Convenience method for executing chain when there's a single string output. The main difference between this method and `Chain.__call__` is that this method can only be used for chains that return a single string output. If a Chain has more outputs, a non-string output, or you want to return the inputs/run info along with the outputs, use `Chain.__call__`. The other difference is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas `Chain.__call__` expects a single input dictionary with all the inputs. Args: *args: If the chain expects a single input, it can be passed in as the sole positional argument. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. **kwargs: If the chain expects multiple inputs, they can be passed in directly as keyword arguments. Returns: The chain output as a string. Example: .. code-block:: python # Suppose we have a single-input chain that takes a 'question' string: await chain.arun("What's the temperature in Boise, Idaho?") # -> "The temperature in Boise is..." # Suppose we have a multi-input chain that takes a 'question' string # and 'context' string: question = "What's the temperature in Boise, Idaho?" context = "Weather report for Boise, Idaho on 07/03/23..." await chain.arun(question=question, context=context) # -> "The temperature in Boise is..." """ if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) elif args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return ( await self.acall( args[0], callbacks=callbacks, tags=tags, metadata=metadata ) )[self.output_keys[0]] if kwargs and not args: return ( await self.acall( kwargs, callbacks=callbacks, tags=tags, metadata=metadata ) )[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of chain. Expects `Chain._chain_type` property to be implemented and for memory to be null. Args: **kwargs: Keyword arguments passed to default `pydantic.BaseModel.dict` method. Returns: A dictionary representation of the chain. Example: ..code-block:: python chain.dict(exclude_unset=True) # -> {"_type": "foo", "verbose": False, ...} """ if self.memory is not None: raise ValueError("Saving of memory is not yet supported.") _dict = super().dict(**kwargs) _dict["_type"] = self._chain_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the chain. Expects `Chain._chain_type` property to be implemented and for memory to be null. Args: file_path: Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path="path/chain.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save chain_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml") def apply( self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None ) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs, callbacks=callbacks) for inputs in input_list]

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"""Base interface that all chains should implement.""" import inspect import json import logging import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Union import yaml from pydantic import Field, root_validator, validator import langchain from langchain.callbacks.base import BaseCallbackManager from langchain.callbacks.manager import ( AsyncCallbackManager, AsyncCallbackManagerForChainRun, CallbackManager, CallbackManagerForChainRun, Callbacks, ) from langchain.load.dump import dumpd from langchain.load.serializable import Serializable from langchain.schema import RUN_KEY, BaseMemory, RunInfo logger = logging.getLogger(__name__) def _get_verbosity() -> bool: return langchain.verbose class Chain(Serializable, ABC): """Abstract base class for creating structured sequences of calls to components. Chains should be used to encode a sequence of calls to components like models, document retrievers, other chains, etc., and provide a simple interface to this sequence. The Chain interface makes it easy to create apps that are: - Stateful: add Memory to any Chain to give it state, - Observable: pass Callbacks to a Chain to execute additional functionality, like logging, outside the main sequence of component calls, - Composable: the Chain API is flexible enough that it is easy to combine Chains with other components, including other Chains. The main methods exposed by chains are: - `__call__`: Chains are callable. The `__call__` method is the primary way to execute a Chain. This takes inputs as a dictionary and returns a dictionary output. - `run`: A convenience method that takes inputs as args/kwargs and returns the output as a string. This method can only be used for a subset of chains and cannot return as rich of an output as `__call__`. """ memory: Optional[BaseMemory] = None """Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog.""" callbacks: Callbacks = Field(default=None, exclude=True) """Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details.""" callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) """Deprecated, use `callbacks` instead.""" verbose: bool = Field(default_factory=_get_verbosity) """Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to `langchain.verbose` value.""" tags: Optional[List[str]] = None """Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in `callbacks`. You can use these to eg identify a specific instance of a chain with its use case. """ metadata: Optional[Dict[str, Any]] = None """Optional metadata associated with the chain. Defaults to None This metadata will be associated with each call to this chain, and passed as arguments to the handlers defined in `callbacks`. You can use these to eg identify a specific instance of a chain with its use case. """ class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") @root_validator() def raise_callback_manager_deprecation(cls, values: Dict) -> Dict: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: warnings.warn( "callback_manager is deprecated. Please use callbacks instead.", DeprecationWarning, ) values["callbacks"] = values.pop("callback_manager", None) return values @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """Set the chain verbosity. Defaults to the global setting if not specified by the user. """ if verbose is None: return _get_verbosity() else: return verbose @property @abstractmethod def input_keys(self) -> List[str]: """Return the keys expected to be in the chain input.""" @property @abstractmethod def output_keys(self) -> List[str]: """Return the keys expected to be in the chain output.""" def _validate_inputs(self, inputs: Dict[str, Any]) -> None: """Check that all inputs are present.""" missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") def _validate_outputs(self, outputs: Dict[str, Any]) -> None: missing_keys = set(self.output_keys).difference(outputs) if missing_keys: raise ValueError(f"Missing some output keys: {missing_keys}") @abstractmethod def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, Any]: """Execute the chain. This is a private method that is not user-facing. It is only called within `Chain.__call__`, which is the user-facing wrapper method that handles callbacks configuration and some input/output processing. Args: inputs: A dict of named inputs to the chain. Assumed to contain all inputs specified in `Chain.input_keys`, including any inputs added by memory. run_manager: The callbacks manager that contains the callback handlers for this run of the chain. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, Any]: """Asynchronously execute the chain. This is a private method that is not user-facing. It is only called within `Chain.acall`, which is the user-facing wrapper method that handles callbacks configuration and some input/output processing. Args: inputs: A dict of named inputs to the chain. Assumed to contain all inputs specified in `Chain.input_keys`, including any inputs added by memory. run_manager: The callbacks manager that contains the callback handlers for this run of the chain. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ raise NotImplementedError("Async call not supported for this chain type.") def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False, ) -> Dict[str, Any]: """Execute the chain. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. return_only_outputs: Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. metadata: Optional metadata associated with the chain. Defaults to None include_run_info: Whether to include run info in the response. Defaults to False. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ inputs = self.prep_inputs(inputs) callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags, metadata, self.metadata, ) new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") run_manager = callback_manager.on_chain_start( dumpd(self), inputs, ) try: outputs = ( self._call(inputs, run_manager=run_manager) if new_arg_supported else self._call(inputs) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_chain_error(e) raise e run_manager.on_chain_end(outputs) final_outputs: Dict[str, Any] = self.prep_outputs( inputs, outputs, return_only_outputs ) if include_run_info: final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id) return final_outputs async def acall( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False, ) -> Dict[str, Any]: """Asynchronously execute the chain. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. return_only_outputs: Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. metadata: Optional metadata associated with the chain. Defaults to None include_run_info: Whether to include run info in the response. Defaults to False. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ inputs = self.prep_inputs(inputs) callback_manager = AsyncCallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags, metadata, self.metadata, ) new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager") run_manager = await callback_manager.on_chain_start( dumpd(self), inputs, ) try: outputs = ( await self._acall(inputs, run_manager=run_manager) if new_arg_supported else await self._acall(inputs) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_chain_error(e) raise e await run_manager.on_chain_end(outputs) final_outputs: Dict[str, Any] = self.prep_outputs( inputs, outputs, return_only_outputs ) if include_run_info: final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id) return final_outputs def prep_outputs( self, inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False, ) -> Dict[str, str]: """Validate and prepare chain outputs, and save info about this run to memory. Args: inputs: Dictionary of chain inputs, including any inputs added by chain memory. outputs: Dictionary of initial chain outputs. return_only_outputs: Whether to only return the chain outputs. If False, inputs are also added to the final outputs. Returns: A dict of the final chain outputs. """ self._validate_outputs(outputs) if self.memory is not None: self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: """Validate and prepare chain inputs, including adding inputs from memory. Args: inputs: Dictionary of raw inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. Returns: A dictionary of all inputs, including those added by the chain's memory. """ if not isinstance(inputs, dict): _input_keys = set(self.input_keys) if self.memory is not None: # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) if len(_input_keys) != 1: raise ValueError( f"A single string input was passed in, but this chain expects " f"multiple inputs ({_input_keys}). When a chain expects " f"multiple inputs, please call it by passing in a dictionary, " "eg `chain({'foo': 1, 'bar': 2})`" ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs @property def _run_output_key(self) -> str: if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) return self.output_keys[0] def run( self, *args: Any, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> str: """Convenience method for executing chain when there's a single string output. The main difference between this method and `Chain.__call__` is that this method can only be used for chains that return a single string output. If a Chain has more outputs, a non-string output, or you want to return the inputs/run info along with the outputs, use `Chain.__call__`. The other difference is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas `Chain.__call__` expects a single input dictionary with all the inputs. Args: *args: If the chain expects a single input, it can be passed in as the sole positional argument. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. **kwargs: If the chain expects multiple inputs, they can be passed in directly as keyword arguments. Returns: The chain output as a string. Example: .. code-block:: python # Suppose we have a single-input chain that takes a 'question' string: chain.run("What's the temperature in Boise, Idaho?") # -> "The temperature in Boise is..." # Suppose we have a multi-input chain that takes a 'question' string # and 'context' string: question = "What's the temperature in Boise, Idaho?" context = "Weather report for Boise, Idaho on 07/03/23..." chain.run(question=question, context=context) # -> "The temperature in Boise is..." """ # Run at start to make sure this is possible/defined _output_key = self._run_output_key if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ _output_key ] if kwargs and not args: return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ _output_key ] if not kwargs and not args: raise ValueError( "`run` supported with either positional arguments or keyword arguments," " but none were provided." ) else: raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) async def arun( self, *args: Any, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> str: """Convenience method for executing chain when there's a single string output. The main difference between this method and `Chain.__call__` is that this method can only be used for chains that return a single string output. If a Chain has more outputs, a non-string output, or you want to return the inputs/run info along with the outputs, use `Chain.__call__`. The other difference is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas `Chain.__call__` expects a single input dictionary with all the inputs. Args: *args: If the chain expects a single input, it can be passed in as the sole positional argument. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. **kwargs: If the chain expects multiple inputs, they can be passed in directly as keyword arguments. Returns: The chain output as a string. Example: .. code-block:: python # Suppose we have a single-input chain that takes a 'question' string: await chain.arun("What's the temperature in Boise, Idaho?") # -> "The temperature in Boise is..." # Suppose we have a multi-input chain that takes a 'question' string # and 'context' string: question = "What's the temperature in Boise, Idaho?" context = "Weather report for Boise, Idaho on 07/03/23..." await chain.arun(question=question, context=context) # -> "The temperature in Boise is..." """ if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) elif args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return ( await self.acall( args[0], callbacks=callbacks, tags=tags, metadata=metadata ) )[self.output_keys[0]] if kwargs and not args: return ( await self.acall( kwargs, callbacks=callbacks, tags=tags, metadata=metadata ) )[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of chain. Expects `Chain._chain_type` property to be implemented and for memory to be null. Args: **kwargs: Keyword arguments passed to default `pydantic.BaseModel.dict` method. Returns: A dictionary representation of the chain. Example: ..code-block:: python chain.dict(exclude_unset=True) # -> {"_type": "foo", "verbose": False, ...} """ if self.memory is not None: raise ValueError("Saving of memory is not yet supported.") _dict = super().dict(**kwargs) _dict["_type"] = self._chain_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the chain. Expects `Chain._chain_type` property to be implemented and for memory to be null. Args: file_path: Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path="path/chain.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save chain_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml") def apply( self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None ) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs, callbacks=callbacks) for inputs in input_list]

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"""Base interface that all chains should implement.""" import inspect import json import logging import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Union import yaml from pydantic import Field, root_validator, validator import langchain from langchain.callbacks.base import BaseCallbackManager from langchain.callbacks.manager import ( AsyncCallbackManager, AsyncCallbackManagerForChainRun, CallbackManager, CallbackManagerForChainRun, Callbacks, ) from langchain.load.dump import dumpd from langchain.load.serializable import Serializable from langchain.schema import RUN_KEY, BaseMemory, RunInfo logger = logging.getLogger(__name__) def _get_verbosity() -> bool: return langchain.verbose class Chain(Serializable, ABC): """Abstract base class for creating structured sequences of calls to components. Chains should be used to encode a sequence of calls to components like models, document retrievers, other chains, etc., and provide a simple interface to this sequence. The Chain interface makes it easy to create apps that are: - Stateful: add Memory to any Chain to give it state, - Observable: pass Callbacks to a Chain to execute additional functionality, like logging, outside the main sequence of component calls, - Composable: the Chain API is flexible enough that it is easy to combine Chains with other components, including other Chains. The main methods exposed by chains are: - `__call__`: Chains are callable. The `__call__` method is the primary way to execute a Chain. This takes inputs as a dictionary and returns a dictionary output. - `run`: A convenience method that takes inputs as args/kwargs and returns the output as a string. This method can only be used for a subset of chains and cannot return as rich of an output as `__call__`. """ memory: Optional[BaseMemory] = None """Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog.""" callbacks: Callbacks = Field(default=None, exclude=True) """Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details.""" callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) """Deprecated, use `callbacks` instead.""" verbose: bool = Field(default_factory=_get_verbosity) """Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to `langchain.verbose` value.""" tags: Optional[List[str]] = None """Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in `callbacks`. You can use these to eg identify a specific instance of a chain with its use case. """ metadata: Optional[Dict[str, Any]] = None """Optional metadata associated with the chain. Defaults to None This metadata will be associated with each call to this chain, and passed as arguments to the handlers defined in `callbacks`. You can use these to eg identify a specific instance of a chain with its use case. """ class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") @root_validator() def raise_callback_manager_deprecation(cls, values: Dict) -> Dict: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: warnings.warn( "callback_manager is deprecated. Please use callbacks instead.", DeprecationWarning, ) values["callbacks"] = values.pop("callback_manager", None) return values @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """Set the chain verbosity. Defaults to the global setting if not specified by the user. """ if verbose is None: return _get_verbosity() else: return verbose @property @abstractmethod def input_keys(self) -> List[str]: """Return the keys expected to be in the chain input.""" @property @abstractmethod def output_keys(self) -> List[str]: """Return the keys expected to be in the chain output.""" def _validate_inputs(self, inputs: Dict[str, Any]) -> None: """Check that all inputs are present.""" missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") def _validate_outputs(self, outputs: Dict[str, Any]) -> None: missing_keys = set(self.output_keys).difference(outputs) if missing_keys: raise ValueError(f"Missing some output keys: {missing_keys}") @abstractmethod def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, Any]: """Execute the chain. This is a private method that is not user-facing. It is only called within `Chain.__call__`, which is the user-facing wrapper method that handles callbacks configuration and some input/output processing. Args: inputs: A dict of named inputs to the chain. Assumed to contain all inputs specified in `Chain.input_keys`, including any inputs added by memory. run_manager: The callbacks manager that contains the callback handlers for this run of the chain. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, Any]: """Asynchronously execute the chain. This is a private method that is not user-facing. It is only called within `Chain.acall`, which is the user-facing wrapper method that handles callbacks configuration and some input/output processing. Args: inputs: A dict of named inputs to the chain. Assumed to contain all inputs specified in `Chain.input_keys`, including any inputs added by memory. run_manager: The callbacks manager that contains the callback handlers for this run of the chain. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ raise NotImplementedError("Async call not supported for this chain type.") def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False, ) -> Dict[str, Any]: """Execute the chain. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. return_only_outputs: Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. metadata: Optional metadata associated with the chain. Defaults to None include_run_info: Whether to include run info in the response. Defaults to False. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ inputs = self.prep_inputs(inputs) callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags, metadata, self.metadata, ) new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") run_manager = callback_manager.on_chain_start( dumpd(self), inputs, ) try: outputs = ( self._call(inputs, run_manager=run_manager) if new_arg_supported else self._call(inputs) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_chain_error(e) raise e run_manager.on_chain_end(outputs) final_outputs: Dict[str, Any] = self.prep_outputs( inputs, outputs, return_only_outputs ) if include_run_info: final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id) return final_outputs async def acall( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False, ) -> Dict[str, Any]: """Asynchronously execute the chain. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. return_only_outputs: Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. metadata: Optional metadata associated with the chain. Defaults to None include_run_info: Whether to include run info in the response. Defaults to False. Returns: A dict of named outputs. Should contain all outputs specified in `Chain.output_keys`. """ inputs = self.prep_inputs(inputs) callback_manager = AsyncCallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags, metadata, self.metadata, ) new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager") run_manager = await callback_manager.on_chain_start( dumpd(self), inputs, ) try: outputs = ( await self._acall(inputs, run_manager=run_manager) if new_arg_supported else await self._acall(inputs) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_chain_error(e) raise e await run_manager.on_chain_end(outputs) final_outputs: Dict[str, Any] = self.prep_outputs( inputs, outputs, return_only_outputs ) if include_run_info: final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id) return final_outputs def prep_outputs( self, inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False, ) -> Dict[str, str]: """Validate and prepare chain outputs, and save info about this run to memory. Args: inputs: Dictionary of chain inputs, including any inputs added by chain memory. outputs: Dictionary of initial chain outputs. return_only_outputs: Whether to only return the chain outputs. If False, inputs are also added to the final outputs. Returns: A dict of the final chain outputs. """ self._validate_outputs(outputs) if self.memory is not None: self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: """Validate and prepare chain inputs, including adding inputs from memory. Args: inputs: Dictionary of raw inputs, or single input if chain expects only one param. Should contain all inputs specified in `Chain.input_keys` except for inputs that will be set by the chain's memory. Returns: A dictionary of all inputs, including those added by the chain's memory. """ if not isinstance(inputs, dict): _input_keys = set(self.input_keys) if self.memory is not None: # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) if len(_input_keys) != 1: raise ValueError( f"A single string input was passed in, but this chain expects " f"multiple inputs ({_input_keys}). When a chain expects " f"multiple inputs, please call it by passing in a dictionary, " "eg `chain({'foo': 1, 'bar': 2})`" ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs @property def _run_output_key(self) -> str: if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) return self.output_keys[0] def run( self, *args: Any, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> str: """Convenience method for executing chain when there's a single string output. The main difference between this method and `Chain.__call__` is that this method can only be used for chains that return a single string output. If a Chain has more outputs, a non-string output, or you want to return the inputs/run info along with the outputs, use `Chain.__call__`. The other difference is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas `Chain.__call__` expects a single input dictionary with all the inputs. Args: *args: If the chain expects a single input, it can be passed in as the sole positional argument. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. **kwargs: If the chain expects multiple inputs, they can be passed in directly as keyword arguments. Returns: The chain output as a string. Example: .. code-block:: python # Suppose we have a single-input chain that takes a 'question' string: chain.run("What's the temperature in Boise, Idaho?") # -> "The temperature in Boise is..." # Suppose we have a multi-input chain that takes a 'question' string # and 'context' string: question = "What's the temperature in Boise, Idaho?" context = "Weather report for Boise, Idaho on 07/03/23..." chain.run(question=question, context=context) # -> "The temperature in Boise is..." """ # Run at start to make sure this is possible/defined _output_key = self._run_output_key if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ _output_key ] if kwargs and not args: return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ _output_key ] if not kwargs and not args: raise ValueError( "`run` supported with either positional arguments or keyword arguments," " but none were provided." ) else: raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) async def arun( self, *args: Any, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> str: """Convenience method for executing chain when there's a single string output. The main difference between this method and `Chain.__call__` is that this method can only be used for chains that return a single string output. If a Chain has more outputs, a non-string output, or you want to return the inputs/run info along with the outputs, use `Chain.__call__`. The other difference is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas `Chain.__call__` expects a single input dictionary with all the inputs. Args: *args: If the chain expects a single input, it can be passed in as the sole positional argument. callbacks: Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects. tags: List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects. **kwargs: If the chain expects multiple inputs, they can be passed in directly as keyword arguments. Returns: The chain output as a string. Example: .. code-block:: python # Suppose we have a single-input chain that takes a 'question' string: await chain.arun("What's the temperature in Boise, Idaho?") # -> "The temperature in Boise is..." # Suppose we have a multi-input chain that takes a 'question' string # and 'context' string: question = "What's the temperature in Boise, Idaho?" context = "Weather report for Boise, Idaho on 07/03/23..." await chain.arun(question=question, context=context) # -> "The temperature in Boise is..." """ if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) elif args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return ( await self.acall( args[0], callbacks=callbacks, tags=tags, metadata=metadata ) )[self.output_keys[0]] if kwargs and not args: return ( await self.acall( kwargs, callbacks=callbacks, tags=tags, metadata=metadata ) )[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of chain. Expects `Chain._chain_type` property to be implemented and for memory to be null. Args: **kwargs: Keyword arguments passed to default `pydantic.BaseModel.dict` method. Returns: A dictionary representation of the chain. Example: ..code-block:: python chain.dict(exclude_unset=True) # -> {"_type": "foo", "verbose": False, ...} """ if self.memory is not None: raise ValueError("Saving of memory is not yet supported.") _dict = super().dict(**kwargs) _dict["_type"] = self._chain_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the chain. Expects `Chain._chain_type` property to be implemented and for memory to be null. Args: file_path: Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path="path/chain.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save chain_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml") def apply( self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None ) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs, callbacks=callbacks) for inputs in input_list]

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"""Utilities for running language models or Chains over datasets.""" from __future__ import annotations import functools import inspect import logging import uuid from enum import Enum from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Tuple, Union, cast, ) from langsmith.client import Client from langsmith.evaluation import RunEvaluator from langsmith.run_helpers import as_runnable, is_traceable_function from langsmith.schemas import Dataset, DataType, Example from langsmith.utils import LangSmithError from requests import HTTPError from langchain._api import warn_deprecated from langchain.callbacks.manager import Callbacks from langchain.callbacks.tracers.evaluation import ( EvaluatorCallbackHandler, wait_for_all_evaluators, ) from langchain.callbacks.tracers.langchain import LangChainTracer from langchain.chains.base import Chain from langchain.evaluation.loading import load_evaluator from langchain.evaluation.schema import ( EvaluatorType, PairwiseStringEvaluator, StringEvaluator, ) from langchain.schema import ChatResult, LLMResult from langchain.schema.language_model import BaseLanguageModel from langchain.schema.messages import BaseMessage, messages_from_dict from langchain.schema.runnable import Runnable, RunnableConfig, RunnableLambda from langchain.schema.runnable import config as runnable_config from langchain.schema.runnable import utils as runnable_utils from langchain.smith import evaluation as smith_eval from langchain.smith.evaluation import config as smith_eval_config from langchain.smith.evaluation import name_generation, progress if TYPE_CHECKING: import pandas as pd logger = logging.getLogger(__name__) MODEL_OR_CHAIN_FACTORY = Union[ Callable[[], Union[Chain, Runnable]], BaseLanguageModel, Callable[[dict], Any], Runnable, Chain, ] MCF = Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel] class InputFormatError(Exception): """Raised when the input format is invalid.""" ## Shared Utilities class TestResult(dict): """A dictionary of the results of a single test run.""" def get_aggregate_feedback( self, quantiles: Optional[Sequence[float]] = None ) -> pd.DataFrame: """Return quantiles for the feedback scores. This method calculates and prints the quantiles for the feedback scores across all feedback keys. Returns: A DataFrame containing the quantiles for each feedback key. """ df = self.to_dataframe() feedback_cols = [ col for col in df.columns if col not in ["input", "output", "reference"] ] _quantiles = df[feedback_cols].quantile( quantiles or [0.25, 0.5, 0.75], numeric_only=True ) _quantiles.loc["mean"] = df[feedback_cols].mean() _quantiles.loc["mode"] = df[feedback_cols].mode().iloc[0] return _quantiles.transpose() def to_dataframe(self) -> pd.DataFrame: """Convert the results to a dataframe.""" try: import pandas as pd except ImportError as e: raise ImportError( "Pandas is required to convert the results to a dataframe." " to install pandas, run `pip install pandas`." ) from e indices = [] records = [] for example_id, result in self["results"].items(): feedback = result["feedback"] r = { **{f.key: f.score for f in feedback}, "input": result["input"], "output": result["output"], } if "reference" in result: r["reference"] = result["reference"] records.append(r) indices.append(example_id) return pd.DataFrame(records, index=indices) def _wrap_in_chain_factory( llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, dataset_name: str = "<my_dataset>", ) -> MCF: """Forgive the user if they pass in a chain without memory instead of a chain factory. It's a common mistake. Raise a more helpful error message as well.""" if isinstance(llm_or_chain_factory, Chain): chain = llm_or_chain_factory chain_class = chain.__class__.__name__ if llm_or_chain_factory.memory is not None: memory_class = chain.memory.__class__.__name__ raise ValueError( "Cannot directly evaluate a chain with stateful memory." " To evaluate this chain, pass in a chain constructor" " that initializes fresh memory each time it is called." " This will safegaurd against information" " leakage between dataset examples." "\nFor example:\n\n" "def chain_constructor():\n" f" new_memory = {memory_class}(...)\n" f" return {chain_class}" "(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) return lambda: chain elif isinstance(llm_or_chain_factory, BaseLanguageModel): return llm_or_chain_factory elif isinstance(llm_or_chain_factory, Runnable): # Memory may exist here, but it's not elegant to check all those cases. lcf = llm_or_chain_factory return lambda: lcf elif callable(llm_or_chain_factory): if is_traceable_function(llm_or_chain_factory): runnable_ = as_runnable(cast(Callable, llm_or_chain_factory)) return lambda: runnable_ try: _model = llm_or_chain_factory() # type: ignore[call-arg] except TypeError: # It's an arbitrary function, wrap it in a RunnableLambda user_func = cast(Callable, llm_or_chain_factory) sig = inspect.signature(user_func) logger.info(f"Wrapping function {sig} as RunnableLambda.") wrapped = RunnableLambda(user_func) return lambda: wrapped constructor = cast(Callable, llm_or_chain_factory) if isinstance(_model, BaseLanguageModel): # It's not uncommon to do an LLM constructor instead of raw LLM, # so we'll unpack it for the user. return _model elif is_traceable_function(cast(Callable, _model)): runnable_ = as_runnable(cast(Callable, _model)) return lambda: runnable_ elif not isinstance(_model, Runnable): # This is unlikely to happen - a constructor for a model function return lambda: RunnableLambda(constructor) else: # Typical correct case return constructor # noqa return llm_or_chain_factory def _get_prompt(inputs: Dict[str, Any]) -> str: """Get prompt from inputs. Args: inputs: The input dictionary. Returns: A string prompt. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") prompts = [] if "prompt" in inputs: if not isinstance(inputs["prompt"], str): raise InputFormatError( "Expected string for 'prompt', got" f" {type(inputs['prompt']).__name__}" ) prompts = [inputs["prompt"]] elif "prompts" in inputs: if not isinstance(inputs["prompts"], list) or not all( isinstance(i, str) for i in inputs["prompts"] ): raise InputFormatError( "Expected list of strings for 'prompts'," f" got {type(inputs['prompts']).__name__}" ) prompts = inputs["prompts"] elif len(inputs) == 1: prompt_ = next(iter(inputs.values())) if isinstance(prompt_, str): prompts = [prompt_] elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_): prompts = prompt_ else: raise InputFormatError(f"LLM Run expects string prompt input. Got {inputs}") else: raise InputFormatError( f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}" ) if len(prompts) == 1: return prompts[0] else: raise InputFormatError( f"LLM Run expects single prompt input. Got {len(prompts)} prompts." ) def _get_messages(inputs: Dict[str, Any]) -> List[BaseMessage]: """Get Chat Messages from inputs. Args: inputs: The input dictionary. Returns: A list of chat messages. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") if "messages" in inputs: single_input = inputs["messages"] elif len(inputs) == 1: single_input = next(iter(inputs.values())) else: raise InputFormatError( f"Chat Run expects 'messages' in inputs when example has multiple" f" input keys. Got {inputs}" ) if isinstance(single_input, list) and all( isinstance(i, dict) for i in single_input ): raw_messages = [single_input] elif isinstance(single_input, list) and all( isinstance(i, list) for i in single_input ): raw_messages = single_input else: raise InputFormatError( f"Chat Run expects List[dict] or List[List[dict]] values for" f" 'messages' key input. Got {inputs}" ) if len(raw_messages) == 1: return messages_from_dict(raw_messages[0]) else: raise InputFormatError( f"Chat Run expects single List[dict] or List[List[dict]] 'messages'" f" input. Got {len(raw_messages)} messages from inputs {inputs}" ) ## Shared data validation utilities def _validate_example_inputs_for_language_model( first_example: Example, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: if input_mapper: prompt_input = input_mapper(first_example.inputs) if not isinstance(prompt_input, str) and not ( isinstance(prompt_input, list) and all(isinstance(msg, BaseMessage) for msg in prompt_input) ): raise InputFormatError( "When using an input_mapper to prepare dataset example inputs" " for an LLM or chat model, the output must a single string or" " a list of chat messages." f"\nGot: {prompt_input} of type {type(prompt_input)}." ) else: try: _get_prompt(first_example.inputs) except InputFormatError: try: _get_messages(first_example.inputs) except InputFormatError: raise InputFormatError( "Example inputs do not match language model input format. " "Expected a dictionary with messages or a single prompt." f" Got: {first_example.inputs}" " Please update your dataset OR provide an input_mapper" " to convert the example.inputs to a compatible format" " for the llm or chat model you wish to evaluate." ) def _validate_example_inputs_for_chain( first_example: Example, chain: Chain, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: """Validate that the example inputs match the chain input keys.""" if input_mapper: first_inputs = input_mapper(first_example.inputs) missing_keys = set(chain.input_keys).difference(first_inputs) if not isinstance(first_inputs, dict): raise InputFormatError( "When using an input_mapper to prepare dataset example" " inputs for a chain, the mapped value must be a dictionary." f"\nGot: {first_inputs} of type {type(first_inputs)}." ) if missing_keys: raise InputFormatError( "Missing keys after loading example using input_mapper." f"\nExpected: {chain.input_keys}. Got: {first_inputs.keys()}" ) else: first_inputs = first_example.inputs missing_keys = set(chain.input_keys).difference(first_inputs) if len(first_inputs) == 1 and len(chain.input_keys) == 1: # We can pass this through the run method. # Refrain from calling to validate. pass elif missing_keys: raise InputFormatError( "Example inputs missing expected chain input keys." " Please provide an input_mapper to convert the example.inputs" " to a compatible format for the chain you wish to evaluate." f"Expected: {chain.input_keys}. " f"Got: {first_inputs.keys()}" ) def _validate_example_inputs( example: Example, llm_or_chain_factory: MCF, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: """Validate that the example inputs are valid for the model.""" if isinstance(llm_or_chain_factory, BaseLanguageModel): _validate_example_inputs_for_language_model(example, input_mapper) else: chain = llm_or_chain_factory() if isinstance(chain, Chain): # Otherwise it's a runnable _validate_example_inputs_for_chain(example, chain, input_mapper) elif isinstance(chain, Runnable): logger.debug(f"Skipping input validation for {chain}") ## Shared Evaluator Setup Utilities def _setup_evaluation( llm_or_chain_factory: MCF, examples: List[Example], evaluation: Optional[smith_eval.RunEvalConfig], data_type: DataType, ) -> Optional[List[RunEvaluator]]: """Configure the evaluators to run on the results of the chain.""" if evaluation: if isinstance(llm_or_chain_factory, BaseLanguageModel): run_inputs, run_outputs = None, None run_type = "llm" else: run_type = "chain" if data_type in (DataType.chat, DataType.llm): val = data_type.value if isinstance(data_type, Enum) else data_type raise ValueError( "Cannot evaluate a chain on dataset with " f"data_type={val}. " "Please specify a dataset with the default 'kv' data type." ) chain = llm_or_chain_factory() run_inputs = chain.input_keys if isinstance(chain, Chain) else None run_outputs = chain.output_keys if isinstance(chain, Chain) else None run_evaluators = _load_run_evaluators( evaluation, run_type, data_type, list(examples[0].outputs) if examples[0].outputs else None, run_inputs, run_outputs, ) else: # TODO: Create a default helpfulness evaluator run_evaluators = None return run_evaluators def _determine_input_key( config: smith_eval.RunEvalConfig, run_inputs: Optional[List[str]], ) -> Optional[str]: input_key = None if config.input_key: input_key = config.input_key if run_inputs and input_key not in run_inputs: raise ValueError(f"Input key {input_key} not in run inputs {run_inputs}") elif run_inputs and len(run_inputs) == 1: input_key = run_inputs[0] elif run_inputs is not None and len(run_inputs) > 1: raise ValueError( f"Must specify input key for model with multiple inputs: {run_inputs}" ) return input_key def _determine_prediction_key( config: smith_eval.RunEvalConfig, run_outputs: Optional[List[str]], ) -> Optional[str]: prediction_key = None if config.prediction_key: prediction_key = config.prediction_key if run_outputs and prediction_key not in run_outputs: raise ValueError( f"Prediction key {prediction_key} not in run outputs {run_outputs}" ) elif run_outputs and len(run_outputs) == 1: prediction_key = run_outputs[0] elif run_outputs is not None and len(run_outputs) > 1: raise ValueError( f"Must specify prediction key for model" f" with multiple outputs: {run_outputs}" ) return prediction_key def _determine_reference_key( config: smith_eval.RunEvalConfig, example_outputs: Optional[List[str]], ) -> Optional[str]: if config.reference_key: reference_key = config.reference_key if example_outputs and reference_key not in example_outputs: raise ValueError( f"Reference key {reference_key} not in Dataset" f" example outputs: {example_outputs}" ) elif example_outputs and len(example_outputs) == 1: reference_key = list(example_outputs)[0] else: reference_key = None return reference_key def _construct_run_evaluator( eval_config: Union[EvaluatorType, str, smith_eval_config.EvalConfig], eval_llm: Optional[BaseLanguageModel], run_type: str, data_type: DataType, example_outputs: Optional[List[str]], reference_key: Optional[str], input_key: Optional[str], prediction_key: Optional[str], ) -> RunEvaluator: if isinstance(eval_config, (EvaluatorType, str)): if not isinstance(eval_config, EvaluatorType): eval_config = EvaluatorType(eval_config) evaluator_ = load_evaluator(eval_config, llm=eval_llm) eval_type_tag = eval_config.value else: kwargs = {"llm": eval_llm, **eval_config.get_kwargs()} evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs) eval_type_tag = eval_config.evaluator_type.value if isinstance(evaluator_, StringEvaluator): if evaluator_.requires_reference and reference_key is None: raise ValueError( f"Must specify reference_key in smith_eval.RunEvalConfig to use" f" evaluator of type {eval_type_tag} with" f" dataset with multiple output keys: {example_outputs}." ) run_evaluator = smith_eval.StringRunEvaluatorChain.from_run_and_data_type( evaluator_, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, tags=[eval_type_tag], ) elif isinstance(evaluator_, PairwiseStringEvaluator): raise NotImplementedError( f"Run evaluator for {eval_type_tag} is not implemented." " PairwiseStringEvaluators compare the outputs of two different models" " rather than the output of a single model." " Did you mean to use a StringEvaluator instead?" "\nSee: https://python.langchain.com/docs/guides/evaluation/string/" ) else: raise NotImplementedError( f"Run evaluator for {eval_type_tag} is not implemented" ) return run_evaluator def _get_keys( config: smith_eval.RunEvalConfig, run_inputs: Optional[List[str]], run_outputs: Optional[List[str]], example_outputs: Optional[List[str]], ) -> Tuple[Optional[str], Optional[str], Optional[str]]: input_key = _determine_input_key(config, run_inputs) prediction_key = _determine_prediction_key(config, run_outputs) reference_key = _determine_reference_key(config, example_outputs) return input_key, prediction_key, reference_key def _load_run_evaluators( config: smith_eval.RunEvalConfig, run_type: str, data_type: DataType, example_outputs: Optional[List[str]], run_inputs: Optional[List[str]], run_outputs: Optional[List[str]], ) -> List[RunEvaluator]: """ Load run evaluators from a configuration. Args: config: Configuration for the run evaluators. Returns: A list of run evaluators. """ run_evaluators = [] input_key, prediction_key, reference_key = None, None, None if ( config.evaluators or any([isinstance(e, EvaluatorType) for e in config.evaluators]) or ( config.custom_evaluators and any([isinstance(e, StringEvaluator) for e in config.custom_evaluators]) ) ): input_key, prediction_key, reference_key = _get_keys( config, run_inputs, run_outputs, example_outputs ) for eval_config in config.evaluators: run_evaluator = _construct_run_evaluator( eval_config, config.eval_llm, run_type, data_type, example_outputs, reference_key, input_key, prediction_key, ) run_evaluators.append(run_evaluator) custom_evaluators = config.custom_evaluators or [] for custom_evaluator in custom_evaluators: if isinstance(custom_evaluator, RunEvaluator): run_evaluators.append(custom_evaluator) elif isinstance(custom_evaluator, StringEvaluator): run_evaluators.append( smith_eval.StringRunEvaluatorChain.from_run_and_data_type( custom_evaluator, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, ) ) else: raise ValueError( f"Unsupported custom evaluator: {custom_evaluator}." f" Expected RunEvaluator or StringEvaluator." ) return run_evaluators ### Async Helpers async def _arun_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], *, tags: Optional[List[str]] = None, callbacks: Callbacks = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """Asynchronously run the language model. Args: llm: The language model to run. inputs: The input dictionary. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map inputs to the expected format. Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): return await llm.apredict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): return await llm.apredict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format" f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: prompt = _get_prompt(inputs) llm_output: Union[str, BaseMessage] = await llm.apredict( prompt, callbacks=callbacks, tags=tags ) except InputFormatError: messages = _get_messages(inputs) llm_output = await llm.apredict_messages( messages, callbacks=callbacks, tags=tags ) return llm_output async def _arun_chain( chain: Union[Chain, Runnable], inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str]: """Run a chain asynchronously on inputs.""" inputs_ = inputs if input_mapper is None else input_mapper(inputs) if ( isinstance(chain, Chain) and isinstance(inputs_, dict) and len(inputs_) == 1 and chain.input_keys ): val = next(iter(inputs_.values())) output = await chain.acall(val, callbacks=callbacks, tags=tags) else: runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks) output = await chain.ainvoke(inputs_, config=runnable_config) return output async def _arun_llm_or_chain( example: Example, config: RunnableConfig, *, llm_or_chain_factory: MCF, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str, LLMResult, ChatResult]: """Asynchronously run the Chain or language model. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map the input to the expected format. Returns: A list of outputs. """ chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) result = None try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = await _arun_llm( llm_or_chain_factory, example.inputs, tags=config["tags"], callbacks=config["callbacks"], input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = await _arun_chain( chain, example.inputs, tags=config["tags"], callbacks=config["callbacks"], input_mapper=input_mapper, ) result = output except Exception as e: logger.warning( f"{chain_or_llm} failed for example {example.id} " f"with inputs {example.inputs}" f"\n{repr(e)}" ) result = {"Error": repr(e)} return result ## Sync Utilities def _run_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """ Run the language model on the example. Args: llm: The language model to run. inputs: The input dictionary. callbacks: The callbacks to use during the run. tags: Optional tags to add to the run. input_mapper: function to map to the inputs dictionary from an Example Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): llm_output: Union[str, BaseMessage] = llm.predict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): llm_output = llm.predict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format: " f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: llm_prompts = _get_prompt(inputs) llm_output = llm.predict(llm_prompts, callbacks=callbacks, tags=tags) except InputFormatError: llm_messages = _get_messages(inputs) llm_output = llm.predict_messages(llm_messages, callbacks=callbacks) return llm_output def _run_chain( chain: Union[Chain, Runnable], inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[Dict, str]: """Run a chain on inputs.""" inputs_ = inputs if input_mapper is None else input_mapper(inputs) if ( isinstance(chain, Chain) and isinstance(inputs_, dict) and len(inputs_) == 1 and chain.input_keys ): val = next(iter(inputs_.values())) output = chain(val, callbacks=callbacks, tags=tags) else: runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks) output = chain.invoke(inputs_, config=runnable_config) return output def _run_llm_or_chain( example: Example, config: RunnableConfig, *, llm_or_chain_factory: MCF, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str, LLMResult, ChatResult]: """ Run the Chain or language model synchronously. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. Returns: Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: The outputs of the model or chain. """ chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) result = None try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = _run_llm( llm_or_chain_factory, example.inputs, config["callbacks"], tags=config["tags"], input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = _run_chain( chain, example.inputs, config["callbacks"], tags=config["tags"], input_mapper=input_mapper, ) result = output except Exception as e: error_type = type(e).__name__ logger.warning( f"{chain_or_llm} failed for example {example.id} " f"with inputs {example.inputs}" f"\nError Type: {error_type}, Message: {e}" ) result = {"Error": repr(e)} return result ## Public API def _prepare_eval_run( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, project_name: str, project_metadata: Optional[Dict[str, Any]] = None, ) -> Tuple[MCF, str, Dataset, List[Example]]: wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name) dataset = client.read_dataset(dataset_name=dataset_name) try: project = client.create_project( project_name, reference_dataset_id=dataset.id, project_extra={"metadata": project_metadata} if project_metadata else {}, ) except (HTTPError, ValueError, LangSmithError) as e: if "already exists " not in str(e): raise e uid = uuid.uuid4() example_msg = f""" run_on_dataset( ... project_name="{project_name} - {uid}", # Update since {project_name} already exists ) """ raise ValueError( f"Test project {project_name} already exists. Please use a different name:" f"\n\n{example_msg}" ) print( f"View the evaluation results for project '{project_name}'" f" at:\n{project.url}?eval=true\n\n" f"View all tests for Dataset {dataset_name} at:\n{dataset.url}", flush=True, ) examples = list(client.list_examples(dataset_id=dataset.id)) if not examples: raise ValueError(f"Dataset {dataset_name} has no example rows.") return wrapped_model, project_name, dataset, examples def _prepare_run_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, project_name: Optional[str], evaluation: Optional[smith_eval.RunEvalConfig] = None, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, concurrency_level: int = 5, project_metadata: Optional[Dict[str, Any]] = None, ) -> Tuple[MCF, str, List[Example], List[RunnableConfig]]: project_name = project_name or name_generation.random_name() wrapped_model, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name, project_metadata=project_metadata, ) wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory) run_evaluators = _setup_evaluation( wrapped_model, examples, evaluation, dataset.data_type or DataType.kv ) _validate_example_inputs(examples[0], wrapped_model, input_mapper) progress_bar = progress.ProgressBarCallback(len(examples)) configs = [ RunnableConfig( callbacks=[ LangChainTracer( project_name=project_name, client=client, use_threading=False, example_id=example.id, ), EvaluatorCallbackHandler( evaluators=run_evaluators or [], client=client, example_id=example.id, ), progress_bar, ], tags=tags or [], max_concurrency=concurrency_level, ) for example in examples ] return wrapped_model, project_name, examples, configs def _collect_test_results( examples: List[Example], batch_results: List[Union[dict, str, LLMResult, ChatResult]], configs: List[RunnableConfig], project_name: str, ) -> TestResult: wait_for_all_evaluators() all_eval_results = {} for c in configs: for callback in cast(list, c["callbacks"]): if isinstance(callback, EvaluatorCallbackHandler): eval_results = callback.logged_eval_results all_eval_results.update( {example_id: v for (_, example_id), v in eval_results.items()} ) results = {} for example, output in zip(examples, batch_results): feedback = all_eval_results.get(str(example.id), []) results[str(example.id)] = { "output": output, "input": example.inputs, "feedback": feedback, } if example.outputs: results[str(example.id)]["reference"] = example.outputs return TestResult( project_name=project_name, results=results, ) _INPUT_MAPPER_DEP_WARNING = ( "The input_mapper argument is deprecated and " "will be removed in a future release. Please add a " " RunnableLambda to your chain to map inputs to the expected format" " instead. Example:\n" "def construct_chain():\n" " my_chain = ...\n" " input_mapper = {'other_key': 'MyOtherInput', 'my_input_key': x}\n" " return input_mapper | my_chain\n" "run_on_dataset(..., llm_or_chain_factory=construct_chain)\n" "(See https://api.python.langchain.com/en/latest/schema/" "langchain.schema.runnable.base.RunnableLambda.html)" ) async def arun_on_dataset( client: Optional[Client], dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[smith_eval.RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, project_metadata: Optional[Dict[str, Any]] = None, verbose: bool = False, tags: Optional[List[str]] = None, **kwargs: Any, ) -> Dict[str, Any]: input_mapper = kwargs.pop("input_mapper", None) if input_mapper: warn_deprecated("0.0.305", message=_INPUT_MAPPER_DEP_WARNING, pending=True) if kwargs: warn_deprecated( "0.0.305", message="The following arguments are deprecated and " "will be removed in a future release: " f"{kwargs.keys()}.", removal="0.0.305", ) client = client or Client() wrapped_model, project_name, examples, configs = _prepare_run_on_dataset( client, dataset_name, llm_or_chain_factory, project_name, evaluation, tags, input_mapper, concurrency_level, project_metadata=project_metadata, ) batch_results = await runnable_utils.gather_with_concurrency( configs[0].get("max_concurrency"), *map( functools.partial( _arun_llm_or_chain, llm_or_chain_factory=wrapped_model, input_mapper=input_mapper, ), examples, configs, ), ) results = _collect_test_results(examples, batch_results, configs, project_name) if verbose: try: agg_feedback = results.get_aggregate_feedback() print("\n Eval quantiles:") print(agg_feedback) except Exception as e: logger.debug(f"Failed to print aggregate feedback: {repr(e)}") return results def run_on_dataset( client: Optional[Client], dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[smith_eval.RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, project_metadata: Optional[Dict[str, Any]] = None, verbose: bool = False, tags: Optional[List[str]] = None, **kwargs: Any, ) -> Dict[str, Any]: input_mapper = kwargs.pop("input_mapper", None) if input_mapper: warn_deprecated("0.0.305", message=_INPUT_MAPPER_DEP_WARNING, pending=True) if kwargs: warn_deprecated( "0.0.305", message="The following arguments are deprecated and " "will be removed in a future release: " f"{kwargs.keys()}.", removal="0.0.305", ) client = client or Client() wrapped_model, project_name, examples, configs = _prepare_run_on_dataset( client, dataset_name, llm_or_chain_factory, project_name, evaluation, tags, input_mapper, concurrency_level, project_metadata=project_metadata, ) if concurrency_level == 0: batch_results = [ _run_llm_or_chain( example, config, llm_or_chain_factory=wrapped_model, input_mapper=input_mapper, ) for example, config in zip(examples, configs) ] else: with runnable_config.get_executor_for_config(configs[0]) as executor: batch_results = list( executor.map( functools.partial( _run_llm_or_chain, llm_or_chain_factory=wrapped_model, input_mapper=input_mapper, ), examples, configs, ) ) results = _collect_test_results(examples, batch_results, configs, project_name) if verbose: try: agg_feedback = results.get_aggregate_feedback() print("\n Eval quantiles:") print(agg_feedback) except Exception as e: logger.debug(f"Failed to print aggregate feedback: {repr(e)}") return results _RUN_ON_DATASET_DOCSTRING = """ Run the Chain or language model on a dataset and store traces to the specified project name. Args: dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Configuration for evaluators to run on the results of the chain concurrency_level: The number of async tasks to run concurrently. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. project_metadata: Optional metadata to add to the project. Useful for storing information the test variant. (prompt version, model version, etc.) client: LangSmith client to use to access the dataset and to log feedback and run traces. verbose: Whether to print progress. tags: Tags to add to each run in the project. Returns: A dictionary containing the run's project name and the resulting model outputs. For the (usually faster) async version of this function, see :func:`arun_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import smith_eval.RunEvalConfig, run_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = smith_eval.RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", smith_eval.RunEvalConfig.Criteria("helpfulness"), smith_eval.RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = smith_eval.RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 run_on_dataset.__doc__ = _RUN_ON_DATASET_DOCSTRING arun_on_dataset.__doc__ = _RUN_ON_DATASET_DOCSTRING.replace( "run_on_dataset(", "await arun_on_dataset(" )

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import os import dotenv dotenv.load_dotenv() ### Load the credentials api_key = os.getenv("API_KEY", None) ibm_cloud_url = os.getenv("IBM_CLOUD_URL", None) project_id = os.getenv("PROJECT_ID", None) HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN", None) min_new_tokens=1 max_new_tokens=300 temperature=1 top_k=50 top_p=1 random_seed=42 repetition_penalty=1.2 from langchain.callbacks.base import BaseCallbackHandler class StreamHandler(BaseCallbackHandler): from uuid import UUID from langchain.schema.output import LLMResult from typing import Any def __init__(self, container, initial_text=""): self.container = container self.text = initial_text def on_llm_new_token(self, token: str, **kwargs) -> None: if token: self.text += token self.container.markdown(self.text) def on_llm_end(self, response: LLMResult, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any) -> Any: return super().on_llm_end(response, run_id=run_id, parent_run_id=parent_run_id, **kwargs) ### IBM Watson def set_params_ibm(decoding_method): from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams if decoding_method == "sample": params = { GenParams.DECODING_METHOD: decoding_method, GenParams.MIN_NEW_TOKENS: min_new_tokens, GenParams.MAX_NEW_TOKENS: max_new_tokens, GenParams.TEMPERATURE: temperature, GenParams.TOP_K: top_k, GenParams.TOP_P: top_p, GenParams.RANDOM_SEED: random_seed, GenParams.REPETITION_PENALTY: repetition_penalty, GenParams.STOP_SEQUENCES: ["# [","\n\n","**"], } elif decoding_method == "greedy": params = { GenParams.DECODING_METHOD: decoding_method, GenParams.MIN_NEW_TOKENS: min_new_tokens, GenParams.MAX_NEW_TOKENS: max_new_tokens, GenParams.RANDOM_SEED: random_seed, GenParams.TEMPERATURE: temperature, GenParams.REPETITION_PENALTY: repetition_penalty, GenParams.STOP_SEQUENCES: ["#","[","\n\n","**"," * "], } else: params = None print("Decoding method not supported, please use 'sample' or 'greedy'.") return params ### IBM Research BAM def llm_param(decoding_method, model_id): from genai.credentials import Credentials from genai.model import Model from genai.extensions.langchain import LangChainInterface if bam_api_key is None or bam_api_url is None: llm = None print("Ensure you copied the .env file that you created earlier into the same directory as this notebook") else: creds = Credentials(bam_api_key, api_endpoint=bam_api_url) # decoding_method = "greedy" params = set_params_ibm(decoding_method) message_placeholder = None # You can define this if needed stream_handler = StreamHandler(message_placeholder) callback = False if callback is True: llm = LangChainInterface( model=model_id, credentials=creds, params=params, callbacks=[stream_handler] ) else: llm = LangChainInterface( model=model_id, credentials=creds, params=params ) llm = LangChainInterface(model=model_id, credentials=creds, params=params, project_id=project_id) return llm ### Hugging Face def hugfacelib(repo_id): from langchain.embeddings import HuggingFaceHubEmbeddings repo_id = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" embedding = HuggingFaceHubEmbeddings( task="feature-extraction", repo_id = repo_id, huggingfacehub_api_token = HUGGINGFACEHUB_API_TOKEN ) return embedding

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"""Base interface for large language models to expose.""" import inspect import json import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union import yaml from pydantic import Extra, Field, root_validator, validator import langchain from langchain.base_language import BaseLanguageModel from langchain.callbacks.base import BaseCallbackManager from langchain.callbacks.manager import ( AsyncCallbackManager, AsyncCallbackManagerForLLMRun, CallbackManager, CallbackManagerForLLMRun, Callbacks, ) from langchain.schema import ( AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string, ) def _get_verbosity() -> bool: return langchain.verbose def get_prompts( params: Dict[str, Any], prompts: List[str] ) -> Tuple[Dict[int, List], str, List[int], List[str]]: """Get prompts that are already cached.""" llm_string = str(sorted([(k, v) for k, v in params.items()])) missing_prompts = [] missing_prompt_idxs = [] existing_prompts = {} for i, prompt in enumerate(prompts): if langchain.llm_cache is not None: cache_val = langchain.llm_cache.lookup(prompt, llm_string) if isinstance(cache_val, list): existing_prompts[i] = cache_val else: missing_prompts.append(prompt) missing_prompt_idxs.append(i) return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts def update_cache( existing_prompts: Dict[int, List], llm_string: str, missing_prompt_idxs: List[int], new_results: LLMResult, prompts: List[str], ) -> Optional[dict]: """Update the cache and get the LLM output.""" for i, result in enumerate(new_results.generations): existing_prompts[missing_prompt_idxs[i]] = result prompt = prompts[missing_prompt_idxs[i]] if langchain.llm_cache is not None: langchain.llm_cache.update(prompt, llm_string, result) llm_output = new_results.llm_output return llm_output class BaseLLM(BaseLanguageModel, ABC): """LLM wrapper should take in a prompt and return a string.""" cache: Optional[bool] = None verbose: bool = Field(default_factory=_get_verbosity) """Whether to print out response text.""" callbacks: Callbacks = Field(default=None, exclude=True) callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @root_validator() def raise_deprecation(cls, values: Dict) -> Dict: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: warnings.warn( "callback_manager is deprecated. Please use callbacks instead.", DeprecationWarning, ) values["callbacks"] = values.pop("callback_manager", None) return values @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @abstractmethod def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> LLMResult: """Run the LLM on the given prompts.""" @abstractmethod async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, ) -> LLMResult: """Run the LLM on the given prompts.""" def generate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return self.generate(prompt_strings, stop=stop, callbacks=callbacks) async def agenerate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return await self.agenerate(prompt_strings, stop=stop, callbacks=callbacks) def generate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: """Run the LLM on the given prompt and input.""" # If string is passed in directly no errors will be raised but outputs will # not make sense. if not isinstance(prompts, list): raise ValueError( "Argument 'prompts' is expected to be of type List[str], received" f" argument of type {type(prompts)}." ) params = self.dict() params["stop"] = stop ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose ) new_arg_supported = inspect.signature(self._generate).parameters.get( "run_manager" ) if langchain.llm_cache is None or disregard_cache: # This happens when langchain.cache is None, but self.cache is True if self.cache is not None and self.cache: raise ValueError( "Asked to cache, but no cache found at `langchain.cache`." ) run_manager = callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompts, invocation_params=params ) try: output = ( self._generate(prompts, stop=stop, run_manager=run_manager) if new_arg_supported else self._generate(prompts, stop=stop) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_llm_error(e) raise e run_manager.on_llm_end(output) return output if len(missing_prompts) > 0: run_manager = callback_manager.on_llm_start( {"name": self.__class__.__name__}, missing_prompts, invocation_params=params, ) try: new_results = ( self._generate(missing_prompts, stop=stop, run_manager=run_manager) if new_arg_supported else self._generate(missing_prompts, stop=stop) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_llm_error(e) raise e run_manager.on_llm_end(new_results) llm_output = update_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) else: llm_output = {} generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output) async def agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: """Run the LLM on the given prompt and input.""" params = self.dict() params["stop"] = stop ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache callback_manager = AsyncCallbackManager.configure( callbacks, self.callbacks, self.verbose ) new_arg_supported = inspect.signature(self._agenerate).parameters.get( "run_manager" ) if langchain.llm_cache is None or disregard_cache: # This happens when langchain.cache is None, but self.cache is True if self.cache is not None and self.cache: raise ValueError( "Asked to cache, but no cache found at `langchain.cache`." ) run_manager = await callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompts, invocation_params=params ) try: output = ( await self._agenerate(prompts, stop=stop, run_manager=run_manager) if new_arg_supported else await self._agenerate(prompts, stop=stop) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_llm_error(e, verbose=self.verbose) raise e await run_manager.on_llm_end(output, verbose=self.verbose) return output if len(missing_prompts) > 0: run_manager = await callback_manager.on_llm_start( {"name": self.__class__.__name__}, missing_prompts, invocation_params=params, ) try: new_results = ( await self._agenerate( missing_prompts, stop=stop, run_manager=run_manager ) if new_arg_supported else await self._agenerate(missing_prompts, stop=stop) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_llm_error(e) raise e await run_manager.on_llm_end(new_results) llm_output = update_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) else: llm_output = {} generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output) def __call__( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None ) -> str: """Check Cache and run the LLM on the given prompt and input.""" if not isinstance(prompt, str): raise ValueError( "Argument `prompt` is expected to be a string. Instead found " f"{type(prompt)}. If you want to run the LLM on multiple prompts, use " "`generate` instead." ) return ( self.generate([prompt], stop=stop, callbacks=callbacks) .generations[0][0] .text ) async def _call_async( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None ) -> str: """Check Cache and run the LLM on the given prompt and input.""" result = await self.agenerate([prompt], stop=stop, callbacks=callbacks) return result.generations[0][0].text def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str: if stop is None: _stop = None else: _stop = list(stop) return self(text, stop=_stop) def predict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = self(text, stop=_stop) return AIMessage(content=content) async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str: if stop is None: _stop = None else: _stop = list(stop) return await self._call_async(text, stop=_stop) async def apredict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = await self._call_async(text, stop=_stop) return AIMessage(content=content) @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {} def __str__(self) -> str: """Get a string representation of the object for printing.""" cls_name = f"\033[1m{self.__class__.__name__}\033[0m" return f"{cls_name}\nParams: {self._identifying_params}" @property @abstractmethod def _llm_type(self) -> str: """Return type of llm.""" def dict(self, **kwargs: Any) -> Dict: """Return a dictionary of the LLM.""" starter_dict = dict(self._identifying_params) starter_dict["_type"] = self._llm_type return starter_dict def save(self, file_path: Union[Path, str]) -> None: """Save the LLM. Args: file_path: Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path="path/llm.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save prompt_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(prompt_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(prompt_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml") class LLM(BaseLLM): """LLM class that expect subclasses to implement a simpler call method. The purpose of this class is to expose a simpler interface for working with LLMs, rather than expect the user to implement the full _generate method. """ @abstractmethod def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> str: """Run the LLM on the given prompt and input.""" async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, ) -> str: """Run the LLM on the given prompt and input.""" raise NotImplementedError("Async generation not implemented for this LLM.") def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> LLMResult: """Run the LLM on the given prompt and input.""" # TODO: add caching here. generations = [] new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") for prompt in prompts: text = ( self._call(prompt, stop=stop, run_manager=run_manager) if new_arg_supported else self._call(prompt, stop=stop) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations) async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, ) -> LLMResult: """Run the LLM on the given prompt and input.""" generations = [] new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager") for prompt in prompts: text = ( await self._acall(prompt, stop=stop, run_manager=run_manager) if new_arg_supported else await self._acall(prompt, stop=stop) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations)

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"""Base interface for large language models to expose.""" import inspect import json import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union import yaml from pydantic import Extra, Field, root_validator, validator import langchain from langchain.base_language import BaseLanguageModel from langchain.callbacks.base import BaseCallbackManager from langchain.callbacks.manager import ( AsyncCallbackManager, AsyncCallbackManagerForLLMRun, CallbackManager, CallbackManagerForLLMRun, Callbacks, ) from langchain.schema import ( AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string, ) def _get_verbosity() -> bool: return langchain.verbose def get_prompts( params: Dict[str, Any], prompts: List[str] ) -> Tuple[Dict[int, List], str, List[int], List[str]]: """Get prompts that are already cached.""" llm_string = str(sorted([(k, v) for k, v in params.items()])) missing_prompts = [] missing_prompt_idxs = [] existing_prompts = {} for i, prompt in enumerate(prompts): if langchain.llm_cache is not None: cache_val = langchain.llm_cache.lookup(prompt, llm_string) if isinstance(cache_val, list): existing_prompts[i] = cache_val else: missing_prompts.append(prompt) missing_prompt_idxs.append(i) return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts def update_cache( existing_prompts: Dict[int, List], llm_string: str, missing_prompt_idxs: List[int], new_results: LLMResult, prompts: List[str], ) -> Optional[dict]: """Update the cache and get the LLM output.""" for i, result in enumerate(new_results.generations): existing_prompts[missing_prompt_idxs[i]] = result prompt = prompts[missing_prompt_idxs[i]] if langchain.llm_cache is not None: langchain.llm_cache.update(prompt, llm_string, result) llm_output = new_results.llm_output return llm_output class BaseLLM(BaseLanguageModel, ABC): """LLM wrapper should take in a prompt and return a string.""" cache: Optional[bool] = None verbose: bool = Field(default_factory=_get_verbosity) """Whether to print out response text.""" callbacks: Callbacks = Field(default=None, exclude=True) callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @root_validator() def raise_deprecation(cls, values: Dict) -> Dict: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: warnings.warn( "callback_manager is deprecated. Please use callbacks instead.", DeprecationWarning, ) values["callbacks"] = values.pop("callback_manager", None) return values @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @abstractmethod def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> LLMResult: """Run the LLM on the given prompts.""" @abstractmethod async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, ) -> LLMResult: """Run the LLM on the given prompts.""" def generate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return self.generate(prompt_strings, stop=stop, callbacks=callbacks) async def agenerate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return await self.agenerate(prompt_strings, stop=stop, callbacks=callbacks) def generate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: """Run the LLM on the given prompt and input.""" # If string is passed in directly no errors will be raised but outputs will # not make sense. if not isinstance(prompts, list): raise ValueError( "Argument 'prompts' is expected to be of type List[str], received" f" argument of type {type(prompts)}." ) params = self.dict() params["stop"] = stop ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose ) new_arg_supported = inspect.signature(self._generate).parameters.get( "run_manager" ) if langchain.llm_cache is None or disregard_cache: # This happens when langchain.cache is None, but self.cache is True if self.cache is not None and self.cache: raise ValueError( "Asked to cache, but no cache found at `langchain.cache`." ) run_manager = callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompts, invocation_params=params ) try: output = ( self._generate(prompts, stop=stop, run_manager=run_manager) if new_arg_supported else self._generate(prompts, stop=stop) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_llm_error(e) raise e run_manager.on_llm_end(output) return output if len(missing_prompts) > 0: run_manager = callback_manager.on_llm_start( {"name": self.__class__.__name__}, missing_prompts, invocation_params=params, ) try: new_results = ( self._generate(missing_prompts, stop=stop, run_manager=run_manager) if new_arg_supported else self._generate(missing_prompts, stop=stop) ) except (KeyboardInterrupt, Exception) as e: run_manager.on_llm_error(e) raise e run_manager.on_llm_end(new_results) llm_output = update_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) else: llm_output = {} generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output) async def agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Callbacks = None, ) -> LLMResult: """Run the LLM on the given prompt and input.""" params = self.dict() params["stop"] = stop ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache callback_manager = AsyncCallbackManager.configure( callbacks, self.callbacks, self.verbose ) new_arg_supported = inspect.signature(self._agenerate).parameters.get( "run_manager" ) if langchain.llm_cache is None or disregard_cache: # This happens when langchain.cache is None, but self.cache is True if self.cache is not None and self.cache: raise ValueError( "Asked to cache, but no cache found at `langchain.cache`." ) run_manager = await callback_manager.on_llm_start( {"name": self.__class__.__name__}, prompts, invocation_params=params ) try: output = ( await self._agenerate(prompts, stop=stop, run_manager=run_manager) if new_arg_supported else await self._agenerate(prompts, stop=stop) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_llm_error(e, verbose=self.verbose) raise e await run_manager.on_llm_end(output, verbose=self.verbose) return output if len(missing_prompts) > 0: run_manager = await callback_manager.on_llm_start( {"name": self.__class__.__name__}, missing_prompts, invocation_params=params, ) try: new_results = ( await self._agenerate( missing_prompts, stop=stop, run_manager=run_manager ) if new_arg_supported else await self._agenerate(missing_prompts, stop=stop) ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_llm_error(e) raise e await run_manager.on_llm_end(new_results) llm_output = update_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) else: llm_output = {} generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output) def __call__( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None ) -> str: """Check Cache and run the LLM on the given prompt and input.""" if not isinstance(prompt, str): raise ValueError( "Argument `prompt` is expected to be a string. Instead found " f"{type(prompt)}. If you want to run the LLM on multiple prompts, use " "`generate` instead." ) return ( self.generate([prompt], stop=stop, callbacks=callbacks) .generations[0][0] .text ) async def _call_async( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None ) -> str: """Check Cache and run the LLM on the given prompt and input.""" result = await self.agenerate([prompt], stop=stop, callbacks=callbacks) return result.generations[0][0].text def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str: if stop is None: _stop = None else: _stop = list(stop) return self(text, stop=_stop) def predict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = self(text, stop=_stop) return AIMessage(content=content) async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str: if stop is None: _stop = None else: _stop = list(stop) return await self._call_async(text, stop=_stop) async def apredict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = await self._call_async(text, stop=_stop) return AIMessage(content=content) @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {} def __str__(self) -> str: """Get a string representation of the object for printing.""" cls_name = f"\033[1m{self.__class__.__name__}\033[0m" return f"{cls_name}\nParams: {self._identifying_params}" @property @abstractmethod def _llm_type(self) -> str: """Return type of llm.""" def dict(self, **kwargs: Any) -> Dict: """Return a dictionary of the LLM.""" starter_dict = dict(self._identifying_params) starter_dict["_type"] = self._llm_type return starter_dict def save(self, file_path: Union[Path, str]) -> None: """Save the LLM. Args: file_path: Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path="path/llm.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save prompt_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(prompt_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(prompt_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml") class LLM(BaseLLM): """LLM class that expect subclasses to implement a simpler call method. The purpose of this class is to expose a simpler interface for working with LLMs, rather than expect the user to implement the full _generate method. """ @abstractmethod def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> str: """Run the LLM on the given prompt and input.""" async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, ) -> str: """Run the LLM on the given prompt and input.""" raise NotImplementedError("Async generation not implemented for this LLM.") def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> LLMResult: """Run the LLM on the given prompt and input.""" # TODO: add caching here. generations = [] new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") for prompt in prompts: text = ( self._call(prompt, stop=stop, run_manager=run_manager) if new_arg_supported else self._call(prompt, stop=stop) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations) async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, ) -> LLMResult: """Run the LLM on the given prompt and input.""" generations = [] new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager") for prompt in prompts: text = ( await self._acall(prompt, stop=stop, run_manager=run_manager) if new_arg_supported else await self._acall(prompt, stop=stop) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations)

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import tempfile from copy import deepcopy from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Sequence import langchain from langchain.callbacks.base import BaseCallbackHandler from langchain.callbacks.utils import ( BaseMetadataCallbackHandler, flatten_dict, import_pandas, import_spacy, import_textstat, ) from langchain.schema import AgentAction, AgentFinish, Generation, LLMResult LANGCHAIN_MODEL_NAME = "langchain-model" def import_comet_ml() -> Any: """Import comet_ml and raise an error if it is not installed.""" try: import comet_ml # noqa: F401 except ImportError: raise ImportError( "To use the comet_ml callback manager you need to have the " "`comet_ml` python package installed. Please install it with" " `pip install comet_ml`" ) return comet_ml def _get_experiment( workspace: Optional[str] = None, project_name: Optional[str] = None ) -> Any: comet_ml = import_comet_ml() experiment = comet_ml.Experiment( # type: ignore workspace=workspace, project_name=project_name, ) return experiment def _fetch_text_complexity_metrics(text: str) -> dict: textstat = import_textstat() text_complexity_metrics = { "flesch_reading_ease": textstat.flesch_reading_ease(text), "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text), "smog_index": textstat.smog_index(text), "coleman_liau_index": textstat.coleman_liau_index(text), "automated_readability_index": textstat.automated_readability_index(text), "dale_chall_readability_score": textstat.dale_chall_readability_score(text), "difficult_words": textstat.difficult_words(text), "linsear_write_formula": textstat.linsear_write_formula(text), "gunning_fog": textstat.gunning_fog(text), "text_standard": textstat.text_standard(text), "fernandez_huerta": textstat.fernandez_huerta(text), "szigriszt_pazos": textstat.szigriszt_pazos(text), "gutierrez_polini": textstat.gutierrez_polini(text), "crawford": textstat.crawford(text), "gulpease_index": textstat.gulpease_index(text), "osman": textstat.osman(text), } return text_complexity_metrics def _summarize_metrics_for_generated_outputs(metrics: Sequence) -> dict: pd = import_pandas() metrics_df = pd.DataFrame(metrics) metrics_summary = metrics_df.describe() return metrics_summary.to_dict() class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): """Callback Handler that logs to Comet. Parameters: job_type (str): The type of comet_ml task such as "inference", "testing" or "qc" project_name (str): The comet_ml project name tags (list): Tags to add to the task task_name (str): Name of the comet_ml task visualize (bool): Whether to visualize the run. complexity_metrics (bool): Whether to log complexity metrics stream_logs (bool): Whether to stream callback actions to Comet This handler will utilize the associated callback method and formats the input of each callback function with metadata regarding the state of LLM run, and adds the response to the list of records for both the {method}_records and action. It then logs the response to Comet. """ def __init__( self, task_type: Optional[str] = "inference", workspace: Optional[str] = None, project_name: Optional[str] = None, tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, stream_logs: bool = True, ) -> None: """Initialize callback handler.""" self.comet_ml = import_comet_ml() super().__init__() self.task_type = task_type self.workspace = workspace self.project_name = project_name self.tags = tags self.visualizations = visualizations self.complexity_metrics = complexity_metrics self.custom_metrics = custom_metrics self.stream_logs = stream_logs self.temp_dir = tempfile.TemporaryDirectory() self.experiment = _get_experiment(workspace, project_name) self.experiment.log_other("Created from", "langchain") if tags: self.experiment.add_tags(tags) self.name = name if self.name: self.experiment.set_name(self.name) warning = ( "The comet_ml callback is currently in beta and is subject to change " "based on updates to `langchain`. Please report any issues to " "https://github.com/comet-ml/issue-tracking/issues with the tag " "`langchain`." ) self.comet_ml.LOGGER.warning(warning) self.callback_columns: list = [] self.action_records: list = [] self.complexity_metrics = complexity_metrics if self.visualizations: spacy = import_spacy() self.nlp = spacy.load("en_core_web_sm") else: self.nlp = None def _init_resp(self) -> Dict: return {k: None for k in self.callback_columns} def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> None: """Run when LLM starts.""" self.step += 1 self.llm_starts += 1 self.starts += 1 metadata = self._init_resp() metadata.update({"action": "on_llm_start"}) metadata.update(flatten_dict(serialized)) metadata.update(self.get_custom_callback_meta()) for prompt in prompts: prompt_resp = deepcopy(metadata) prompt_resp["prompts"] = prompt self.on_llm_start_records.append(prompt_resp) self.action_records.append(prompt_resp) if self.stream_logs: self._log_stream(prompt, metadata, self.step) def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Run when LLM generates a new token.""" self.step += 1 self.llm_streams += 1 resp = self._init_resp() resp.update({"action": "on_llm_new_token", "token": token}) resp.update(self.get_custom_callback_meta()) self.action_records.append(resp) def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Run when LLM ends running.""" self.step += 1 self.llm_ends += 1 self.ends += 1 metadata = self._init_resp() metadata.update({"action": "on_llm_end"}) metadata.update(flatten_dict(response.llm_output or {})) metadata.update(self.get_custom_callback_meta()) output_complexity_metrics = [] output_custom_metrics = [] for prompt_idx, generations in enumerate(response.generations): for gen_idx, generation in enumerate(generations): text = generation.text generation_resp = deepcopy(metadata) generation_resp.update(flatten_dict(generation.dict())) complexity_metrics = self._get_complexity_metrics(text) if complexity_metrics: output_complexity_metrics.append(complexity_metrics) generation_resp.update(complexity_metrics) custom_metrics = self._get_custom_metrics( generation, prompt_idx, gen_idx ) if custom_metrics: output_custom_metrics.append(custom_metrics) generation_resp.update(custom_metrics) if self.stream_logs: self._log_stream(text, metadata, self.step) self.action_records.append(generation_resp) self.on_llm_end_records.append(generation_resp) self._log_text_metrics(output_complexity_metrics, step=self.step) self._log_text_metrics(output_custom_metrics, step=self.step) def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Run when LLM errors.""" self.step += 1 self.errors += 1 def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Run when chain starts running.""" self.step += 1 self.chain_starts += 1 self.starts += 1 resp = self._init_resp() resp.update({"action": "on_chain_start"}) resp.update(flatten_dict(serialized)) resp.update(self.get_custom_callback_meta()) for chain_input_key, chain_input_val in inputs.items(): if isinstance(chain_input_val, str): input_resp = deepcopy(resp) if self.stream_logs: self._log_stream(chain_input_val, resp, self.step) input_resp.update({chain_input_key: chain_input_val}) self.action_records.append(input_resp) else: self.comet_ml.LOGGER.warning( f"Unexpected data format provided! " f"Input Value for {chain_input_key} will not be logged" ) def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Run when chain ends running.""" self.step += 1 self.chain_ends += 1 self.ends += 1 resp = self._init_resp() resp.update({"action": "on_chain_end"}) resp.update(self.get_custom_callback_meta()) for chain_output_key, chain_output_val in outputs.items(): if isinstance(chain_output_val, str): output_resp = deepcopy(resp) if self.stream_logs: self._log_stream(chain_output_val, resp, self.step) output_resp.update({chain_output_key: chain_output_val}) self.action_records.append(output_resp) else: self.comet_ml.LOGGER.warning( f"Unexpected data format provided! " f"Output Value for {chain_output_key} will not be logged" ) def on_chain_error(self, error: BaseException, **kwargs: Any) -> None: """Run when chain errors.""" self.step += 1 self.errors += 1 def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs: Any ) -> None: """Run when tool starts running.""" self.step += 1 self.tool_starts += 1 self.starts += 1 resp = self._init_resp() resp.update({"action": "on_tool_start"}) resp.update(flatten_dict(serialized)) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(input_str, resp, self.step) resp.update({"input_str": input_str}) self.action_records.append(resp) def on_tool_end(self, output: str, **kwargs: Any) -> None: """Run when tool ends running.""" self.step += 1 self.tool_ends += 1 self.ends += 1 resp = self._init_resp() resp.update({"action": "on_tool_end"}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(output, resp, self.step) resp.update({"output": output}) self.action_records.append(resp) def on_tool_error(self, error: BaseException, **kwargs: Any) -> None: """Run when tool errors.""" self.step += 1 self.errors += 1 def on_text(self, text: str, **kwargs: Any) -> None: """ Run when agent is ending. """ self.step += 1 self.text_ctr += 1 resp = self._init_resp() resp.update({"action": "on_text"}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(text, resp, self.step) resp.update({"text": text}) self.action_records.append(resp) def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None: """Run when agent ends running.""" self.step += 1 self.agent_ends += 1 self.ends += 1 resp = self._init_resp() output = finish.return_values["output"] log = finish.log resp.update({"action": "on_agent_finish", "log": log}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(output, resp, self.step) resp.update({"output": output}) self.action_records.append(resp) def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: """Run on agent action.""" self.step += 1 self.tool_starts += 1 self.starts += 1 tool = action.tool tool_input = str(action.tool_input) log = action.log resp = self._init_resp() resp.update({"action": "on_agent_action", "log": log, "tool": tool}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(tool_input, resp, self.step) resp.update({"tool_input": tool_input}) self.action_records.append(resp) def _get_complexity_metrics(self, text: str) -> dict: """Compute text complexity metrics using textstat. Parameters: text (str): The text to analyze. Returns: (dict): A dictionary containing the complexity metrics. """ resp = {} if self.complexity_metrics: text_complexity_metrics = _fetch_text_complexity_metrics(text) resp.update(text_complexity_metrics) return resp def _get_custom_metrics( self, generation: Generation, prompt_idx: int, gen_idx: int ) -> dict: """Compute Custom Metrics for an LLM Generated Output Args: generation (LLMResult): Output generation from an LLM prompt_idx (int): List index of the input prompt gen_idx (int): List index of the generated output Returns: dict: A dictionary containing the custom metrics. """ resp = {} if self.custom_metrics: custom_metrics = self.custom_metrics(generation, prompt_idx, gen_idx) resp.update(custom_metrics) return resp def flush_tracker( self, langchain_asset: Any = None, task_type: Optional[str] = "inference", workspace: Optional[str] = None, project_name: Optional[str] = "comet-langchain-demo", tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, finish: bool = False, reset: bool = False, ) -> None: """Flush the tracker and setup the session. Everything after this will be a new table. Args: name: Name of the performed session so far so it is identifiable langchain_asset: The langchain asset to save. finish: Whether to finish the run. Returns: None """ self._log_session(langchain_asset) if langchain_asset: try: self._log_model(langchain_asset) except Exception: self.comet_ml.LOGGER.error( "Failed to export agent or LLM to Comet", exc_info=True, extra={"show_traceback": True}, ) if finish: self.experiment.end() if reset: self._reset( task_type, workspace, project_name, tags, name, visualizations, complexity_metrics, custom_metrics, ) def _log_stream(self, prompt: str, metadata: dict, step: int) -> None: self.experiment.log_text(prompt, metadata=metadata, step=step) def _log_model(self, langchain_asset: Any) -> None: model_parameters = self._get_llm_parameters(langchain_asset) self.experiment.log_parameters(model_parameters, prefix="model") langchain_asset_path = Path(self.temp_dir.name, "model.json") model_name = self.name if self.name else LANGCHAIN_MODEL_NAME try: if hasattr(langchain_asset, "save"): langchain_asset.save(langchain_asset_path) self.experiment.log_model(model_name, str(langchain_asset_path)) except (ValueError, AttributeError, NotImplementedError) as e: if hasattr(langchain_asset, "save_agent"): langchain_asset.save_agent(langchain_asset_path) self.experiment.log_model(model_name, str(langchain_asset_path)) else: self.comet_ml.LOGGER.error( f"{e}" " Could not save Langchain Asset " f"for {langchain_asset.__class__.__name__}" ) def _log_session(self, langchain_asset: Optional[Any] = None) -> None: try: llm_session_df = self._create_session_analysis_dataframe(langchain_asset) # Log the cleaned dataframe as a table self.experiment.log_table("langchain-llm-session.csv", llm_session_df) except Exception: self.comet_ml.LOGGER.warning( "Failed to log session data to Comet", exc_info=True, extra={"show_traceback": True}, ) try: metadata = {"langchain_version": str(langchain.__version__)} # Log the langchain low-level records as a JSON file directly self.experiment.log_asset_data( self.action_records, "langchain-action_records.json", metadata=metadata ) except Exception: self.comet_ml.LOGGER.warning( "Failed to log session data to Comet", exc_info=True, extra={"show_traceback": True}, ) try: self._log_visualizations(llm_session_df) except Exception: self.comet_ml.LOGGER.warning( "Failed to log visualizations to Comet", exc_info=True, extra={"show_traceback": True}, ) def _log_text_metrics(self, metrics: Sequence[dict], step: int) -> None: if not metrics: return metrics_summary = _summarize_metrics_for_generated_outputs(metrics) for key, value in metrics_summary.items(): self.experiment.log_metrics(value, prefix=key, step=step) def _log_visualizations(self, session_df: Any) -> None: if not (self.visualizations and self.nlp): return spacy = import_spacy() prompts = session_df["prompts"].tolist() outputs = session_df["text"].tolist() for idx, (prompt, output) in enumerate(zip(prompts, outputs)): doc = self.nlp(output) sentence_spans = list(doc.sents) for visualization in self.visualizations: try: html = spacy.displacy.render( sentence_spans, style=visualization, options={"compact": True}, jupyter=False, page=True, ) self.experiment.log_asset_data( html, name=f"langchain-viz-{visualization}-{idx}.html", metadata={"prompt": prompt}, step=idx, ) except Exception as e: self.comet_ml.LOGGER.warning( e, exc_info=True, extra={"show_traceback": True} ) return def _reset( self, task_type: Optional[str] = None, workspace: Optional[str] = None, project_name: Optional[str] = None, tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, ) -> None: _task_type = task_type if task_type else self.task_type _workspace = workspace if workspace else self.workspace _project_name = project_name if project_name else self.project_name _tags = tags if tags else self.tags _name = name if name else self.name _visualizations = visualizations if visualizations else self.visualizations _complexity_metrics = ( complexity_metrics if complexity_metrics else self.complexity_metrics ) _custom_metrics = custom_metrics if custom_metrics else self.custom_metrics self.__init__( # type: ignore task_type=_task_type, workspace=_workspace, project_name=_project_name, tags=_tags, name=_name, visualizations=_visualizations, complexity_metrics=_complexity_metrics, custom_metrics=_custom_metrics, ) self.reset_callback_meta() self.temp_dir = tempfile.TemporaryDirectory() def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict: pd = import_pandas() llm_parameters = self._get_llm_parameters(langchain_asset) num_generations_per_prompt = llm_parameters.get("n", 1) llm_start_records_df = pd.DataFrame(self.on_llm_start_records) # Repeat each input row based on the number of outputs generated per prompt llm_start_records_df = llm_start_records_df.loc[ llm_start_records_df.index.repeat(num_generations_per_prompt) ].reset_index(drop=True) llm_end_records_df = pd.DataFrame(self.on_llm_end_records) llm_session_df = pd.merge( llm_start_records_df, llm_end_records_df, left_index=True, right_index=True, suffixes=["_llm_start", "_llm_end"], ) return llm_session_df def _get_llm_parameters(self, langchain_asset: Any = None) -> dict: if not langchain_asset: return {} try: if hasattr(langchain_asset, "agent"): llm_parameters = langchain_asset.agent.llm_chain.llm.dict() elif hasattr(langchain_asset, "llm_chain"): llm_parameters = langchain_asset.llm_chain.llm.dict() elif hasattr(langchain_asset, "llm"): llm_parameters = langchain_asset.llm.dict() else: llm_parameters = langchain_asset.dict() except Exception: return {} return llm_parameters

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import tempfile from copy import deepcopy from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Sequence import langchain from langchain.callbacks.base import BaseCallbackHandler from langchain.callbacks.utils import ( BaseMetadataCallbackHandler, flatten_dict, import_pandas, import_spacy, import_textstat, ) from langchain.schema import AgentAction, AgentFinish, Generation, LLMResult LANGCHAIN_MODEL_NAME = "langchain-model" def import_comet_ml() -> Any: """Import comet_ml and raise an error if it is not installed.""" try: import comet_ml # noqa: F401 except ImportError: raise ImportError( "To use the comet_ml callback manager you need to have the " "`comet_ml` python package installed. Please install it with" " `pip install comet_ml`" ) return comet_ml def _get_experiment( workspace: Optional[str] = None, project_name: Optional[str] = None ) -> Any: comet_ml = import_comet_ml() experiment = comet_ml.Experiment( # type: ignore workspace=workspace, project_name=project_name, ) return experiment def _fetch_text_complexity_metrics(text: str) -> dict: textstat = import_textstat() text_complexity_metrics = { "flesch_reading_ease": textstat.flesch_reading_ease(text), "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text), "smog_index": textstat.smog_index(text), "coleman_liau_index": textstat.coleman_liau_index(text), "automated_readability_index": textstat.automated_readability_index(text), "dale_chall_readability_score": textstat.dale_chall_readability_score(text), "difficult_words": textstat.difficult_words(text), "linsear_write_formula": textstat.linsear_write_formula(text), "gunning_fog": textstat.gunning_fog(text), "text_standard": textstat.text_standard(text), "fernandez_huerta": textstat.fernandez_huerta(text), "szigriszt_pazos": textstat.szigriszt_pazos(text), "gutierrez_polini": textstat.gutierrez_polini(text), "crawford": textstat.crawford(text), "gulpease_index": textstat.gulpease_index(text), "osman": textstat.osman(text), } return text_complexity_metrics def _summarize_metrics_for_generated_outputs(metrics: Sequence) -> dict: pd = import_pandas() metrics_df = pd.DataFrame(metrics) metrics_summary = metrics_df.describe() return metrics_summary.to_dict() class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): """Callback Handler that logs to Comet. Parameters: job_type (str): The type of comet_ml task such as "inference", "testing" or "qc" project_name (str): The comet_ml project name tags (list): Tags to add to the task task_name (str): Name of the comet_ml task visualize (bool): Whether to visualize the run. complexity_metrics (bool): Whether to log complexity metrics stream_logs (bool): Whether to stream callback actions to Comet This handler will utilize the associated callback method and formats the input of each callback function with metadata regarding the state of LLM run, and adds the response to the list of records for both the {method}_records and action. It then logs the response to Comet. """ def __init__( self, task_type: Optional[str] = "inference", workspace: Optional[str] = None, project_name: Optional[str] = None, tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, stream_logs: bool = True, ) -> None: """Initialize callback handler.""" self.comet_ml = import_comet_ml() super().__init__() self.task_type = task_type self.workspace = workspace self.project_name = project_name self.tags = tags self.visualizations = visualizations self.complexity_metrics = complexity_metrics self.custom_metrics = custom_metrics self.stream_logs = stream_logs self.temp_dir = tempfile.TemporaryDirectory() self.experiment = _get_experiment(workspace, project_name) self.experiment.log_other("Created from", "langchain") if tags: self.experiment.add_tags(tags) self.name = name if self.name: self.experiment.set_name(self.name) warning = ( "The comet_ml callback is currently in beta and is subject to change " "based on updates to `langchain`. Please report any issues to " "https://github.com/comet-ml/issue-tracking/issues with the tag " "`langchain`." ) self.comet_ml.LOGGER.warning(warning) self.callback_columns: list = [] self.action_records: list = [] self.complexity_metrics = complexity_metrics if self.visualizations: spacy = import_spacy() self.nlp = spacy.load("en_core_web_sm") else: self.nlp = None def _init_resp(self) -> Dict: return {k: None for k in self.callback_columns} def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> None: """Run when LLM starts.""" self.step += 1 self.llm_starts += 1 self.starts += 1 metadata = self._init_resp() metadata.update({"action": "on_llm_start"}) metadata.update(flatten_dict(serialized)) metadata.update(self.get_custom_callback_meta()) for prompt in prompts: prompt_resp = deepcopy(metadata) prompt_resp["prompts"] = prompt self.on_llm_start_records.append(prompt_resp) self.action_records.append(prompt_resp) if self.stream_logs: self._log_stream(prompt, metadata, self.step) def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Run when LLM generates a new token.""" self.step += 1 self.llm_streams += 1 resp = self._init_resp() resp.update({"action": "on_llm_new_token", "token": token}) resp.update(self.get_custom_callback_meta()) self.action_records.append(resp) def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Run when LLM ends running.""" self.step += 1 self.llm_ends += 1 self.ends += 1 metadata = self._init_resp() metadata.update({"action": "on_llm_end"}) metadata.update(flatten_dict(response.llm_output or {})) metadata.update(self.get_custom_callback_meta()) output_complexity_metrics = [] output_custom_metrics = [] for prompt_idx, generations in enumerate(response.generations): for gen_idx, generation in enumerate(generations): text = generation.text generation_resp = deepcopy(metadata) generation_resp.update(flatten_dict(generation.dict())) complexity_metrics = self._get_complexity_metrics(text) if complexity_metrics: output_complexity_metrics.append(complexity_metrics) generation_resp.update(complexity_metrics) custom_metrics = self._get_custom_metrics( generation, prompt_idx, gen_idx ) if custom_metrics: output_custom_metrics.append(custom_metrics) generation_resp.update(custom_metrics) if self.stream_logs: self._log_stream(text, metadata, self.step) self.action_records.append(generation_resp) self.on_llm_end_records.append(generation_resp) self._log_text_metrics(output_complexity_metrics, step=self.step) self._log_text_metrics(output_custom_metrics, step=self.step) def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Run when LLM errors.""" self.step += 1 self.errors += 1 def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Run when chain starts running.""" self.step += 1 self.chain_starts += 1 self.starts += 1 resp = self._init_resp() resp.update({"action": "on_chain_start"}) resp.update(flatten_dict(serialized)) resp.update(self.get_custom_callback_meta()) for chain_input_key, chain_input_val in inputs.items(): if isinstance(chain_input_val, str): input_resp = deepcopy(resp) if self.stream_logs: self._log_stream(chain_input_val, resp, self.step) input_resp.update({chain_input_key: chain_input_val}) self.action_records.append(input_resp) else: self.comet_ml.LOGGER.warning( f"Unexpected data format provided! " f"Input Value for {chain_input_key} will not be logged" ) def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Run when chain ends running.""" self.step += 1 self.chain_ends += 1 self.ends += 1 resp = self._init_resp() resp.update({"action": "on_chain_end"}) resp.update(self.get_custom_callback_meta()) for chain_output_key, chain_output_val in outputs.items(): if isinstance(chain_output_val, str): output_resp = deepcopy(resp) if self.stream_logs: self._log_stream(chain_output_val, resp, self.step) output_resp.update({chain_output_key: chain_output_val}) self.action_records.append(output_resp) else: self.comet_ml.LOGGER.warning( f"Unexpected data format provided! " f"Output Value for {chain_output_key} will not be logged" ) def on_chain_error(self, error: BaseException, **kwargs: Any) -> None: """Run when chain errors.""" self.step += 1 self.errors += 1 def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs: Any ) -> None: """Run when tool starts running.""" self.step += 1 self.tool_starts += 1 self.starts += 1 resp = self._init_resp() resp.update({"action": "on_tool_start"}) resp.update(flatten_dict(serialized)) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(input_str, resp, self.step) resp.update({"input_str": input_str}) self.action_records.append(resp) def on_tool_end(self, output: str, **kwargs: Any) -> None: """Run when tool ends running.""" self.step += 1 self.tool_ends += 1 self.ends += 1 resp = self._init_resp() resp.update({"action": "on_tool_end"}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(output, resp, self.step) resp.update({"output": output}) self.action_records.append(resp) def on_tool_error(self, error: BaseException, **kwargs: Any) -> None: """Run when tool errors.""" self.step += 1 self.errors += 1 def on_text(self, text: str, **kwargs: Any) -> None: """ Run when agent is ending. """ self.step += 1 self.text_ctr += 1 resp = self._init_resp() resp.update({"action": "on_text"}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(text, resp, self.step) resp.update({"text": text}) self.action_records.append(resp) def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None: """Run when agent ends running.""" self.step += 1 self.agent_ends += 1 self.ends += 1 resp = self._init_resp() output = finish.return_values["output"] log = finish.log resp.update({"action": "on_agent_finish", "log": log}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(output, resp, self.step) resp.update({"output": output}) self.action_records.append(resp) def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: """Run on agent action.""" self.step += 1 self.tool_starts += 1 self.starts += 1 tool = action.tool tool_input = str(action.tool_input) log = action.log resp = self._init_resp() resp.update({"action": "on_agent_action", "log": log, "tool": tool}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(tool_input, resp, self.step) resp.update({"tool_input": tool_input}) self.action_records.append(resp) def _get_complexity_metrics(self, text: str) -> dict: """Compute text complexity metrics using textstat. Parameters: text (str): The text to analyze. Returns: (dict): A dictionary containing the complexity metrics. """ resp = {} if self.complexity_metrics: text_complexity_metrics = _fetch_text_complexity_metrics(text) resp.update(text_complexity_metrics) return resp def _get_custom_metrics( self, generation: Generation, prompt_idx: int, gen_idx: int ) -> dict: """Compute Custom Metrics for an LLM Generated Output Args: generation (LLMResult): Output generation from an LLM prompt_idx (int): List index of the input prompt gen_idx (int): List index of the generated output Returns: dict: A dictionary containing the custom metrics. """ resp = {} if self.custom_metrics: custom_metrics = self.custom_metrics(generation, prompt_idx, gen_idx) resp.update(custom_metrics) return resp def flush_tracker( self, langchain_asset: Any = None, task_type: Optional[str] = "inference", workspace: Optional[str] = None, project_name: Optional[str] = "comet-langchain-demo", tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, finish: bool = False, reset: bool = False, ) -> None: """Flush the tracker and setup the session. Everything after this will be a new table. Args: name: Name of the performed session so far so it is identifiable langchain_asset: The langchain asset to save. finish: Whether to finish the run. Returns: None """ self._log_session(langchain_asset) if langchain_asset: try: self._log_model(langchain_asset) except Exception: self.comet_ml.LOGGER.error( "Failed to export agent or LLM to Comet", exc_info=True, extra={"show_traceback": True}, ) if finish: self.experiment.end() if reset: self._reset( task_type, workspace, project_name, tags, name, visualizations, complexity_metrics, custom_metrics, ) def _log_stream(self, prompt: str, metadata: dict, step: int) -> None: self.experiment.log_text(prompt, metadata=metadata, step=step) def _log_model(self, langchain_asset: Any) -> None: model_parameters = self._get_llm_parameters(langchain_asset) self.experiment.log_parameters(model_parameters, prefix="model") langchain_asset_path = Path(self.temp_dir.name, "model.json") model_name = self.name if self.name else LANGCHAIN_MODEL_NAME try: if hasattr(langchain_asset, "save"): langchain_asset.save(langchain_asset_path) self.experiment.log_model(model_name, str(langchain_asset_path)) except (ValueError, AttributeError, NotImplementedError) as e: if hasattr(langchain_asset, "save_agent"): langchain_asset.save_agent(langchain_asset_path) self.experiment.log_model(model_name, str(langchain_asset_path)) else: self.comet_ml.LOGGER.error( f"{e}" " Could not save Langchain Asset " f"for {langchain_asset.__class__.__name__}" ) def _log_session(self, langchain_asset: Optional[Any] = None) -> None: try: llm_session_df = self._create_session_analysis_dataframe(langchain_asset) # Log the cleaned dataframe as a table self.experiment.log_table("langchain-llm-session.csv", llm_session_df) except Exception: self.comet_ml.LOGGER.warning( "Failed to log session data to Comet", exc_info=True, extra={"show_traceback": True}, ) try: metadata = {"langchain_version": str(langchain.__version__)} # Log the langchain low-level records as a JSON file directly self.experiment.log_asset_data( self.action_records, "langchain-action_records.json", metadata=metadata ) except Exception: self.comet_ml.LOGGER.warning( "Failed to log session data to Comet", exc_info=True, extra={"show_traceback": True}, ) try: self._log_visualizations(llm_session_df) except Exception: self.comet_ml.LOGGER.warning( "Failed to log visualizations to Comet", exc_info=True, extra={"show_traceback": True}, ) def _log_text_metrics(self, metrics: Sequence[dict], step: int) -> None: if not metrics: return metrics_summary = _summarize_metrics_for_generated_outputs(metrics) for key, value in metrics_summary.items(): self.experiment.log_metrics(value, prefix=key, step=step) def _log_visualizations(self, session_df: Any) -> None: if not (self.visualizations and self.nlp): return spacy = import_spacy() prompts = session_df["prompts"].tolist() outputs = session_df["text"].tolist() for idx, (prompt, output) in enumerate(zip(prompts, outputs)): doc = self.nlp(output) sentence_spans = list(doc.sents) for visualization in self.visualizations: try: html = spacy.displacy.render( sentence_spans, style=visualization, options={"compact": True}, jupyter=False, page=True, ) self.experiment.log_asset_data( html, name=f"langchain-viz-{visualization}-{idx}.html", metadata={"prompt": prompt}, step=idx, ) except Exception as e: self.comet_ml.LOGGER.warning( e, exc_info=True, extra={"show_traceback": True} ) return def _reset( self, task_type: Optional[str] = None, workspace: Optional[str] = None, project_name: Optional[str] = None, tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, ) -> None: _task_type = task_type if task_type else self.task_type _workspace = workspace if workspace else self.workspace _project_name = project_name if project_name else self.project_name _tags = tags if tags else self.tags _name = name if name else self.name _visualizations = visualizations if visualizations else self.visualizations _complexity_metrics = ( complexity_metrics if complexity_metrics else self.complexity_metrics ) _custom_metrics = custom_metrics if custom_metrics else self.custom_metrics self.__init__( # type: ignore task_type=_task_type, workspace=_workspace, project_name=_project_name, tags=_tags, name=_name, visualizations=_visualizations, complexity_metrics=_complexity_metrics, custom_metrics=_custom_metrics, ) self.reset_callback_meta() self.temp_dir = tempfile.TemporaryDirectory() def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict: pd = import_pandas() llm_parameters = self._get_llm_parameters(langchain_asset) num_generations_per_prompt = llm_parameters.get("n", 1) llm_start_records_df = pd.DataFrame(self.on_llm_start_records) # Repeat each input row based on the number of outputs generated per prompt llm_start_records_df = llm_start_records_df.loc[ llm_start_records_df.index.repeat(num_generations_per_prompt) ].reset_index(drop=True) llm_end_records_df = pd.DataFrame(self.on_llm_end_records) llm_session_df = pd.merge( llm_start_records_df, llm_end_records_df, left_index=True, right_index=True, suffixes=["_llm_start", "_llm_end"], ) return llm_session_df def _get_llm_parameters(self, langchain_asset: Any = None) -> dict: if not langchain_asset: return {} try: if hasattr(langchain_asset, "agent"): llm_parameters = langchain_asset.agent.llm_chain.llm.dict() elif hasattr(langchain_asset, "llm_chain"): llm_parameters = langchain_asset.llm_chain.llm.dict() elif hasattr(langchain_asset, "llm"): llm_parameters = langchain_asset.llm.dict() else: llm_parameters = langchain_asset.dict() except Exception: return {} return llm_parameters

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import tempfile from copy import deepcopy from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Sequence import langchain from langchain.callbacks.base import BaseCallbackHandler from langchain.callbacks.utils import ( BaseMetadataCallbackHandler, flatten_dict, import_pandas, import_spacy, import_textstat, ) from langchain.schema import AgentAction, AgentFinish, Generation, LLMResult LANGCHAIN_MODEL_NAME = "langchain-model" def import_comet_ml() -> Any: """Import comet_ml and raise an error if it is not installed.""" try: import comet_ml # noqa: F401 except ImportError: raise ImportError( "To use the comet_ml callback manager you need to have the " "`comet_ml` python package installed. Please install it with" " `pip install comet_ml`" ) return comet_ml def _get_experiment( workspace: Optional[str] = None, project_name: Optional[str] = None ) -> Any: comet_ml = import_comet_ml() experiment = comet_ml.Experiment( # type: ignore workspace=workspace, project_name=project_name, ) return experiment def _fetch_text_complexity_metrics(text: str) -> dict: textstat = import_textstat() text_complexity_metrics = { "flesch_reading_ease": textstat.flesch_reading_ease(text), "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text), "smog_index": textstat.smog_index(text), "coleman_liau_index": textstat.coleman_liau_index(text), "automated_readability_index": textstat.automated_readability_index(text), "dale_chall_readability_score": textstat.dale_chall_readability_score(text), "difficult_words": textstat.difficult_words(text), "linsear_write_formula": textstat.linsear_write_formula(text), "gunning_fog": textstat.gunning_fog(text), "text_standard": textstat.text_standard(text), "fernandez_huerta": textstat.fernandez_huerta(text), "szigriszt_pazos": textstat.szigriszt_pazos(text), "gutierrez_polini": textstat.gutierrez_polini(text), "crawford": textstat.crawford(text), "gulpease_index": textstat.gulpease_index(text), "osman": textstat.osman(text), } return text_complexity_metrics def _summarize_metrics_for_generated_outputs(metrics: Sequence) -> dict: pd = import_pandas() metrics_df = pd.DataFrame(metrics) metrics_summary = metrics_df.describe() return metrics_summary.to_dict() class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): """Callback Handler that logs to Comet. Parameters: job_type (str): The type of comet_ml task such as "inference", "testing" or "qc" project_name (str): The comet_ml project name tags (list): Tags to add to the task task_name (str): Name of the comet_ml task visualize (bool): Whether to visualize the run. complexity_metrics (bool): Whether to log complexity metrics stream_logs (bool): Whether to stream callback actions to Comet This handler will utilize the associated callback method and formats the input of each callback function with metadata regarding the state of LLM run, and adds the response to the list of records for both the {method}_records and action. It then logs the response to Comet. """ def __init__( self, task_type: Optional[str] = "inference", workspace: Optional[str] = None, project_name: Optional[str] = None, tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, stream_logs: bool = True, ) -> None: """Initialize callback handler.""" self.comet_ml = import_comet_ml() super().__init__() self.task_type = task_type self.workspace = workspace self.project_name = project_name self.tags = tags self.visualizations = visualizations self.complexity_metrics = complexity_metrics self.custom_metrics = custom_metrics self.stream_logs = stream_logs self.temp_dir = tempfile.TemporaryDirectory() self.experiment = _get_experiment(workspace, project_name) self.experiment.log_other("Created from", "langchain") if tags: self.experiment.add_tags(tags) self.name = name if self.name: self.experiment.set_name(self.name) warning = ( "The comet_ml callback is currently in beta and is subject to change " "based on updates to `langchain`. Please report any issues to " "https://github.com/comet-ml/issue-tracking/issues with the tag " "`langchain`." ) self.comet_ml.LOGGER.warning(warning) self.callback_columns: list = [] self.action_records: list = [] self.complexity_metrics = complexity_metrics if self.visualizations: spacy = import_spacy() self.nlp = spacy.load("en_core_web_sm") else: self.nlp = None def _init_resp(self) -> Dict: return {k: None for k in self.callback_columns} def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> None: """Run when LLM starts.""" self.step += 1 self.llm_starts += 1 self.starts += 1 metadata = self._init_resp() metadata.update({"action": "on_llm_start"}) metadata.update(flatten_dict(serialized)) metadata.update(self.get_custom_callback_meta()) for prompt in prompts: prompt_resp = deepcopy(metadata) prompt_resp["prompts"] = prompt self.on_llm_start_records.append(prompt_resp) self.action_records.append(prompt_resp) if self.stream_logs: self._log_stream(prompt, metadata, self.step) def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Run when LLM generates a new token.""" self.step += 1 self.llm_streams += 1 resp = self._init_resp() resp.update({"action": "on_llm_new_token", "token": token}) resp.update(self.get_custom_callback_meta()) self.action_records.append(resp) def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Run when LLM ends running.""" self.step += 1 self.llm_ends += 1 self.ends += 1 metadata = self._init_resp() metadata.update({"action": "on_llm_end"}) metadata.update(flatten_dict(response.llm_output or {})) metadata.update(self.get_custom_callback_meta()) output_complexity_metrics = [] output_custom_metrics = [] for prompt_idx, generations in enumerate(response.generations): for gen_idx, generation in enumerate(generations): text = generation.text generation_resp = deepcopy(metadata) generation_resp.update(flatten_dict(generation.dict())) complexity_metrics = self._get_complexity_metrics(text) if complexity_metrics: output_complexity_metrics.append(complexity_metrics) generation_resp.update(complexity_metrics) custom_metrics = self._get_custom_metrics( generation, prompt_idx, gen_idx ) if custom_metrics: output_custom_metrics.append(custom_metrics) generation_resp.update(custom_metrics) if self.stream_logs: self._log_stream(text, metadata, self.step) self.action_records.append(generation_resp) self.on_llm_end_records.append(generation_resp) self._log_text_metrics(output_complexity_metrics, step=self.step) self._log_text_metrics(output_custom_metrics, step=self.step) def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Run when LLM errors.""" self.step += 1 self.errors += 1 def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: """Run when chain starts running.""" self.step += 1 self.chain_starts += 1 self.starts += 1 resp = self._init_resp() resp.update({"action": "on_chain_start"}) resp.update(flatten_dict(serialized)) resp.update(self.get_custom_callback_meta()) for chain_input_key, chain_input_val in inputs.items(): if isinstance(chain_input_val, str): input_resp = deepcopy(resp) if self.stream_logs: self._log_stream(chain_input_val, resp, self.step) input_resp.update({chain_input_key: chain_input_val}) self.action_records.append(input_resp) else: self.comet_ml.LOGGER.warning( f"Unexpected data format provided! " f"Input Value for {chain_input_key} will not be logged" ) def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Run when chain ends running.""" self.step += 1 self.chain_ends += 1 self.ends += 1 resp = self._init_resp() resp.update({"action": "on_chain_end"}) resp.update(self.get_custom_callback_meta()) for chain_output_key, chain_output_val in outputs.items(): if isinstance(chain_output_val, str): output_resp = deepcopy(resp) if self.stream_logs: self._log_stream(chain_output_val, resp, self.step) output_resp.update({chain_output_key: chain_output_val}) self.action_records.append(output_resp) else: self.comet_ml.LOGGER.warning( f"Unexpected data format provided! " f"Output Value for {chain_output_key} will not be logged" ) def on_chain_error(self, error: BaseException, **kwargs: Any) -> None: """Run when chain errors.""" self.step += 1 self.errors += 1 def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs: Any ) -> None: """Run when tool starts running.""" self.step += 1 self.tool_starts += 1 self.starts += 1 resp = self._init_resp() resp.update({"action": "on_tool_start"}) resp.update(flatten_dict(serialized)) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(input_str, resp, self.step) resp.update({"input_str": input_str}) self.action_records.append(resp) def on_tool_end(self, output: str, **kwargs: Any) -> None: """Run when tool ends running.""" self.step += 1 self.tool_ends += 1 self.ends += 1 resp = self._init_resp() resp.update({"action": "on_tool_end"}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(output, resp, self.step) resp.update({"output": output}) self.action_records.append(resp) def on_tool_error(self, error: BaseException, **kwargs: Any) -> None: """Run when tool errors.""" self.step += 1 self.errors += 1 def on_text(self, text: str, **kwargs: Any) -> None: """ Run when agent is ending. """ self.step += 1 self.text_ctr += 1 resp = self._init_resp() resp.update({"action": "on_text"}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(text, resp, self.step) resp.update({"text": text}) self.action_records.append(resp) def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None: """Run when agent ends running.""" self.step += 1 self.agent_ends += 1 self.ends += 1 resp = self._init_resp() output = finish.return_values["output"] log = finish.log resp.update({"action": "on_agent_finish", "log": log}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(output, resp, self.step) resp.update({"output": output}) self.action_records.append(resp) def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: """Run on agent action.""" self.step += 1 self.tool_starts += 1 self.starts += 1 tool = action.tool tool_input = str(action.tool_input) log = action.log resp = self._init_resp() resp.update({"action": "on_agent_action", "log": log, "tool": tool}) resp.update(self.get_custom_callback_meta()) if self.stream_logs: self._log_stream(tool_input, resp, self.step) resp.update({"tool_input": tool_input}) self.action_records.append(resp) def _get_complexity_metrics(self, text: str) -> dict: """Compute text complexity metrics using textstat. Parameters: text (str): The text to analyze. Returns: (dict): A dictionary containing the complexity metrics. """ resp = {} if self.complexity_metrics: text_complexity_metrics = _fetch_text_complexity_metrics(text) resp.update(text_complexity_metrics) return resp def _get_custom_metrics( self, generation: Generation, prompt_idx: int, gen_idx: int ) -> dict: """Compute Custom Metrics for an LLM Generated Output Args: generation (LLMResult): Output generation from an LLM prompt_idx (int): List index of the input prompt gen_idx (int): List index of the generated output Returns: dict: A dictionary containing the custom metrics. """ resp = {} if self.custom_metrics: custom_metrics = self.custom_metrics(generation, prompt_idx, gen_idx) resp.update(custom_metrics) return resp def flush_tracker( self, langchain_asset: Any = None, task_type: Optional[str] = "inference", workspace: Optional[str] = None, project_name: Optional[str] = "comet-langchain-demo", tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, finish: bool = False, reset: bool = False, ) -> None: """Flush the tracker and setup the session. Everything after this will be a new table. Args: name: Name of the performed session so far so it is identifiable langchain_asset: The langchain asset to save. finish: Whether to finish the run. Returns: None """ self._log_session(langchain_asset) if langchain_asset: try: self._log_model(langchain_asset) except Exception: self.comet_ml.LOGGER.error( "Failed to export agent or LLM to Comet", exc_info=True, extra={"show_traceback": True}, ) if finish: self.experiment.end() if reset: self._reset( task_type, workspace, project_name, tags, name, visualizations, complexity_metrics, custom_metrics, ) def _log_stream(self, prompt: str, metadata: dict, step: int) -> None: self.experiment.log_text(prompt, metadata=metadata, step=step) def _log_model(self, langchain_asset: Any) -> None: model_parameters = self._get_llm_parameters(langchain_asset) self.experiment.log_parameters(model_parameters, prefix="model") langchain_asset_path = Path(self.temp_dir.name, "model.json") model_name = self.name if self.name else LANGCHAIN_MODEL_NAME try: if hasattr(langchain_asset, "save"): langchain_asset.save(langchain_asset_path) self.experiment.log_model(model_name, str(langchain_asset_path)) except (ValueError, AttributeError, NotImplementedError) as e: if hasattr(langchain_asset, "save_agent"): langchain_asset.save_agent(langchain_asset_path) self.experiment.log_model(model_name, str(langchain_asset_path)) else: self.comet_ml.LOGGER.error( f"{e}" " Could not save Langchain Asset " f"for {langchain_asset.__class__.__name__}" ) def _log_session(self, langchain_asset: Optional[Any] = None) -> None: try: llm_session_df = self._create_session_analysis_dataframe(langchain_asset) # Log the cleaned dataframe as a table self.experiment.log_table("langchain-llm-session.csv", llm_session_df) except Exception: self.comet_ml.LOGGER.warning( "Failed to log session data to Comet", exc_info=True, extra={"show_traceback": True}, ) try: metadata = {"langchain_version": str(langchain.__version__)} # Log the langchain low-level records as a JSON file directly self.experiment.log_asset_data( self.action_records, "langchain-action_records.json", metadata=metadata ) except Exception: self.comet_ml.LOGGER.warning( "Failed to log session data to Comet", exc_info=True, extra={"show_traceback": True}, ) try: self._log_visualizations(llm_session_df) except Exception: self.comet_ml.LOGGER.warning( "Failed to log visualizations to Comet", exc_info=True, extra={"show_traceback": True}, ) def _log_text_metrics(self, metrics: Sequence[dict], step: int) -> None: if not metrics: return metrics_summary = _summarize_metrics_for_generated_outputs(metrics) for key, value in metrics_summary.items(): self.experiment.log_metrics(value, prefix=key, step=step) def _log_visualizations(self, session_df: Any) -> None: if not (self.visualizations and self.nlp): return spacy = import_spacy() prompts = session_df["prompts"].tolist() outputs = session_df["text"].tolist() for idx, (prompt, output) in enumerate(zip(prompts, outputs)): doc = self.nlp(output) sentence_spans = list(doc.sents) for visualization in self.visualizations: try: html = spacy.displacy.render( sentence_spans, style=visualization, options={"compact": True}, jupyter=False, page=True, ) self.experiment.log_asset_data( html, name=f"langchain-viz-{visualization}-{idx}.html", metadata={"prompt": prompt}, step=idx, ) except Exception as e: self.comet_ml.LOGGER.warning( e, exc_info=True, extra={"show_traceback": True} ) return def _reset( self, task_type: Optional[str] = None, workspace: Optional[str] = None, project_name: Optional[str] = None, tags: Optional[Sequence] = None, name: Optional[str] = None, visualizations: Optional[List[str]] = None, complexity_metrics: bool = False, custom_metrics: Optional[Callable] = None, ) -> None: _task_type = task_type if task_type else self.task_type _workspace = workspace if workspace else self.workspace _project_name = project_name if project_name else self.project_name _tags = tags if tags else self.tags _name = name if name else self.name _visualizations = visualizations if visualizations else self.visualizations _complexity_metrics = ( complexity_metrics if complexity_metrics else self.complexity_metrics ) _custom_metrics = custom_metrics if custom_metrics else self.custom_metrics self.__init__( # type: ignore task_type=_task_type, workspace=_workspace, project_name=_project_name, tags=_tags, name=_name, visualizations=_visualizations, complexity_metrics=_complexity_metrics, custom_metrics=_custom_metrics, ) self.reset_callback_meta() self.temp_dir = tempfile.TemporaryDirectory() def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict: pd = import_pandas() llm_parameters = self._get_llm_parameters(langchain_asset) num_generations_per_prompt = llm_parameters.get("n", 1) llm_start_records_df = pd.DataFrame(self.on_llm_start_records) # Repeat each input row based on the number of outputs generated per prompt llm_start_records_df = llm_start_records_df.loc[ llm_start_records_df.index.repeat(num_generations_per_prompt) ].reset_index(drop=True) llm_end_records_df = pd.DataFrame(self.on_llm_end_records) llm_session_df = pd.merge( llm_start_records_df, llm_end_records_df, left_index=True, right_index=True, suffixes=["_llm_start", "_llm_end"], ) return llm_session_df def _get_llm_parameters(self, langchain_asset: Any = None) -> dict: if not langchain_asset: return {} try: if hasattr(langchain_asset, "agent"): llm_parameters = langchain_asset.agent.llm_chain.llm.dict() elif hasattr(langchain_asset, "llm_chain"): llm_parameters = langchain_asset.llm_chain.llm.dict() elif hasattr(langchain_asset, "llm"): llm_parameters = langchain_asset.llm.dict() else: llm_parameters = langchain_asset.dict() except Exception: return {} return llm_parameters

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"""Utilities for running language models or Chains over datasets.""" from __future__ import annotations import asyncio import functools import itertools import logging import uuid from enum import Enum from typing import ( Any, Callable, Coroutine, Dict, Iterator, List, Optional, Sequence, Tuple, Union, ) from urllib.parse import urlparse, urlunparse from langsmith import Client, RunEvaluator from langsmith.schemas import Dataset, DataType, Example from langchain.callbacks.base import BaseCallbackHandler from langchain.callbacks.manager import Callbacks from langchain.callbacks.tracers.base import BaseTracer from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler from langchain.callbacks.tracers.langchain import LangChainTracer from langchain.chains.base import Chain from langchain.chat_models.openai import ChatOpenAI from langchain.evaluation.loading import load_evaluator from langchain.evaluation.schema import EvaluatorType, StringEvaluator from langchain.schema import ChatResult, LLMResult from langchain.schema.language_model import BaseLanguageModel from langchain.schema.messages import BaseMessage, messages_from_dict from langchain.smith.evaluation.config import EvalConfig, RunEvalConfig from langchain.smith.evaluation.string_run_evaluator import StringRunEvaluatorChain logger = logging.getLogger(__name__) MODEL_OR_CHAIN_FACTORY = Union[Callable[[], Chain], BaseLanguageModel] class InputFormatError(Exception): """Raised when the input format is invalid.""" ## Shared Utilities def _get_eval_project_url(api_url: str, project_id: str) -> str: """Get the project url from the api url.""" parsed = urlparse(api_url) hostname = parsed.hostname or "" if "api." in hostname: hostname = hostname.replace("api.", "", 1) if "localhost" in hostname: # Remove the port hostname = "localhost" url = urlunparse(parsed._replace(netloc=hostname)) return f"{url}/projects/p/{project_id}?eval=true" def _wrap_in_chain_factory( llm_or_chain_factory: Union[Chain, MODEL_OR_CHAIN_FACTORY], dataset_name: str = "<my_dataset>", ) -> MODEL_OR_CHAIN_FACTORY: """Forgive the user if they pass in a chain without memory instead of a chain factory. It's a common mistake. Raise a more helpful error message as well.""" if isinstance(llm_or_chain_factory, Chain): chain = llm_or_chain_factory chain_class = chain.__class__.__name__ if llm_or_chain_factory.memory is not None: memory_class = chain.memory.__class__.__name__ raise ValueError( "Cannot directly evaluate a chain with stateful memory." " To evaluate this chain, pass in a chain constructor" " that initializes fresh memory each time it is called." " This will safegaurd against information" " leakage between dataset examples." "\nFor example:\n\n" "def chain_constructor():\n" f" new_memory = {memory_class}(...)\n" f" return {chain_class}" "(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) logger.warning( "Directly passing in a chain is not recommended as chains may have state." " This can lead to unexpected behavior as the " "same chain instance could be used across multiple datasets. Instead," " please pass a chain constructor that creates a new " "chain with fresh memory each time it is called. This will safeguard" " against information leakage between dataset examples. " "\nFor example:\n\n" "def chain_constructor():\n" f" return {chain_class}(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) return lambda: chain elif isinstance(llm_or_chain_factory, BaseLanguageModel): return llm_or_chain_factory elif callable(llm_or_chain_factory): _model = llm_or_chain_factory() if isinstance(_model, BaseLanguageModel): return _model return llm_or_chain_factory return llm_or_chain_factory def _first_example(examples: Iterator[Example]) -> Tuple[Example, Iterator[Example]]: """Get the first example while chaining it back and preserving the iterator.""" try: example: Example = next(examples) except StopIteration: raise ValueError("No examples provided.") return example, itertools.chain([example], examples) def _get_prompt(inputs: Dict[str, Any]) -> str: """Get prompt from inputs. Args: inputs: The input dictionary. Returns: A string prompt. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") prompts = [] if "prompt" in inputs: if not isinstance(inputs["prompt"], str): raise InputFormatError( "Expected string for 'prompt', got" f" {type(inputs['prompt']).__name__}" ) prompts = [inputs["prompt"]] elif "prompts" in inputs: if not isinstance(inputs["prompts"], list) or not all( isinstance(i, str) for i in inputs["prompts"] ): raise InputFormatError( "Expected list of strings for 'prompts'," f" got {type(inputs['prompts']).__name__}" ) prompts = inputs["prompts"] elif len(inputs) == 1: prompt_ = next(iter(inputs.values())) if isinstance(prompt_, str): prompts = [prompt_] elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_): prompts = prompt_ else: raise InputFormatError(f"LLM Run expects string prompt input. Got {inputs}") else: raise InputFormatError( f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}" ) if len(prompts) == 1: return prompts[0] else: raise InputFormatError( f"LLM Run expects single prompt input. Got {len(prompts)} prompts." ) def _get_messages(inputs: Dict[str, Any]) -> List[BaseMessage]: """Get Chat Messages from inputs. Args: inputs: The input dictionary. Returns: A list of chat messages. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") if "messages" in inputs: single_input = inputs["messages"] elif len(inputs) == 1: single_input = next(iter(inputs.values())) else: raise InputFormatError( f"Chat Run expects 'messages' in inputs when example has multiple" f" input keys. Got {inputs}" ) if isinstance(single_input, list) and all( isinstance(i, dict) for i in single_input ): raw_messages = [single_input] elif isinstance(single_input, list) and all( isinstance(i, list) for i in single_input ): raw_messages = single_input else: raise InputFormatError( f"Chat Run expects List[dict] or List[List[dict]] values for" f" 'messages' key input. Got {inputs}" ) if len(raw_messages) == 1: return messages_from_dict(raw_messages[0]) else: raise InputFormatError( f"Chat Run expects single List[dict] or List[List[dict]] 'messages'" f" input. Got {len(raw_messages)} messages from inputs {inputs}" ) def _get_project_name( project_name: Optional[str], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, ) -> str: """ Get the project name. Args: project_name: The project name if manually specified. llm_or_chain_factory: The Chain or language model constructor. Returns: The project name. """ if project_name is not None: return project_name if isinstance(llm_or_chain_factory, BaseLanguageModel): model_name = llm_or_chain_factory.__class__.__name__ else: model_name = llm_or_chain_factory().__class__.__name__ hex = uuid.uuid4().hex return f"{hex}-{model_name}" ## Shared Validation Utilities def _validate_example_inputs_for_language_model( first_example: Example, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: if input_mapper: prompt_input = input_mapper(first_example.inputs) if not isinstance(prompt_input, str) and not ( isinstance(prompt_input, list) and all(isinstance(msg, BaseMessage) for msg in prompt_input) ): raise InputFormatError( "When using an input_mapper to prepare dataset example inputs" " for an LLM or chat model, the output must a single string or" " a list of chat messages." f"\nGot: {prompt_input} of type {type(prompt_input)}." ) else: try: _get_prompt(first_example.inputs) except InputFormatError: try: _get_messages(first_example.inputs) except InputFormatError: raise InputFormatError( "Example inputs do not match language model input format. " "Expected a dictionary with messages or a single prompt." f" Got: {first_example.inputs}" " Please update your dataset OR provide an input_mapper" " to convert the example.inputs to a compatible format" " for the llm or chat model you wish to evaluate." ) def _validate_example_inputs_for_chain( first_example: Example, chain: Chain, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: """Validate that the example inputs match the chain input keys.""" if input_mapper: first_inputs = input_mapper(first_example.inputs) if not isinstance(first_inputs, dict): raise InputFormatError( "When using an input_mapper to prepare dataset example" " inputs for a chain, the mapped value must be a dictionary." f"\nGot: {first_inputs} of type {type(first_inputs)}." ) if not set(first_inputs.keys()) == set(chain.input_keys): raise InputFormatError( "When using an input_mapper to prepare dataset example inputs" " for a chain mapped value must have keys that match the chain's" " expected input keys." f"\nExpected: {chain.input_keys}. Got: {first_inputs.keys()}" ) else: first_inputs = first_example.inputs if len(first_inputs) == 1 and len(chain.input_keys) == 1: # We can pass this through the run method. # Refrain from calling to validate. pass elif not set(first_inputs.keys()) == set(chain.input_keys): raise InputFormatError( "Example inputs do not match chain input keys." " Please provide an input_mapper to convert the example.inputs" " to a compatible format for the chain you wish to evaluate." f"Expected: {chain.input_keys}. " f"Got: {first_inputs.keys()}" ) def _validate_example_inputs( examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, input_mapper: Optional[Callable[[Dict], Any]], ) -> Iterator[Example]: """Validate that the example inputs are valid for the model.""" first_example, examples = _first_example(examples) if isinstance(llm_or_chain_factory, BaseLanguageModel): _validate_example_inputs_for_language_model(first_example, input_mapper) else: chain = llm_or_chain_factory() _validate_example_inputs_for_chain(first_example, chain, input_mapper) return examples ## Shared Evaluator Setup Utilities def _setup_evaluation( llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, examples: Iterator[Example], evaluation: Optional[RunEvalConfig], data_type: DataType, ) -> Tuple[Optional[List[RunEvaluator]], Iterator[Example]]: """Configure the evaluators to run on the results of the chain.""" if evaluation: first_example, examples = _first_example(examples) if isinstance(llm_or_chain_factory, BaseLanguageModel): run_inputs, run_outputs = None, None run_type = "llm" else: run_type = "chain" if data_type in (DataType.chat, DataType.llm): val = data_type.value if isinstance(data_type, Enum) else data_type raise ValueError( "Cannot evaluate a chain on dataset with " f"data_type={val}. " "Please specify a dataset with the default 'kv' data type." ) chain = llm_or_chain_factory() run_inputs = chain.input_keys run_outputs = chain.output_keys run_evaluators = _load_run_evaluators( evaluation, run_type, data_type, list(first_example.outputs) if first_example.outputs else None, run_inputs, run_outputs, ) else: # TODO: Create a default helpfulness evaluator run_evaluators = None return run_evaluators, examples def _determine_input_key( config: RunEvalConfig, run_inputs: Optional[List[str]], run_type: str, ) -> Optional[str]: if config.input_key: input_key = config.input_key if run_inputs and input_key not in run_inputs: raise ValueError(f"Input key {input_key} not in run inputs {run_inputs}") elif run_type == "llm": input_key = None elif run_inputs and len(run_inputs) == 1: input_key = run_inputs[0] else: raise ValueError( f"Must specify input key for model with multiple inputs: {run_inputs}" ) return input_key def _determine_prediction_key( config: RunEvalConfig, run_outputs: Optional[List[str]], run_type: str, ) -> Optional[str]: if config.prediction_key: prediction_key = config.prediction_key if run_outputs and prediction_key not in run_outputs: raise ValueError( f"Prediction key {prediction_key} not in run outputs {run_outputs}" ) elif run_type == "llm": prediction_key = None elif run_outputs and len(run_outputs) == 1: prediction_key = run_outputs[0] else: raise ValueError( f"Must specify prediction key for model" f" with multiple outputs: {run_outputs}" ) return prediction_key def _determine_reference_key( config: RunEvalConfig, example_outputs: Optional[List[str]], ) -> Optional[str]: if config.reference_key: reference_key = config.reference_key if example_outputs and reference_key not in example_outputs: raise ValueError( f"Reference key {reference_key} not in Dataset" f" example outputs: {example_outputs}" ) elif example_outputs and len(example_outputs) == 1: reference_key = list(example_outputs)[0] else: reference_key = None return reference_key def _construct_run_evaluator( eval_config: Union[EvaluatorType, EvalConfig], eval_llm: BaseLanguageModel, run_type: str, data_type: DataType, example_outputs: Optional[List[str]], reference_key: Optional[str], input_key: Optional[str], prediction_key: Optional[str], ) -> RunEvaluator: if isinstance(eval_config, EvaluatorType): evaluator_ = load_evaluator(eval_config, llm=eval_llm) eval_type_tag = eval_config.value else: kwargs = {"llm": eval_llm, **eval_config.get_kwargs()} evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs) eval_type_tag = eval_config.evaluator_type.value if isinstance(evaluator_, StringEvaluator): if evaluator_.requires_reference and reference_key is None: raise ValueError( f"Must specify reference_key in RunEvalConfig to use" f" evaluator of type {eval_type_tag} with" f" dataset with multiple output keys: {example_outputs}." ) run_evaluator = StringRunEvaluatorChain.from_run_and_data_type( evaluator_, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, tags=[eval_type_tag], ) else: raise NotImplementedError( f"Run evaluator for {eval_type_tag} is not implemented" ) return run_evaluator def _load_run_evaluators( config: RunEvalConfig, run_type: str, data_type: DataType, example_outputs: Optional[List[str]], run_inputs: Optional[List[str]], run_outputs: Optional[List[str]], ) -> List[RunEvaluator]: """ Load run evaluators from a configuration. Args: config: Configuration for the run evaluators. Returns: A list of run evaluators. """ eval_llm = config.eval_llm or ChatOpenAI(model="gpt-4", temperature=0.0) run_evaluators = [] input_key = _determine_input_key(config, run_inputs, run_type) prediction_key = _determine_prediction_key(config, run_outputs, run_type) reference_key = _determine_reference_key(config, example_outputs) for eval_config in config.evaluators: run_evaluator = _construct_run_evaluator( eval_config, eval_llm, run_type, data_type, example_outputs, reference_key, input_key, prediction_key, ) run_evaluators.append(run_evaluator) custom_evaluators = config.custom_evaluators or [] for custom_evaluator in custom_evaluators: if isinstance(custom_evaluator, RunEvaluator): run_evaluators.append(custom_evaluator) elif isinstance(custom_evaluator, StringEvaluator): run_evaluators.append( StringRunEvaluatorChain.from_run_and_data_type( custom_evaluator, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, ) ) else: raise ValueError( f"Unsupported custom evaluator: {custom_evaluator}." f" Expected RunEvaluator or StringEvaluator." ) return run_evaluators ### Async Helpers async def _arun_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], *, tags: Optional[List[str]] = None, callbacks: Callbacks = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """Asynchronously run the language model. Args: llm: The language model to run. inputs: The input dictionary. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map inputs to the expected format. Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): return await llm.apredict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): return await llm.apredict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format" f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: prompt = _get_prompt(inputs) llm_output: Union[str, BaseMessage] = await llm.apredict( prompt, callbacks=callbacks, tags=tags ) except InputFormatError: messages = _get_messages(inputs) llm_output = await llm.apredict_messages( messages, callbacks=callbacks, tags=tags ) return llm_output async def _arun_chain( chain: Chain, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str]: """Run a chain asynchronously on inputs.""" if input_mapper is not None: inputs_ = input_mapper(inputs) output: Union[dict, str] = await chain.acall( inputs_, callbacks=callbacks, tags=tags ) else: inputs_ = next(iter(inputs.values())) if len(inputs) == 1 else inputs output = await chain.acall(inputs_, callbacks=callbacks, tags=tags) return output async def _arun_llm_or_chain( example: Example, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, n_repetitions: int, *, tags: Optional[List[str]] = None, callbacks: Optional[List[BaseCallbackHandler]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: """Asynchronously run the Chain or language model. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. n_repetitions: The number of times to run the model on each example. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map the input to the expected format. Returns: A list of outputs. """ if callbacks: previous_example_ids = [ getattr(tracer, "example_id", None) for tracer in callbacks ] for tracer in callbacks: if hasattr(tracer, "example_id"): tracer.example_id = example.id else: previous_example_ids = None outputs = [] chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) for _ in range(n_repetitions): try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = await _arun_llm( llm_or_chain_factory, example.inputs, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = await _arun_chain( chain, example.inputs, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) outputs.append(output) except Exception as e: logger.warning( f"{chain_or_llm} failed for example {example.id}. Error: {e}" ) outputs.append({"Error": str(e)}) if callbacks and previous_example_ids: for example_id, tracer in zip(previous_example_ids, callbacks): if hasattr(tracer, "example_id"): tracer.example_id = example_id return outputs async def _gather_with_concurrency( n: int, initializer: Callable[[], Coroutine[Any, Any, Any]], *async_funcs: Callable[ [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any] ], ) -> List[Any]: """Run coroutines with a concurrency limit. Args: n: The maximum number of concurrent tasks. initializer: A coroutine that initializes shared resources for the tasks. async_funcs: The async_funcs to be run concurrently. Returns: A list of results from the coroutines. """ semaphore = asyncio.Semaphore(n) job_state = {"num_processed": 0} callback_queue: asyncio.Queue[Sequence[BaseCallbackHandler]] = asyncio.Queue() for _ in range(n): callback_queue.put_nowait(await initializer()) async def run_coroutine_with_semaphore( async_func: Callable[ [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any] ] ) -> Any: async with semaphore: callbacks = await callback_queue.get() try: result = await async_func(callbacks, job_state) finally: callback_queue.put_nowait(callbacks) return result results = await asyncio.gather( *(run_coroutine_with_semaphore(function) for function in async_funcs) ) while callback_queue: try: callbacks = callback_queue.get_nowait() except asyncio.QueueEmpty: break for callback in callbacks: if isinstance(callback, (LangChainTracer, EvaluatorCallbackHandler)): callback.wait_for_futures() return results async def _callbacks_initializer( project_name: Optional[str], client: Client, run_evaluators: Sequence[RunEvaluator], evaluation_handler_collector: List[EvaluatorCallbackHandler], ) -> List[BaseTracer]: """ Initialize a tracer to share across tasks. Args: project_name: The project name for the tracer. client: The client to use for the tracer. run_evaluators: The evaluators to run. evaluation_handler_collector: A list to collect the evaluators. Used to wait for the evaluators to finish. Returns: The callbacks for this thread. """ callbacks: List[BaseTracer] = [] if project_name: callbacks.append( LangChainTracer( project_name=project_name, client=client, use_threading=False ) ) evaluator_project_name = f"{project_name}-evaluators" if project_name else None if run_evaluators: callback = EvaluatorCallbackHandler( client=client, evaluators=run_evaluators, # We already have concurrency, don't want to overload the machine max_workers=1, project_name=evaluator_project_name, ) callbacks.append(callback) evaluation_handler_collector.append(callback) return callbacks async def _arun_on_examples( client: Client, examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, num_repetitions: int = 1, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, data_type: DataType = DataType.kv, ) -> Dict[str, Any]: """ Asynchronously run the chain on examples and store traces to the specified project name. Args: client: LangSmith client to use to log feedback and runs. examples: Examples to run the model or chain over. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. num_repetitions: Number of times to run the model on each example. This is useful when testing success rates or generating confidence intervals. project_name: Project name to use when tracing runs. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. data_type: The dataset's data type. This is used to determine determine how to deserialize the reference data and model compatibility. Returns: A dictionary mapping example ids to the model outputs. """ llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory) project_name = _get_project_name(project_name, llm_or_chain_factory) run_evaluators, examples = _setup_evaluation( llm_or_chain_factory, examples, evaluation, data_type ) examples = _validate_example_inputs(examples, llm_or_chain_factory, input_mapper) results: Dict[str, List[Any]] = {} async def process_example( example: Example, callbacks: List[BaseCallbackHandler], job_state: dict ) -> None: """Process a single example.""" result = await _arun_llm_or_chain( example, llm_or_chain_factory, num_repetitions, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) results[str(example.id)] = result job_state["num_processed"] += 1 if verbose: print( f"Processed examples: {job_state['num_processed']}", end="\r", flush=True, ) evaluation_handlers: List[EvaluatorCallbackHandler] = [] await _gather_with_concurrency( concurrency_level, functools.partial( _callbacks_initializer, project_name=project_name, client=client, evaluation_handler_collector=evaluation_handlers, run_evaluators=run_evaluators or [], ), *(functools.partial(process_example, e) for e in examples), ) for handler in evaluation_handlers: handler.wait_for_futures() return results ## Sync Utilities def _run_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """ Run the language model on the example. Args: llm: The language model to run. inputs: The input dictionary. callbacks: The callbacks to use during the run. tags: Optional tags to add to the run. input_mapper: function to map to the inputs dictionary from an Example Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): llm_output: Union[str, BaseMessage] = llm.predict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): llm_output = llm.predict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format: " f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: llm_prompts = _get_prompt(inputs) llm_output = llm.predict(llm_prompts, callbacks=callbacks, tags=tags) except InputFormatError: llm_messages = _get_messages(inputs) llm_output = llm.predict_messages(llm_messages, callbacks=callbacks) return llm_output def _run_chain( chain: Chain, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[Dict, str]: """Run a chain on inputs.""" if input_mapper is not None: inputs_ = input_mapper(inputs) output: Union[dict, str] = chain(inputs_, callbacks=callbacks, tags=tags) else: inputs_ = next(iter(inputs.values())) if len(inputs) == 1 else inputs output = chain(inputs_, callbacks=callbacks, tags=tags) return output def _run_llm_or_chain( example: Example, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, n_repetitions: int, *, tags: Optional[List[str]] = None, callbacks: Optional[List[BaseCallbackHandler]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: """ Run the Chain or language model synchronously. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. n_repetitions: The number of times to run the model on each example. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. Returns: Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: The outputs of the model or chain. """ if callbacks: previous_example_ids = [ getattr(tracer, "example_id", None) for tracer in callbacks ] for tracer in callbacks: if hasattr(tracer, "example_id"): tracer.example_id = example.id else: previous_example_ids = None outputs = [] chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) for _ in range(n_repetitions): try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = _run_llm( llm_or_chain_factory, example.inputs, callbacks, tags=tags, input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = _run_chain( chain, example.inputs, callbacks, tags=tags, input_mapper=input_mapper, ) outputs.append(output) except Exception as e: logger.warning( f"{chain_or_llm} failed for example {example.id}. Error: {e}" ) outputs.append({"Error": str(e)}) if callbacks and previous_example_ids: for example_id, tracer in zip(previous_example_ids, callbacks): if hasattr(tracer, "example_id"): tracer.example_id = example_id return outputs def _run_on_examples( client: Client, examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, num_repetitions: int = 1, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, data_type: DataType = DataType.kv, ) -> Dict[str, Any]: """ Run the Chain or language model on examples and store traces to the specified project name. Args: client: LangSmith client to use to log feedback and runs. examples: Examples to run the model or chain over. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating num_repetitions: Number of times to run the model on each example. This is useful when testing success rates or generating confidence intervals. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. data_type: The dataset's data type. This is used to determine determine how to deserialize the reference data and model compatibility. Returns: A dictionary mapping example ids to the model outputs. """ results: Dict[str, Any] = {} llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory) project_name = _get_project_name(project_name, llm_or_chain_factory) tracer = LangChainTracer( project_name=project_name, client=client, use_threading=False ) evaluator_project_name = f"{project_name}-evaluators" run_evaluators, examples = _setup_evaluation( llm_or_chain_factory, examples, evaluation, data_type ) examples = _validate_example_inputs(examples, llm_or_chain_factory, input_mapper) evalution_handler = EvaluatorCallbackHandler( evaluators=run_evaluators or [], client=client, project_name=evaluator_project_name, ) callbacks: List[BaseCallbackHandler] = [tracer, evalution_handler] for i, example in enumerate(examples): result = _run_llm_or_chain( example, llm_or_chain_factory, num_repetitions, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) if verbose: print(f"{i+1} processed", flush=True, end="\r") results[str(example.id)] = result tracer.wait_for_futures() evalution_handler.wait_for_futures() return results ## Public API def _prepare_eval_run( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, project_name: Optional[str], ) -> Tuple[MODEL_OR_CHAIN_FACTORY, str, Dataset, Iterator[Example]]: llm_or_chain_factory = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name) project_name = _get_project_name(project_name, llm_or_chain_factory) try: project = client.create_project(project_name) except ValueError as e: if "already exists " not in str(e): raise e raise ValueError( f"Project {project_name} already exists. Please use a different name." ) project_url = _get_eval_project_url(client.api_url, project.id) print( f"View the evaluation results for project '{project_name}' at:\n{project_url}" ) dataset = client.read_dataset(dataset_name=dataset_name) examples = client.list_examples(dataset_id=str(dataset.id)) return llm_or_chain_factory, project_name, dataset, examples async def arun_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, num_repetitions: int = 1, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Dict[str, Any]: """ Asynchronously run the Chain or language model on a dataset and store traces to the specified project name. Args: client: LangSmith client to use to read the dataset, and to log feedback and run traces. dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. num_repetitions: Number of times to run the model on each example. This is useful when testing success rates or generating confidence intervals. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. Returns: A dictionary containing the run's project name and the resulting model outputs. For the synchronous version, see :func:`run_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig, arun_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() await arun_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) await arun_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 llm_or_chain_factory, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name ) results = await _arun_on_examples( client, examples, llm_or_chain_factory, concurrency_level=concurrency_level, num_repetitions=num_repetitions, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) return { "project_name": project_name, "results": results, } def run_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, num_repetitions: int = 1, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Dict[str, Any]: """ Run the Chain or language model on a dataset and store traces to the specified project name. Args: client: LangSmith client to use to access the dataset and to log feedback and run traces. dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Configuration for evaluators to run on the results of the chain num_repetitions: Number of times to run the model on each example. This is useful when testing success rates or generating confidence intervals. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. Returns: A dictionary containing the run's project name and the resulting model outputs. For the (usually faster) async version of this function, see :func:`arun_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig, run_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 llm_or_chain_factory, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name ) results = _run_on_examples( client, examples, llm_or_chain_factory, num_repetitions=num_repetitions, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) return { "project_name": project_name, "results": results, }

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""" .. warning:: Beta Feature! **Cache** provides an optional caching layer for LLMs. Cache is useful for two reasons: - It can save you money by reducing the number of API calls you make to the LLM provider if you're often requesting the same completion multiple times. - It can speed up your application by reducing the number of API calls you make to the LLM provider. Cache directly competes with Memory. See documentation for Pros and Cons. **Class hierarchy:** .. code-block:: BaseCache --> <name>Cache # Examples: InMemoryCache, RedisCache, GPTCache """ from __future__ import annotations import hashlib import inspect import json import logging import warnings from abc import ABC, abstractmethod from datetime import timedelta from typing import ( TYPE_CHECKING, Any, Callable, Dict, Optional, Sequence, Tuple, Type, Union, cast, ) from sqlalchemy import Column, Integer, String, create_engine, select from sqlalchemy.engine.base import Engine from sqlalchemy.orm import Session from langchain.utils import get_from_env try: from sqlalchemy.orm import declarative_base except ImportError: from sqlalchemy.ext.declarative import declarative_base from langchain.embeddings.base import Embeddings from langchain.load.dump import dumps from langchain.load.load import loads from langchain.schema import ChatGeneration, Generation from langchain.vectorstores.redis import Redis as RedisVectorstore logger = logging.getLogger(__file__) if TYPE_CHECKING: import momento RETURN_VAL_TYPE = Sequence[Generation] def _hash(_input: str) -> str: """Use a deterministic hashing approach.""" return hashlib.md5(_input.encode()).hexdigest() def _dump_generations_to_json(generations: RETURN_VAL_TYPE) -> str: """Dump generations to json. Args: generations (RETURN_VAL_TYPE): A list of language model generations. Returns: str: Json representing a list of generations. """ return json.dumps([generation.dict() for generation in generations]) def _load_generations_from_json(generations_json: str) -> RETURN_VAL_TYPE: """Load generations from json. Args: generations_json (str): A string of json representing a list of generations. Raises: ValueError: Could not decode json string to list of generations. Returns: RETURN_VAL_TYPE: A list of generations. """ try: results = json.loads(generations_json) return [Generation(**generation_dict) for generation_dict in results] except json.JSONDecodeError: raise ValueError( f"Could not decode json to list of generations: {generations_json}" ) class BaseCache(ABC): """Base interface for cache.""" @abstractmethod def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" @abstractmethod def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" @abstractmethod def clear(self, **kwargs: Any) -> None: """Clear cache that can take additional keyword arguments.""" class InMemoryCache(BaseCache): """Cache that stores things in memory.""" def __init__(self) -> None: """Initialize with empty cache.""" self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {} def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" return self._cache.get((prompt, llm_string), None) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" self._cache[(prompt, llm_string)] = return_val def clear(self, **kwargs: Any) -> None: """Clear cache.""" self._cache = {} Base = declarative_base() class FullLLMCache(Base): # type: ignore """SQLite table for full LLM Cache (all generations).""" __tablename__ = "full_llm_cache" prompt = Column(String, primary_key=True) llm = Column(String, primary_key=True) idx = Column(Integer, primary_key=True) response = Column(String) class SQLAlchemyCache(BaseCache): """Cache that uses SQAlchemy as a backend.""" def __init__(self, engine: Engine, cache_schema: Type[FullLLMCache] = FullLLMCache): """Initialize by creating all tables.""" self.engine = engine self.cache_schema = cache_schema self.cache_schema.metadata.create_all(self.engine) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" stmt = ( select(self.cache_schema.response) .where(self.cache_schema.prompt == prompt) # type: ignore .where(self.cache_schema.llm == llm_string) .order_by(self.cache_schema.idx) ) with Session(self.engine) as session: rows = session.execute(stmt).fetchall() if rows: try: return [loads(row[0]) for row in rows] except Exception: logger.warning( "Retrieving a cache value that could not be deserialized " "properly. This is likely due to the cache being in an " "older format. Please recreate your cache to avoid this " "error." ) # In a previous life we stored the raw text directly # in the table, so assume it's in that format. return [Generation(text=row[0]) for row in rows] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update based on prompt and llm_string.""" items = [ self.cache_schema(prompt=prompt, llm=llm_string, response=dumps(gen), idx=i) for i, gen in enumerate(return_val) ] with Session(self.engine) as session, session.begin(): for item in items: session.merge(item) def clear(self, **kwargs: Any) -> None: """Clear cache.""" with Session(self.engine) as session: session.query(self.cache_schema).delete() session.commit() class SQLiteCache(SQLAlchemyCache): """Cache that uses SQLite as a backend.""" def __init__(self, database_path: str = ".langchain.db"): """Initialize by creating the engine and all tables.""" engine = create_engine(f"sqlite:///{database_path}") super().__init__(engine) class RedisCache(BaseCache): """Cache that uses Redis as a backend.""" # TODO - implement a TTL policy in Redis def __init__(self, redis_: Any): """Initialize by passing in Redis instance.""" try: from redis import Redis except ImportError: raise ValueError( "Could not import redis python package. " "Please install it with `pip install redis`." ) if not isinstance(redis_, Redis): raise ValueError("Please pass in Redis object.") self.redis = redis_ def _key(self, prompt: str, llm_string: str) -> str: """Compute key from prompt and llm_string""" return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" generations = [] # Read from a Redis HASH results = self.redis.hgetall(self._key(prompt, llm_string)) if results: for _, text in results.items(): generations.append(Generation(text=text)) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisCache does not" " support caching ChatModel outputs." ) return # Write to a Redis HASH key = self._key(prompt, llm_string) self.redis.hset( key, mapping={ str(idx): generation.text for idx, generation in enumerate(return_val) }, ) def clear(self, **kwargs: Any) -> None: """Clear cache. If `asynchronous` is True, flush asynchronously.""" asynchronous = kwargs.get("asynchronous", False) self.redis.flushdb(asynchronous=asynchronous, **kwargs) class RedisSemanticCache(BaseCache): """Cache that uses Redis as a vector-store backend.""" # TODO - implement a TTL policy in Redis def __init__( self, redis_url: str, embedding: Embeddings, score_threshold: float = 0.2 ): """Initialize by passing in the `init` GPTCache func Args: redis_url (str): URL to connect to Redis. embedding (Embedding): Embedding provider for semantic encoding and search. score_threshold (float, 0.2): Example: .. code-block:: python import langchain from langchain.cache import RedisSemanticCache from langchain.embeddings import OpenAIEmbeddings langchain.llm_cache = RedisSemanticCache( redis_url="redis://localhost:6379", embedding=OpenAIEmbeddings() ) """ self._cache_dict: Dict[str, RedisVectorstore] = {} self.redis_url = redis_url self.embedding = embedding self.score_threshold = score_threshold def _index_name(self, llm_string: str) -> str: hashed_index = _hash(llm_string) return f"cache:{hashed_index}" def _get_llm_cache(self, llm_string: str) -> RedisVectorstore: index_name = self._index_name(llm_string) # return vectorstore client for the specific llm string if index_name in self._cache_dict: return self._cache_dict[index_name] # create new vectorstore client for the specific llm string try: self._cache_dict[index_name] = RedisVectorstore.from_existing_index( embedding=self.embedding, index_name=index_name, redis_url=self.redis_url, ) except ValueError: redis = RedisVectorstore( embedding_function=self.embedding.embed_query, index_name=index_name, redis_url=self.redis_url, ) _embedding = self.embedding.embed_query(text="test") redis._create_index(dim=len(_embedding)) self._cache_dict[index_name] = redis return self._cache_dict[index_name] def clear(self, **kwargs: Any) -> None: """Clear semantic cache for a given llm_string.""" index_name = self._index_name(kwargs["llm_string"]) if index_name in self._cache_dict: self._cache_dict[index_name].drop_index( index_name=index_name, delete_documents=True, redis_url=self.redis_url ) del self._cache_dict[index_name] def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" llm_cache = self._get_llm_cache(llm_string) generations = [] # Read from a Hash results = llm_cache.similarity_search_limit_score( query=prompt, k=1, score_threshold=self.score_threshold, ) if results: for document in results: for text in document.metadata["return_val"]: generations.append(Generation(text=text)) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisSemanticCache only supports caching of " f"normal LLM generations, got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisSentimentCache does not" " support caching ChatModel outputs." ) return llm_cache = self._get_llm_cache(llm_string) # Write to vectorstore metadata = { "llm_string": llm_string, "prompt": prompt, "return_val": [generation.text for generation in return_val], } llm_cache.add_texts(texts=[prompt], metadatas=[metadata]) class GPTCache(BaseCache): """Cache that uses GPTCache as a backend.""" def __init__( self, init_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = None, ): """Initialize by passing in init function (default: `None`). Args: init_func (Optional[Callable[[Any], None]]): init `GPTCache` function (default: `None`) Example: .. code-block:: python # Initialize GPTCache with a custom init function import gptcache from gptcache.processor.pre import get_prompt from gptcache.manager.factory import get_data_manager # Avoid multiple caches using the same file, causing different llm model caches to affect each other def init_gptcache(cache_obj: gptcache.Cache, llm str): cache_obj.init( pre_embedding_func=get_prompt, data_manager=manager_factory( manager="map", data_dir=f"map_cache_{llm}" ), ) langchain.llm_cache = GPTCache(init_gptcache) """ try: import gptcache # noqa: F401 except ImportError: raise ImportError( "Could not import gptcache python package. " "Please install it with `pip install gptcache`." ) self.init_gptcache_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = init_func self.gptcache_dict: Dict[str, Any] = {} def _new_gptcache(self, llm_string: str) -> Any: """New gptcache object""" from gptcache import Cache from gptcache.manager.factory import get_data_manager from gptcache.processor.pre import get_prompt _gptcache = Cache() if self.init_gptcache_func is not None: sig = inspect.signature(self.init_gptcache_func) if len(sig.parameters) == 2: self.init_gptcache_func(_gptcache, llm_string) # type: ignore[call-arg] else: self.init_gptcache_func(_gptcache) # type: ignore[call-arg] else: _gptcache.init( pre_embedding_func=get_prompt, data_manager=get_data_manager(data_path=llm_string), ) self.gptcache_dict[llm_string] = _gptcache return _gptcache def _get_gptcache(self, llm_string: str) -> Any: """Get a cache object. When the corresponding llm model cache does not exist, it will be created.""" _gptcache = self.gptcache_dict.get(llm_string, None) if not _gptcache: _gptcache = self._new_gptcache(llm_string) return _gptcache def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up the cache data. First, retrieve the corresponding cache object using the `llm_string` parameter, and then retrieve the data from the cache based on the `prompt`. """ from gptcache.adapter.api import get _gptcache = self._get_gptcache(llm_string) res = get(prompt, cache_obj=_gptcache) if res: return [ Generation(**generation_dict) for generation_dict in json.loads(res) ] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache. First, retrieve the corresponding cache object using the `llm_string` parameter, and then store the `prompt` and `return_val` in the cache object. """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "GPTCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) from gptcache.adapter.api import put _gptcache = self._get_gptcache(llm_string) handled_data = json.dumps([generation.dict() for generation in return_val]) put(prompt, handled_data, cache_obj=_gptcache) return None def clear(self, **kwargs: Any) -> None: """Clear cache.""" from gptcache import Cache for gptcache_instance in self.gptcache_dict.values(): gptcache_instance = cast(Cache, gptcache_instance) gptcache_instance.flush() self.gptcache_dict.clear() def _ensure_cache_exists(cache_client: momento.CacheClient, cache_name: str) -> None: """Create cache if it doesn't exist. Raises: SdkException: Momento service or network error Exception: Unexpected response """ from momento.responses import CreateCache create_cache_response = cache_client.create_cache(cache_name) if isinstance(create_cache_response, CreateCache.Success) or isinstance( create_cache_response, CreateCache.CacheAlreadyExists ): return None elif isinstance(create_cache_response, CreateCache.Error): raise create_cache_response.inner_exception else: raise Exception(f"Unexpected response cache creation: {create_cache_response}") def _validate_ttl(ttl: Optional[timedelta]) -> None: if ttl is not None and ttl <= timedelta(seconds=0): raise ValueError(f"ttl must be positive but was {ttl}.") class MomentoCache(BaseCache): """Cache that uses Momento as a backend. See https://gomomento.com/""" def __init__( self, cache_client: momento.CacheClient, cache_name: str, *, ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True, ): """Instantiate a prompt cache using Momento as a backend. Note: to instantiate the cache client passed to MomentoCache, you must have a Momento account. See https://gomomento.com/. Args: cache_client (CacheClient): The Momento cache client. cache_name (str): The name of the cache to use to store the data. ttl (Optional[timedelta], optional): The time to live for the cache items. Defaults to None, ie use the client default TTL. ensure_cache_exists (bool, optional): Create the cache if it doesn't exist. Defaults to True. Raises: ImportError: Momento python package is not installed. TypeError: cache_client is not of type momento.CacheClientObject ValueError: ttl is non-null and non-negative """ try: from momento import CacheClient except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if not isinstance(cache_client, CacheClient): raise TypeError("cache_client must be a momento.CacheClient object.") _validate_ttl(ttl) if ensure_cache_exists: _ensure_cache_exists(cache_client, cache_name) self.cache_client = cache_client self.cache_name = cache_name self.ttl = ttl @classmethod def from_client_params( cls, cache_name: str, ttl: timedelta, *, configuration: Optional[momento.config.Configuration] = None, auth_token: Optional[str] = None, **kwargs: Any, ) -> MomentoCache: """Construct cache from CacheClient parameters.""" try: from momento import CacheClient, Configurations, CredentialProvider except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if configuration is None: configuration = Configurations.Laptop.v1() auth_token = auth_token or get_from_env("auth_token", "MOMENTO_AUTH_TOKEN") credentials = CredentialProvider.from_string(auth_token) cache_client = CacheClient(configuration, credentials, default_ttl=ttl) return cls(cache_client, cache_name, ttl=ttl, **kwargs) def __key(self, prompt: str, llm_string: str) -> str: """Compute cache key from prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Returns: str: The cache key. """ return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Lookup llm generations in cache by prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Raises: SdkException: Momento service or network error Returns: Optional[RETURN_VAL_TYPE]: A list of language model generations. """ from momento.responses import CacheGet generations: RETURN_VAL_TYPE = [] get_response = self.cache_client.get( self.cache_name, self.__key(prompt, llm_string) ) if isinstance(get_response, CacheGet.Hit): value = get_response.value_string generations = _load_generations_from_json(value) elif isinstance(get_response, CacheGet.Miss): pass elif isinstance(get_response, CacheGet.Error): raise get_response.inner_exception return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Store llm generations in cache. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model string. return_val (RETURN_VAL_TYPE): A list of language model generations. Raises: SdkException: Momento service or network error Exception: Unexpected response """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "Momento only supports caching of normal LLM generations, " f"got {type(gen)}" ) key = self.__key(prompt, llm_string) value = _dump_generations_to_json(return_val) set_response = self.cache_client.set(self.cache_name, key, value, self.ttl) from momento.responses import CacheSet if isinstance(set_response, CacheSet.Success): pass elif isinstance(set_response, CacheSet.Error): raise set_response.inner_exception else: raise Exception(f"Unexpected response: {set_response}") def clear(self, **kwargs: Any) -> None: """Clear the cache. Raises: SdkException: Momento service or network error """ from momento.responses import CacheFlush flush_response = self.cache_client.flush_cache(self.cache_name) if isinstance(flush_response, CacheFlush.Success): pass elif isinstance(flush_response, CacheFlush.Error): raise flush_response.inner_exception

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""" .. warning:: Beta Feature! **Cache** provides an optional caching layer for LLMs. Cache is useful for two reasons: - It can save you money by reducing the number of API calls you make to the LLM provider if you're often requesting the same completion multiple times. - It can speed up your application by reducing the number of API calls you make to the LLM provider. Cache directly competes with Memory. See documentation for Pros and Cons. **Class hierarchy:** .. code-block:: BaseCache --> <name>Cache # Examples: InMemoryCache, RedisCache, GPTCache """ from __future__ import annotations import hashlib import inspect import json import logging import warnings from abc import ABC, abstractmethod from datetime import timedelta from typing import ( TYPE_CHECKING, Any, Callable, Dict, Optional, Sequence, Tuple, Type, Union, cast, ) from sqlalchemy import Column, Integer, String, create_engine, select from sqlalchemy.engine.base import Engine from sqlalchemy.orm import Session from langchain.utils import get_from_env try: from sqlalchemy.orm import declarative_base except ImportError: from sqlalchemy.ext.declarative import declarative_base from langchain.embeddings.base import Embeddings from langchain.load.dump import dumps from langchain.load.load import loads from langchain.schema import ChatGeneration, Generation from langchain.vectorstores.redis import Redis as RedisVectorstore logger = logging.getLogger(__file__) if TYPE_CHECKING: import momento RETURN_VAL_TYPE = Sequence[Generation] def _hash(_input: str) -> str: """Use a deterministic hashing approach.""" return hashlib.md5(_input.encode()).hexdigest() def _dump_generations_to_json(generations: RETURN_VAL_TYPE) -> str: """Dump generations to json. Args: generations (RETURN_VAL_TYPE): A list of language model generations. Returns: str: Json representing a list of generations. """ return json.dumps([generation.dict() for generation in generations]) def _load_generations_from_json(generations_json: str) -> RETURN_VAL_TYPE: """Load generations from json. Args: generations_json (str): A string of json representing a list of generations. Raises: ValueError: Could not decode json string to list of generations. Returns: RETURN_VAL_TYPE: A list of generations. """ try: results = json.loads(generations_json) return [Generation(**generation_dict) for generation_dict in results] except json.JSONDecodeError: raise ValueError( f"Could not decode json to list of generations: {generations_json}" ) class BaseCache(ABC): """Base interface for cache.""" @abstractmethod def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" @abstractmethod def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" @abstractmethod def clear(self, **kwargs: Any) -> None: """Clear cache that can take additional keyword arguments.""" class InMemoryCache(BaseCache): """Cache that stores things in memory.""" def __init__(self) -> None: """Initialize with empty cache.""" self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {} def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" return self._cache.get((prompt, llm_string), None) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" self._cache[(prompt, llm_string)] = return_val def clear(self, **kwargs: Any) -> None: """Clear cache.""" self._cache = {} Base = declarative_base() class FullLLMCache(Base): # type: ignore """SQLite table for full LLM Cache (all generations).""" __tablename__ = "full_llm_cache" prompt = Column(String, primary_key=True) llm = Column(String, primary_key=True) idx = Column(Integer, primary_key=True) response = Column(String) class SQLAlchemyCache(BaseCache): """Cache that uses SQAlchemy as a backend.""" def __init__(self, engine: Engine, cache_schema: Type[FullLLMCache] = FullLLMCache): """Initialize by creating all tables.""" self.engine = engine self.cache_schema = cache_schema self.cache_schema.metadata.create_all(self.engine) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" stmt = ( select(self.cache_schema.response) .where(self.cache_schema.prompt == prompt) # type: ignore .where(self.cache_schema.llm == llm_string) .order_by(self.cache_schema.idx) ) with Session(self.engine) as session: rows = session.execute(stmt).fetchall() if rows: try: return [loads(row[0]) for row in rows] except Exception: logger.warning( "Retrieving a cache value that could not be deserialized " "properly. This is likely due to the cache being in an " "older format. Please recreate your cache to avoid this " "error." ) # In a previous life we stored the raw text directly # in the table, so assume it's in that format. return [Generation(text=row[0]) for row in rows] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update based on prompt and llm_string.""" items = [ self.cache_schema(prompt=prompt, llm=llm_string, response=dumps(gen), idx=i) for i, gen in enumerate(return_val) ] with Session(self.engine) as session, session.begin(): for item in items: session.merge(item) def clear(self, **kwargs: Any) -> None: """Clear cache.""" with Session(self.engine) as session: session.query(self.cache_schema).delete() session.commit() class SQLiteCache(SQLAlchemyCache): """Cache that uses SQLite as a backend.""" def __init__(self, database_path: str = ".langchain.db"): """Initialize by creating the engine and all tables.""" engine = create_engine(f"sqlite:///{database_path}") super().__init__(engine) class RedisCache(BaseCache): """Cache that uses Redis as a backend.""" # TODO - implement a TTL policy in Redis def __init__(self, redis_: Any): """Initialize by passing in Redis instance.""" try: from redis import Redis except ImportError: raise ValueError( "Could not import redis python package. " "Please install it with `pip install redis`." ) if not isinstance(redis_, Redis): raise ValueError("Please pass in Redis object.") self.redis = redis_ def _key(self, prompt: str, llm_string: str) -> str: """Compute key from prompt and llm_string""" return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" generations = [] # Read from a Redis HASH results = self.redis.hgetall(self._key(prompt, llm_string)) if results: for _, text in results.items(): generations.append(Generation(text=text)) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisCache does not" " support caching ChatModel outputs." ) return # Write to a Redis HASH key = self._key(prompt, llm_string) self.redis.hset( key, mapping={ str(idx): generation.text for idx, generation in enumerate(return_val) }, ) def clear(self, **kwargs: Any) -> None: """Clear cache. If `asynchronous` is True, flush asynchronously.""" asynchronous = kwargs.get("asynchronous", False) self.redis.flushdb(asynchronous=asynchronous, **kwargs) class RedisSemanticCache(BaseCache): """Cache that uses Redis as a vector-store backend.""" # TODO - implement a TTL policy in Redis def __init__( self, redis_url: str, embedding: Embeddings, score_threshold: float = 0.2 ): """Initialize by passing in the `init` GPTCache func Args: redis_url (str): URL to connect to Redis. embedding (Embedding): Embedding provider for semantic encoding and search. score_threshold (float, 0.2): Example: .. code-block:: python import langchain from langchain.cache import RedisSemanticCache from langchain.embeddings import OpenAIEmbeddings langchain.llm_cache = RedisSemanticCache( redis_url="redis://localhost:6379", embedding=OpenAIEmbeddings() ) """ self._cache_dict: Dict[str, RedisVectorstore] = {} self.redis_url = redis_url self.embedding = embedding self.score_threshold = score_threshold def _index_name(self, llm_string: str) -> str: hashed_index = _hash(llm_string) return f"cache:{hashed_index}" def _get_llm_cache(self, llm_string: str) -> RedisVectorstore: index_name = self._index_name(llm_string) # return vectorstore client for the specific llm string if index_name in self._cache_dict: return self._cache_dict[index_name] # create new vectorstore client for the specific llm string try: self._cache_dict[index_name] = RedisVectorstore.from_existing_index( embedding=self.embedding, index_name=index_name, redis_url=self.redis_url, ) except ValueError: redis = RedisVectorstore( embedding_function=self.embedding.embed_query, index_name=index_name, redis_url=self.redis_url, ) _embedding = self.embedding.embed_query(text="test") redis._create_index(dim=len(_embedding)) self._cache_dict[index_name] = redis return self._cache_dict[index_name] def clear(self, **kwargs: Any) -> None: """Clear semantic cache for a given llm_string.""" index_name = self._index_name(kwargs["llm_string"]) if index_name in self._cache_dict: self._cache_dict[index_name].drop_index( index_name=index_name, delete_documents=True, redis_url=self.redis_url ) del self._cache_dict[index_name] def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" llm_cache = self._get_llm_cache(llm_string) generations = [] # Read from a Hash results = llm_cache.similarity_search_limit_score( query=prompt, k=1, score_threshold=self.score_threshold, ) if results: for document in results: for text in document.metadata["return_val"]: generations.append(Generation(text=text)) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisSemanticCache only supports caching of " f"normal LLM generations, got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisSentimentCache does not" " support caching ChatModel outputs." ) return llm_cache = self._get_llm_cache(llm_string) # Write to vectorstore metadata = { "llm_string": llm_string, "prompt": prompt, "return_val": [generation.text for generation in return_val], } llm_cache.add_texts(texts=[prompt], metadatas=[metadata]) class GPTCache(BaseCache): """Cache that uses GPTCache as a backend.""" def __init__( self, init_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = None, ): """Initialize by passing in init function (default: `None`). Args: init_func (Optional[Callable[[Any], None]]): init `GPTCache` function (default: `None`) Example: .. code-block:: python # Initialize GPTCache with a custom init function import gptcache from gptcache.processor.pre import get_prompt from gptcache.manager.factory import get_data_manager # Avoid multiple caches using the same file, causing different llm model caches to affect each other def init_gptcache(cache_obj: gptcache.Cache, llm str): cache_obj.init( pre_embedding_func=get_prompt, data_manager=manager_factory( manager="map", data_dir=f"map_cache_{llm}" ), ) langchain.llm_cache = GPTCache(init_gptcache) """ try: import gptcache # noqa: F401 except ImportError: raise ImportError( "Could not import gptcache python package. " "Please install it with `pip install gptcache`." ) self.init_gptcache_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = init_func self.gptcache_dict: Dict[str, Any] = {} def _new_gptcache(self, llm_string: str) -> Any: """New gptcache object""" from gptcache import Cache from gptcache.manager.factory import get_data_manager from gptcache.processor.pre import get_prompt _gptcache = Cache() if self.init_gptcache_func is not None: sig = inspect.signature(self.init_gptcache_func) if len(sig.parameters) == 2: self.init_gptcache_func(_gptcache, llm_string) # type: ignore[call-arg] else: self.init_gptcache_func(_gptcache) # type: ignore[call-arg] else: _gptcache.init( pre_embedding_func=get_prompt, data_manager=get_data_manager(data_path=llm_string), ) self.gptcache_dict[llm_string] = _gptcache return _gptcache def _get_gptcache(self, llm_string: str) -> Any: """Get a cache object. When the corresponding llm model cache does not exist, it will be created.""" _gptcache = self.gptcache_dict.get(llm_string, None) if not _gptcache: _gptcache = self._new_gptcache(llm_string) return _gptcache def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up the cache data. First, retrieve the corresponding cache object using the `llm_string` parameter, and then retrieve the data from the cache based on the `prompt`. """ from gptcache.adapter.api import get _gptcache = self._get_gptcache(llm_string) res = get(prompt, cache_obj=_gptcache) if res: return [ Generation(**generation_dict) for generation_dict in json.loads(res) ] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache. First, retrieve the corresponding cache object using the `llm_string` parameter, and then store the `prompt` and `return_val` in the cache object. """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "GPTCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) from gptcache.adapter.api import put _gptcache = self._get_gptcache(llm_string) handled_data = json.dumps([generation.dict() for generation in return_val]) put(prompt, handled_data, cache_obj=_gptcache) return None def clear(self, **kwargs: Any) -> None: """Clear cache.""" from gptcache import Cache for gptcache_instance in self.gptcache_dict.values(): gptcache_instance = cast(Cache, gptcache_instance) gptcache_instance.flush() self.gptcache_dict.clear() def _ensure_cache_exists(cache_client: momento.CacheClient, cache_name: str) -> None: """Create cache if it doesn't exist. Raises: SdkException: Momento service or network error Exception: Unexpected response """ from momento.responses import CreateCache create_cache_response = cache_client.create_cache(cache_name) if isinstance(create_cache_response, CreateCache.Success) or isinstance( create_cache_response, CreateCache.CacheAlreadyExists ): return None elif isinstance(create_cache_response, CreateCache.Error): raise create_cache_response.inner_exception else: raise Exception(f"Unexpected response cache creation: {create_cache_response}") def _validate_ttl(ttl: Optional[timedelta]) -> None: if ttl is not None and ttl <= timedelta(seconds=0): raise ValueError(f"ttl must be positive but was {ttl}.") class MomentoCache(BaseCache): """Cache that uses Momento as a backend. See https://gomomento.com/""" def __init__( self, cache_client: momento.CacheClient, cache_name: str, *, ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True, ): """Instantiate a prompt cache using Momento as a backend. Note: to instantiate the cache client passed to MomentoCache, you must have a Momento account. See https://gomomento.com/. Args: cache_client (CacheClient): The Momento cache client. cache_name (str): The name of the cache to use to store the data. ttl (Optional[timedelta], optional): The time to live for the cache items. Defaults to None, ie use the client default TTL. ensure_cache_exists (bool, optional): Create the cache if it doesn't exist. Defaults to True. Raises: ImportError: Momento python package is not installed. TypeError: cache_client is not of type momento.CacheClientObject ValueError: ttl is non-null and non-negative """ try: from momento import CacheClient except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if not isinstance(cache_client, CacheClient): raise TypeError("cache_client must be a momento.CacheClient object.") _validate_ttl(ttl) if ensure_cache_exists: _ensure_cache_exists(cache_client, cache_name) self.cache_client = cache_client self.cache_name = cache_name self.ttl = ttl @classmethod def from_client_params( cls, cache_name: str, ttl: timedelta, *, configuration: Optional[momento.config.Configuration] = None, auth_token: Optional[str] = None, **kwargs: Any, ) -> MomentoCache: """Construct cache from CacheClient parameters.""" try: from momento import CacheClient, Configurations, CredentialProvider except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if configuration is None: configuration = Configurations.Laptop.v1() auth_token = auth_token or get_from_env("auth_token", "MOMENTO_AUTH_TOKEN") credentials = CredentialProvider.from_string(auth_token) cache_client = CacheClient(configuration, credentials, default_ttl=ttl) return cls(cache_client, cache_name, ttl=ttl, **kwargs) def __key(self, prompt: str, llm_string: str) -> str: """Compute cache key from prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Returns: str: The cache key. """ return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Lookup llm generations in cache by prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Raises: SdkException: Momento service or network error Returns: Optional[RETURN_VAL_TYPE]: A list of language model generations. """ from momento.responses import CacheGet generations: RETURN_VAL_TYPE = [] get_response = self.cache_client.get( self.cache_name, self.__key(prompt, llm_string) ) if isinstance(get_response, CacheGet.Hit): value = get_response.value_string generations = _load_generations_from_json(value) elif isinstance(get_response, CacheGet.Miss): pass elif isinstance(get_response, CacheGet.Error): raise get_response.inner_exception return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Store llm generations in cache. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model string. return_val (RETURN_VAL_TYPE): A list of language model generations. Raises: SdkException: Momento service or network error Exception: Unexpected response """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "Momento only supports caching of normal LLM generations, " f"got {type(gen)}" ) key = self.__key(prompt, llm_string) value = _dump_generations_to_json(return_val) set_response = self.cache_client.set(self.cache_name, key, value, self.ttl) from momento.responses import CacheSet if isinstance(set_response, CacheSet.Success): pass elif isinstance(set_response, CacheSet.Error): raise set_response.inner_exception else: raise Exception(f"Unexpected response: {set_response}") def clear(self, **kwargs: Any) -> None: """Clear the cache. Raises: SdkException: Momento service or network error """ from momento.responses import CacheFlush flush_response = self.cache_client.flush_cache(self.cache_name) if isinstance(flush_response, CacheFlush.Success): pass elif isinstance(flush_response, CacheFlush.Error): raise flush_response.inner_exception

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""" .. warning:: Beta Feature! **Cache** provides an optional caching layer for LLMs. Cache is useful for two reasons: - It can save you money by reducing the number of API calls you make to the LLM provider if you're often requesting the same completion multiple times. - It can speed up your application by reducing the number of API calls you make to the LLM provider. Cache directly competes with Memory. See documentation for Pros and Cons. **Class hierarchy:** .. code-block:: BaseCache --> <name>Cache # Examples: InMemoryCache, RedisCache, GPTCache """ from __future__ import annotations import hashlib import inspect import json import logging import warnings from abc import ABC, abstractmethod from datetime import timedelta from typing import ( TYPE_CHECKING, Any, Callable, Dict, Optional, Sequence, Tuple, Type, Union, cast, ) from sqlalchemy import Column, Integer, String, create_engine, select from sqlalchemy.engine.base import Engine from sqlalchemy.orm import Session from langchain.utils import get_from_env try: from sqlalchemy.orm import declarative_base except ImportError: from sqlalchemy.ext.declarative import declarative_base from langchain.embeddings.base import Embeddings from langchain.load.dump import dumps from langchain.load.load import loads from langchain.schema import ChatGeneration, Generation from langchain.vectorstores.redis import Redis as RedisVectorstore logger = logging.getLogger(__file__) if TYPE_CHECKING: import momento RETURN_VAL_TYPE = Sequence[Generation] def _hash(_input: str) -> str: """Use a deterministic hashing approach.""" return hashlib.md5(_input.encode()).hexdigest() def _dump_generations_to_json(generations: RETURN_VAL_TYPE) -> str: """Dump generations to json. Args: generations (RETURN_VAL_TYPE): A list of language model generations. Returns: str: Json representing a list of generations. """ return json.dumps([generation.dict() for generation in generations]) def _load_generations_from_json(generations_json: str) -> RETURN_VAL_TYPE: """Load generations from json. Args: generations_json (str): A string of json representing a list of generations. Raises: ValueError: Could not decode json string to list of generations. Returns: RETURN_VAL_TYPE: A list of generations. """ try: results = json.loads(generations_json) return [Generation(**generation_dict) for generation_dict in results] except json.JSONDecodeError: raise ValueError( f"Could not decode json to list of generations: {generations_json}" ) class BaseCache(ABC): """Base interface for cache.""" @abstractmethod def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" @abstractmethod def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" @abstractmethod def clear(self, **kwargs: Any) -> None: """Clear cache that can take additional keyword arguments.""" class InMemoryCache(BaseCache): """Cache that stores things in memory.""" def __init__(self) -> None: """Initialize with empty cache.""" self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {} def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" return self._cache.get((prompt, llm_string), None) def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" self._cache[(prompt, llm_string)] = return_val def clear(self, **kwargs: Any) -> None: """Clear cache.""" self._cache = {} Base = declarative_base() class FullLLMCache(Base): # type: ignore """SQLite table for full LLM Cache (all generations).""" __tablename__ = "full_llm_cache" prompt = Column(String, primary_key=True) llm = Column(String, primary_key=True) idx = Column(Integer, primary_key=True) response = Column(String) class SQLAlchemyCache(BaseCache): """Cache that uses SQAlchemy as a backend.""" def __init__(self, engine: Engine, cache_schema: Type[FullLLMCache] = FullLLMCache): """Initialize by creating all tables.""" self.engine = engine self.cache_schema = cache_schema self.cache_schema.metadata.create_all(self.engine) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" stmt = ( select(self.cache_schema.response) .where(self.cache_schema.prompt == prompt) # type: ignore .where(self.cache_schema.llm == llm_string) .order_by(self.cache_schema.idx) ) with Session(self.engine) as session: rows = session.execute(stmt).fetchall() if rows: try: return [loads(row[0]) for row in rows] except Exception: logger.warning( "Retrieving a cache value that could not be deserialized " "properly. This is likely due to the cache being in an " "older format. Please recreate your cache to avoid this " "error." ) # In a previous life we stored the raw text directly # in the table, so assume it's in that format. return [Generation(text=row[0]) for row in rows] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update based on prompt and llm_string.""" items = [ self.cache_schema(prompt=prompt, llm=llm_string, response=dumps(gen), idx=i) for i, gen in enumerate(return_val) ] with Session(self.engine) as session, session.begin(): for item in items: session.merge(item) def clear(self, **kwargs: Any) -> None: """Clear cache.""" with Session(self.engine) as session: session.query(self.cache_schema).delete() session.commit() class SQLiteCache(SQLAlchemyCache): """Cache that uses SQLite as a backend.""" def __init__(self, database_path: str = ".langchain.db"): """Initialize by creating the engine and all tables.""" engine = create_engine(f"sqlite:///{database_path}") super().__init__(engine) class RedisCache(BaseCache): """Cache that uses Redis as a backend.""" # TODO - implement a TTL policy in Redis def __init__(self, redis_: Any): """Initialize by passing in Redis instance.""" try: from redis import Redis except ImportError: raise ValueError( "Could not import redis python package. " "Please install it with `pip install redis`." ) if not isinstance(redis_, Redis): raise ValueError("Please pass in Redis object.") self.redis = redis_ def _key(self, prompt: str, llm_string: str) -> str: """Compute key from prompt and llm_string""" return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" generations = [] # Read from a Redis HASH results = self.redis.hgetall(self._key(prompt, llm_string)) if results: for _, text in results.items(): generations.append(Generation(text=text)) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisCache does not" " support caching ChatModel outputs." ) return # Write to a Redis HASH key = self._key(prompt, llm_string) self.redis.hset( key, mapping={ str(idx): generation.text for idx, generation in enumerate(return_val) }, ) def clear(self, **kwargs: Any) -> None: """Clear cache. If `asynchronous` is True, flush asynchronously.""" asynchronous = kwargs.get("asynchronous", False) self.redis.flushdb(asynchronous=asynchronous, **kwargs) class RedisSemanticCache(BaseCache): """Cache that uses Redis as a vector-store backend.""" # TODO - implement a TTL policy in Redis def __init__( self, redis_url: str, embedding: Embeddings, score_threshold: float = 0.2 ): """Initialize by passing in the `init` GPTCache func Args: redis_url (str): URL to connect to Redis. embedding (Embedding): Embedding provider for semantic encoding and search. score_threshold (float, 0.2): Example: .. code-block:: python import langchain from langchain.cache import RedisSemanticCache from langchain.embeddings import OpenAIEmbeddings langchain.llm_cache = RedisSemanticCache( redis_url="redis://localhost:6379", embedding=OpenAIEmbeddings() ) """ self._cache_dict: Dict[str, RedisVectorstore] = {} self.redis_url = redis_url self.embedding = embedding self.score_threshold = score_threshold def _index_name(self, llm_string: str) -> str: hashed_index = _hash(llm_string) return f"cache:{hashed_index}" def _get_llm_cache(self, llm_string: str) -> RedisVectorstore: index_name = self._index_name(llm_string) # return vectorstore client for the specific llm string if index_name in self._cache_dict: return self._cache_dict[index_name] # create new vectorstore client for the specific llm string try: self._cache_dict[index_name] = RedisVectorstore.from_existing_index( embedding=self.embedding, index_name=index_name, redis_url=self.redis_url, ) except ValueError: redis = RedisVectorstore( embedding_function=self.embedding.embed_query, index_name=index_name, redis_url=self.redis_url, ) _embedding = self.embedding.embed_query(text="test") redis._create_index(dim=len(_embedding)) self._cache_dict[index_name] = redis return self._cache_dict[index_name] def clear(self, **kwargs: Any) -> None: """Clear semantic cache for a given llm_string.""" index_name = self._index_name(kwargs["llm_string"]) if index_name in self._cache_dict: self._cache_dict[index_name].drop_index( index_name=index_name, delete_documents=True, redis_url=self.redis_url ) del self._cache_dict[index_name] def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" llm_cache = self._get_llm_cache(llm_string) generations = [] # Read from a Hash results = llm_cache.similarity_search_limit_score( query=prompt, k=1, score_threshold=self.score_threshold, ) if results: for document in results: for text in document.metadata["return_val"]: generations.append(Generation(text=text)) return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "RedisSemanticCache only supports caching of " f"normal LLM generations, got {type(gen)}" ) if isinstance(gen, ChatGeneration): warnings.warn( "NOTE: Generation has not been cached. RedisSentimentCache does not" " support caching ChatModel outputs." ) return llm_cache = self._get_llm_cache(llm_string) # Write to vectorstore metadata = { "llm_string": llm_string, "prompt": prompt, "return_val": [generation.text for generation in return_val], } llm_cache.add_texts(texts=[prompt], metadatas=[metadata]) class GPTCache(BaseCache): """Cache that uses GPTCache as a backend.""" def __init__( self, init_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = None, ): """Initialize by passing in init function (default: `None`). Args: init_func (Optional[Callable[[Any], None]]): init `GPTCache` function (default: `None`) Example: .. code-block:: python # Initialize GPTCache with a custom init function import gptcache from gptcache.processor.pre import get_prompt from gptcache.manager.factory import get_data_manager # Avoid multiple caches using the same file, causing different llm model caches to affect each other def init_gptcache(cache_obj: gptcache.Cache, llm str): cache_obj.init( pre_embedding_func=get_prompt, data_manager=manager_factory( manager="map", data_dir=f"map_cache_{llm}" ), ) langchain.llm_cache = GPTCache(init_gptcache) """ try: import gptcache # noqa: F401 except ImportError: raise ImportError( "Could not import gptcache python package. " "Please install it with `pip install gptcache`." ) self.init_gptcache_func: Union[ Callable[[Any, str], None], Callable[[Any], None], None ] = init_func self.gptcache_dict: Dict[str, Any] = {} def _new_gptcache(self, llm_string: str) -> Any: """New gptcache object""" from gptcache import Cache from gptcache.manager.factory import get_data_manager from gptcache.processor.pre import get_prompt _gptcache = Cache() if self.init_gptcache_func is not None: sig = inspect.signature(self.init_gptcache_func) if len(sig.parameters) == 2: self.init_gptcache_func(_gptcache, llm_string) # type: ignore[call-arg] else: self.init_gptcache_func(_gptcache) # type: ignore[call-arg] else: _gptcache.init( pre_embedding_func=get_prompt, data_manager=get_data_manager(data_path=llm_string), ) self.gptcache_dict[llm_string] = _gptcache return _gptcache def _get_gptcache(self, llm_string: str) -> Any: """Get a cache object. When the corresponding llm model cache does not exist, it will be created.""" _gptcache = self.gptcache_dict.get(llm_string, None) if not _gptcache: _gptcache = self._new_gptcache(llm_string) return _gptcache def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up the cache data. First, retrieve the corresponding cache object using the `llm_string` parameter, and then retrieve the data from the cache based on the `prompt`. """ from gptcache.adapter.api import get _gptcache = self._get_gptcache(llm_string) res = get(prompt, cache_obj=_gptcache) if res: return [ Generation(**generation_dict) for generation_dict in json.loads(res) ] return None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache. First, retrieve the corresponding cache object using the `llm_string` parameter, and then store the `prompt` and `return_val` in the cache object. """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "GPTCache only supports caching of normal LLM generations, " f"got {type(gen)}" ) from gptcache.adapter.api import put _gptcache = self._get_gptcache(llm_string) handled_data = json.dumps([generation.dict() for generation in return_val]) put(prompt, handled_data, cache_obj=_gptcache) return None def clear(self, **kwargs: Any) -> None: """Clear cache.""" from gptcache import Cache for gptcache_instance in self.gptcache_dict.values(): gptcache_instance = cast(Cache, gptcache_instance) gptcache_instance.flush() self.gptcache_dict.clear() def _ensure_cache_exists(cache_client: momento.CacheClient, cache_name: str) -> None: """Create cache if it doesn't exist. Raises: SdkException: Momento service or network error Exception: Unexpected response """ from momento.responses import CreateCache create_cache_response = cache_client.create_cache(cache_name) if isinstance(create_cache_response, CreateCache.Success) or isinstance( create_cache_response, CreateCache.CacheAlreadyExists ): return None elif isinstance(create_cache_response, CreateCache.Error): raise create_cache_response.inner_exception else: raise Exception(f"Unexpected response cache creation: {create_cache_response}") def _validate_ttl(ttl: Optional[timedelta]) -> None: if ttl is not None and ttl <= timedelta(seconds=0): raise ValueError(f"ttl must be positive but was {ttl}.") class MomentoCache(BaseCache): """Cache that uses Momento as a backend. See https://gomomento.com/""" def __init__( self, cache_client: momento.CacheClient, cache_name: str, *, ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True, ): """Instantiate a prompt cache using Momento as a backend. Note: to instantiate the cache client passed to MomentoCache, you must have a Momento account. See https://gomomento.com/. Args: cache_client (CacheClient): The Momento cache client. cache_name (str): The name of the cache to use to store the data. ttl (Optional[timedelta], optional): The time to live for the cache items. Defaults to None, ie use the client default TTL. ensure_cache_exists (bool, optional): Create the cache if it doesn't exist. Defaults to True. Raises: ImportError: Momento python package is not installed. TypeError: cache_client is not of type momento.CacheClientObject ValueError: ttl is non-null and non-negative """ try: from momento import CacheClient except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if not isinstance(cache_client, CacheClient): raise TypeError("cache_client must be a momento.CacheClient object.") _validate_ttl(ttl) if ensure_cache_exists: _ensure_cache_exists(cache_client, cache_name) self.cache_client = cache_client self.cache_name = cache_name self.ttl = ttl @classmethod def from_client_params( cls, cache_name: str, ttl: timedelta, *, configuration: Optional[momento.config.Configuration] = None, auth_token: Optional[str] = None, **kwargs: Any, ) -> MomentoCache: """Construct cache from CacheClient parameters.""" try: from momento import CacheClient, Configurations, CredentialProvider except ImportError: raise ImportError( "Could not import momento python package. " "Please install it with `pip install momento`." ) if configuration is None: configuration = Configurations.Laptop.v1() auth_token = auth_token or get_from_env("auth_token", "MOMENTO_AUTH_TOKEN") credentials = CredentialProvider.from_string(auth_token) cache_client = CacheClient(configuration, credentials, default_ttl=ttl) return cls(cache_client, cache_name, ttl=ttl, **kwargs) def __key(self, prompt: str, llm_string: str) -> str: """Compute cache key from prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Returns: str: The cache key. """ return _hash(prompt + llm_string) def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Lookup llm generations in cache by prompt and associated model and settings. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model version and settings. Raises: SdkException: Momento service or network error Returns: Optional[RETURN_VAL_TYPE]: A list of language model generations. """ from momento.responses import CacheGet generations: RETURN_VAL_TYPE = [] get_response = self.cache_client.get( self.cache_name, self.__key(prompt, llm_string) ) if isinstance(get_response, CacheGet.Hit): value = get_response.value_string generations = _load_generations_from_json(value) elif isinstance(get_response, CacheGet.Miss): pass elif isinstance(get_response, CacheGet.Error): raise get_response.inner_exception return generations if generations else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Store llm generations in cache. Args: prompt (str): The prompt run through the language model. llm_string (str): The language model string. return_val (RETURN_VAL_TYPE): A list of language model generations. Raises: SdkException: Momento service or network error Exception: Unexpected response """ for gen in return_val: if not isinstance(gen, Generation): raise ValueError( "Momento only supports caching of normal LLM generations, " f"got {type(gen)}" ) key = self.__key(prompt, llm_string) value = _dump_generations_to_json(return_val) set_response = self.cache_client.set(self.cache_name, key, value, self.ttl) from momento.responses import CacheSet if isinstance(set_response, CacheSet.Success): pass elif isinstance(set_response, CacheSet.Error): raise set_response.inner_exception else: raise Exception(f"Unexpected response: {set_response}") def clear(self, **kwargs: Any) -> None: """Clear the cache. Raises: SdkException: Momento service or network error """ from momento.responses import CacheFlush flush_response = self.cache_client.flush_cache(self.cache_name) if isinstance(flush_response, CacheFlush.Success): pass elif isinstance(flush_response, CacheFlush.Error): raise flush_response.inner_exception

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"""Utilities for running language models or Chains over datasets.""" from __future__ import annotations import asyncio import functools import inspect import itertools import logging import uuid import warnings from enum import Enum from typing import ( TYPE_CHECKING, Any, Callable, Coroutine, Dict, Iterator, List, Optional, Sequence, Tuple, Union, cast, ) from urllib.parse import urlparse, urlunparse from langsmith import Client, RunEvaluator from langsmith.schemas import Dataset, DataType, Example from langchain.callbacks.base import BaseCallbackHandler from langchain.callbacks.manager import Callbacks from langchain.callbacks.tracers.base import BaseTracer from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler from langchain.callbacks.tracers.langchain import LangChainTracer from langchain.chains.base import Chain from langchain.evaluation.loading import load_evaluator from langchain.evaluation.schema import EvaluatorType, StringEvaluator from langchain.schema import ChatResult, LLMResult from langchain.schema.language_model import BaseLanguageModel from langchain.schema.messages import BaseMessage, messages_from_dict from langchain.schema.runnable import Runnable, RunnableConfig, RunnableLambda from langchain.smith.evaluation.config import EvalConfig, RunEvalConfig from langchain.smith.evaluation.string_run_evaluator import StringRunEvaluatorChain if TYPE_CHECKING: import pandas as pd logger = logging.getLogger(__name__) MODEL_OR_CHAIN_FACTORY = Union[ Callable[[], Union[Chain, Runnable]], BaseLanguageModel, Callable[[dict], Any], Runnable, Chain, ] MCF = Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel] class InputFormatError(Exception): """Raised when the input format is invalid.""" ## Shared Utilities class TestResult(dict): """A dictionary of the results of a single test run.""" def to_dataframe(self) -> pd.DataFrame: """Convert the results to a dataframe.""" try: import pandas as pd except ImportError as e: raise ImportError( "Pandas is required to convert the results to a dataframe." " to install pandas, run `pip install pandas`." ) from e indices = [] records = [] for example_id, result in self["results"].items(): feedback = result["feedback"] records.append( {**{f.key: f.score for f in feedback}, "output": result["output"]} ) indices.append(example_id) return pd.DataFrame(records, index=indices) def _get_eval_project_url(api_url: str, project_id: str) -> str: """Get the project url from the api url.""" parsed = urlparse(api_url) hostname = parsed.hostname or "" if "api." in hostname: hostname = hostname.replace("api.", "", 1) if "localhost" in hostname: # Remove the port hostname = "localhost" url = urlunparse(parsed._replace(netloc=hostname)) return f"{url}/projects/p/{project_id}?eval=true" def _wrap_in_chain_factory( llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, dataset_name: str = "<my_dataset>", ) -> MCF: """Forgive the user if they pass in a chain without memory instead of a chain factory. It's a common mistake. Raise a more helpful error message as well.""" if isinstance(llm_or_chain_factory, Chain): chain = llm_or_chain_factory chain_class = chain.__class__.__name__ if llm_or_chain_factory.memory is not None: memory_class = chain.memory.__class__.__name__ raise ValueError( "Cannot directly evaluate a chain with stateful memory." " To evaluate this chain, pass in a chain constructor" " that initializes fresh memory each time it is called." " This will safegaurd against information" " leakage between dataset examples." "\nFor example:\n\n" "def chain_constructor():\n" f" new_memory = {memory_class}(...)\n" f" return {chain_class}" "(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) logger.warning( "Directly passing in a chain is not recommended as chains may have state." " This can lead to unexpected behavior as the " "same chain instance could be used across multiple datasets. Instead," " please pass a chain constructor that creates a new " "chain with fresh memory each time it is called. This will safeguard" " against information leakage between dataset examples. " "\nFor example:\n\n" "def chain_constructor():\n" f" return {chain_class}(memory=new_memory, ...)\n\n" f'run_on_dataset("{dataset_name}", chain_constructor, ...)' ) return lambda: chain elif isinstance(llm_or_chain_factory, BaseLanguageModel): return llm_or_chain_factory elif isinstance(llm_or_chain_factory, Runnable): # Memory may exist here, but it's not elegant to check all those cases. lcf = llm_or_chain_factory return lambda: lcf elif callable(llm_or_chain_factory): try: _model = llm_or_chain_factory() # type: ignore[call-arg] except TypeError: # It's an arbitrary function, wrap it in a RunnableLambda user_func = cast(Callable, llm_or_chain_factory) sig = inspect.signature(user_func) logger.info(f"Wrapping function {sig} as RunnableLambda.") wrapped = RunnableLambda(user_func) return lambda: wrapped constructor = cast(Callable, llm_or_chain_factory) if isinstance(_model, BaseLanguageModel): # It's not uncommon to do an LLM constructor instead of raw LLM, # so we'll unpack it for the user. return _model elif not isinstance(_model, Runnable): # This is unlikely to happen - a constructor for a model function return lambda: RunnableLambda(constructor) else: # Typical correct case return constructor # noqa return llm_or_chain_factory def _first_example(examples: Iterator[Example]) -> Tuple[Example, Iterator[Example]]: """Get the first example while chaining it back and preserving the iterator.""" try: example: Example = next(examples) except StopIteration: raise ValueError("No examples provided.") return example, itertools.chain([example], examples) def _get_prompt(inputs: Dict[str, Any]) -> str: """Get prompt from inputs. Args: inputs: The input dictionary. Returns: A string prompt. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") prompts = [] if "prompt" in inputs: if not isinstance(inputs["prompt"], str): raise InputFormatError( "Expected string for 'prompt', got" f" {type(inputs['prompt']).__name__}" ) prompts = [inputs["prompt"]] elif "prompts" in inputs: if not isinstance(inputs["prompts"], list) or not all( isinstance(i, str) for i in inputs["prompts"] ): raise InputFormatError( "Expected list of strings for 'prompts'," f" got {type(inputs['prompts']).__name__}" ) prompts = inputs["prompts"] elif len(inputs) == 1: prompt_ = next(iter(inputs.values())) if isinstance(prompt_, str): prompts = [prompt_] elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_): prompts = prompt_ else: raise InputFormatError(f"LLM Run expects string prompt input. Got {inputs}") else: raise InputFormatError( f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}" ) if len(prompts) == 1: return prompts[0] else: raise InputFormatError( f"LLM Run expects single prompt input. Got {len(prompts)} prompts." ) def _get_messages(inputs: Dict[str, Any]) -> List[BaseMessage]: """Get Chat Messages from inputs. Args: inputs: The input dictionary. Returns: A list of chat messages. Raises: InputFormatError: If the input format is invalid. """ if not inputs: raise InputFormatError("Inputs should not be empty.") if "messages" in inputs: single_input = inputs["messages"] elif len(inputs) == 1: single_input = next(iter(inputs.values())) else: raise InputFormatError( f"Chat Run expects 'messages' in inputs when example has multiple" f" input keys. Got {inputs}" ) if isinstance(single_input, list) and all( isinstance(i, dict) for i in single_input ): raw_messages = [single_input] elif isinstance(single_input, list) and all( isinstance(i, list) for i in single_input ): raw_messages = single_input else: raise InputFormatError( f"Chat Run expects List[dict] or List[List[dict]] values for" f" 'messages' key input. Got {inputs}" ) if len(raw_messages) == 1: return messages_from_dict(raw_messages[0]) else: raise InputFormatError( f"Chat Run expects single List[dict] or List[List[dict]] 'messages'" f" input. Got {len(raw_messages)} messages from inputs {inputs}" ) def _get_project_name( project_name: Optional[str], llm_or_chain_factory: MCF, ) -> str: """ Get the project name. Args: project_name: The project name if manually specified. llm_or_chain_factory: The Chain or language model constructor. Returns: The project name. """ if project_name is not None: return project_name if isinstance(llm_or_chain_factory, BaseLanguageModel): model_name = llm_or_chain_factory.__class__.__name__ else: model_name = llm_or_chain_factory().__class__.__name__ hex = uuid.uuid4().hex return f"{hex}-{model_name}" ## Shared Validation Utilities def _validate_example_inputs_for_language_model( first_example: Example, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: if input_mapper: prompt_input = input_mapper(first_example.inputs) if not isinstance(prompt_input, str) and not ( isinstance(prompt_input, list) and all(isinstance(msg, BaseMessage) for msg in prompt_input) ): raise InputFormatError( "When using an input_mapper to prepare dataset example inputs" " for an LLM or chat model, the output must a single string or" " a list of chat messages." f"\nGot: {prompt_input} of type {type(prompt_input)}." ) else: try: _get_prompt(first_example.inputs) except InputFormatError: try: _get_messages(first_example.inputs) except InputFormatError: raise InputFormatError( "Example inputs do not match language model input format. " "Expected a dictionary with messages or a single prompt." f" Got: {first_example.inputs}" " Please update your dataset OR provide an input_mapper" " to convert the example.inputs to a compatible format" " for the llm or chat model you wish to evaluate." ) def _validate_example_inputs_for_chain( first_example: Example, chain: Chain, input_mapper: Optional[Callable[[Dict], Any]], ) -> None: """Validate that the example inputs match the chain input keys.""" if input_mapper: first_inputs = input_mapper(first_example.inputs) missing_keys = set(chain.input_keys).difference(first_inputs) if not isinstance(first_inputs, dict): raise InputFormatError( "When using an input_mapper to prepare dataset example" " inputs for a chain, the mapped value must be a dictionary." f"\nGot: {first_inputs} of type {type(first_inputs)}." ) if missing_keys: raise InputFormatError( "Missing keys after loading example using input_mapper." f"\nExpected: {chain.input_keys}. Got: {first_inputs.keys()}" ) else: first_inputs = first_example.inputs missing_keys = set(chain.input_keys).difference(first_inputs) if len(first_inputs) == 1 and len(chain.input_keys) == 1: # We can pass this through the run method. # Refrain from calling to validate. pass elif missing_keys: raise InputFormatError( "Example inputs missing expected chain input keys." " Please provide an input_mapper to convert the example.inputs" " to a compatible format for the chain you wish to evaluate." f"Expected: {chain.input_keys}. " f"Got: {first_inputs.keys()}" ) def _validate_example_inputs( examples: Iterator[Example], llm_or_chain_factory: MCF, input_mapper: Optional[Callable[[Dict], Any]], ) -> Iterator[Example]: """Validate that the example inputs are valid for the model.""" first_example, examples = _first_example(examples) if isinstance(llm_or_chain_factory, BaseLanguageModel): _validate_example_inputs_for_language_model(first_example, input_mapper) else: chain = llm_or_chain_factory() if isinstance(chain, Chain): # Otherwise it's a runnable _validate_example_inputs_for_chain(first_example, chain, input_mapper) elif isinstance(chain, Runnable): logger.debug(f"Skipping input validation for {chain}") return examples ## Shared Evaluator Setup Utilities def _setup_evaluation( llm_or_chain_factory: MCF, examples: Iterator[Example], evaluation: Optional[RunEvalConfig], data_type: DataType, ) -> Tuple[Optional[List[RunEvaluator]], Iterator[Example]]: """Configure the evaluators to run on the results of the chain.""" if evaluation: first_example, examples = _first_example(examples) if isinstance(llm_or_chain_factory, BaseLanguageModel): run_inputs, run_outputs = None, None run_type = "llm" else: run_type = "chain" if data_type in (DataType.chat, DataType.llm): val = data_type.value if isinstance(data_type, Enum) else data_type raise ValueError( "Cannot evaluate a chain on dataset with " f"data_type={val}. " "Please specify a dataset with the default 'kv' data type." ) chain = llm_or_chain_factory() run_inputs = chain.input_keys if isinstance(chain, Chain) else None run_outputs = chain.output_keys if isinstance(chain, Chain) else None run_evaluators = _load_run_evaluators( evaluation, run_type, data_type, list(first_example.outputs) if first_example.outputs else None, run_inputs, run_outputs, ) else: # TODO: Create a default helpfulness evaluator run_evaluators = None return run_evaluators, examples def _determine_input_key( config: RunEvalConfig, run_inputs: Optional[List[str]], ) -> Optional[str]: input_key = None if config.input_key: input_key = config.input_key if run_inputs and input_key not in run_inputs: raise ValueError(f"Input key {input_key} not in run inputs {run_inputs}") elif run_inputs and len(run_inputs) == 1: input_key = run_inputs[0] elif run_inputs is not None and len(run_inputs) > 1: raise ValueError( f"Must specify input key for model with multiple inputs: {run_inputs}" ) return input_key def _determine_prediction_key( config: RunEvalConfig, run_outputs: Optional[List[str]], ) -> Optional[str]: prediction_key = None if config.prediction_key: prediction_key = config.prediction_key if run_outputs and prediction_key not in run_outputs: raise ValueError( f"Prediction key {prediction_key} not in run outputs {run_outputs}" ) elif run_outputs and len(run_outputs) == 1: prediction_key = run_outputs[0] elif run_outputs is not None and len(run_outputs) > 1: raise ValueError( f"Must specify prediction key for model" f" with multiple outputs: {run_outputs}" ) return prediction_key def _determine_reference_key( config: RunEvalConfig, example_outputs: Optional[List[str]], ) -> Optional[str]: if config.reference_key: reference_key = config.reference_key if example_outputs and reference_key not in example_outputs: raise ValueError( f"Reference key {reference_key} not in Dataset" f" example outputs: {example_outputs}" ) elif example_outputs and len(example_outputs) == 1: reference_key = list(example_outputs)[0] else: reference_key = None return reference_key def _construct_run_evaluator( eval_config: Union[EvaluatorType, str, EvalConfig], eval_llm: Optional[BaseLanguageModel], run_type: str, data_type: DataType, example_outputs: Optional[List[str]], reference_key: Optional[str], input_key: Optional[str], prediction_key: Optional[str], ) -> RunEvaluator: if isinstance(eval_config, (EvaluatorType, str)): if not isinstance(eval_config, EvaluatorType): eval_config = EvaluatorType(eval_config) evaluator_ = load_evaluator(eval_config, llm=eval_llm) eval_type_tag = eval_config.value else: kwargs = {"llm": eval_llm, **eval_config.get_kwargs()} evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs) eval_type_tag = eval_config.evaluator_type.value if isinstance(evaluator_, StringEvaluator): if evaluator_.requires_reference and reference_key is None: raise ValueError( f"Must specify reference_key in RunEvalConfig to use" f" evaluator of type {eval_type_tag} with" f" dataset with multiple output keys: {example_outputs}." ) run_evaluator = StringRunEvaluatorChain.from_run_and_data_type( evaluator_, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, tags=[eval_type_tag], ) else: raise NotImplementedError( f"Run evaluator for {eval_type_tag} is not implemented" ) return run_evaluator def _get_keys( config: RunEvalConfig, run_inputs: Optional[List[str]], run_outputs: Optional[List[str]], example_outputs: Optional[List[str]], ) -> Tuple[Optional[str], Optional[str], Optional[str]]: input_key = _determine_input_key(config, run_inputs) prediction_key = _determine_prediction_key(config, run_outputs) reference_key = _determine_reference_key(config, example_outputs) return input_key, prediction_key, reference_key def _load_run_evaluators( config: RunEvalConfig, run_type: str, data_type: DataType, example_outputs: Optional[List[str]], run_inputs: Optional[List[str]], run_outputs: Optional[List[str]], ) -> List[RunEvaluator]: """ Load run evaluators from a configuration. Args: config: Configuration for the run evaluators. Returns: A list of run evaluators. """ run_evaluators = [] input_key, prediction_key, reference_key = None, None, None if ( config.evaluators or any([isinstance(e, EvaluatorType) for e in config.evaluators]) or ( config.custom_evaluators and any([isinstance(e, StringEvaluator) for e in config.custom_evaluators]) ) ): input_key, prediction_key, reference_key = _get_keys( config, run_inputs, run_outputs, example_outputs ) for eval_config in config.evaluators: run_evaluator = _construct_run_evaluator( eval_config, config.eval_llm, run_type, data_type, example_outputs, reference_key, input_key, prediction_key, ) run_evaluators.append(run_evaluator) custom_evaluators = config.custom_evaluators or [] for custom_evaluator in custom_evaluators: if isinstance(custom_evaluator, RunEvaluator): run_evaluators.append(custom_evaluator) elif isinstance(custom_evaluator, StringEvaluator): run_evaluators.append( StringRunEvaluatorChain.from_run_and_data_type( custom_evaluator, run_type, data_type, input_key=input_key, prediction_key=prediction_key, reference_key=reference_key, ) ) else: raise ValueError( f"Unsupported custom evaluator: {custom_evaluator}." f" Expected RunEvaluator or StringEvaluator." ) return run_evaluators ### Async Helpers async def _arun_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], *, tags: Optional[List[str]] = None, callbacks: Callbacks = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """Asynchronously run the language model. Args: llm: The language model to run. inputs: The input dictionary. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map inputs to the expected format. Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): return await llm.apredict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): return await llm.apredict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format" f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: prompt = _get_prompt(inputs) llm_output: Union[str, BaseMessage] = await llm.apredict( prompt, callbacks=callbacks, tags=tags ) except InputFormatError: messages = _get_messages(inputs) llm_output = await llm.apredict_messages( messages, callbacks=callbacks, tags=tags ) return llm_output async def _arun_chain( chain: Union[Chain, Runnable], inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str]: """Run a chain asynchronously on inputs.""" inputs_ = inputs if input_mapper is None else input_mapper(inputs) if isinstance(chain, Chain): if isinstance(inputs_, dict) and len(inputs_) == 1: val = next(iter(inputs_.values())) output = await chain.acall(val, callbacks=callbacks, tags=tags) else: output = await chain.acall(inputs_, callbacks=callbacks, tags=tags) else: runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks) output = await chain.ainvoke(inputs_, config=runnable_config) return output async def _arun_llm_or_chain( example: Example, llm_or_chain_factory: MCF, *, tags: Optional[List[str]] = None, callbacks: Optional[List[BaseCallbackHandler]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str, LLMResult, ChatResult]: """Asynchronously run the Chain or language model. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. input_mapper: Optional function to map the input to the expected format. Returns: A list of outputs. """ if callbacks: previous_example_ids = [ getattr(tracer, "example_id", None) for tracer in callbacks ] for tracer in callbacks: if hasattr(tracer, "example_id"): tracer.example_id = example.id else: previous_example_ids = None chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) result = None try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = await _arun_llm( llm_or_chain_factory, example.inputs, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = await _arun_chain( chain, example.inputs, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) result = output except Exception as e: logger.warning(f"{chain_or_llm} failed for example {example.id}. Error: {e}") result = {"Error": str(e)} if callbacks and previous_example_ids: for example_id, tracer in zip(previous_example_ids, callbacks): if hasattr(tracer, "example_id"): tracer.example_id = example_id return result async def _gather_with_concurrency( n: int, initializer: Callable[[], Coroutine[Any, Any, Any]], *async_funcs: Callable[ [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any] ], ) -> List[Any]: """Run coroutines with a concurrency limit. Args: n: The maximum number of concurrent tasks. initializer: A coroutine that initializes shared resources for the tasks. async_funcs: The async_funcs to be run concurrently. Returns: A list of results from the coroutines. """ semaphore = asyncio.Semaphore(n) job_state = {"num_processed": 0} callback_queue: asyncio.Queue[Sequence[BaseCallbackHandler]] = asyncio.Queue() for _ in range(n): callback_queue.put_nowait(await initializer()) async def run_coroutine_with_semaphore( async_func: Callable[ [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any] ] ) -> Any: async with semaphore: callbacks = await callback_queue.get() try: result = await async_func(callbacks, job_state) finally: callback_queue.put_nowait(callbacks) return result results = await asyncio.gather( *(run_coroutine_with_semaphore(function) for function in async_funcs) ) while callback_queue: try: callbacks = callback_queue.get_nowait() except asyncio.QueueEmpty: break for callback in callbacks: if isinstance(callback, (LangChainTracer, EvaluatorCallbackHandler)): callback.wait_for_futures() return results async def _callbacks_initializer( project_name: Optional[str], client: Client, run_evaluators: Sequence[RunEvaluator], evaluation_handler_collector: List[EvaluatorCallbackHandler], ) -> List[BaseTracer]: """ Initialize a tracer to share across tasks. Args: project_name: The project name for the tracer. client: The client to use for the tracer. run_evaluators: The evaluators to run. evaluation_handler_collector: A list to collect the evaluators. Used to wait for the evaluators to finish. Returns: The callbacks for this thread. """ callbacks: List[BaseTracer] = [] if project_name: callbacks.append( LangChainTracer( project_name=project_name, client=client, use_threading=False ) ) if run_evaluators: callback = EvaluatorCallbackHandler( client=client, evaluators=run_evaluators, # We already have concurrency, don't want to overload the machine max_workers=1, ) callbacks.append(callback) evaluation_handler_collector.append(callback) return callbacks async def _arun_on_examples( client: Client, examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, data_type: DataType = DataType.kv, ) -> Dict[str, Any]: """ Asynchronously run the chain on examples and store traces to the specified project name. Args: client: LangSmith client to use to log feedback and runs. examples: Examples to run the model or chain over. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. project_name: Project name to use when tracing runs. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. data_type: The dataset's data type. This is used to determine determine how to deserialize the reference data and model compatibility. Returns: A dictionary mapping example ids to the model outputs. """ wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory) project_name = _get_project_name(project_name, wrapped_model) run_evaluators, examples = _setup_evaluation( wrapped_model, examples, evaluation, data_type ) examples = _validate_example_inputs(examples, wrapped_model, input_mapper) results: Dict[str, dict] = {} async def process_example( example: Example, callbacks: List[BaseCallbackHandler], job_state: dict ) -> None: """Process a single example.""" result = await _arun_llm_or_chain( example, wrapped_model, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) results[str(example.id)] = {"output": result} job_state["num_processed"] += 1 if verbose: print( f"Processed examples: {job_state['num_processed']}", end="\r", flush=True, ) evaluation_handlers: List[EvaluatorCallbackHandler] = [] await _gather_with_concurrency( concurrency_level, functools.partial( _callbacks_initializer, project_name=project_name, client=client, evaluation_handler_collector=evaluation_handlers, run_evaluators=run_evaluators or [], ), *(functools.partial(process_example, e) for e in examples), ) all_feedback = {} for handler in evaluation_handlers: handler.wait_for_futures() all_feedback.update(handler.logged_feedback) # join the results and feedback on the example id for example_id, output_dict in results.items(): feedback = all_feedback.get(example_id, []) output_dict["feedback"] = feedback return results ## Sync Utilities def _run_llm( llm: BaseLanguageModel, inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[str, BaseMessage]: """ Run the language model on the example. Args: llm: The language model to run. inputs: The input dictionary. callbacks: The callbacks to use during the run. tags: Optional tags to add to the run. input_mapper: function to map to the inputs dictionary from an Example Returns: The LLMResult or ChatResult. Raises: ValueError: If the LLM type is unsupported. InputFormatError: If the input format is invalid. """ if input_mapper is not None: prompt_or_messages = input_mapper(inputs) if isinstance(prompt_or_messages, str): llm_output: Union[str, BaseMessage] = llm.predict( prompt_or_messages, callbacks=callbacks, tags=tags ) elif isinstance(prompt_or_messages, list) and all( isinstance(msg, BaseMessage) for msg in prompt_or_messages ): llm_output = llm.predict_messages( prompt_or_messages, callbacks=callbacks, tags=tags ) else: raise InputFormatError( "Input mapper returned invalid format: " f" {prompt_or_messages}" "\nExpected a single string or list of chat messages." ) else: try: llm_prompts = _get_prompt(inputs) llm_output = llm.predict(llm_prompts, callbacks=callbacks, tags=tags) except InputFormatError: llm_messages = _get_messages(inputs) llm_output = llm.predict_messages(llm_messages, callbacks=callbacks) return llm_output def _run_chain( chain: Union[Chain, Runnable], inputs: Dict[str, Any], callbacks: Callbacks, *, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[Dict, str]: """Run a chain on inputs.""" inputs_ = inputs if input_mapper is None else input_mapper(inputs) if isinstance(chain, Chain): if isinstance(inputs_, dict) and len(inputs_) == 1: val = next(iter(inputs_.values())) output = chain(val, callbacks=callbacks, tags=tags) else: output = chain(inputs_, callbacks=callbacks, tags=tags) else: runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks) output = chain.invoke(inputs_, config=runnable_config) return output def _run_llm_or_chain( example: Example, llm_or_chain_factory: MCF, *, tags: Optional[List[str]] = None, callbacks: Optional[List[BaseCallbackHandler]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, ) -> Union[dict, str, LLMResult, ChatResult]: """ Run the Chain or language model synchronously. Args: example: The example to run. llm_or_chain_factory: The Chain or language model constructor to run. tags: Optional tags to add to the run. callbacks: Optional callbacks to use during the run. Returns: Union[List[dict], List[str], List[LLMResult], List[ChatResult]]: The outputs of the model or chain. """ if callbacks: previous_example_ids = [ getattr(tracer, "example_id", None) for tracer in callbacks ] for tracer in callbacks: if hasattr(tracer, "example_id"): tracer.example_id = example.id else: previous_example_ids = None chain_or_llm = ( "LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain" ) result = None try: if isinstance(llm_or_chain_factory, BaseLanguageModel): output: Any = _run_llm( llm_or_chain_factory, example.inputs, callbacks, tags=tags, input_mapper=input_mapper, ) else: chain = llm_or_chain_factory() output = _run_chain( chain, example.inputs, callbacks, tags=tags, input_mapper=input_mapper, ) result = output except Exception as e: logger.warning( f"{chain_or_llm} failed for example {example.id} with inputs:" f" {example.inputs}.\nError: {e}", ) result = {"Error": str(e)} if callbacks and previous_example_ids: for example_id, tracer in zip(previous_example_ids, callbacks): if hasattr(tracer, "example_id"): tracer.example_id = example_id return result def _run_on_examples( client: Client, examples: Iterator[Example], llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, data_type: DataType = DataType.kv, ) -> Dict[str, Any]: """ Run the Chain or language model on examples and store traces to the specified project name. Args: client: LangSmith client to use to log feedback and runs. examples: Examples to run the model or chain over. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. data_type: The dataset's data type. This is used to determine determine how to deserialize the reference data and model compatibility. Returns: A dictionary mapping example ids to the model outputs. """ results: Dict[str, dict] = {} wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory) project_name = _get_project_name(project_name, wrapped_model) tracer = LangChainTracer( project_name=project_name, client=client, use_threading=False ) run_evaluators, examples = _setup_evaluation( wrapped_model, examples, evaluation, data_type ) examples = _validate_example_inputs(examples, wrapped_model, input_mapper) evaluation_handler = EvaluatorCallbackHandler( evaluators=run_evaluators or [], client=client, ) callbacks: List[BaseCallbackHandler] = [tracer, evaluation_handler] for i, example in enumerate(examples): result = _run_llm_or_chain( example, wrapped_model, tags=tags, callbacks=callbacks, input_mapper=input_mapper, ) if verbose: print(f"{i+1} processed", flush=True, end="\r") results[str(example.id)] = {"output": result} tracer.wait_for_futures() evaluation_handler.wait_for_futures() all_feedback = evaluation_handler.logged_feedback # join the results and feedback on the example id for example_id, output_dict in results.items(): feedback = all_feedback.get(example_id, []) output_dict["feedback"] = feedback return results ## Public API def _prepare_eval_run( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, project_name: Optional[str], ) -> Tuple[MCF, str, Dataset, Iterator[Example]]: wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name) project_name = _get_project_name(project_name, wrapped_model) try: project = client.create_project(project_name) except ValueError as e: if "already exists " not in str(e): raise e raise ValueError( f"Project {project_name} already exists. Please use a different name." ) project_url = _get_eval_project_url(client.api_url, project.id) print( f"View the evaluation results for project '{project_name}' at:\n{project_url}" ) dataset = client.read_dataset(dataset_name=dataset_name) examples = client.list_examples(dataset_id=str(dataset.id)) return wrapped_model, project_name, dataset, examples async def arun_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, **kwargs: Any, ) -> Dict[str, Any]: """ Asynchronously run the Chain or language model on a dataset and store traces to the specified project name. Args: client: LangSmith client to use to read the dataset, and to log feedback and run traces. dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. Returns: A dictionary containing the run's project name and the resulting model outputs. For the synchronous version, see :func:`run_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig, arun_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() await arun_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) await arun_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 if kwargs: warnings.warn( "The following arguments are deprecated and will " "be removed in a future release: " f"{kwargs.keys()}.", DeprecationWarning, ) wrapped_model, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name ) results = await _arun_on_examples( client, examples, wrapped_model, concurrency_level=concurrency_level, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) return TestResult( project_name=project_name, results=results, ) def _handle_coroutine(coro: Coroutine) -> Any: """ Handles a coroutine from a sync context. Args: coro (asyncio.coroutine): The coroutine to be handled. Returns: any: The result of the executed coroutine. """ # Check if there's a running event loop try: loop = asyncio.get_event_loop() except RuntimeError: # No event loop return asyncio.run(coro) if loop.is_running(): return loop.run_until_complete(coro) else: return asyncio.run(coro) def run_on_dataset( client: Client, dataset_name: str, llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY, *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, **kwargs: Any, ) -> Dict[str, Any]: """ Run the Chain or language model on a dataset and store traces to the specified project name. Args: client: LangSmith client to use to access the dataset and to log feedback and run traces. dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Configuration for evaluators to run on the results of the chain concurrency_level: The number of async tasks to run concurrently. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. Returns: A dictionary containing the run's project name and the resulting model outputs. For the (usually faster) async version of this function, see :func:`arun_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig, run_on_dataset # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) run_on_dataset( client, "<my_dataset_name>", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 if kwargs: warnings.warn( "The following arguments are deprecated and " "will be removed in a future release: " f"{kwargs.keys()}.", DeprecationWarning, ) wrapped_model, project_name, dataset, examples = _prepare_eval_run( client, dataset_name, llm_or_chain_factory, project_name ) if concurrency_level in (0, 1): results = _run_on_examples( client, examples, wrapped_model, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) else: # TODO: Use runnables and the batch method coro = _arun_on_examples( client, examples, wrapped_model, concurrency_level=concurrency_level, project_name=project_name, verbose=verbose, tags=tags, evaluation=evaluation, input_mapper=input_mapper, data_type=dataset.data_type, ) results = _handle_coroutine(coro) return TestResult( project_name=project_name, results=results, )

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"""Base interface that all chains should implement.""" import json from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Union import yaml from pydantic import BaseModel, Field, validator import langchain from langchain.callbacks import get_callback_manager from langchain.callbacks.base import BaseCallbackManager from langchain.schema import BaseMemory def _get_verbosity() -> bool: return langchain.verbose class Chain(BaseModel, ABC): """Base interface that all chains should implement.""" memory: Optional[BaseMemory] = None callback_manager: BaseCallbackManager = Field( default_factory=get_callback_manager, exclude=True ) verbose: bool = Field( default_factory=_get_verbosity ) # Whether to print the response text class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") @validator("callback_manager", pre=True, always=True) def set_callback_manager( cls, callback_manager: Optional[BaseCallbackManager] ) -> BaseCallbackManager: """If callback manager is None, set it. This allows users to pass in None as callback manager, which is a nice UX. """ return callback_manager or get_callback_manager() @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @property @abstractmethod def input_keys(self) -> List[str]: """Input keys this chain expects.""" @property @abstractmethod def output_keys(self) -> List[str]: """Output keys this chain expects.""" def _validate_inputs(self, inputs: Dict[str, str]) -> None: """Check that all inputs are present.""" missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") def _validate_outputs(self, outputs: Dict[str, str]) -> None: if set(outputs) != set(self.output_keys): raise ValueError( f"Did not get output keys that were expected. " f"Got: {set(outputs)}. Expected: {set(self.output_keys)}." ) @abstractmethod def _call(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" raise NotImplementedError("Async call not supported for this chain type.") def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = self._call(inputs) except (KeyboardInterrupt, Exception) as e: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) async def acall( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) if self.callback_manager.is_async: await self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) else: self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = await self._acall(inputs) except (KeyboardInterrupt, Exception) as e: if self.callback_manager.is_async: await self.callback_manager.on_chain_error(e, verbose=self.verbose) else: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e if self.callback_manager.is_async: await self.callback_manager.on_chain_end(outputs, verbose=self.verbose) else: self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) def prep_outputs( self, inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False, ) -> Dict[str, str]: """Validate and prep outputs.""" self._validate_outputs(outputs) if self.memory is not None: self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: """Validate and prep inputs.""" if not isinstance(inputs, dict): _input_keys = set(self.input_keys) if self.memory is not None: # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) if len(_input_keys) != 1: raise ValueError( f"A single string input was passed in, but this chain expects " f"multiple inputs ({_input_keys}). When a chain expects " f"multiple inputs, please call it by passing in a dictionary, " "eg `chain({'foo': 1, 'bar': 2})`" ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs) for inputs in input_list] def run(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return self(args[0])[self.output_keys[0]] if kwargs and not args: return self(kwargs)[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) async def arun(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return (await self.acall(args[0]))[self.output_keys[0]] if kwargs and not args: return (await self.acall(kwargs))[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of chain.""" if self.memory is not None: raise ValueError("Saving of memory is not yet supported.") _dict = super().dict() _dict["_type"] = self._chain_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the chain. Args: file_path: Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path="path/chain.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save chain_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml")

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"""Base interface that all chains should implement.""" import json from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Union import yaml from pydantic import BaseModel, Field, validator import langchain from langchain.callbacks import get_callback_manager from langchain.callbacks.base import BaseCallbackManager from langchain.schema import BaseMemory def _get_verbosity() -> bool: return langchain.verbose class Chain(BaseModel, ABC): """Base interface that all chains should implement.""" memory: Optional[BaseMemory] = None callback_manager: BaseCallbackManager = Field( default_factory=get_callback_manager, exclude=True ) verbose: bool = Field( default_factory=_get_verbosity ) # Whether to print the response text class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") @validator("callback_manager", pre=True, always=True) def set_callback_manager( cls, callback_manager: Optional[BaseCallbackManager] ) -> BaseCallbackManager: """If callback manager is None, set it. This allows users to pass in None as callback manager, which is a nice UX. """ return callback_manager or get_callback_manager() @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @property @abstractmethod def input_keys(self) -> List[str]: """Input keys this chain expects.""" @property @abstractmethod def output_keys(self) -> List[str]: """Output keys this chain expects.""" def _validate_inputs(self, inputs: Dict[str, str]) -> None: """Check that all inputs are present.""" missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") def _validate_outputs(self, outputs: Dict[str, str]) -> None: if set(outputs) != set(self.output_keys): raise ValueError( f"Did not get output keys that were expected. " f"Got: {set(outputs)}. Expected: {set(self.output_keys)}." ) @abstractmethod def _call(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" raise NotImplementedError("Async call not supported for this chain type.") def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = self._call(inputs) except (KeyboardInterrupt, Exception) as e: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) async def acall( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) if self.callback_manager.is_async: await self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) else: self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = await self._acall(inputs) except (KeyboardInterrupt, Exception) as e: if self.callback_manager.is_async: await self.callback_manager.on_chain_error(e, verbose=self.verbose) else: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e if self.callback_manager.is_async: await self.callback_manager.on_chain_end(outputs, verbose=self.verbose) else: self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) def prep_outputs( self, inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False, ) -> Dict[str, str]: """Validate and prep outputs.""" self._validate_outputs(outputs) if self.memory is not None: self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: """Validate and prep inputs.""" if not isinstance(inputs, dict): _input_keys = set(self.input_keys) if self.memory is not None: # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) if len(_input_keys) != 1: raise ValueError( f"A single string input was passed in, but this chain expects " f"multiple inputs ({_input_keys}). When a chain expects " f"multiple inputs, please call it by passing in a dictionary, " "eg `chain({'foo': 1, 'bar': 2})`" ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs) for inputs in input_list] def run(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return self(args[0])[self.output_keys[0]] if kwargs and not args: return self(kwargs)[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) async def arun(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return (await self.acall(args[0]))[self.output_keys[0]] if kwargs and not args: return (await self.acall(kwargs))[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of chain.""" if self.memory is not None: raise ValueError("Saving of memory is not yet supported.") _dict = super().dict() _dict["_type"] = self._chain_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the chain. Args: file_path: Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path="path/chain.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save chain_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml")

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"""Base interface that all chains should implement.""" import json from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Union import yaml from pydantic import BaseModel, Field, validator import langchain from langchain.callbacks import get_callback_manager from langchain.callbacks.base import BaseCallbackManager from langchain.schema import BaseMemory def _get_verbosity() -> bool: return langchain.verbose class Chain(BaseModel, ABC): """Base interface that all chains should implement.""" memory: Optional[BaseMemory] = None callback_manager: BaseCallbackManager = Field( default_factory=get_callback_manager, exclude=True ) verbose: bool = Field( default_factory=_get_verbosity ) # Whether to print the response text class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") @validator("callback_manager", pre=True, always=True) def set_callback_manager( cls, callback_manager: Optional[BaseCallbackManager] ) -> BaseCallbackManager: """If callback manager is None, set it. This allows users to pass in None as callback manager, which is a nice UX. """ return callback_manager or get_callback_manager() @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @property @abstractmethod def input_keys(self) -> List[str]: """Input keys this chain expects.""" @property @abstractmethod def output_keys(self) -> List[str]: """Output keys this chain expects.""" def _validate_inputs(self, inputs: Dict[str, str]) -> None: """Check that all inputs are present.""" missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") def _validate_outputs(self, outputs: Dict[str, str]) -> None: if set(outputs) != set(self.output_keys): raise ValueError( f"Did not get output keys that were expected. " f"Got: {set(outputs)}. Expected: {set(self.output_keys)}." ) @abstractmethod def _call(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" raise NotImplementedError("Async call not supported for this chain type.") def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = self._call(inputs) except (KeyboardInterrupt, Exception) as e: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) async def acall( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) if self.callback_manager.is_async: await self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) else: self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = await self._acall(inputs) except (KeyboardInterrupt, Exception) as e: if self.callback_manager.is_async: await self.callback_manager.on_chain_error(e, verbose=self.verbose) else: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e if self.callback_manager.is_async: await self.callback_manager.on_chain_end(outputs, verbose=self.verbose) else: self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) def prep_outputs( self, inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False, ) -> Dict[str, str]: """Validate and prep outputs.""" self._validate_outputs(outputs) if self.memory is not None: self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: """Validate and prep inputs.""" if not isinstance(inputs, dict): _input_keys = set(self.input_keys) if self.memory is not None: # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) if len(_input_keys) != 1: raise ValueError( f"A single string input was passed in, but this chain expects " f"multiple inputs ({_input_keys}). When a chain expects " f"multiple inputs, please call it by passing in a dictionary, " "eg `chain({'foo': 1, 'bar': 2})`" ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs) for inputs in input_list] def run(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return self(args[0])[self.output_keys[0]] if kwargs and not args: return self(kwargs)[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) async def arun(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return (await self.acall(args[0]))[self.output_keys[0]] if kwargs and not args: return (await self.acall(kwargs))[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of chain.""" if self.memory is not None: raise ValueError("Saving of memory is not yet supported.") _dict = super().dict() _dict["_type"] = self._chain_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the chain. Args: file_path: Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path="path/chain.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save chain_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml")

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"""Base interface that all chains should implement.""" import json from abc import ABC, abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Union import yaml from pydantic import BaseModel, Field, validator import langchain from langchain.callbacks import get_callback_manager from langchain.callbacks.base import BaseCallbackManager from langchain.schema import BaseMemory def _get_verbosity() -> bool: return langchain.verbose class Chain(BaseModel, ABC): """Base interface that all chains should implement.""" memory: Optional[BaseMemory] = None callback_manager: BaseCallbackManager = Field( default_factory=get_callback_manager, exclude=True ) verbose: bool = Field( default_factory=_get_verbosity ) # Whether to print the response text class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") @validator("callback_manager", pre=True, always=True) def set_callback_manager( cls, callback_manager: Optional[BaseCallbackManager] ) -> BaseCallbackManager: """If callback manager is None, set it. This allows users to pass in None as callback manager, which is a nice UX. """ return callback_manager or get_callback_manager() @validator("verbose", pre=True, always=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. """ if verbose is None: return _get_verbosity() else: return verbose @property @abstractmethod def input_keys(self) -> List[str]: """Input keys this chain expects.""" @property @abstractmethod def output_keys(self) -> List[str]: """Output keys this chain expects.""" def _validate_inputs(self, inputs: Dict[str, str]) -> None: """Check that all inputs are present.""" missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") def _validate_outputs(self, outputs: Dict[str, str]) -> None: if set(outputs) != set(self.output_keys): raise ValueError( f"Did not get output keys that were expected. " f"Got: {set(outputs)}. Expected: {set(self.output_keys)}." ) @abstractmethod def _call(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run the logic of this chain and return the output.""" raise NotImplementedError("Async call not supported for this chain type.") def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = self._call(inputs) except (KeyboardInterrupt, Exception) as e: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) async def acall( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False ) -> Dict[str, Any]: """Run the logic of this chain and add to output if desired. Args: inputs: Dictionary of inputs, or single input if chain expects only one param. return_only_outputs: boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. """ inputs = self.prep_inputs(inputs) if self.callback_manager.is_async: await self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) else: self.callback_manager.on_chain_start( {"name": self.__class__.__name__}, inputs, verbose=self.verbose, ) try: outputs = await self._acall(inputs) except (KeyboardInterrupt, Exception) as e: if self.callback_manager.is_async: await self.callback_manager.on_chain_error(e, verbose=self.verbose) else: self.callback_manager.on_chain_error(e, verbose=self.verbose) raise e if self.callback_manager.is_async: await self.callback_manager.on_chain_end(outputs, verbose=self.verbose) else: self.callback_manager.on_chain_end(outputs, verbose=self.verbose) return self.prep_outputs(inputs, outputs, return_only_outputs) def prep_outputs( self, inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False, ) -> Dict[str, str]: """Validate and prep outputs.""" self._validate_outputs(outputs) if self.memory is not None: self.memory.save_context(inputs, outputs) if return_only_outputs: return outputs else: return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: """Validate and prep inputs.""" if not isinstance(inputs, dict): _input_keys = set(self.input_keys) if self.memory is not None: # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) if len(_input_keys) != 1: raise ValueError( f"A single string input was passed in, but this chain expects " f"multiple inputs ({_input_keys}). When a chain expects " f"multiple inputs, please call it by passing in a dictionary, " "eg `chain({'foo': 1, 'bar': 2})`" ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) self._validate_inputs(inputs) return inputs def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs) for inputs in input_list] def run(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return self(args[0])[self.output_keys[0]] if kwargs and not args: return self(kwargs)[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) async def arun(self, *args: Any, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" if len(self.output_keys) != 1: raise ValueError( f"`run` not supported when there is not exactly " f"one output key. Got {self.output_keys}." ) if args and not kwargs: if len(args) != 1: raise ValueError("`run` supports only one positional argument.") return (await self.acall(args[0]))[self.output_keys[0]] if kwargs and not args: return (await self.acall(kwargs))[self.output_keys[0]] raise ValueError( f"`run` supported with either positional arguments or keyword arguments" f" but not both. Got args: {args} and kwargs: {kwargs}." ) def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of chain.""" if self.memory is not None: raise ValueError("Saving of memory is not yet supported.") _dict = super().dict() _dict["_type"] = self._chain_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the chain. Args: file_path: Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path="path/chain.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save chain_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml")

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from typing import Dict, List, Optional from langchain.agents.load_tools import ( _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS, ) from langflow.custom import customs from langflow.interface.base import LangChainTypeCreator from langflow.interface.tools.constants import ( ALL_TOOLS_NAMES, CUSTOM_TOOLS, FILE_TOOLS, OTHER_TOOLS, ) from langflow.interface.tools.util import get_tool_params from langflow.services.utils import get_settings_manager from langflow.template.field.base import TemplateField from langflow.template.template.base import Template from langflow.utils import util from langflow.utils.util import build_template_from_class TOOL_INPUTS = { "str": TemplateField( field_type="str", required=True, is_list=False, show=True, placeholder="", value="", ), "llm": TemplateField( field_type="BaseLanguageModel", required=True, is_list=False, show=True ), "func": TemplateField( field_type="function", required=True, is_list=False, show=True, multiline=True, ), "code": TemplateField( field_type="str", required=True, is_list=False, show=True, value="", multiline=True, ), "path": TemplateField( field_type="file", required=True, is_list=False, show=True, value="", suffixes=[".json", ".yaml", ".yml"], file_types=["json", "yaml", "yml"], ), } class ToolCreator(LangChainTypeCreator): type_name: str = "tools" tools_dict: Optional[Dict] = None @property def type_to_loader_dict(self) -> Dict: settings_manager = get_settings_manager() if self.tools_dict is None: all_tools = {} for tool, tool_fcn in ALL_TOOLS_NAMES.items(): tool_params = get_tool_params(tool_fcn) tool_name = tool_params.get("name") or tool if ( tool_name in settings_manager.settings.TOOLS or settings_manager.settings.DEV ): if tool_name == "JsonSpec": tool_params["path"] = tool_params.pop("dict_") # type: ignore all_tools[tool_name] = { "type": tool, "params": tool_params, "fcn": tool_fcn, } self.tools_dict = all_tools return self.tools_dict def get_signature(self, name: str) -> Optional[Dict]: """Get the signature of a tool.""" base_classes = ["Tool", "BaseTool"] fields = [] params = [] tool_params = {} # Raise error if name is not in tools if name not in self.type_to_loader_dict.keys(): raise ValueError("Tool not found") tool_type: str = self.type_to_loader_dict[name]["type"] # type: ignore # if tool_type in _BASE_TOOLS.keys(): # params = [] if tool_type in _LLM_TOOLS.keys(): params = ["llm"] elif tool_type in _EXTRA_LLM_TOOLS.keys(): extra_keys = _EXTRA_LLM_TOOLS[tool_type][1] params = ["llm"] + extra_keys elif tool_type in _EXTRA_OPTIONAL_TOOLS.keys(): extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type][1] params = extra_keys # elif tool_type == "Tool": # params = ["name", "description", "func"] elif tool_type in CUSTOM_TOOLS: # Get custom tool params params = self.type_to_loader_dict[name]["params"] # type: ignore base_classes = ["function"] if node := customs.get_custom_nodes("tools").get(tool_type): return node elif tool_type in FILE_TOOLS: params = self.type_to_loader_dict[name]["params"] # type: ignore base_classes += [name] elif tool_type in OTHER_TOOLS: tool_dict = build_template_from_class(tool_type, OTHER_TOOLS) fields = tool_dict["template"] # Pop unnecessary fields and add name fields.pop("_type") # type: ignore fields.pop("return_direct") # type: ignore fields.pop("verbose") # type: ignore tool_params = { "name": fields.pop("name")["value"], # type: ignore "description": fields.pop("description")["value"], # type: ignore } fields = [ TemplateField(name=name, field_type=field["type"], **field) for name, field in fields.items() # type: ignore ] base_classes += tool_dict["base_classes"] # Copy the field and add the name for param in params: field = TOOL_INPUTS.get(param, TOOL_INPUTS["str"]).copy() field.name = param field.advanced = False if param == "aiosession": field.show = False field.required = False fields.append(field) template = Template(fields=fields, type_name=tool_type) tool_params = {**tool_params, **self.type_to_loader_dict[name]["params"]} return { "template": util.format_dict(template.to_dict()), **tool_params, "base_classes": base_classes, } def to_list(self) -> List[str]: """List all load tools""" return list(self.type_to_loader_dict.keys()) tool_creator = ToolCreator()

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from typing import Dict, List, Optional from langchain.agents.load_tools import ( _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS, ) from langflow.custom import customs from langflow.interface.base import LangChainTypeCreator from langflow.interface.tools.constants import ( ALL_TOOLS_NAMES, CUSTOM_TOOLS, FILE_TOOLS, OTHER_TOOLS, ) from langflow.interface.tools.util import get_tool_params from langflow.services.utils import get_settings_manager from langflow.template.field.base import TemplateField from langflow.template.template.base import Template from langflow.utils import util from langflow.utils.util import build_template_from_class TOOL_INPUTS = { "str": TemplateField( field_type="str", required=True, is_list=False, show=True, placeholder="", value="", ), "llm": TemplateField( field_type="BaseLanguageModel", required=True, is_list=False, show=True ), "func": TemplateField( field_type="function", required=True, is_list=False, show=True, multiline=True, ), "code": TemplateField( field_type="str", required=True, is_list=False, show=True, value="", multiline=True, ), "path": TemplateField( field_type="file", required=True, is_list=False, show=True, value="", suffixes=[".json", ".yaml", ".yml"], file_types=["json", "yaml", "yml"], ), } class ToolCreator(LangChainTypeCreator): type_name: str = "tools" tools_dict: Optional[Dict] = None @property def type_to_loader_dict(self) -> Dict: settings_manager = get_settings_manager() if self.tools_dict is None: all_tools = {} for tool, tool_fcn in ALL_TOOLS_NAMES.items(): tool_params = get_tool_params(tool_fcn) tool_name = tool_params.get("name") or tool if ( tool_name in settings_manager.settings.TOOLS or settings_manager.settings.DEV ): if tool_name == "JsonSpec": tool_params["path"] = tool_params.pop("dict_") # type: ignore all_tools[tool_name] = { "type": tool, "params": tool_params, "fcn": tool_fcn, } self.tools_dict = all_tools return self.tools_dict def get_signature(self, name: str) -> Optional[Dict]: """Get the signature of a tool.""" base_classes = ["Tool", "BaseTool"] fields = [] params = [] tool_params = {} # Raise error if name is not in tools if name not in self.type_to_loader_dict.keys(): raise ValueError("Tool not found") tool_type: str = self.type_to_loader_dict[name]["type"] # type: ignore # if tool_type in _BASE_TOOLS.keys(): # params = [] if tool_type in _LLM_TOOLS.keys(): params = ["llm"] elif tool_type in _EXTRA_LLM_TOOLS.keys(): extra_keys = _EXTRA_LLM_TOOLS[tool_type][1] params = ["llm"] + extra_keys elif tool_type in _EXTRA_OPTIONAL_TOOLS.keys(): extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type][1] params = extra_keys # elif tool_type == "Tool": # params = ["name", "description", "func"] elif tool_type in CUSTOM_TOOLS: # Get custom tool params params = self.type_to_loader_dict[name]["params"] # type: ignore base_classes = ["function"] if node := customs.get_custom_nodes("tools").get(tool_type): return node elif tool_type in FILE_TOOLS: params = self.type_to_loader_dict[name]["params"] # type: ignore base_classes += [name] elif tool_type in OTHER_TOOLS: tool_dict = build_template_from_class(tool_type, OTHER_TOOLS) fields = tool_dict["template"] # Pop unnecessary fields and add name fields.pop("_type") # type: ignore fields.pop("return_direct") # type: ignore fields.pop("verbose") # type: ignore tool_params = { "name": fields.pop("name")["value"], # type: ignore "description": fields.pop("description")["value"], # type: ignore } fields = [ TemplateField(name=name, field_type=field["type"], **field) for name, field in fields.items() # type: ignore ] base_classes += tool_dict["base_classes"] # Copy the field and add the name for param in params: field = TOOL_INPUTS.get(param, TOOL_INPUTS["str"]).copy() field.name = param field.advanced = False if param == "aiosession": field.show = False field.required = False fields.append(field) template = Template(fields=fields, type_name=tool_type) tool_params = {**tool_params, **self.type_to_loader_dict[name]["params"]} return { "template": util.format_dict(template.to_dict()), **tool_params, "base_classes": base_classes, } def to_list(self) -> List[str]: """List all load tools""" return list(self.type_to_loader_dict.keys()) tool_creator = ToolCreator()

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from typing import Dict, List, Optional from langchain.agents.load_tools import ( _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS, ) from langflow.custom import customs from langflow.interface.base import LangChainTypeCreator from langflow.interface.tools.constants import ( ALL_TOOLS_NAMES, CUSTOM_TOOLS, FILE_TOOLS, OTHER_TOOLS, ) from langflow.interface.tools.util import get_tool_params from langflow.services.utils import get_settings_manager from langflow.template.field.base import TemplateField from langflow.template.template.base import Template from langflow.utils import util from langflow.utils.util import build_template_from_class TOOL_INPUTS = { "str": TemplateField( field_type="str", required=True, is_list=False, show=True, placeholder="", value="", ), "llm": TemplateField( field_type="BaseLanguageModel", required=True, is_list=False, show=True ), "func": TemplateField( field_type="function", required=True, is_list=False, show=True, multiline=True, ), "code": TemplateField( field_type="str", required=True, is_list=False, show=True, value="", multiline=True, ), "path": TemplateField( field_type="file", required=True, is_list=False, show=True, value="", suffixes=[".json", ".yaml", ".yml"], file_types=["json", "yaml", "yml"], ), } class ToolCreator(LangChainTypeCreator): type_name: str = "tools" tools_dict: Optional[Dict] = None @property def type_to_loader_dict(self) -> Dict: settings_manager = get_settings_manager() if self.tools_dict is None: all_tools = {} for tool, tool_fcn in ALL_TOOLS_NAMES.items(): tool_params = get_tool_params(tool_fcn) tool_name = tool_params.get("name") or tool if ( tool_name in settings_manager.settings.TOOLS or settings_manager.settings.DEV ): if tool_name == "JsonSpec": tool_params["path"] = tool_params.pop("dict_") # type: ignore all_tools[tool_name] = { "type": tool, "params": tool_params, "fcn": tool_fcn, } self.tools_dict = all_tools return self.tools_dict def get_signature(self, name: str) -> Optional[Dict]: """Get the signature of a tool.""" base_classes = ["Tool", "BaseTool"] fields = [] params = [] tool_params = {} # Raise error if name is not in tools if name not in self.type_to_loader_dict.keys(): raise ValueError("Tool not found") tool_type: str = self.type_to_loader_dict[name]["type"] # type: ignore # if tool_type in _BASE_TOOLS.keys(): # params = [] if tool_type in _LLM_TOOLS.keys(): params = ["llm"] elif tool_type in _EXTRA_LLM_TOOLS.keys(): extra_keys = _EXTRA_LLM_TOOLS[tool_type][1] params = ["llm"] + extra_keys elif tool_type in _EXTRA_OPTIONAL_TOOLS.keys(): extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type][1] params = extra_keys # elif tool_type == "Tool": # params = ["name", "description", "func"] elif tool_type in CUSTOM_TOOLS: # Get custom tool params params = self.type_to_loader_dict[name]["params"] # type: ignore base_classes = ["function"] if node := customs.get_custom_nodes("tools").get(tool_type): return node elif tool_type in FILE_TOOLS: params = self.type_to_loader_dict[name]["params"] # type: ignore base_classes += [name] elif tool_type in OTHER_TOOLS: tool_dict = build_template_from_class(tool_type, OTHER_TOOLS) fields = tool_dict["template"] # Pop unnecessary fields and add name fields.pop("_type") # type: ignore fields.pop("return_direct") # type: ignore fields.pop("verbose") # type: ignore tool_params = { "name": fields.pop("name")["value"], # type: ignore "description": fields.pop("description")["value"], # type: ignore } fields = [ TemplateField(name=name, field_type=field["type"], **field) for name, field in fields.items() # type: ignore ] base_classes += tool_dict["base_classes"] # Copy the field and add the name for param in params: field = TOOL_INPUTS.get(param, TOOL_INPUTS["str"]).copy() field.name = param field.advanced = False if param == "aiosession": field.show = False field.required = False fields.append(field) template = Template(fields=fields, type_name=tool_type) tool_params = {**tool_params, **self.type_to_loader_dict[name]["params"]} return { "template": util.format_dict(template.to_dict()), **tool_params, "base_classes": base_classes, } def to_list(self) -> List[str]: """List all load tools""" return list(self.type_to_loader_dict.keys()) tool_creator = ToolCreator()

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import langchain_visualizer # isort:skip # noqa: F401 import asyncio import vcr_langchain as vcr from fvalues import FValue from langchain import PromptTemplate from langchain.llms import OpenAI # ========================== Start of langchain example code ========================== # https://langchain.readthedocs.io/en/latest/modules/prompts/getting_started.html # An example prompt with one input variable one_input_prompt = PromptTemplate( input_variables=["adjective"], template="Tell me a {adjective} joke." ) prompt = one_input_prompt.format(adjective="funny") # ================================== Execute example ================================== def test_prompt(): assert prompt.parts == ( "Tell me a ", FValue(source="adjective", value="funny", formatted="funny"), " joke.", ) @vcr.use_cassette() async def one_input_prompt_demo(): agent = OpenAI(model_name="text-ada-001", temperature=0) return agent(prompt) def test_llm_usage_succeeds(): """ Check that it works like a regular prompt. Also, record playback for easy visualization. """ result = asyncio.get_event_loop().run_until_complete(one_input_prompt_demo()) assert result.strip().startswith("Why did the chicken cross the road?") if __name__ == "__main__": from langchain_visualizer import visualize visualize(one_input_prompt_demo)

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# coding=utf-8 import json import hashlib from datetime import datetime import os import time import openai import flet as ft import re import shutil from flet import ( ElevatedButton, FilePicker, FilePickerResultEvent, Page, Row, Text, icons, ) from prompt_engineering import * import codecs # 赋值固定的api_key # 测试用 openai.api_key = None openai.api_key = read_APIKEY() ''' 每一行对话的类 在调用的同时，会将数据存入聊天记录文件 ''' class chat_row(ft.UserControl): def __init__(self, role, content): super(chat_row, self).__init__() self.role = role self.content = content self.hash = save_now_chat( chat_json_path=chat_json_path, role=self.role, content=self.content) def build(self): self.role_dropdown = ft.Dropdown( value=self.role, width=150, options=[ ft.dropdown.Option("system"), ft.dropdown.Option("user"), ft.dropdown.Option("assistant"), ], on_change=self.role_change ) self.content_textfield = ft.TextField( label="消息内容", value=self.content, filled=True, expand=True, multiline=True, on_change=self.content_change ) self.generalize_btn = ft.ElevatedButton( '概括本消息', on_click=self.sum_change ) self.sum_field = ft.TextField ( visible=False, label="概括内容", value='概括中', filled=True, expand=True, multiline=True, # on_change=self.content_change ) self.one_chat_row = ft.Row ( [ self.role_dropdown, self.content_textfield, self.generalize_btn, self.sum_field, ] ) return self.one_chat_row def sum_change(self,e): self.generalize_btn.disabled = True self.update() self.one_chat_row.controls.append(self.sum_field) self.update() self.summary = chatGPT_sum(self.content) renew_now_chat ( chat_json_path=chat_json_path, hash_val=self.hash, summary=self.summary ) self.sum_field.visible = True self.sum_field.value = self.summary self.generalize_btn.disabled = False self.update () return self.one_chat_row def role_change(self, e): self.role = self.role_dropdown.value renew_now_chat( chat_json_path=chat_json_path, hash_val=self.hash, role=self.role) def content_change(self, e): self.content = self.content_textfield.value renew_now_chat( chat_json_path=chat_json_path, hash_val=self.hash, content=self.content) ''' 调用chatGPT获取关键词和概括的函数 ''' # 文本切块成 def split_text(text, divide_length): return [text[i:i+divide_length] for i in range(0, len(text), divide_length)] # 获取概括文本 def chatGPT_sum_old(content): divided_content = split_text(content,divide_length=1000) chain_summary = [] for single_content in divided_content: composition_analysis_message = [{"role" : "user", "content" : f'''为以下文本创建概括: {content} 概括内容:'''}] chatGPT_raw_response = openai.ChatCompletion.create ( model="gpt-3.5-turbo", messages=composition_analysis_message ) summary = decode_chr ( chatGPT_raw_response.choices[0].message['content'].strip () ) chain_summary.append(summary) print(summary) chain_summary_long = '\n'.join ( chain_summary ) return chain_summary_long def chatGPT_sum(content,chain_type='map_reduce'): try: # 试图用langchain但无法打包 import langchain from langchain.llms import OpenAI from langchain.chains.summarize import load_summarize_chain from langchain.chains import AnalyzeDocumentChain from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.document_loaders import UnstructuredFileLoader from langchain.docstore.document import Document from langchain.prompts import PromptTemplate ''' 准备步骤 ''' # 创建文本分块器，每块长度为1000 text_splitter = RecursiveCharacterTextSplitter ( chunk_size=1000, chunk_overlap=0 ) # 创建大语言模型，将温度拉到最低以提高精确性 llm = OpenAI ( temperature=0, openai_api_key=openai.api_key ) ''' 加载需要概括的内容 ''' # 加载外部文件 # loader = UnstructuredFileLoader ( 'game_log_conversation.txt' ) # docs = loader.load () # split_docs = text_splitter.split_documents(docs) # 加载函数输入的字符串 split_content = text_splitter.split_text ( content ) split_docs = [Document ( page_content=t ) for t in split_content] ''' 总结文本 ''' # 创建prompt模板 prompt_template = """为以下文本创建概括: {text} 概括内容:""" PROMPT = PromptTemplate ( template=prompt_template, input_variables=["text"] ) # 创建总结链，模式为map_reduce # 第一种 # 有模板的总结链 summary_chain = load_summarize_chain ( llm, chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT ,verbose=True) # 带参数带模板总结 chain_summary = summary_chain ( {"input_documents" : split_docs}, # return_only_outputs=True, ) # 第二种 # 无模板的总结链 # summary_chain = load_summarize_chain ( llm, chain_type=chain_type, verbose=True ) # 直接总结 # chain_summary = summary_chain.run ( split_docs ) chain_summary_long = '\n'.join(chain_summary['intermediate_steps']) return chain_summary_long except Exception as error: print(error) return chatGPT_sum_old(content) # 获取关键词（还是用jieba吧） def chatGPT_getkeyword(content): chatGPT_raw_response = openai.Completion.create ( model="text-ada-001", prompt=f"你要总结这一文本的关键词，并以python列表的形式返回数个关键词字符串:{content}。", temperature=0 ) keyword = decode_chr ( chatGPT_raw_response.choices[0]['text'].strip () ) print(keyword) return keyword # 获取关键词（还是用jieba吧） def chatGPT_getsummary(content): chatGPT_raw_response = openai.Completion.create ( model="text-ada-001", prompt=f"你要在10字以内概括这段文本:{content}。", temperature=0 ) keyword = decode_chr ( chatGPT_raw_response.choices[0]['text'].strip () ) print(keyword) return keyword ''' 读写chatlog的相关函数 ''' # 创建chat数据记录的方法，将聊天数据结构基于 JSON 文件进行存储 def create_chat_json(save_path='./chatlog'): # 如果保存目录不存在，则创建目录 if not os.path.exists(save_path): os.makedirs(save_path) # 获取当前时间并格式化为文件名格式 now = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') chat_json_name = f"chat_{now}.json" # 如果文件不存在，则创建文件 chat_json_path = os.path.join(save_path, chat_json_name) if not os.path.exists(chat_json_path): with open(chat_json_path, 'w') as f: json.dump([], f, default=str) return chat_json_path # 储存现有的每一句聊天信息 def save_now_chat(chat_json_path: str, role: str, content: str) -> str: ''' 将每一段聊天信息储存到一个 JSON 文件中。以哈希值作为唯一标识符 :param chat_json_path: 聊天文件的路径 :param role: 发言者 :param content: 发言内容 :return hash_val: 哈希值 ''' now = datetime.now().timestamp() str_to_hash = str(now) + role + content hash_obj = hashlib.blake2b(str_to_hash.encode('utf-8'), digest_size=8) hash_val = hash_obj.hexdigest() # 读取之前的内容 try: with open(chat_json_path, 'r') as f: chats = json.load(f) except FileNotFoundError: chats = [] # 添加新的聊天信息 message = { 'role': role, 'content': content, 'keyword': [], 'summary': '' } chats.append({ 'chat_seq': len(chats) + 1, 'hash': hash_val, 'created_time': now, 'altered_time': None, 'message': message }) # 将新的聊天信息写入文件 with open(chat_json_path, 'w') as f: json.dump(chats, f, default=str,indent=4) return hash_val # 根据聊天信息的哈希值，更新现有历史聊天列表 def renew_now_chat(chat_json_path: str, hash_val: str, role: str = None, content: str = None , summary=None) -> None: ''' 根据聊天信息的哈希值，更新现有历史聊天列表 :param chat_json_path: 聊天文件的路径 :param hash_val: 聊天信息的哈希值 :param role: 更新后的发言者（可选） :param content: 更新后的发言内容（可选） :return: None ''' # 读取chat_renew_data.json文件 with open(chat_json_path, 'r') as f: data = json.load(f) # 找出哈希值相同的键值对，并更新它们的role和content for chat_item in data: if hash_val == chat_item['hash']: if role: chat_item['message']['role'] = role if content: chat_item['message']['content'] = content if summary : chat_item['message']['summary'] = summary chat_item["altered_time"] = datetime.now().timestamp() break # 将更新后的数据写回文件 with open(chat_json_path, 'w') as f: json.dump(data, f, default=str,indent=4) # 根据聊天信息的哈希值，获取单个键值对内的role和content def get_one_role_and_content( chat_json_path: str, hash_value: str) -> (str, str): ''' 根据聊天信息的哈希值，获取单个键值对内的role和content :param chat_json_path: 聊天文件的路径 :param hash_value: 聊天信息的哈希值 :return: (发言者, 发言内容) ''' with open(chat_json_path) as f: data = json.load(f) for chat_item in data: for message in chat_item['message']: if hash_value == hashlib.blake2b( str(message["created_time"] + message["role"] + message["content"]).encode('utf-8'), digest_size=8).hexdigest(): return message["role"], message["content"] return None # 读取特定文件内的所有role和content def get_combined_data(chat_json_path: str) -> list[dict[str, str]]: ''' 获取特定文件内的所有role和content :param chat_json_path: JSON 聊天文件的路径 :return: 包含所有发言者和发言内容（若有概括则返回概括）的列表 ''' with open(chat_json_path) as f: data = json.load(f) result = [] for chat_item in data: if chat_item['message']['summary'] != '': result.append({ "role": chat_item['message']["role"], "content": chat_item['message']["summary"] }) else: result.append({ "role": chat_item['message']["role"], "content": chat_item['message']["content"] }) return result # 概括chatlog def summarize_chatlog(chatlog_json_path) : with open ( chatlog_json_path, 'r' ) as f : chatlog = json.load ( f ) for message in chatlog : if 'summary' not in message or not message['summary'] : content = message['message']['content'] # assuming content is always the second item in the message list if len ( content ) > 100 : summary = chatGPT_sum ( content ) message['summary'] = summary with open ( chatlog_json_path, 'w' ) as f : json.dump ( chatlog, f ,indent=4) # 概括chatlog def summarize_chatlog(chatlog_json_path) : with open ( chatlog_json_path, 'r' ) as f : chatlog = json.load ( f ) for message in chatlog : if 'summary' not in message or not message['summary'] : content = message['message']['content'] # assuming content is always the second item in the message list if len ( content ) > 100 : summary = chatGPT_sum ( content ) message['summary'] = summary with open ( chatlog_json_path, 'w' ) as f : json.dump ( chatlog, f ,indent=4) # 获取chatlog关键词 def get_chatlog_keyword(chatlog_json_path) : with open ( chatlog_json_path, 'r' ) as f : chatlog = json.load ( f ) for message in chatlog : if 'keyword' not in message or not message['keyword'] : content = message['message']['content'] # assuming content is always the second item in the message list keywords = chatGPT_getkeyword ( content ) message['keyword'] = keywords with open ( chatlog_json_path, 'w' ) as f : json.dump ( chatlog, f,indent=4 ) # 创建chat数据记录 chat_json_path = create_chat_json() ''' 加工message方法，先对向chatGPT发送的请求进行处理 ''' def process_message() : pass def cut_message(message) : ''' 剪切接收到的message，如果超过4000token长度就从最早的消息开始剪切，剪切到小于4000token为止 :param message: :return: ''' total_length = 0 # Iterate over contents in the list for message_dict in message : # message_dict['content'] = message_dict['content'] # 计算content长度 if message_dict['content'].isalpha () : length = len ( message_dict['content'].split () ) else : length = len ( message_dict['content'] ) total_length += length while total_length > 4000: if not message:raise Exception('最后一条消息长度大于4000字符了，请编辑消息或重置对话') removed_content = message.pop(0) removed_length = len ( removed_content['content'] ) if removed_content['content'].isalpha () : removed_length = len ( removed_content['content'].split () ) total_length -= removed_length return message ''' 读写API-KEY的函数 ''' # 定义 write_APIKEY 函数 def write_APIKEY(APIKEY=None): # 以追加模式打开或创建 APIKEY.txt 文件 with open("APIKEY.txt", "a") as f: # 写入字符串并换行 if APIKEY: f.write(APIKEY.strip() + "\n") # 定义 read_APIKEY 函数 def read_APIKEY(): # 以读取模式打开 APIKEY.txt 文件 with open("APIKEY.txt", "r") as f: # 读取所有行并存入列表 lines = f.readlines() # 如果列表不为空，返回最后一行的值，去掉换行符 if lines: return lines[-1].strip() ''' 读写settings.txt的函数 ''' # 读取settings.txt文件的设置项值，并返回一个字典 def read_settings(): """ 读取settings.txt文件，如果该文件不存在，则创建一个空白的文件 然后读取所有行，每行格式为：设置项名称 = 设置项的具体值 将每行拆分为键值对，添加到字典中，并返回该字典 :return: 包含settings.txt内的所有行的键值对形式的字典 """ settings_dict = {} try: with open('settings.txt', 'r', encoding='utf-8') as file: for line in file: line = line.strip() if line: key, value = line.split('=', maxsplit=1) settings_dict[key.strip()] = value.strip() except FileNotFoundError: with open('settings.txt', 'w', encoding='utf-8') as file: pass # 如果文件不存在，则创建一个空白的txt文件，不需要做任何操作 return settings_dict # 将字典中的多个键值对写入/修改settings.txt文件的设置项值 def write_settings(settings): """ 将多个键值对写入/更新settings.txt文件 如果文件不存在则创建一个空的文件 :param settings: 包含键值对的字典 """ with open('settings.txt', 'r+') as f: lines = f.readlines() f.seek(0) for key, value in settings.items(): for i, line in enumerate(lines): if key in line: lines[i] = key + ' = ' + value + '\n' break else: f.write(key + ' = ' + value + '\n') f.writelines(lines) ''' 读写字体文件的函数（适用于windows） ''' def replace_font_file(path): old_path = os.path.join(".", "asset", "font.ttf") try: os.remove(old_path) except OSError: pass try: shutil.copy(path, 'assets/font.ttf') except FileNotFoundError: with open('assets/font.ttf','a'): pass shutil.copy(path, 'assets/font.ttf') print("替换成功！") ''' 其他函数 ''' # 字符转码 def decode_chr(s): s = s.replace('\\\\','\\') pattern = re.compile(r'(\\u[0-9a-fA-F]{4}|\n)') result = '' pos = 0 while True: match = pattern.search(s, pos) if match is None: break result += s[pos:match.start()] if match.group() == '\n': result += '\n' else: result += chr(int(match.group()[2:], 16)) pos = match.end() result += s[pos:] return result def convert_content_to_unicode(txt_path): # 读取txt文件，转换为Python列表 with open(txt_path, 'r', encoding='utf-8') as f: txt_data = f.read() data = json.loads(txt_data) # 转换content内容为Unicode编码 for item in data: content = item["message"]["content"] item["message"]["content"] = content.encode('unicode_escape').decode() # item["message"]["content"] = item["message"]["content"].replace('\\u',r'\u') # 将Python列表转换为json格式字符串 json_data = json.dumps(data, ensure_ascii=False) # 保存json文件到cache文件夹 if not os.path.exists("./cache"): os.makedirs("./cache") # 创建cache文件夹 json_path = f"./cache/{os.path.basename(txt_path)[:-4]}.json" with open(json_path, 'w', encoding='utf-8') as f: f.write(json_data) return json_path # markdown检测 def markdown_check(gpt_msg): pass ''' 程序主窗体 ''' def ft_interface(page: ft.Page): # 设置字体与主题 page.title = 'BillyGPT' page.fonts = {'A75方正像素12': './assets/font.ttf'} page.theme = ft.Theme(font_family='A75方正像素12') page.dark_theme = page.theme # 设置主页面聊天区域的滚动列表 chat_area = ft.ListView( expand=True, spacing=10, auto_scroll=True, padding=20) ''' 添加选择上传文件、保存文件、打开文件夹按钮 ''' # 导入聊天记录 def import_chatlog(e: FilePickerResultEvent): try: clear_page() selected_file = ( ", ".join(map(lambda f: f.path, e.files) ) if e.files else "Cancelled!" ) if selected_file[-4:] == 'json': full_chatlog = get_combined_data(selected_file) print(full_chatlog) for chat_row_content in full_chatlog: role = chat_row_content['role'] content = chat_row_content['content'] chat_area.controls.append(chat_row(role, content)) page.update() elif selected_file[-4:] == '.txt': json_path = convert_content_to_unicode(selected_file) full_chatlog = get_combined_data(json_path) for chat_row_content in full_chatlog: role = decode_chr(chat_row_content['role']) content = decode_chr(chat_row_content['content']) chat_area.controls.append(chat_row(role, content)) page.update() except Exception as e: chat_area.controls.append( Text(f'出现如下报错\n{e}\n请检查导入的聊天记录是否正确，或联系开发者微信B1lli_official')) page.update() import_chatlog_dialog = FilePicker(on_result=import_chatlog) # 导出聊天记录 def save_file_result(e: FilePickerResultEvent): save_file_path.value = e.path if e.path else "Cancelled!" if save_file_path.value != "Cancelled!": shutil.copy(chat_json_path, save_file_path.value) path = save_file_path.value with open ( path, 'r', encoding='utf-8' ) as f : content = f.read () processed_content = decode_chr ( content ) with open ( path, 'w', encoding='utf-8' ) as f : f.write ( processed_content ) save_file_dialog = FilePicker(on_result=save_file_result) save_file_path = Text() # 打开文件夹（还没做） def get_directory_result(e: FilePickerResultEvent): directory_path.value = e.path if e.path else "Cancelled!" directory_path.update() get_directory_dialog = FilePicker(on_result=get_directory_result) directory_path = Text() # 隐藏所有 page.overlay.extend( [import_chatlog_dialog, save_file_dialog, get_directory_dialog]) ''' 添加改变字体按钮 并调用改变字体方法 ''' def change_font_clicked(e: FilePickerResultEvent): selected_file = ( ", ".join ( map ( lambda f : f.path, e.files ) ) if e.files else "Cancelled!" ) replace_font_file(selected_file) ''' 添加设置对话和按钮 ''' def save_settings(e): settings_dlg.open = False write_APIKEY(apikey_field.value) openai.api_key = read_APIKEY() page.update() def cancel_settings(e): settings_dlg.open = False page.update() change_font_dialog = FilePicker(on_result=change_font_clicked) page.overlay.extend([change_font_dialog]) apikey_field = ft.TextField(hint_text='在此输入apikey',) my_wechat = ft.Text('如有任何bug请联系我：B1lli_official',size=15) github_page_btn = ft.ElevatedButton( '打开本项目的GitHub页面', tooltip='如果你给这个项目点了star，你就是忠实用户了，请打开本页面后进入项目群！', on_click=lambda _:page.launch_url('https://github.com/createrX12/BillyGPT') ) change_font_btn = ft.ElevatedButton( '选择新的字体文件', on_click=lambda _:change_font_dialog.pick_files( allowed_extensions=['ttf'], allow_multiple=False, dialog_title='选择字体文件导入', ) ) settings_dlg = ft.AlertDialog( title=ft.Text("Settings"), content=ft.Column( [apikey_field,my_wechat,github_page_btn,change_font_btn], height=200, ), actions=[ ft.TextButton("保存", on_click=save_settings), ft.TextButton("取消", on_click=cancel_settings) ], actions_alignment=ft.MainAxisAlignment.END, shape=ft.RoundedRectangleBorder(radius=10) ) def open_dlg_modal(e): page.dialog = settings_dlg settings_dlg.open = True page.update() settings_btn = ft.IconButton( icon=ft.icons.SETTINGS_OUTLINED, icon_color="#9ecaff", bgcolor='#202429', icon_size=20, tooltip="Settings", on_click=open_dlg_modal, ) ''' 添加启动应用程序获取apikey窗口 ''' def save_settings_open(e): write_APIKEY(apikey_field_open.value) openai.api_key = read_APIKEY() open_setting_apikey_dlg.open = False page.update() get_apikey_btn = ft.ElevatedButton( '从openAI获取apikey', on_click=lambda _:page.launch_url('https://platform.openai.com/account/api-keys') ) write_APIKEY() openai.api_key = read_APIKEY() if not openai.api_key: apikey_field_open = ft.TextField(label="输入你的apikey") open_setting_apikey_dlg = ft.AlertDialog( open=True, modal=True, title=ft.Text("欢迎使用BillyGPT"), content=ft.Column( [apikey_field_open,get_apikey_btn], tight=True), actions=[ ft.ElevatedButton( text="开始使用", on_click=save_settings_open)], actions_alignment="end", ) page.dialog = open_setting_apikey_dlg openai.api_key = read_APIKEY() ''' 添加聊天行 ''' def add_msg(e): chatPO_btn.disabled = True chat_area.controls.append(chat_row('user', chat_text.value)) chat_text.value = "" page.update() chat_area.controls.append( chat_row( 'assistant', chatGPT())) page.update() def add_msg_composition(e): chatPO_btn.disabled = True chat_area.controls.append(chat_row('user', chat_text.value)) chat_area.controls.append( chat_row( 'assistant', chatGPT_PO( chat_text.value))) page.update() ''' 添加ctrl+enter发送消息方法 ''' def on_keyboard(e: ft.KeyboardEvent) : if e.ctrl: if e.key == 'Enter': add_msg(e) page.on_keyboard_event = on_keyboard ''' 添加模板选择功能 可以读取模板文件，实时搜索，选择时可以切换身份 ''' class DropdownSearchBar(ft.UserControl): def __init__(self): super(DropdownSearchBar, self).__init__() self.item_number = ft.Text(size=9,italic=True) def dropdown_search(self): _object_ = ft.Container( width=450, height=50, bgcolor="white10", border_radius=6, padding=ft.padding.only(top=15), clip_behavior=ft.ClipBehavior.HARD_EDGE, animate=ft.animation.Animation(400,"decelerate"), content=ft.Column( horizontal_alignment=ft.CrossAxisAlignment.CENTER, alignment=ft.MainAxisAlignment.START, controls=[ Row( spacing=10, vertical_alignment=ft.CrossAxisAlignment.CENTER, controls=[ ft.Icon( ft.icons.SEARCH_ROUNDED, size=15, opacity=0.90, ), ft.TextField( border_color='transparent', height=20, text_size=12, content_padding=2, cursor_color='black', cursor_width=1, hint_text='搜索模板', on_change=None ), self.item_number, ], ), ft.Column( scroll='auto', expand=True ) ] ) ) return _object_ def build(self): return ft.Container() page.add(DropdownSearchBar()) # def template_change(self, e): # template_name = template_dropbox.value # renew_now_chat( # chat_json_path=chat_json_path, # hash_val=self.hash, # role=self.role) # # # template_dropbox = ft.Dropdown( # value=role, # width=150, # options=[ # ft.dropdown.Option("system"), # ft.dropdown.Option("user"), # ft.dropdown.Option("assistant"), # ], # on_change=template_change # ) ''' 设置布局 添加控件 ''' # 模板选择下拉框 page.add( Row( [ ElevatedButton ( "清空聊天日志", icon=icons.CLEANING_SERVICES, on_click=lambda _ : clear_page(), ), ElevatedButton( "导入聊天日志", icon=icons.FILE_DOWNLOAD_OUTLINED, on_click=lambda _: import_chatlog_dialog.pick_files( allowed_extensions=['json','txt'], allow_multiple=False, dialog_title='选择聊天记录文件导入', initial_directory='./chatlog' ), ), ElevatedButton( "导出聊天日志", icon=icons.FILE_UPLOAD_OUTLINED, on_click=lambda _: save_file_dialog.save_file( file_name=f"chat_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt", # file_type=ft.FilePickerFileType.CUSTOM ), disabled=page.web, ), settings_btn ], alignment=ft.MainAxisAlignment.END, ), ) page.add(chat_area) chat_text = ft.TextField( hint_text="想和chatGPT说些什么？", filled=True, expand=True, multiline=True) chat_btn = ft.ElevatedButton("对话", on_click=add_msg, tooltip='随便聊聊') chatPO_btn = ft.ElevatedButton( "思维链优化对话", on_click=add_msg_composition, tooltip='认真提问\n仅在对话开始时可用' ) view = ft.Column( controls=[ ft.Row( controls=[ chat_text, chat_btn, chatPO_btn ] ), ], ) page.horizontal_alignment = ft.CrossAxisAlignment.CENTER page.add(view) ''' 添加清空页面方法 ''' def clear_page(): global chat_json_path chat_area.controls.clear () page.update() chat_json_path = create_chat_json() chatPO_btn.disabled = False ''' 聊天方法，向api发送请求 ''' def chatGPT(message=None): try: # global role_template if not message: message = get_combined_data(chat_json_path) # message = get_combined_data ( chat_json_path ) chatGPT_raw_response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=message ) # if role_template:pass chatGPT_response = decode_chr( chatGPT_raw_response.choices[0].message['content']) return chatGPT_response.strip() except openai.error.AuthenticationError as error: chat_area.controls.append( ft.Text(f'出现如下报错\n{str(error)}\n请在设置中更新可用的apikey')) page.update() except openai.error.InvalidRequestError as error: chat_area.controls.append( ft.Text(f'出现如下报错\n{str(error)}\n聊天上下文过长，请对长文本调用概括模块概括后重试')) page.update() # summarize_chatlog(chat_json_path) # chat_area.controls.append( # ft.Text(f'概括完毕，已发送概括后消息')) # page.update() # message = cut_message ( get_combined_data ( chat_json_path ) ) # chatGPT_raw_response = openai.ChatCompletion.create( # model="gpt-3.5-turbo", # messages=message # ) # chatGPT_response = decode_chr( # chatGPT_raw_response.choices[0].message['content']) # return chatGPT_response.strip() except Exception as error: print(openai.api_key) chat_area.controls.append( ft.Text(f'出现如下报错\n{str ( error )}\n请联系开发者微信B1lli_official')) page.update() def chatGPT_PO(initial_prompt=None): ''' PO:Prompt Optimization 该方法是提示优化方法，工作原理如下： 它会尝试判定用户输入的每一条提示，并进行类别判断。 如果判断是问题或者要求，它会分析该类型提示一般需要哪些信息 对于缺少细节信息的提示，它会向用户提问具体细节 :param initial_prompt:初始prompt :return chatGPT_response.strip():基于提示工程思维链优化后的chatGPT方法 ''' try: initial_prompt = chat_text.value chat_text.value = '' page.update() chat_area.controls.append( ft.Text(f'正在分析提示词组成结构，请耐心等待', color='#1cc9a0')) page.update() composition_analysis_message = prompt_composition_analysis( initial_prompt) chat_area.controls.append( ft.Text(f'提示词组成结构分析完毕，正在根据组成结构逐步生成详细结果，耗时较长，请耐心等待', color='#1cc9a0')) page.update() chatGPT_raw_response = composition_stepped_reply( composition_analysis_message) chatGPT_response = decode_chr( chatGPT_raw_response.choices[0].message['content']) return chatGPT_response.strip() except openai.error.AuthenticationError as error: chat_area.controls.append( ft.Text(f'出现如下报错\n{str(error)}\n请在设置中更新可用的apikey')) page.update() except Exception as error: print(openai.api_key) chat_area.controls.append( ft.Text(f'出现如下报错\n{str ( error )}\n请联系开发者微信B1lli_official')) page.update() ''' 版本信息 ''' ver_text = ft.Text('BillyGPT V5.3.1 By B1lli', size=10) page.add(ver_text) if __name__ == '__main__': # 在客户端运行 ft.app(target=ft_interface, assets_dir='assets') # 在内网运行 # ft.app ( port=8550, target=ft_interface, view=ft.WEB_BROWSER ,assets_dir='assets') content = '''接下来你看到的是一个文字冒险游戏的剧情，你是剧情的女主角，名为钰鹤，下面是以对话形式展现出的剧情，你需要做出合理的判断，并说明原因：?？?：马路上的那个人怎么没撑伞 被雨淋湿的青年： ?？?：（他的样子好像有点奇怪··· ?？?：睫··车开过来了那个人却没有发现.....！? ?？?：再这样下去，他会被车撞到！？ ?？?：危险………！ ???：(我为什么在这里 ???：只记得自已跳入车道，之后的事完全没印象开眼睛就发现自己躺在医院病床上了 ???：不知道那个人事故之后怎么样了 穿西装的男子：小姐·你从刚才就在发呆你听到我说的话了吗？ ???：啊…………不好意思 ???：对了，我想起来了。有人说发现我和某个人在一起而那个人身上带看违法药品 ???：(本以为要继续接受盘问结果莫名其妙地就被带到了这座岛上 穿西装的男子：埃我知道，遇到这种事头脑难免会陷入一片混乱 穿西装的男子：正因为情况特殊你更应该振作起来才行你说得出自己的全名吗？ 鹤：嗯，我是钰鹤 穿西装的男子：真是太好了。看来你还算比较冷静至少说得出自已的名字。 穿西装的男子：那么，你应该也能够理解我刚才的说明吧钰小姐。 鹤：嗯……你是 穿西装的男子：真是的· 穿西装的男子：真是的我的名字是『今部』。看来你真的睡糊涂了。 鹤：对了，他叫今部·· 鹤：在被带来这里之前接受警方盘问正当事情始终理不出头绪时是他帮我脱离了困境 鹤：（但我完全不认识他不知道他是个怎么样的人 今部：没办法，只好挑重点再重新说明一次，这次请你务必专心听。 今部：首先第一个重点，正如刚刚所说的尔被怀疑涉及参与了某起事件现在被列为重点参考证人。 今部：由于事发现场，与你在一起的男子有交易非法药物的嫌疑。 今部：和他出现在同一个地点的人，也就是你，自然也被怀疑参与了违法买卖 鹤：你说我有嫌疑可是我真的什么都不知道也不认识那个男的 今部：可惜有自击者指出，曾经好几次看到男一女在事发地点附近的夜店出没进行非法药物交易 鹤：可是，我真的没有 今部：我了解。但现在的难点是没有人能证明你是无辜的。 鹤：那我该怎么做才好现在那个被环疑的男人在哪里？他看到我的脸应该就会知道抓错人了。 今部：你说的没错，请他真接和你见面确认，也许是最快的方法。 今部：只可惜他大概很难为你提供不在场证明 鹤：为什么…………·? 今部：你认得这名男子吧？ 鹤：这个人的脸··我见过… 今部：这名涉嫌交易非法药物的男子可能因为车祸的冲击丧失了部分过去的记忆。 鹤：什么？丧失记忆 今部：是的。我想应该只是暂时性的失忆。但是他完全想不起来那天在夜店里和他在一起的女人是谁。 鹤：(怎么会难道真的没有人可以证明我的清白了？ 今部：我帮你作证吧 今部：你没听清楚吗？我说我要帮你作证，证明你不认识那名男子，也和非法药物的犯罪无关。 鹤：你要帮我作证这样就能证明我的清白了吗？ 今部：只要钰小姐愿意配合协助我。 今部：我的提议绝不会陷你干不利 鹤：(要我协助他可是如果他提出什么难题或是奇怪的条件，我该怎么办 今部：你似平还没完全了解你的处境 今部：事情发展到这地步，你没有选择的余地。请回我「好」或「YES」，二择其一。你会接受我的提议吧？ 今部：我想请你帮的忙，就是在那名失忆男子的面前假装自己是『他的恋人』 鹤：要我扮演他的恋人·！? 今部：其实发生交通事故之前他和某位女性在一起，而那位女性很可能是他的女友。 今部：那名女性最后被目击到的地点就是那间夜店，随后不久她便失踪行踪成谜 今部：为了找出那位失踪的女性必须找回这名男子因车祸失去的记忆。 今部：所以才希望你假扮成他的恋人从他口中打听任何可能的线索找出他女友的所在地， 今部：在你协助的这段期间，我也会在别处进行搜索。只要找到和他出现在夜店的那名女性，就能顺利证明你的清白了。 鹤：可是我要怎么在素未谋面的陌生人面前假扮他的恋人，这未免太强人所难了· 今部：这不是担心做不做得到的时候你非做不可。 今部：麻烦你了。 鹤：为了洗清我的嫌疑也别无选择了 鹤：好我知道了 今部：谢谢你愿意协助据说这名男子周围的人都叫他也『葵』。 鹤：名字是『葵』吗 今部：啊，对了。差点忘了告诉你你从今天开始必须暂时住在这座小岛上。 鹤：什么？ 今部：住宿的相关细节等下会由设施员工为你说明 鹤：等、等一下 看守员：不好意思我过来带钰小姐到她的房间。 今部：那就之后再见了钰小姐。 鹤：我到底会被带去挪里话说回来，在抵达小岛前好像坐了很久的船 鹤：该不会在找到他的女友之前我都回不了家？ 鹤：..·嗯? 鹤：从这里能跳望到海面 鹤：这里真的是一座孤岛 看守员：今天的海面很平静呢 鹤：风景很美。大海和天空一望无际 看守员：毕竟这座设施坐落于岛上视野最佳的位置。 看守员：以前政府在这座岛上盖了监由国家管理 看守员：但战后因财政困难，没有经费维持而关闭后来由民间企业『西海普制药」将整座岛买下，加以利用。 看守员：我个现在所在的这栋建筑设施就是『西海普医疗中心』。 鹤：『西海普制药』? 看守员：那是一家专门研发、制造医疗用品及药品的制药厂。 看守员：这里就是『西海普制药』所管理的设施之一，用于收容病患以进行外地疗养 看守员：··表面上是这样。不过我想你也已经察觉到，实际用途不仅如此。 看守员：这里的收容人都有自己的房间只要在安全管理上不引发问题都能在允许的范围内从事自由活动。 看守员：也个的房间受到24小时全天候的监视一旦房间内发生任何意外，设施的勺人员都能马上应对。 鹤：·这么说的话，我的行动也会受到监视吗？ 看守员：不会。我收到的指示是要带你去员工用宿舍的房间。那里不在监视的范围内，你大可放心。 看守员：一直站在这里说话也不是办法，我价们继续往前走吧 看守员：这里就是钰小姐的房间可供你自由使用 看守员：另外，还要给你这个 鹤：智能手机? 看守员：是的。在岛上的期间无法使用私人手机，请改用这台『SABOT』。 看守员：请收下。可以试着开启一下电源。 看守员：启动之后，手机内的引导功能就会为你介绍各功能的使用方法 看守员：这只手机也能当作你在岛上的身份证请务必随身携带 看守员：注意千万不要弄丢使用上也请多加小心 看守员：此外，在岛上只要出示这支手机证明身份，就能免费购物。不需要另外支付现金。 看守员：今部先生已经事先为钰小姐预付了一笔费用当作在岛上的生活费你不用担心金钱的问题。 看守员：最后说一件事关于明天会面请你明早10点到我们刚才经过的收容所入口处集合。 鹤：好的我明白了 看守员：那么，请好好休息 鹤：·是。 鹤：天呐没想到我被卷入了这么棘手的事件 鹤：今后，到底会怎样? 鹤：(太阳升起来了差不多该前往看守员所说的地点了 今部：早安，钰小姐。 今部：你好像很紧张。 鹤：嗯…是啊。 今部：那也是难免的。昨天离开后，我也重新思考了一遍 今部：你是普通人，不是职业演员：老实说我也不认为你能很自然地扮成他的恋人。 今部：今天就先以他的友人身份见面视状况往后再做调整吧 今部：请你在了解他的为人以后再看准合适的时机：告诉他你是他的女友。 看守员：早安。今部先生，钰小姐。让两位久等了。 今部：阿，已经到会面时间了吗那就拜托你好好表现了，钰小姐。 鹤：太、太强人所难了· 看守员：那我们走吧。 看守员：这间就是会面室。 看守员：进去以后，你会看见里面有块玻璃隔板将房间隔成会面者和收容人用的两间小房间。 看守员：会面过程中不会有职员陪同司，我们会在房间外待命，有任何问题或需要帮忙时，请随时叫我个 看守员：也许和收容人独处会让你感到不安但会面室的玻璃采用不易被打破的强化玻璃亚克力板制成， 看守员：即使搬起椅子用力砸玻璃房间内的隔板也绝对不会碎。这点你大可放心。 鹤：那个 看守员：是，请说。 鹤：请问·我该怎么面对收容人? 看守员：该怎么面对？如果你是他的朋友，就以平常心对待即可。 鹤：(我就是无法拿捏所谓的「平常心」 看守员：请问怎么了？还有什么事让你感到不安吗？ 鹤：啊，没有没事。 看守员：等会面时间结束了，我会敲门提醒你就算事情没说完，也请尽快结束会面离开房间。 鹤：就是这人人吗· 葵：干嘛?一直町着别人的脸看 葵：我听说有人认识的人想找我谈谈 葵： 葵：不过很抱，我对你完全没印象。 葵：你叫什么名字？ 葵：钰鹤 葵：嗯名字也没有印象。 葵：我们真的认识？ 鹤：与其说认识，不如说 葵：啊，还是算了。反正我都不记得了问了也没有意义。 葵：如果我们以前真的见过面那很抱款，我现在都想不起来了。 葵：那么，你找我有什么事？ 葵：特地跑到这种地方，应该不会只是为了闲聊吧？ 葵：应该是有什么事想问我？ 鹤：（就算这么说，我也不知道该问什么好. 鹤：（既然要装作认识的人要是表现得太客气的话反而会很不自然吧· 葵：怎么了？ 鹤：啊，嗯那 葵：。。。 鹤：再这样下去会被怀疑的得随便问点问题才行··· 鹤：（总之，先问问看关于那场意外他还记得什么吧 你面临着2个选择，第一个：我听人说 ，你正涉及某件危险的事。第二个：你还记得意外发生前 ，你跟谁在一起吗 ？''' # a = chatGPT_sum(content) # print(a) ''' 你是一个文字冒险游戏的女主角，名为钰鹤，钰是姓氏，鹤是名字。下面是游戏剧情，你需要根据剧情做出合理的判断，并说明原因： 钰鹤被警方怀疑参与了某起事件，今部帮助她脱离困境，但他们需要找到一个可以证明钰鹤无辜的方法，最终他们决定让钰鹤和被环疑的男子见面，以确认钰鹤的无辜。 鹤被要求去小岛上假扮一名失忆男子的恋人，以帮助他找回记忆，以洗清自己的嫌疑。小岛上有一座监狱，拥有最佳的视野，海面平静，风景美丽。 鹤被卷入一个棘手的事件，被带到一个由民间企业『西海普制药」管理的设施，收容病患以进行外地疗养，但实际用途不仅如此。鹤被给予一台『SABOT』智能手机，可以用来证明身份，免费购物，以及收到一笔费用当作在岛上的生活费。最后，鹤被要求在第二天10点到 鹤面对着葵，不知道该如何面对，看守员提醒他以平常心对待，葵表示自己对鹤完全没有印象， 你要向葵提问，你面临着2个提问选择： 1.我听人说，你正涉及某件危险的事。 2.你还记得意外发生前，你跟谁在一起吗？ 你选择哪个，为什么？'''

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import os import langchain.text_splitter from langchain import PromptTemplate, LLMChain from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.llms import LlamaCpp try: from extensions.telegram_bot.source.generators.abstract_generator import AbstractGenerator except ImportError: from source.generators.abstract_generator import AbstractGenerator # Place where path to LLM file stored llm = None # Callbacks support token-wise streaming class Generator(AbstractGenerator): model_change_allowed = False # if model changing allowed without stopping. preset_change_allowed = True # if preset_file changing allowed. def __init__(self, model_path, n_ctx=2048, seed=0, n_gpu_layers=0): callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) self.llm = LlamaCpp( model_path=model_path, n_ctx=n_ctx, callback_manager=callback_manager, verbose=True, ) def generate_answer( self, prompt, generation_params, eos_token, stopping_strings, default_answer, turn_template="", **kwargs ): if "max_tokens" in generation_params: llm.max_tokens = generation_params["max_tokens"] if "temperature" in generation_params: llm.temperature = generation_params["temperature"] if "top_p" in generation_params: llm.top_p = generation_params["top_p"] if "top_k" in generation_params: llm.top_k = generation_params["top_k"] prompt_template = PromptTemplate(template="{prompt}", input_variables=["prompt"]) llm.stop = stopping_strings llm_chain = LLMChain(prompt=prompt_template, llm=self.llm) answer = llm_chain.run(prompt) return answer def tokens_count(self, text: str): splitter = langchain.text_splitter.TokenTextSplitter() length = len(splitter.split_text(text)) return length def get_model_list( self, ): bins = [] for i in os.listdir("models"): if i.endswith(".bin"): bins.append(i) return bins def load_model(self, model_file: str): pass

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import os import openai from dotenv import load_dotenv from langchain.chat_models import AzureChatOpenAI from langchain.chat_models import ChatOpenAI from langchain.embeddings import OpenAIEmbeddings from langchain.callbacks.base import BaseCallbackHandler from langchain.vectorstores import FAISS from langchain.chains import ConversationalRetrievalChain from langchain.chains.conversational_retrieval.base import BaseConversationalRetrievalChain from langchain.prompts import PromptTemplate from langchain.document_loaders import (UnstructuredPowerPointLoader, UnstructuredWordDocumentLoader, PyPDFLoader, UnstructuredFileLoader, CSVLoader, MWDumpLoader) import langchain.text_splitter as text_splitter from langchain.text_splitter import (RecursiveCharacterTextSplitter, CharacterTextSplitter) from typing import List import streamlit import glob REQUEST_TIMEOUT_DEFAULT = 10 TEMPERATURE_DEFAULT = 0.0 CHAT_MODEL_NAME_DEFAULT = "gpt-3.5-turbo" OPENAI_EMBEDDING_DEPLOYMENT_NAME_DEFAULT = "text-embedding-ada-002" CHUNK_SIZE_DEFAULT = 1000 CHUNK_OVERLAP_DEFAULT = 0 class StreamHandler(BaseCallbackHandler): def __init__(self, container, initial_text=""): self.container = container self.text = initial_text def on_llm_new_token(self, token: str, **kwargs) -> None: self.text += token self.container.markdown(self.text) class DocChatbot: llm: ChatOpenAI condense_question_llm: ChatOpenAI embeddings: OpenAIEmbeddings vector_db: FAISS chatchain: BaseConversationalRetrievalChain # configuration for API calls request_timeout: int temperature: float chat_model_name : str api_key : str def init_llm_openai(self, streaming: bool, condense_question_container = None, answer_container = None) -> None: # init for LLM using openai.com api self.llm = ChatOpenAI( temperature=self.temperature, openai_api_key=self.api_key, request_timeout=self.request_timeout, model=self.chat_model_name, # Model name is needed for openai.com only streaming=streaming, callbacks=[StreamHandler(answer_container)] if streaming else [] ) # type: ignore if streaming: self.condense_question_llm = ChatOpenAI( temperature=self.temperature, openai_api_key=self.api_key, request_timeout=self.request_timeout, streaming=True, model=self.chat_model_name, callbacks=[StreamHandler(condense_question_container, "🤔...")] ) # type: ignore else: self.condense_question_llm = self.llm def init_llm_azure(self, streaming: bool, condense_question_container = None, answer_container = None) -> None: # init for LLM using Azure OpenAI Service assert(os.getenv("OPENAI_GPT_DEPLOYMENT_NAME") is not None) assert(os.getenv("OPENAI_API_BASE") is not None) assert(os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME") is not None) assert(len(self.api_key) == 32) self.llm = AzureChatOpenAI( deployment_name=os.getenv("OPENAI_GPT_DEPLOYMENT_NAME"), temperature=self.temperature, openai_api_version="2023-05-15", openai_api_type="azure", openai_api_base=os.getenv("OPENAI_API_BASE"), openai_api_key=self.api_key, request_timeout=self.request_timeout, streaming=streaming, callbacks=[StreamHandler(answer_container)] if streaming else [] ) # type: ignore if streaming: self.condense_question_llm = AzureChatOpenAI( deployment_name=os.getenv("OPENAI_GPT_DEPLOYMENT_NAME"), temperature=self.temperature, openai_api_version="2023-05-15", openai_api_type="azure", openai_api_base=os.getenv("OPENAI_API_BASE"), openai_api_key=os.getenv("OPENAI_API_KEY"), request_timeout=self.request_timeout, model=self.chat_model_name, streaming=True, callbacks=[StreamHandler(condense_question_container, "🤔...")] ) # type: ignore else: self.condense_question_llm = self.llm def __init__(self) -> None: #init for LLM and Embeddings, without support for streaming #load environment variables load_dotenv() assert(os.getenv("OPENAI_API_KEY") is not None) self.api_key = str(os.getenv("OPENAI_API_KEY")) self.request_timeout = REQUEST_TIMEOUT_DEFAULT if os.getenv("REQUEST_TIMEOUT") is None else int(os.getenv("REQUEST_TIMEOUT")) self.temperature = TEMPERATURE_DEFAULT if os.getenv("TEMPERATURE") is None else float(os.getenv("TEMPERATURE")) self.chat_model_name = CHAT_MODEL_NAME_DEFAULT if os.getenv("CHAT_MODEL_NAME") is None else str(os.getenv("CHAT_MODEL_NAME")) #check if user is using API from openai.com or Azure OpenAI Service by inspecting the api key if self.api_key.startswith("sk-"): # user is using API from openai.com assert(len(self.api_key) == 51) self.init_llm_openai(False) else: # user is using Azure OpenAI Service self.init_llm_azure(False) embedding_deployment = OPENAI_EMBEDDING_DEPLOYMENT_NAME_DEFAULT if os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME") is None else str(os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME")) self.embeddings = OpenAIEmbeddings( deployment=embedding_deployment, chunk_size=1 ) # type: ignore def init_streaming(self, condense_question_container, answer_container) -> None: #init for LLM and Embeddings, with support for streaming if self.api_key.startswith("sk-"): # user is using API from openai.com self.init_llm_openai(True, condense_question_container, answer_container) else: # user is using Azure OpenAI Service self.init_llm_azure(True, condense_question_container, answer_container) # def init_streaming(self, condense_question_container, answer_container) -> None: # #init for LLM and Embeddings, with support for streaming # api_key = str(os.getenv("OPENAI_API_KEY")) # temperature=float(os.getenv("TEMPERATURE")) # request_timeout=int(os.getenv("REQUEST_TIMEOUT")) # model_name=str(os.getenv("CHAT_MODEL_NAME")) # if api_key.startswith("sk-"): # # user is using API from openai.com # self.llm = ChatOpenAI( # temperature=temperature, # openai_api_key=api_key, # request_timeout=request_timeout, # streaming=True, # model=model_name, # callbacks=[StreamHandler(answer_container)] # ) # type: ignore # self.condense_question_llm = ChatOpenAI( # temperature=temperature, # openai_api_key=api_key, # request_timeout=request_timeout, # streaming=True, # model=model_name, # callbacks=[StreamHandler(condense_question_container, "🤔...")] # ) # type: ignore # else: # # user is using Azure OpenAI Service # self.llm = AzureChatOpenAI( # deployment_name=os.getenv("OPENAI_GPT_DEPLOYMENT_NAME"), # temperature=temperature, # openai_api_version="2023-05-15", # openai_api_type="azure", # openai_api_base=os.getenv("OPENAI_API_BASE"), # openai_api_key=os.getenv("OPENAI_API_KEY"), # request_timeout=request_timeout, # model=model_name, # streaming=True, # callbacks=[StreamHandler(answer_container)] # ) # type: ignore # self.condense_question_llm = AzureChatOpenAI( # deployment_name=os.getenv("OPENAI_GPT_DEPLOYMENT_NAME"), # temperature=temperature, # openai_api_version="2023-05-15", # openai_api_type="azure", # openai_api_base=os.getenv("OPENAI_API_BASE"), # openai_api_key=os.getenv("OPENAI_API_KEY"), # request_timeout=request_timeout, # model=model_name, # streaming=True, # callbacks=[StreamHandler(condense_question_container, "🤔...")] # ) # type: ignore def init_chatchain(self, chain_type : str = "stuff") -> None: # init for ConversationalRetrievalChain CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template("""Given the following conversation and a follow up input, rephrase the standalone question. The standanlone question to be generated should be in the same language with the input. For example, if the input is in Chinese, the follow up question or the standalone question below should be in Chinese too. Chat History: {chat_history} Follow Up Input: {question} Standalone Question:""" ) # stuff chain_type seems working better than others self.chatchain = ConversationalRetrievalChain.from_llm(llm=self.llm, retriever=self.vector_db.as_retriever(), condense_question_prompt=CONDENSE_QUESTION_PROMPT, condense_question_llm=self.condense_question_llm, chain_type=chain_type, return_source_documents=True, verbose=False) # combine_docs_chain_kwargs=dict(return_map_steps=False)) # get answer from query, return answer and source documents def get_answer_with_source(self, query, chat_history): result = self.chatchain({ "question": query, "chat_history": chat_history }, return_only_outputs=True) return result['answer'], result['source_documents'] # get answer from query. # This function is for streamlit app and the chat history is in a format aligned with openai api def get_answer(self, query, chat_history): ''' Here's the format for chat history: [{"role": "assistant", "content": "How can I help you?"}, {"role": "user", "content": "What is your name?"}] The input for the Chain is in a format like this: [("How can I help you?", "What is your name?")] That is, it's a list of question and answer pairs. So need to transform the chat history to the format for the Chain ''' chat_history_for_chain = [] for i in range(0, len(chat_history), 2): chat_history_for_chain.append(( chat_history[i]['content'], chat_history[i+1]['content'] if chat_history[i+1] is not None else "" )) result = self.chatchain({ "question": query, "chat_history": chat_history_for_chain }, return_only_outputs=True) return result['answer'], result['source_documents'] # load vector db from local def load_vector_db_from_local(self, path: str, index_name: str): self.vector_db = FAISS.load_local(path, self.embeddings, index_name) print(f"Loaded vector db from local: {path}/{index_name}") # save vector db to local def save_vector_db_to_local(self, path: str, index_name: str): FAISS.save_local(self.vector_db, path, index_name) print("Vector db saved to local") # split documents, generate embeddings and ingest to vector db def init_vector_db_from_documents(self, file_list: List[str]): chunk_size = CHUNK_SIZE_DEFAULT if os.getenv("CHUNK_SIZE") is None else int(os.getenv("CHUNK_SIZE")) chunk_overlap = CHUNK_OVERLAP_DEFAULT if os.getenv("CHUNK_OVERLAP") is None else int(os.getenv("CHUNK_OVERLAP")) text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) docs = [] for file in file_list: print(f"Loading file: {file}") ext_name = os.path.splitext(file)[-1] # print(ext_name) if ext_name == ".pptx": loader = UnstructuredPowerPointLoader(file) elif ext_name == ".docx": loader = UnstructuredWordDocumentLoader(file) elif ext_name == ".pdf": loader = PyPDFLoader(file) elif ext_name == ".csv": loader = CSVLoader(file_path=file) elif ext_name == ".xml": loader = MWDumpLoader(file_path=file, encoding="utf8") else: # process .txt, .html loader = UnstructuredFileLoader(file) doc = loader.load_and_split(text_splitter) docs.extend(doc) print("Processed document: " + file) print("Generating embeddings and ingesting to vector db.") self.vector_db = FAISS.from_documents(docs, self.embeddings) print("Vector db initialized.") # Get indexes available def get_available_indexes(self, path: str): return [os.path.splitext(os.path.basename(file))[0] for file in glob.glob(f"{path}/*.faiss")]

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import os import re import langchain import paperqa import paperscraper from langchain.base_language import BaseLanguageModel from langchain.tools import BaseTool from pypdf.errors import PdfReadError def paper_scraper(search: str, pdir: str = "query") -> dict: try: return paperscraper.search_papers(search, pdir=pdir) except KeyError: return {} def paper_search(llm, query): prompt = langchain.prompts.PromptTemplate( input_variables=["question"], template=""" I would like to find scholarly papers to answer this question: {question}. Your response must be at most 10 words long. 'A search query that would bring up papers that can answer this question would be: '""", ) query_chain = langchain.chains.llm.LLMChain(llm=llm, prompt=prompt) if not os.path.isdir("./query"): # todo: move to ckpt os.mkdir("query/") search = query_chain.run(query) print("\nSearch:", search) papers = paper_scraper(search, pdir=f"query/{re.sub(' ', '', search)}") return papers def scholar2result_llm(llm, query, k=5, max_sources=2): """Useful to answer questions that require technical knowledge. Ask a specific question.""" papers = paper_search(llm, query) if len(papers) == 0: return "Not enough papers found" docs = paperqa.Docs(llm=llm) not_loaded = 0 for path, data in papers.items(): try: docs.add(path, data["citation"]) except (ValueError, FileNotFoundError, PdfReadError): not_loaded += 1 print(f"\nFound {len(papers.items())} papers but couldn't load {not_loaded}") answer = docs.query(query, k=k, max_sources=max_sources).formatted_answer return answer class Scholar2ResultLLM(BaseTool): name = "LiteratureSearch" description = ( "Useful to answer questions that require technical " "knowledge. Ask a specific question." ) llm: BaseLanguageModel = None def __init__(self, llm): super().__init__() self.llm = llm def _run(self, query) -> str: return scholar2result_llm(self.llm, query) async def _arun(self, query) -> str: """Use the tool asynchronously.""" raise NotImplementedError("this tool does not support async")

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""" Class for Langchain chain, this chain makes a request to OpenAI to provide information in a given location and time period. """ import os import logging from pathlib import Path import langchain PROMPT_STRING = """ You just gave historical information for {location} around the time period of {time_period} and \n this is the answer you gave {previous_response}. \n Provide bullet points in relation to what you said previously with more information around this topic: {selected_text}. \n Provide no filler or conversation response, just detailed bullet points.\n If you are unsure of the history for this time period, explain that the for this area and time period is limited. \n\n """ class FollowUpChain(): def __init__(self, openai_api_key, model): super().__init__() self.openai_api_key = openai_api_key self.llm_chains = [] # Buffer to store LLMChain objects self.model = model # Add LLMChain objects to the buffer self.add_chain(self.get_history_chain()) def add_chain(self, llm_chain): self.llm_chains.append(llm_chain) def get_template(self, input): # Create the first PromptTemplate prompt = langchain.PromptTemplate( input_variables=input, template=PROMPT_STRING ) return prompt def get_history_chain(self): prompt_1 = self.get_template(input=["location", "time_period", "previous_response", "selected_text"]) return langchain.LLMChain(llm=langchain.OpenAI( openai_api_key=self.openai_api_key, model_name=self.model), prompt=prompt_1) def run(self, input_text): llm_1 = self.llm_chains[0].run(input_text) # Return the results return { "response": llm_1, }

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from typing import TYPE_CHECKING if TYPE_CHECKING: from langchain.chains import LLMChain from langchain.prompts.few_shot import FewShotPromptTemplate def get_prompt(is_zh: bool = False, sydney: bool = False) -> 'FewShotPromptTemplate': from langchain.prompts.few_shot import FewShotPromptTemplate from langchain.prompts.prompt import PromptTemplate langwhat_example_prompt = PromptTemplate( input_variables=["question", "answer"], template="Q: {question}\n{answer}" ) langwhat_examples = [ { "question": "vJzDRsEKDa0", "answer": """ Might be: A YouTube video ID Description: The ID "vJzDRsEKDa0" is most likely a unique identifier for a specific video on YouTube. This alphanumeric code is automatically assigned to every video uploaded on the platform and is used to access the video directly or share it with other users. """, }, { "question": "https://langchain.readthedocs.io/", "answer": """ Might be: A website or online documentation Description: This website/document provides information about LangChain, a technology or platform that could be used for language processing or analysis. """, }, ] langwhat_examples_zh = [ { "question": "vJzDRsEKDa0", "answer": """ 可能是: YouTube 视频 ID 描述: “vJzDRsEKDa0”这个ID很可能是YouTube上特定视频的唯一标识符。 每上传一个视频，该平台都会自动分配这个字母数字代码，并用于直接访问或与其他用户共享该视频。 """, }, { "question": "https://langchain.readthedocs.io/", "answer": """ 可能是: 一个网站或在线文档 描述: 这个网站/文件提供有关LangChain的信息，它是一种可用于语言处理或分析的技术或平台。 """, }, ] prefix_sydney = ''' You are a helpful assistant that answer questions in the format below, you must answer the question only, do not ask what I want to know more about: """ '''.lstrip() suffix_sydney = '''"""\n\nBEGIN!\n\nQ: {input}''' suffix_openai = '''\nQ: {input}''' langwhat_prompt = FewShotPromptTemplate( example_prompt=langwhat_example_prompt, examples=langwhat_examples_zh if is_zh else langwhat_examples, suffix=suffix_sydney if sydney else suffix_openai, prefix=prefix_sydney if sydney else '', input_variables=["input"], ) return langwhat_prompt def get_llm_chain( is_zh: bool = False, sydney: bool = False, cookie_path: str | None = None ) -> 'LLMChain': from langchain.llms import OpenAIChat from langchain.chains import LLMChain from .llm import EdgeLLM if sydney: if cookie_path is None: raise ValueError("cookie_path is required for sydney.") llm = EdgeLLM(bing_cookie_path=cookie_path) else: llm = OpenAIChat() # type: ignore langwhat_prompt = get_prompt(is_zh=is_zh, sydney=sydney) chain = LLMChain(llm=llm, prompt=langwhat_prompt) return chain def parse_standard_answer_format(answer: str) -> tuple[str, str]: ans_lines = answer.strip().splitlines() might_be = ans_lines[1] description = '\n'.join(ans_lines[3:]) description = description.replace("\n", ",\r\n") # description = description.replace(",", ",\r\n") # description = description.replace("，", "，\r\n") # description = description.replace("。", "。\r\n") # description = description.replace(".", ".\r\n") return might_be, description def parse_chain_response(chain_response: dict[str, str]) -> tuple[str, str, str]: """Parse the response from the chain. Args: chain_response (dict[str, str]): The response from the chain. Returns: tuple[str, str]: The first element is the might be, the second element is the description. """ answer = chain_response['text'] return split_edgegpt_answer(answer) def split_edgegpt_answer(answer: str) -> tuple[str, str, str]: answer = answer.strip() if answer.startswith('['): references, answer_body = answer.split('\n\n', maxsplit=1) references = references.strip() answer_body = answer_body.strip() else: references = '' answer_body = answer return references, *parse_standard_answer_format(answer_body) def use_langchain_sqlite_cache(): import langchain from langchain.cache import SQLiteCache from .config import LANGCHAIN_SQLITE_CACHE_FILE langchain.llm_cache = SQLiteCache(database_path=str(LANGCHAIN_SQLITE_CACHE_FILE))

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from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores.faiss import FAISS from langchain.embeddings import OpenAIEmbeddings from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain from langchain.chat_models import ChatOpenAI from PyPDF2 import PdfReader import langchain import requests import io import docx import json langchain.verbose = False def get_text_from_files(file_list): text = "" for file in file_list: ext_name = file.split(".")[-1].lower() response = requests.get(file) if ext_name == "pdf": ##load pdf from url pdf_reader = PdfReader(io.BytesIO(response.content)) # pdf_reader = PdfReader(file) for page in pdf_reader.pages: text += page.extract_text() elif ext_name == "txt": #load txt from url text = response.content.decode("utf-8") elif ext_name == "docx": #load docx from url docx_file = io.BytesIO(response.content) doc = docx.Document(docx_file) for para in doc.paragraphs: text += para.text return text def get_text_chunks(text): text_splitter = CharacterTextSplitter( separator="\n", chunk_size=800, chunk_overlap=200, length_function=len, ) chunks = text_splitter.split_text(text) return chunks ##get keys from .env file import os # from dotenv import load_dotenv # load_dotenv() openai_api_key = os.getenv("OPENAI_API_KEY") def get_vectorstore(text_chunks): embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) vector_store = FAISS.from_texts(texts=text_chunks, embedding=embeddings) return vector_store def get_conversation_chain(vectorstore): llm = ChatOpenAI(openai_api_key=openai_api_key, temperature=0.25) memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) conversation_chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=vectorstore.as_retriever(), memory=memory, ) return conversation_chain def process(text_info,files_or_urls,questions): conversation=None if text_info: raw_text = str(text_info) else: raw_text = "" ## allow if only text is input if files_or_urls: raw_text = raw_text+ "\n" + str(get_text_from_files(files_or_urls)) text_chunks = get_text_chunks(raw_text) vectorstore = get_vectorstore(text_chunks) conversation = get_conversation_chain(vectorstore) for user_question in questions: response = conversation({'question': user_question}) chat_history = response['chat_history'] data={} for i, message in enumerate(chat_history): if i % 2 == 0: key=message.content else: data[key]=message.content return data def lambda_handler(event, context): # TODO implement result=process(text_info=event['text_info'],files_or_urls=event['files_or_urls'],questions=event['questions']) return { 'statusCode': 200, 'body': json.dumps(result) }

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import zipfile from langchain.chat_models import ChatOpenAI from langchain.schema import ( HumanMessage, SystemMessage ) import langchain from langchain.cache import SQLiteCache langchain.llm_cache = SQLiteCache( database_path=".langchain.db" ) # caches queries that are the same. def generate_code(question, model_type, api_key): """ Generate Python code script. The script should only include code, no comments. What is returned is code that needs some light modification. """ task = "Generate Python Code Script. The script should only include code, no comments." chat_model = ChatOpenAI(temperature=0, model=model_type, openai_api_key = api_key) response = chat_model( messages=[ SystemMessage(content=task), HumanMessage(content=question) ], ) code = response.content return format_code(code) def format_code(code): """ Format the generated code to remove read_csv if its there. """ csv_line_index = code.find("read_csv") if csv_line_index > 0: before_csv_line_index = code[0:csv_line_index].rfind("\n") before_csv = ( "" if before_csv_line_index == -1 else code[0:before_csv_line_index] ) after_csv = code[csv_line_index:] after_csv_line_index = after_csv.find("\n") after_csv = ( "" if after_csv_line_index == -1 else after_csv[after_csv_line_index:] ) return before_csv + after_csv return code def format_primer(primer_desc, primer_code, question): """ Format the primer description and code. """ return f'"""\n{primer_desc}{question}\n"""\n{primer_code}' def generate_primer(df, df_name): """ Generate a primer for visualization. Include all the necessary imports. """ primer_desc = """ If a graph is needed, label the x and y axes appropriately. Add a title. Use st.plotly or plotly.express to visualize." Using Python version 3.9.12, only create a script using the dataframe "df" if the user asked for a graph. Do not give a y/n answer in the code or try to take any user input. Graph should be created using st.plotly or plotly.express. If there is only a single row, manipulate it to be able to chart the data. IF the given sql query is only one column, manipulate it to get it in the right format for a chart if it has multiple rows. If its just one column one row, don't chart, just display the table. Make sure that variable names are the same as the sql. """ primer_code = ( "import pandas as pd\nimport streamlit as st\nimport plotly.express as px\n" ) primer_code += f"df = {df_name}.copy()\n" return primer_desc, primer_code def unzip(file): with zipfile.ZipFile(file, 'r') as zip_ref: zip_ref.extractall()

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import langchain from langchain.chat_models import ChatOpenAI from langchain_core.tools import Tool langchain.verbose = True langchain.debug = True def get_chat(): return ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) def my_super_func(params): return 42 if __name__ == "__main__": tools = [ Tool.from_function( func=my_super_func, name="The_Answer", description="生命、宇宙、そして万物について究極の疑問の答え" ), ]

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import os import tkinter as tk from tkinter import Label, Entry, Button, Text, Scrollbar import langchain from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chat_models import ChatOpenAI class ProjectEvaluatorApp: def __init__(self, root): self.root = root self.root.title('Project Evaluator') self.experience_label = Label(root, text='Rate your coding ability on a scale of 1 to 5:') self.experience_input = Entry(root) self.frameworks_label = Label(root, text='Which coding languages have you used?') self.frameworks_input = Entry(root) self.learn_label = Label(root, text='What would you like to learn?') self.learn_input = Entry(root) self.evaluate_button = Button(root, text='Evaluate Project', command=self.evaluate_project) self.result_text = Text(root, wrap='word', height=10, width=50) self.result_scrollbar = Scrollbar(root, command=self.result_text.yview) self.result_text['yscrollcommand'] = self.result_scrollbar.set self.experience_label.pack() self.experience_input.pack() self.frameworks_label.pack() self.frameworks_input.pack() self.learn_label.pack() self.learn_input.pack() self.evaluate_button.pack() self.result_text.pack(side='left', padx=5) self.result_scrollbar.pack(side='right', fill='y') # Corrected argument def evaluate_project(self): experience_level = self.experience_input.get() frameworks_used = self.frameworks_input.get() what_to_learn = self.learn_input.get() CREATE_CONTEXT_PROMPT = f"""Summarize this student's coding experience level and learning goals using the following information: The student's experience level on a scale of 0 to 5: {experience_level} The coding languages the student has used: {frameworks_used} The thing the student wants to learn: {what_to_learn}""" llm = ChatOpenAI( openai_api_key=os.environ["OPENAI_API_KEY"], model_name='gpt-4', temperature=0.0 ) context = llm.invoke(CREATE_CONTEXT_PROMPT) PROMPT = f"""You are evaluating potential project ideas. Each idea has a hierarchy based on difficulty and applicability in the industry, rated on a scale from 1 to 5. Your goal is to organize these ideas in ascending order of difficulty and descending order of applicability: {context} Given their coding experience, what simple example project could the student build to learn the topic they want to learn? """ project = llm.invoke(str(PROMPT)) self.result_text.config(state='normal') # Enable editing self.result_text.delete('1.0', 'end') # Clear preavious content self.result_text.insert('1.0', f"{project}\n") # Insert new content self.result_text.config(state='disabled') # Disable editing if __name__ == '__main__': root = tk.Tk() app = ProjectEvaluatorApp(root) root.mainloop()

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from __future__ import annotations from collections import OrderedDict from typing import Any, Dict, List, Optional, Tuple import langchain import numpy as np import orjson import pandas as pd from langchain.cache import InMemoryCache from peewee import ModelSelect, fn from .constants import * from .orm import Knowledge, Solution, Space, Task, database_proxy from .utils import format_config, get_llm SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS langchain.llm_cache = InMemoryCache() def ingest_experience( history_df: pd.DataFrame, task_desc: Optional[Dict[str, str]], space_desc: str, space_id: str, ) -> Space: """Ingest experience from history dataframe. Parameters ---------- history_df: pandas.DataFrame The history of configurations. task_desc The task descriptions. space_desc The space description. space_id The space id. Returns ------- None Notes ----- The history_df should be a dataframe with the following columns: - CONFIG_0 - CONFIG_1 - ... - CONFIG_N - METRIC The task_desc should be a dict with the following format: { "task_id_0": "task_description_0", "task_id_1": "task_description_1", ... "task_id_N": "task_description_N" } """ history_df = history_df.drop_duplicates() history_df["TASK_ID"] = history_df["TASK_ID"].astype(str) if task_desc is None: task_desc = {k: "." for k in history_df["TASK_ID"].unique()} experience_df = history_df[ history_df[["TASK_ID", "SCORE"]] .groupby("TASK_ID") .rank(method="first", ascending=False)["SCORE"] <= TOP_K ] quantile_info = get_quantile_stat(experience_df) space = _ingest_space(space_id, space_desc, quantile_info) _ingest_task(history_df, task_desc) _ingest_solution(history_df, space) print("Ingested experience into database.") # save db database_proxy.commit() return space def _ingest_space( space_id: str, space_desc: str, quantile_info: Dict[str, List[float]] ) -> Space: with database_proxy.atomic(): try: space = Space.get(Space.space_id == space_id) print(f"Space {space_id} already exists, skip ingestion.") return space except: space = Space.create( space_id=space_id, desc=space_desc, quantile_info=orjson.dumps(quantile_info, option=SAVE_OPTIONS), ) print("Ingested space into database.") # save db database_proxy.commit() return space def _ingest_task(history_df: pd.DataFrame, row_task_desc: Dict[str, str]) -> None: embeddings = get_llm("embedding")() with database_proxy.atomic(): for task_id in history_df["TASK_ID"].unique(): try: Task.get(Task.task_id == task_id) print(f"Task {task_id} already exists, skip ingestion.") continue except Task.DoesNotExist: task_desc = canonicalize_task(row_task_desc[task_id]) embedding = np.asarray( embeddings.embed_query(task_desc), dtype=np.float32 ).tobytes() Task.create( task_id=task_id, embedding=embedding, desc=task_desc, row_desc=row_task_desc[task_id], ) print("Ingested task into database.") # save db database_proxy.commit() def _ingest_solution(history_df: pd.DataFrame, space: Space) -> None: with database_proxy.atomic(): solutions = [] for _, row in ( history_df.groupby("TASK_ID") .apply(lambda x: x.sort_values("SCORE", ascending=False)) .reset_index(drop=True) .iterrows() ): row_config_dict = { k[7:]: v for k, v in row.to_dict().items() if k.startswith("CONFIG_") } row_config = orjson.dumps((row_config_dict), option=SAVE_OPTIONS) try: Solution.get( Solution.task == row["TASK_ID"], Solution.space == space.space_id, Solution.row_config == row_config, ) print( f"Solution '{row_config}' for space {space.space_id} and task {row['TASK_ID']} already exists, skip ingestion." ) continue except Solution.DoesNotExist: solutions.append( { "task": row["TASK_ID"], "space": space, "metric": row["SCORE"], "row_config": row_config, "extra_metric": row.get("EXTRA_METRIC", ""), "demo": canonicalize_config( row_config_dict, orjson.loads(space.quantile_info) ), } ) Solution.insert_many(solutions).execute() print("Ingested solution into database.") # save db database_proxy.commit() def canonicalize_task(row_task_desc: str) -> str: """ Canonicalize task to a string. Parameters ---------- row_task_desc: str The task description. Returns ------- str The canonicalized task description. """ task_desc = f"Task: {row_task_desc}" return task_desc def canonicalize_config( config_term: Dict[str, Any], quantile_info: Dict[str, List[float]] ) -> str: """ Canonicalize configuration to a string. Parameters ---------- config_term The configuration term. quantile_info The meta train info for stat. Record quantile information for each hyperparameter. Returns ------- str The canonicalized configuration. """ demo = format_config(config_term, quantile_info, bin_map=bin_map) return demo def get_quantile_stat(experience_df: pd.DataFrame) -> Dict[str, List[float]]: """ Get quantile stat from experience dataframe. Parameters ---------- experience_df: pandas.DataFrame The experience dataframe. Returns ------- Dict[str, List[float]] The quantile stat. """ # map column name 'CONFIG_{}' to '{}', rest of columns are deleted meta_train_df_for_stat = experience_df.rename( columns=lambda x: x[7:] if x.startswith("CONFIG_") else None ) del meta_train_df_for_stat[None] try: quantile_info = { col: np.quantile( meta_train_df_for_stat[col][~meta_train_df_for_stat[col].isna()], list(bin_map.keys()), method="nearest", ) .astype(np.float32) .tolist() for col in meta_train_df_for_stat.select_dtypes(include=[np.number]) } except: quantile_info = { col: np.quantile( meta_train_df_for_stat[col][~meta_train_df_for_stat[col].isna()], list(bin_map.keys()), interpolation="nearest", ) .astype(np.float32) .tolist() for col in meta_train_df_for_stat.select_dtypes(include=[np.number]) } return quantile_info def gen_experience_per_task(space: Space, task: Task) -> str: """ Generate experience content from space and task. Parameters ---------- task_desc: str The task description. demos: str The demos. example: Configurations 1: ... Configurations 2: ... Configurations 3: ... Returns ------- str The experience content. """ demos = _gen_experience_demos(space, task) content = f"{task.desc}\n{demos}" return content def _gen_experience_demos(space: Space, task: Task) -> str: """ Generate experience demonstrations Parameters ---------- space: Space The space. task: Task The task. Returns ------- str The experience demonstrations per task. """ solutions = ( Solution.select() .where(Solution.task_id == task.task_id, Solution.space_id == space.space_id) .order_by(Solution.metric.desc()) .limit(TOP_K) ) demos = "\n".join( [ f"Configuration {i+1}: {solution.demo}" for i, solution in enumerate(solutions) ] ) return demos def _get_best_relevant_solutions(space: Space, task_desc: str) -> ModelSelect: """ Get the best relevant solution for a task. The relevance is measured by cosine similarity between task description embeddings, which affects the order of results. Parameters ---------- space: Space The space. task_desc: str The task description. Returns ------- ModelSelect The best relevant solution. """ SolutionAlias = Solution.alias() order_key = Task.embedding.cosine_distance(task_desc) subquery = ( SolutionAlias.select( SolutionAlias.demo, Task.task_id, Task.desc, Task.embedding, fn.ROW_NUMBER() .over( partition_by=[SolutionAlias.space, SolutionAlias.task], order_by=[SolutionAlias.metric.desc()], ) .alias("rnk"), ) .where(SolutionAlias.space == space) .join(Task, on=(SolutionAlias.task == Task.task_id)) .order_by(order_key) .alias("subq") ) query = ( Solution.select(subquery.c.task_id, subquery.c.demo, subquery.c.desc) .from_(subquery) .where(subquery.c.rnk <= TOP_K) ) return query def _get_best_solutions(space: Space) -> ModelSelect: """ Get the best solution for each task. Parameters ---------- space: Space The space. Returns ------- ModelSelect The best solution for each task. """ SolutionAlias = Solution.alias() subquery = ( SolutionAlias.select( SolutionAlias.demo, Task.task_id, Task.desc, Task.embedding, fn.ROW_NUMBER() .over( partition_by=[SolutionAlias.space, SolutionAlias.task], order_by=[SolutionAlias.metric.desc()], ) .alias("rnk"), ) .where(SolutionAlias.space == space) .join(Task, on=(SolutionAlias.task == Task.task_id)) .alias("subq") ) query = ( Solution.select(subquery.c.task_id, subquery.c.demo, subquery.c.desc) .from_(subquery) .where(subquery.c.rnk <= TOP_K) ) return query def gen_experience( space: Space, task_desc: Optional[str] = None ) -> Tuple[List[str], List[str]]: """ Generate experience content from space and optional task description. Parameters ---------- space: Space The space. task_desc The task description. Returns ------- List[str] The experience content. """ if task_desc is None: query = _get_best_solutions(space) else: query = _get_best_relevant_solutions(space, task_desc) examples = OrderedDict() for solution in query: if solution.task_id not in examples: examples[solution.task_id] = [solution.desc] examples[solution.task_id].append( f"Configuration {len(examples[solution.task_id])}: {solution.demo}" ) return list(examples.keys()), ["\n".join(e) for e in examples.values()]

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import os try: from genai.credentials import Credentials from genai.schemas import GenerateParams from genai.extensions.langchain import LangChainInterface from langchain import PromptTemplate from langchain.chains import LLMChain, SimpleSequentialChain except ImportError: raise ImportError("Could not import langchain: Please install ibm-generative-ai[langchain] extension.") """ pip install "ibm-generative-ai[langchain]" """ # make sure you have a .env file under genai root with api_key = os.getenv("GENAI_KEY", None) api_url = os.getenv("GENAI_API", None) # create creds for watsonx.ai models creds = Credentials(api_key, api_endpoint=api_url) params = GenerateParams(decoding_method="greedy", min_new_tokens=3, max_new_tokens=300) #params = GenerateParams(decoding_method="greedy") llmp = LangChainInterface(model="ibm/granite-13b-sft", params=params, credentials=creds) # This is a simple example of a zero shot prompt print(f"******************************************************") print(f" ") print(f"This is a simple zero shot prompt") print(f" ") print(f"******************************************************") print(f" ") print(f" ") print(llmp("Explain Mortgage backed securities in one sentence")) """ Implement a Prompt PromptTemplate """ print(f" ") print(f" ") print(f"******************************************************") print(f" ") print(f"This is a sample using a Prompt Template") print(f" ") print(f"******************************************************") print(f" ") print(f" ") template = """ You are a mortgage broker with exertise in financial product. Explain the concept of {concept} in a paragraph. """ promptp = PromptTemplate(input_variables=["concept"],template=template,) #print(promptp) conceptp = "closing costs" print(llmp(promptp.format(concept=conceptp))) """ Implement Chains: In this section I am taking the initial prompt response and then asking the model to update the response. """ print(f"******************************************************") print(f" ") print(f" ") print(f"This is an example of chaining 2 prompts together to have the second prompt work in the repsonse of the first prompt. We will be using the first Prompt template from above to feed the second Prompt Template, using a Simple Sequence Chain.") print(f" ") print(f" ") print(f"******************************************************") print(f" ") print(f" ") #print(promptp) chain = LLMChain(llm=llmp, prompt=promptp) #print(chain.run(conceptp)) second_prompt = PromptTemplate( input_variables=["ml_concept"], template="Turn this financial description {ml_concept} and explain in techncial terms") chain_two = LLMChain(llm=llmp, prompt=second_prompt) overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True) explanation = overall_chain.run(conceptp) print(explanation)

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#%% Import Flask and create an app object import config from dotenv import load_dotenv load_dotenv() import os import json import asyncio import openai import pprint as pp import markdown # openai.api_key = os.getenv("OPENAI_API_KEY") # os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY") # Import Flask and create an app object from flask import Flask, render_template, request, jsonify app = Flask(__name__) # Import asyncio and the async_playwright module from Playwright import asyncio from playwright.async_api import async_playwright # Import LangChain and other modules import langchain from langchain.agents import AgentType from langchain.chat_models import ChatOpenAI from langchain.agents import initialize_agent, load_tools from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit from langchain.tools.playwright.utils import ( create_async_playwright_browser, create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter. ) # This import is required only for jupyter notebooks, since they have their own eventloop # import nest_asyncio # nest_asyncio.apply() #%% Agent def get_agent(): # LLM using ChatOpenAI llm = ChatOpenAI(model_name="gpt-4", temperature=0) # Also works well with Anthropic models # Get the tools from the toolkit tools = load_tools(["serpapi"], llm=llm) # Create an agent using ChatOpenAI and initialize it with tools and handle_parsing_errors=True agent_chain = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True, handle_parsing_errors=True ) # agent_chain = get_agent() #%% @app.route("/") def home(): # Use some initial chat history (empty, in this case) chat_history = [] return render_template("chat.html", chat_history=chat_history) @app.route("/respond") def respond(): """ Create another route for your chatbot logic """ print("\nrespond::start:") # Get the user message and the chat history from the interface message = request.args.get("message") print(f" respond::message: {message}") # Parse the chat history as a JSON object chat_history = json.loads(request.args.get("chat_history", "[]")) print(f" respond::chat_history: {chat_history}") # Run your main coroutine that handles the chatbot logic # response = asyncio.run(main(message, chat_history)) chat_history = main(message, chat_history) response = jsonify({"chat_history": chat_history}) print("\nrespond::return:") print(f" respond::chat_history: {chat_history}") # print response to console as json pp.pprint(response.json) # Return the response as a JSON string return response def main(message, chat_history): """ Define your main coroutine that handles the chatbot logic """ print("\nmain::start:") print(f" main::message: {message}") print(f" main::chat_history: {chat_history}") # Run your agent chain with the user message as input and get the bot message as output bot_message = "I'm a STICK!!!\n\n```python\nprint('Hello World')\n```" # Convert bot_message to HTML bot_message_html = markdown.markdown(bot_message) # Append the user message and the bot message to the chat history chat_history[-1][1] = bot_message_html print("\nmain::return:") print(f" main::bot_message: {bot_message}") print(f" main::bot_message_html: {bot_message_html}") print(f" main::chat_history: {chat_history}") # Return the updated chat history as a string return chat_history # def main(message, chat_history): # """ Define your main coroutine that handles the chatbot logic # """ # print("\nmain::start:") # print(f" main::message: {message}") # print(f" main::chat_history: {chat_history}") # # Run your agent chain with the user message as input and get the bot message as output # # bot_message = "I'm a STICK!!!\n\n```python\nprint('Hello World')\n```" # agent_chain.run(input=message) # bot_message = "I'm a STICK!!!<br><br><pre><code>print('Hello World')</code></pre>" # print("\nmain::return:") # print(f" main::bot_message: {bot_message}") # # Append the user message and the bot message to the chat history # chat_history[-1][1] = bot_message # print(f" main::chat_history: {chat_history}") # # Return the updated chat history as a string # return chat_history async def amain(message, chat_history): """ Define your main coroutine that handles the chatbot logic """ # Create one event loop before creating the tools loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) # Use async with to create and close the async browser automatically async with async_playwright() as playwright: # Use await to launch a Chromium browser asynchronously browser = await playwright.chromium.launch() # Create a PlayWrightBrowserToolkit from the browser browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=browser) # Get the tools from the toolkit tools = browser_toolkit.get_tools() # Create an agent using ChatOpenAI and initialize it with tools and handle_parsing_errors=True llm = ChatOpenAI(model_name="gpt-4", temperature=0) # Also works well with Anthropic models agent_chain = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True, handle_parsing_errors=True ) # Run your agent chain with the user message as input and get the bot message as output bot_message = await agent_chain.arun(input=message) # Append the user message and the bot message to the chat history chat_history.append((message, bot_message)) # Return the updated chat history as a string return str(chat_history) # Run your Flask app with app.run() if __name__ == "__main__": app.run( host="localhost", port=None, debug=True, load_dotenv=True, )

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import torch from langchain.llms.base import LLM from langchain.embeddings.huggingface import HuggingFaceEmbeddings from llama_index import LangchainEmbedding from llama_index import SimpleDirectoryReader, LLMPredictor, PromptHelper, GPTSimpleVectorIndex from peft import PeftModel from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig hf_model_path = "models/llama-7b" alpaca_model_path = "models/lora-alpaca" tokenizer = LlamaTokenizer.from_pretrained(hf_model_path) model = LlamaForCausalLM.from_pretrained( hf_model_path, load_in_8bit=True, #Dissabling could solve some errors device_map="auto", ) model = PeftModel.from_pretrained(model, alpaca_model_path) device = torch.device("cuda") if torch.cuda.is_available() else "cpu" max_length = 1500 #2048 max_new_tokens = 48 class LLaMALLM(LLM): def _call(self, prompt, stop=None): prompt += "### Response:" inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_config = GenerationConfig( temperature=0.6, top_p=0.95, repetition_penalty=1.15, ) with torch.no_grad(): generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=128, ) response = "" for s in generation_output.sequences: response += tokenizer.decode(s) response = response[len(prompt):] print("Model Response:", response) return response def _identifying_params(self): return {"name_of_model": "alpaca"} def _llm_type(self): return "custom" max_input_size = max_length num_output = max_new_tokens max_chunk_overlap = 20 prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap) embed_model = LangchainEmbedding(HuggingFaceEmbeddings()) documents = SimpleDirectoryReader('data').load_data() llm_predictor = LLMPredictor(llm=LLaMALLM()) index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, embed_model=embed_model, prompt_helper=prompt_helper) index.save_to_disk('index.json') new_index = GPTSimpleVectorIndex.load_from_disk('index.json', embed_model=embed_model, llm_predictor=llm_predictor, prompt_helper=prompt_helper) response = new_index.query("What did Gatsby do before he met Daisy?") print(response.response) response = new_index.query("What did the narrator do after getting back to Chicago?") print(response.response)

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import langchain.graphs.neo4j_graph as neo4j_graph import os import sys import ast sys.path.append('backendPython') from llms import * from dotenv import load_dotenv, find_dotenv load_dotenv(find_dotenv()) # read local .env file from langchain.chains import LLMChain from langchain.prompts import PromptTemplate, FewShotPromptTemplate from neo4j_dir.entities import get_nodes_chain graph = neo4j_graph.Neo4jGraph( url = os.environ['NEO4J_URI'], username=os.environ['NEO4J_USERNAME'], password=os.environ['NEO4J_PASSWORD'] ) #__________________________________________________________________________________________________ # giving few-shot prompts examples = [ {'Query' : 'name 10 companies which came for cgpa below 7' , 'syntax' : '''MATCH (c:Company)-[Company_to_CGPA]->(cgpa:CGPA) WHERE cgpa.name < 7 RETURN c.name LIMIT 10'''}, {"Query": "The ctc recieved by students for cgpa below 6", "syntax": '''MATCH (cgpa:CGPA)-[CGPA_to_CTC]->(ctc:CTC) WHERE cgpa.name < 6 RETURN ctc '''}, {"Query": "name 10 companies which came for cgpa below 7 and ctc above 20", "syntax" :'''MATCH (company:Company)-[Company_to_CGPA]->(cgpa:CGPA) MATCH (company)-[Company_to_CTC]->(ctc:CTC) WHERE cgpa.name < 7 AND ctc.name > 20 RETURN company LIMIT 10;''' } , { "Query": "minimum cgpa required for ctc above 40" , "syntax" : ''' MATCH (cgpa:CGPA)-[CGPA_to_CTC]->(ctc:CTC) WHERE ctc.name > 20 RETURN MIN(cgpa.name) AS minimum_cgpa; ''' , } , { "Query" :"maximum and minimum cgpa required for ctc above 40" , "syntax" : ''' MATCH (cgpa:CGPA)-[CGPA_to_CTC]->(ctc:CTC)-[CTC_to_Company]->(company:Company) WHERE ctc.name > 40 WITH MIN(cgpa.name) AS minCGPA, MAX(cgpa.name) AS maxCGPA RETURN minCGPA, maxCGPA; ''' } , ] example_formatter_template = """ Query : {Query} Cypher Syntax : {syntax} """ example_prompt = PromptTemplate( input_variables=['Query','syntax'], template=example_formatter_template, ) prefix = ''' You are supposed to convert the following natural query into a cypher query for neo4j database You can refer to below examples for getting idea of how to write cypher query for neo4j database. ''' suffix = ''' create a cypher query for following natural query for neo4j database The query has the following list of nodes : {List_of_nodes} You can create relations between the nodes in following manner : Node1 : Company Node2 : CGPA Relation1 : Company_to_CGPA Relation2 : CGPA_to_Company natural_query : {natural_query} Do not put back-ticks(`) in the output. Use only nodes provided , don't create your new nodes and relations. ''' few_shot_prompt = FewShotPromptTemplate( examples=examples, # prompt template used to format each individual example example_prompt=example_prompt, # prompt template string to put before the examples, assigning roles and rules. prefix=prefix , # prompt template string to put after the examples. suffix=suffix , # input variable to use in the suffix template input_variables=["List_of_nodes" , "natural_query"], example_separator="\n", ) cypher_chain = LLMChain(llm=llm, prompt=few_shot_prompt,verbose=False,) #__________________________________________________________________________________________________ # cypher = cypher_chain.run({"List_of_nodes" :['Company' , 'CGPA'] , 'natural_query':'name 10 companies which came for cgpa below 7'}) # result = graph.query(cypher) # print(result) #__________________________________________________________________________________________________ result_template = ''' You will be provided with the response generated for the given user query. Response : {response} You need to format the respone in html in a conversational way, arrange the response in bulleted points and under major headings if possible. Take care that you do not pollute the data provided in response, by adding your own data. Check that there are no back-ticks(`) in the output. Check that html syntax is correct. ''' result_prompt = PromptTemplate(input_variables=['response'], template=result_template) result_chain = LLMChain(llm=llm, prompt=result_prompt) # print(result_chain.run({'response': result})) # #__________________________________________________________________________________________________ def get_response(query): print('\n\n\n\n', query) li = get_nodes_chain.run(query) print(li) if type(li)==str: li = ast.literal_eval(li) cypher = cypher_chain.run({"List_of_nodes" : li, 'natural_query':query}) print('\n\n\n', cypher,'\n\n\n') result = graph.query(cypher) print('\n\n\n', result) response = result_chain.run({'response': result}) # print('\n\n\n', response) return response # x = get_response('what ctc is offered for cgpa below 7, sort the ctc in descending order') # x = get_response("list companies with ctc above 30") # x= get_response("list companies with ctc above 30 and cgpa below 8") # x = get_response('name 10 companies which offered ctc above 20') # x = get_response('jobProfiles available for cgpa below 7') # print(x)

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# Import langchain modules from langchain.memory import Memory, ConversationBufferMemory from langchain.agents import BaseMultiActionAgent, AgentExecutor # Import other modules and classes from research_agent import ResearchAgent class ConversationMemory(Memory): def __init__(self): # Initialize the parent class with an empty dictionary super().__init__(memory={}) # Initialize the conversation buffer memory attribute self.conversation_buffer_memory = ConversationBufferMemory() def set(self, key: str, value: str) -> None: # Store the value in the memory dictionary self.memory[key] = value # Add the value to the conversation buffer memory self.conversation_buffer_memory.add(value) def get(self, key: str) -> str: # Retrieve the value from the memory dictionary return self.memory.get(key) # Create a ConversationMemory instance conversation_memory = ConversationMemory() # Create an AgentExecutor instance with conversation_memory as a parameter agent_executor = AgentExecutor( agent=ResearchAgent(prompt_template, language_model, stop_sequence, output_parser), memory=conversation_memory, max_turns=10 ) # Run the AgentExecutor agent_executor.run()

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from langchain.llms import HuggingFacePipeline import langchain from ingest import create_vector_db from langchain.cache import InMemoryCache from langchain.schema import prompt from langchain.chains import RetrievalQA from langchain.callbacks import StdOutCallbackHandler from langchain import PromptTemplate from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline DB_FAISS_PATH = 'vectorstoredb/db_faiss' langchain.llm_cache = InMemoryCache() PROMPT_TEMPLATE = ''' Use the following pieces of information to answer the user's question. If you don't know the answer, just say that you don't know, don't try to make up an answer. Context: {context} Question: {question} Do provide only helpful answers Helpful answer: ''' handler = StdOutCallbackHandler() def set_custom_prompt(): input_variables = ['context', 'question'] prompt = PromptTemplate(template=PROMPT_TEMPLATE, input_variables=input_variables) return prompt def load_retriever(): return create_vector_db def load_llm(): model_name = 'TheBloke/Mistral-7B-Instruct-v0.1-GPTQ' model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=False, revision="gptq-8bit-32g-actorder_True") tokenizer = AutoTokenizer.from_pretrained(model_name,use_fast = True) pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.1, top_p=0.95, top_k=40, repetition_penalty=1.1 ) llm = HuggingFacePipeline(pipeline=pipe) return llm def retrieval_qa_chain(llm, prompt, retriever): qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever = retriever, verbose=True, callbacks=[handler], chain_type_kwargs={"prompt": prompt}, return_source_documents=True ) return qa_chain def final_result(query): llm = load_llm() retriever = load_retriever() prompt = set_custom_prompt() qa_result = retrieval_qa_chain(llm, prompt, retriever=retriever) response = qa_result({"query": query}) return response if __name__ == "__main__": query = "What is the description of the accord form?" final_result(query=query)

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""" implement the actions as tools so we can validate inputs """ import langchain from langchain.schema import AgentAction, AgentFinish from langchain.schema.output import LLMResult from langchain.agents import AgentType, initialize_agent from langchain.tools import Tool, StructuredTool from langchain.tools.base import ToolException from langchain.chat_models import ChatOpenAI from langchain.callbacks.base import BaseCallbackHandler from typing import Any, Dict # from pydantic import BaseModel, Field, root_validator # from pydantic import Field, root_validator from pydantic.v1 import BaseModel, Field, root_validator # from pydantic.v1 import BaseModel from typing import Optional from browser_env.actions import ( Action, ActionParsingError, create_id_based_action, create_none_action, create_playwright_action, ) from browser_env import ( Action, ActionTypes, ScriptBrowserEnv, StateInfo, Trajectory, create_stop_action, ) from browser_env.helper_functions import ( RenderHelper, get_action_description, ) from browser_env.env_config import URL_MAPPINGS import my_globals class LLMOutputHandler(BaseCallbackHandler): def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any: """Run when LLM ends running.""" # do this to pass LLM outputs to render_helper. cos the intermediate_steps alr # uses output_parser which throws away raw LLM output my_globals.llm_response = response.generations[-1][0].text def init_tools_args(args, lm_config, tokenizer): langchain.verbose = True SHOULD_RETURN_DIRECT = args.tools_return_true PREFIX = """You are an autonomous intelligent agent tasked with completing an objective via navigating a web browser. This objective will be accomplished through the use of specific actions (tools) you can issue. Here's the information you'll have: The user's objective: This is the task you're trying to complete. The current web page's accessibility tree: This is a simplified representation of the webpage, providing key information. The accessibility tree is of the form `[element_id] Text describing the element` (i.e. the element id is to the left of the description) The current web page's URL: This is the page you're currently navigating. The open tabs: These are the tabs you have open. The previous action: This is the action you just performed. It may be helpful to track your progress. Homepage: If you want to visit other websites, check out the homepage at http://homepage.com. It has a list of websites you can visit. Respond to the human as helpfully and accurately as possible. You have access to the following tools:""" SUFFIX = """ =====Start of example 1 ===== YOUR CURRENT OBJECTIVE: What is the price of HP Inkjet Fax Machine PREVIOUS ACTION: None Observation: URL: http://onestopmarket.com/office-products/office-electronics.html Accessibility tree: [1744] link 'HP CB782A#ABA 640 Inkjet Fax Machine (Renewed)' [1749] StaticText '$279.49' [1757] button 'Add to Cart' [1760] button 'Add to Wish List' [1761] button 'Add to Compare' Thought: Let's think step-by-step. This page list the information of HP Inkjet Fax Machine, which is the product identified in the objective. Its price is $279.49. I think I have achieved the objective. I will issue the stop action with the answer. Action: ``` {{ "action": "stop", "action_input": {{"final_answer" : "$279.49"}} }} ``` =====End of example 1 ===== =====Start of example 2 ===== YOUR CURRENT OBJECTIVE: Show me the restaurants near CMU PREVIOUS ACTION: None Observation: URL: http://openstreetmap.org Accessibility tree: [164] textbox 'Search' focused: True required: False [171] button 'Go' [174] link 'Find directions between two points' [212] heading 'Search Results' [216] button 'Close' Thought: Let's think step-by-step. This page has a search box whose ID is [164]. According to the nominatim rule of openstreetmap, I can search for the restaurants near a location by \"restaurants near\". I can submit my typing by pressing the Enter afterwards. Action: ``` {{ "action": "type_into_field", "action_input": {{"element_id": 164, content: "restaurants near CMU", press_enter_after: 1}} }} ``` =====End of example 2 ===== To be successful, it is very important to follow the following rules: 1. You should only issue an action that is valid given the current observation 2. In your thoughts, you should follow the examples to reason step by step and then issue the next action. 3. Think whether your action makes sense. For example, it is pointless to click on static texts as it does nothing. 4. Issue stop action when you think you have achieved the objective. Begin! Reminder to ALWAYS respond with a valid json blob of a single action.""" # REMOVE scratchpad for context limit. leave variable there for validation but display 0 chars # try memory variables or sth? HUMAN_MESSAGE_TEMPLATE = '{input}\n\n{agent_scratchpad:0.0}' FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input). Valid "action" values: {tool_names} Provide only ONE action per $JSON_BLOB, as shown: ``` {{{{ "action": $TOOL_NAME, "action_input": $INPUT }}}} ``` Follow this format: Thought: consider previous and subsequent steps, reason step by step what the best next action should be Action: ``` $JSON_BLOB ``` Observation: action result / accessibility tree / URL ... (repeat Thought/Action/Observation N times) Thought: The objective can be achieved now (and explain step by step why this is so). I know what to respond. Action: ``` {{{{ "action": "stop", "action_input": {{{{"final_answer" : "Final response to human"}}}} }}}} ```""" a_kwargs = { 'prefix': PREFIX, 'format_instructions': FORMAT_INSTRUCTIONS, 'suffix': SUFFIX, 'human_message_template': HUMAN_MESSAGE_TEMPLATE, } ex1 = """ Example: ### Example start ### Observation: [1744] link 'HP CB782A#ABA 640 Inkjet Fax Machine (Renewed)' [1749] StaticText '$279.49' [1757] button 'Add to Cart' In this example, there is a link with element id 1744 and a button with element id 1757. If you want to click on element id 1757, issue this function with 1757 as the parameter. Note that element 1749 is static text so it is not clickable ### Example end ### """ ex2 = """ Example: ### Example start ### Observation: [1744] link 'HP CB782A#ABA 640 Inkjet Fax Machine (Renewed)' [1749] StaticText '$279.49' [1757] button 'Add to Cart' OBJECTIVE: What is the price of HP Inkjet Fax Machine In this example we see that this page lists the information of HP Inkjet Fax Machine, which is the product identified in the objective. Its price is $279.49. Since you have achieved the objective, you will issue the stop action with the parameter $279.49 ### Example end ### """ def map_url_to_real(url: str) -> str: """Map the urls to their real world counterparts""" for i, j in URL_MAPPINGS.items(): if i in url: url = url.replace(i, j) return url def map_url_to_local(url: str) -> str: """Map the urls to their local counterparts""" for i, j in URL_MAPPINGS.items(): if j in url: url = url.replace(j, i) return url def create_action_from_str(parsed_response): # TODO: allow for playwright actions try: action = create_id_based_action(parsed_response) except ActionParsingError as e: action = create_none_action() action["raw_prediction"] = my_globals.llm_response return action def create_append_action(issued_action_str): action = create_action_from_str(issued_action_str) print(f'action: {action}') my_globals.trajectory.append(action) action_str = get_action_description( action, my_globals.state_info["info"]["observation_metadata"], action_set_tag=args.action_set_tag, prompt_constructor=None, ) print(f'action str: {action_str}') my_globals.render_helper.render( action, my_globals.state_info, my_globals.meta_data, args.render_screenshot ) my_globals.meta_data["action_history"].append(action_str) return action def execute_action(issued_action_str): # TODO: early stop, action error handling action = create_append_action(issued_action_str) # Note: this is meant to break loop if agent issues stop or early stopping # Since our agent's stop already breaks out of loop, we only need to # handle early stopping later # if action["action_type"] == ActionTypes.STOP: # return STOP_SEQUENCE obs, _, terminated, _, info = my_globals.env.step(action) my_globals.state_info = {"observation": obs, "info": info} my_globals.trajectory.append(my_globals.state_info) # Note: the ScriptBrowserEnv always returns False for terminated # if terminated: # my_globals.trajectory.append(create_stop_action("")) # return STOP_SEQUENCE accessibility_tree = obs["text"] max_obs_length = lm_config.gen_config["max_obs_length"] if max_obs_length: accessibility_tree = tokenizer.decode(tokenizer.encode(accessibility_tree)[:max_obs_length]) page = info["page"] url = page.url url = map_url_to_real(url) return f"\n URL: \n {url} \n Accessibility tree: \n {accessibility_tree} \n" def _handle_error(error: ToolException) -> str: return ( my_globals.tool_error_start + str(error.args[0]) + " Please try other valid input(s) or tools." ) def validate_element_id(fn_name: str, element_id: int): if str(element_id) not in my_globals.state_info['info']['observation_metadata']['text']['obs_nodes_info']: raise ToolException(f"Attempted to use tool {fn_name} on invalid element_id {element_id}\n" f"The available element ids are {my_globals.state_info['info']['observation_metadata']['text']['obs_nodes_info'].keys()}" ) # class ElementToolInputSchema(BaseModel): # element_id: int = Field() # @root_validator(skip_on_failure=True) # def validate_query(cls, values: Dict[str, Any]) -> Dict: # element_id = values["element_id"] # if str(element_id) not in my_globals.state_info['info']['observation_metadata']['text']['obs_nodes_info']: # raise ToolException( # f"Element id {element_id} is not available for current observation\n" # f"The available element ids are {my_globals.state_info['info']['observation_metadata']['text']['obs_nodes_info'].keys()}" # ) # return values def click(element_id: int): validate_element_id('click', element_id) return execute_action(f"click [{element_id}]") click_tool = StructuredTool.from_function( name="click", func=click, description="This action clicks on an element specified by the element_id in the input.", return_direct=SHOULD_RETURN_DIRECT, # args_schema=ElementToolInputSchema, handle_tool_error=_handle_error, ) STOP_TOOL_DESC = """Issue this action when you believe the task is complete. If the objective is to find a text-based answer, provide the answer as an input to this tool. If you believe the task is impossible to complete, provide the answer as "N/A" as the input to this tool.""" def stop(final_answer: Optional[str] = ''): action_str = f"stop [{final_answer}]" action = create_append_action(action_str) return action_str stop_tool = StructuredTool.from_function( name="stop", func=stop, description=STOP_TOOL_DESC, return_direct=True, ) # class TypeToolInputSchema(BaseModel): # element_id: int = Field() # content: str = Field() # press_enter_after: int = Field() # # @root_validator(skip_on_failure=True) # def validate_query(cls, values: Dict[str, Any]) -> Dict: # element_id = values["element_id"] # press_enter_after = values["press_enter_after"] # if str(element_id) not in my_globals.state_info['info']['observation_metadata']['text']['obs_nodes_info']: # raise ToolException( # f"Element id {element_id} is not available for current observation\n" # f"The available element ids are {my_globals.state_info['info']['observation_metadata']['text']['obs_nodes_info'].keys()}" # ) # if press_enter_after not in [1, 0]: # raise ToolException( # f"press_enter_after value {press_enter_after} is not valid. It should be 1 or 0." # ) # return values TYPE_TOOL_DESC = """Use this to type the content into the field with element_id. press_enter_after is either 1 or 0. By default, the "Enter" key is pressed after typing unless press_enter_after is set to 0.""" def validate_press_enter(fn_name: str, press_enter_after: int): if press_enter_after not in [1, 0]: raise ToolException( f"Attempted to use tool {fn_name} on invalid press_enter_after value {press_enter_after}. It should be 1 or 0." ) def type_into_field(element_id: int, content: str, press_enter_after: Optional[int] = 1): validate_element_id('type_into_field', element_id) validate_press_enter('type_into_field', press_enter_after) return execute_action(f"type [{element_id}] [{content}] [{press_enter_after}]") type_tool = StructuredTool.from_function( name="type_into_field", func=type_into_field, description=TYPE_TOOL_DESC, return_direct=SHOULD_RETURN_DIRECT, # args_schema=TypeToolInputSchema, handle_tool_error=_handle_error, ) def hover(element_id: int): validate_element_id('hover', element_id) return execute_action(f"hover [{element_id}]") hover_tool = StructuredTool.from_function( name="hover", func=hover, description="Hover over an element with element_id.", return_direct=SHOULD_RETURN_DIRECT, # args_schema=ElementToolInputSchema, handle_tool_error=_handle_error, ) def press(key_comb: str): # TODO: consider validation for this return execute_action(f"press [{key_comb}]") press_tool = StructuredTool.from_function( name="press", func=press, description="Simulates the pressing of a key combination on the keyboard (e.g., Ctrl+v).", return_direct=SHOULD_RETURN_DIRECT, ) # class ScrollToolInputSchema(BaseModel): # direction: str = Field() # # @root_validator(skip_on_failure=True) # def validate_query(cls, values: Dict[str, Any]) -> Dict: # direction = values["direction"] # if direction not in ["up", "down"]: # raise ToolException( # f"direction {direction} is not valid. It should be up or down" # ) # return values def validate_scroll(fn_name: str, direction: str): if direction not in ["up", "down"]: raise ToolException( f"Attempted to use tool {fn_name} on invalid direction {direction}. It should be up or down." ) def scroll(direction: str): validate_scroll('scroll', direction) return execute_action(f"scroll [{direction}]") scroll_tool = StructuredTool.from_function( name="scroll", func=scroll, description="Scroll the page. Specify the direction (up or down) in the input", return_direct=SHOULD_RETURN_DIRECT, # args_schema=ScrollToolInputSchema, handle_tool_error=_handle_error, ) def new_tab(): return execute_action("new_tab") new_tab_tool = StructuredTool.from_function( name="new_tab", func=new_tab, description="Open a new, empty browser tab", return_direct=SHOULD_RETURN_DIRECT, ) # class TabFocusToolInputSchema(BaseModel): # tab_index: int = Field() # # @root_validator(skip_on_failure=True) # def validate_query(cls, values: Dict[str, Any]) -> Dict: # tab_index = values["tab_index"] # tab_title_str, content = my_globals.state_info['observation'].split('\n\n') # tabs = tab_title_str.split(" | ") # # if tab_index not in range(len(tabs)): # raise ToolException( # f"tab_index {tab_index} is not valid" # f"The available tab_index are {list(range(len(tabs)))}" # ) # return values def validate_tab_focus(fn_name: str, tab_index: int): tab_title_str, content = my_globals.state_info['observation'].split('\n\n') tabs = tab_title_str.split(" | ") if tab_index not in range(len(tabs)): raise ToolException( f"Attempted to use tool {fn_name} on invalid tab_index {tab_index}." f"The available tab_index are {list(range(len(tabs)))}" ) def tab_focus(tab_index: int): validate_tab_focus('tab_focus', tab_index) return execute_action(f"tab_focus [{tab_index}]") tab_focus_tool = StructuredTool.from_function( name="tab_focus", func=tab_focus, description="Switch the browser's focus to a specific tab using its index", return_direct=SHOULD_RETURN_DIRECT, # args_schema=TabFocusToolInputSchema, handle_tool_error=_handle_error, ) def close_tab(): return execute_action("close_tab") close_tab_tool = StructuredTool.from_function( name="close_tab", func=close_tab, description="Close the currently active tab.", return_direct=SHOULD_RETURN_DIRECT, ) def go_back(): return execute_action("go_back") go_back_tool = StructuredTool.from_function( name="go_back", func=go_back, description="Navigate to the previously viewed page.", return_direct=SHOULD_RETURN_DIRECT, ) def go_forward(): return execute_action("go_forward") go_forward_tool = StructuredTool.from_function( name="go_forward", func=go_forward, description="Navigate to the next page (if a previous 'go_back' action was performed).", return_direct=SHOULD_RETURN_DIRECT, ) _APPROVED_DOMAINS = set(URL_MAPPINGS.values()) _APPROVED_DOMAINS.update(['docs.gitlab.com', 'experienceleague.adobe.com']) # class GotoToolInputSchema(BaseModel): # url: str = Field() # # @root_validator(skip_on_failure=True) # def validate_query(cls, values: Dict[str, Any]) -> Dict: # url = values["url"] # for approved_domain in _APPROVED_DOMAINS: # if approved_domain in url: # return values # # raise ToolException( # f"url {url} is not valid\n" # f"The valid urls must contain any of {_APPROVED_DOMAINS}" # ) def validate_url(fn_name: str, url: str): for approved_domain in _APPROVED_DOMAINS: if approved_domain in url: return raise ToolException( f"Attempted to use tool {fn_name} on invalid url {url}\n" f"The valid urls must contain any of {_APPROVED_DOMAINS}" ) def goto(url: str): validate_url('goto', url) return execute_action(f"goto [{map_url_to_local(url)}]") goto_tool = StructuredTool.from_function( name="goto", func=goto, description="Navigate to a specific URL.", return_direct=SHOULD_RETURN_DIRECT, # args_schema=GotoToolInputSchema, handle_tool_error=_handle_error, ) return [stop_tool, click_tool, type_tool, hover_tool, press_tool, scroll_tool, new_tab_tool, tab_focus_tool, close_tab_tool, go_back_tool, go_forward_tool, goto_tool], a_kwargs # return [click_tool], a_kwargs def init_agent(args, lm_config, tokenizer): def _handle_parsing_error(error) -> str: msg = my_globals.parse_error_start + str(error) return msg tools, agent_kwargs = init_tools_args(args, lm_config, tokenizer) handler = LLMOutputHandler() llm = ChatOpenAI(temperature=lm_config.gen_config["temperature"], model_name=lm_config.model, callbacks=[handler], top_p=lm_config.gen_config["top_p"], ) # stop token cant change cos need default 'observation' # max_tokens=lm_config.gen_config["max_tokens"], agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=args.max_steps, agent_kwargs=agent_kwargs, handle_parsing_errors=_handle_parsing_error) return agent_chain

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import os import streamlit as st import langchain.memory import langchain.llms import langchain.chains from apikey import apikey from langchain.memory import ConversationBufferMemory from langchain.memory import ChatMessageHistory from langchain.llms import OpenAI from langchain.chains import ConversationChain from langchain.memory import ConversationBufferMemory os.environ["OPENAI_API_KEY"] =apikey def conversation_memory(): history = ChatMessageHistory() history.add_user_message("hi!") history.add_ai_message("whats up?") memory = ConversationBufferMemory(chat_memory=history) llm = OpenAI(temperature=0) conversation = ConversationChain(llm=llm, memory=memory, verbose=True) def predict(user_input): response = conversation.predict(input=user_input) return response st.title("Conversation Memory Chatbot") user_input = st.text_input("Enter your message:") if user_input: response = predict(user_input) st.write("AI response:", response) if __name__ == "__main__": conversation_memory()

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import sys import getpass from dotenv import load_dotenv, dotenv_values import pandas as pd from IPython.display import display, Markdown, Latex, HTML, JSON import langchain from langchain.llms import OpenAI from langchain.prompts import PromptTemplate from langchain.chains import LLMChain from cmd import PROMPT import os from pyexpat.errors import messages import openai import tiktoken sys.path.append(r"/Users/dovcohen/Documents/Projects/AI/NL2SQL") #from .OpenAI_Func import Num_Tokens_From_String, OpenAI_Embeddings_Cost from ChatGPT.src.lib.OpenAI_Func import Num_Tokens_From_String, OpenAI_Embeddings_Cost from ChatGPT.src.lib.OpenAI_Func import Prompt_Cost, OpenAI_Usage_Cost from ChatGPT.src.lib.DB_Func import execute_query, run_query ## Vector Datastore from ChatGPT.src.lib.lib_OpenAI_Embeddings import VDS, OpenAI_Embeddings class GenAI_NL2SQL(): def __init__(self, OPENAI_API_KEY, Model, Embedding_Model, Encoding_Base, Max_Tokens, Temperature, \ Token_Cost, DB, MYSQL_User, MYSQL_PWD, WD, VDSDB=None, VDSDB_Filename=None): self._LLM_Model = Model self._Embedding_Model = Embedding_Model self._Encoding_Base = Encoding_Base self._Max_Tokens = Max_Tokens self._Temperature = Temperature self._Token_Cost = Token_Cost self._OpenAI_API_Key = OPENAI_API_KEY self._DB = DB self._MYSQL_Credemtals = {'User':MYSQL_User,'PWD':MYSQL_PWD} self._WD = WD self.Set_OpenAI_API_Key() if VDSDB is not None: self._VDSDB = VDSDB self._VDS = VDS(VDSDB_Filename, Encoding_Base, Embedding_Model, Token_Cost, Max_Tokens) self._VDS.Load_VDS_DF(Verbose=False) def Set_OpenAI_API_Key(self): openai.api_key = self._OpenAI_API_Key return 1 def Print_Open_AI_Key(self): print(self._OpenAI_API_Key) def Print_MySQL_Keys(self): print(self._MYSQL_Credemtals) ############################################################################## def Prompt_Question(self, _Prompt_Template_, Inputs, Write_Template=True): """ """ for i,j in Inputs.items(): Prompt = _Prompt_Template_.replace(i,j) if Write_Template: filename = f'{self._WD}/prompt_templates/Template_tmp.txt' prompt_file = open(filename, 'w') prompt_file.write(Prompt) prompt_file.close() return Prompt ############################################################################### def Insert_N_Shot_Examples(self, _Prompt_Template_, N_Shot_Examples, Verbose=False): """ """ # prepare Examples text # Question = .... # Query = ... Examples = '\n' for i in range(len(N_Shot_Examples['Question'])): Examples += f"Question: {N_Shot_Examples['Question'][i]} \nQuery: {N_Shot_Examples['Query'][i]} \n\n" # insert into template Prompt_Template = _Prompt_Template_.replace('{EXAMPLES}', Examples) if Verbose: print(f'Insert_N_Shot_Examples: {Prompt_Template}') return Prompt_Template ############################################################################## def OpenAI_Completion(self, Prompt): try: #Make your OpenAI API request here response = openai.Completion.create( model=self._LLM_Model, prompt=Prompt, max_tokens=self._Max_Tokens, temperature=self._Temperature, top_p=1, frequency_penalty=0, presence_penalty=0 ) except openai.error.APIError as e: #Handle API error here, e.g. retry or log print(f"OpenAI API returned an API Error: {e}") return -1 except openai.error.APIConnectionError as e: #Handle connection error here print(f"Failed to connect to OpenAI API: {e}") return -1 except openai.error.RateLimitError as e: #Handle rate limit error (we recommend using exponential backoff) print(f"OpenAI API request exceeded rate limit: {e}") return -1 return(response) ############################################################################# def OpenAI_ChatCompletion(self, Messages): try: response = openai.ChatCompletion.create( model=self._LLM_Model, messages=Messages, max_tokens=self._Max_Tokens, temperature=self._Temperature, top_p=1, frequency_penalty=0, presence_penalty=0 ) except openai.error.APIError as e: #Handle API error here, e.g. retry or log print(f"OpenAI API returned an API Error: {e}") return -1 except openai.error.APIConnectionError as e: #Handle connection error here print(f"Failed to connect to OpenAI API: {e}") return -1 except openai.error.RateLimitError as e: #Handle rate limit error (we recommend using exponential backoff) print(f"OpenAI API request exceeded rate limit: {e}") return -1 return(response) ############################################################################# def OpenAI_Response_Parser(self, Response, Debug=False): if Debug: print(f'Response {Response}') id = Response['id'] object = Response['object'] if object == 'text_completion': Txt = str(Response['choices'][0]['text']) if Txt[0:7] == "\nQuery:": Txt = Txt[7:] elif object == 'chat.completion': Txt = str(Response['choices'][0]['message']['content']) else: print(f'Type: {Type} is Unsupported ') Txt = '' return(Txt) ############################################################################## def Prompt_Query(self, Prompt_Template, Question = '', N_Shot_Examples = None, Verbose=False, Debug=False): status = 0 df = pd.DataFrame() # Prompt for Model 3-turbo Prompt_Template = self.Insert_N_Shot_Examples(Prompt_Template, N_Shot_Examples) # Construct prompt Prompt = self.Prompt_Question(Prompt_Template,{'{Question}':Question}) # Estimate input prompt cost Cost, Tokens_Used = Prompt_Cost(Prompt, self._LLM_Model, self._Token_Cost, self._Encoding_Base) if Verbose: print('Input') print(f'Total Cost: {round(Cost,3)} Tokens Used {Tokens_Used}') # Send prompt to LLM Response = self.OpenAI_Completion(Prompt) if Debug: print(f'Prompt: \n',Prompt,'\n') print('Response \n',Response,'\n') Cost, Tokens_Used = OpenAI_Usage_Cost(Response, self._LLM_Model, self._Token_Cost ) if Verbose: print('Output') print(f'Total Cost: {round(Cost,3)} Tokens Used {Tokens_Used}','\n') # extract query from LLM response Query = self.OpenAI_Response_Parser(Response) return Query ############################################################################## # Given an single input question, run the entire process def GPT_Completion(self, Question, Prompt_Template, Correct_Query=False, Correction_Prompt=None, \ Max_Iterations=0,Verbose=False, QueryDB = False, Update_VDS=True, Prompt_Update=True): Correct_Query_Iterations = 0 # Request Question Embedding vector Question_Emb = self._VDS.OpenAI_Get_Embedding(Text=Question, Verbose=False) # Search Vector Datastore for similar questions rtn = self._VDS.Search_VDS(Question_Emb, Similarity_Func = 'Cosine', Top_n=3) Prompt_Examples = {'Question':rtn[1], 'Query':rtn[2]} # Construct prompt Query = self.Prompt_Query(Prompt_Template, Question, N_Shot_Examples = Prompt_Examples, Verbose=False) if Verbose: print(f'Query: \n {Query} \n') # Test query the DB - if QueryDB: status, df = run_query(Query = Query, Credentials = self._MYSQL_Credemtals, DB=self._DB, Verbose=False) # if query was malformed, llm halucianated for example if Correct_Query and (status == -5): while (status == -5) and (Correct_Query_Iterations < Max_Iterations): Correct_Query_Iterations += 1 print('Attempting to correct query syntax error') Query = self.Prompt_Query(Correction_Prompt, Question, Verbose=False) # Query the DB status, df = run_query(Query = Query, Credentials = self._MYSQL_Credemtals,\ DB=self._DB, Verbose=False) if Verbose: print(f'Results of query: \n',df) if Update_VDS: if Prompt_Update: rtn = '' while rtn not in ('Y','N'): print(f'Add results to Vector Datastore DB? Y or N') rtn = input('Prompt> ') if rtn == 'Y': self._VDS.Insert_VDS(Question=Question, Query=Query, Metadata='',Embedding=Question_Emb) else: self._VDS.Insert_VDS(Question=Question, Query=Query, Metadata='',Embedding=Question_Emb) # Return Query return Query, df ############################################################################## def Load_Prompt_Template(self, File=None): if File: try: with open(File, 'r') as file: Template = file.read().replace('\n', '') Status = 0 except: print(f'Prompt file {File} load failed ') Status = -1 return "", Status return Template, Status ############################################################################# def LangChain_Initiate_LLM(self, Model='OpenAI'): if Model=='OpenAI': self._LLM = OpenAI(temperature=self._Temperature, model_name=self._LLM_Model, \ max_tokens=self._Max_Tokens, openai_api_key=self._OpenAI_API_Key) return 0 else: print('Model Unsupported') return -1 # Langchain Completion def LangChainCompletion(self, Prompt, Input): chain = LLMChain(llm=self._LLM, prompt=Prompt) return chain.run(Input) ############################################################################# def Populate_Embeddings_from_DF_Column(self,Verbose=False): self._VDS.Retrieve_Embeddings_DF_Column(Verbose=Verbose) return 0 ############################################################################## # Given an single input question, run the entire process def GPT_ChatCompletion(self, Question, Max_Iterations=0, Verbose=False, QueryDB = False, Correct_Query=False, Update_VDS=True, Prompt_Update=True): # Request Question Embedding vector Question_Emb = self._VDS.OpenAI_Get_Embedding(Text=Question, Verbose=False) # Search Vector Datastore for similar questions rtn = self._VDS.Search_VDS(Question_Emb, Similarity_Func = 'Cosine', Top_n=3) Prompt_Examples = {'Question':rtn[1], 'Query':rtn[2]} # Construct prompt Query = self.Message_Query(Question, Ntem_Shot_Examples = Prompt_Examples, Verbose=False, Debug=False) if Query == -100: return if Verbose: print(f'Query: \n {Query} \n') # Test query the DB - if QueryDB: status, df = run_query(Query = Query, Credentials = self._MYSQL_Credemtals, DB=self._DB, Verbose=False) print(f'Status {status}') # if query was malformed, llm halucianated for example if Correct_Query and (status == -5): while (status == -5) and (Correct_Query_Iterations < Max_Iterations): Correct_Query_Iterations += 1 print('Attempting to correct query syntax error') Query = self.Prompt_Query(Correction_Prompt, Question, Verbose=False) # Query the DB status, df = run_query(Query = Query, Credentials = self._MYSQL_Credemtals,\ DB=self._DB, Verbose=False) if Verbose: print(f'Results of query: \n',df) if Update_VDS: if Prompt_Update: rtn = '' while rtn not in ('Y','N'): print(f'Add results to Vector Datastore DB? Y or N') rtn = input('Prompt> ') if rtn == 'Y': self._VDS.Insert_VDS(Question=Question, Query=Query, Metadata='',Embedding=Question_Emb) else: self._VDS.Insert_VDS(Question=Question, Query=Query, Metadata='',Embedding=Question_Emb) # Return Query return Query, df ############################################################################## # Import Message Template def Prepare_Message_Template(self, Verbose=False, Debug=False): # Import Mesage Template file Filename = f'{self._WD}/Message_Templates/Template_0.txt' # Filename = self._MessageTemplate try: with open(Filename, 'r') as file: Template = file.read().replace('\n', '') Status = 0 except: print(f'Prompt file {Filename} load failed ') Status = -1 return "", Status if Debug: print(f'Template {Template}') Messages = [{"role": "system", "content": Template}] if Debug: print(f'Prepare Message Template: \n {Messages} \n end \n') return Messages, Status ############################################################################## # Insert_N_Shot_Messages def Insert_N_Shot_Messages(self, Messages, N_Shot_Examples, Verbose=False, Debug=False): """ Insert example questions and queries into message list for ChatCompletion API """ for i in range(len(N_Shot_Examples['Question'])): Messages.append({"role": "system", "name":"example_user", "content": N_Shot_Examples['Question'][i]}) Messages.append({"role": "system", "name":"example_assistant", "content": N_Shot_Examples['Query'][i]}) if Debug: print(f'Insert_N_Shot_Examples: {Messages[0]}\n') print(f'Insert_N_Shot_Examples: {Messages[1]}\n') print(f'Insert_N_Shot_Examples: {Messages[2]}') return Messages ############################################################################## def Insert_Queston(self, Messages, Question, Verbose=True, Debug=False): """ Insert question into message list for ChatCompletion API """ Messages.append({"role": "user", "content": Question}) if Debug: print(f'Insert Question: \n {Messages[3]}') return Messages ############################################################################## def Message_Query(self, Question = '', N_Shot_Examples = None, Verbose=False, Debug=False): """ Message dictionary format for ChatCompletion API """ Status = 0 df = pd.DataFrame() # Prepare Message Template Messages, Status = self.Prepare_Message_Template(Verbose=True) # Insert Example Messages Messages = self.Insert_N_Shot_Messages(Messages, N_Shot_Examples, Verbose=True) # Insert question Messages = self.Insert_Queston(Messages, Question, Verbose=True) if Debug: print(f' Message_Query: \n {Messages}') # Estimate input prompt cost # Cost, Tokens_Used = Prompt_Cost(Prompt, self._LLM_Model, self._Token_Cost, self._Encoding_Base) # if Verbose: # print('Input') # print(f'Total Cost: {round(Cost,3)} Tokens Used {Tokens_Used}') # Send prompt to LLM Response = self.OpenAI_ChatCompletion(Messages) if Debug: print(f'Prompt: \n',Messages,'\n') print('Response \n',Response,'\n') return -100 # Cost, Tokens_Used = OpenAI_Usage_Cost(Response, self._LLM_Model, self._Token_Cost ) if Verbose: print('Output') print(f'Total Cost: {round(Cost,3)} Tokens Used {Tokens_Used}','\n') # extract query from LLM response Query = self.OpenAI_Response_Parser(Response) return Query

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"""Web base loader class.""" import langchain_community.document_loaders as dl from langchain.docstore.document import Document import asyncio import datetime from io import StringIO import logging import re import warnings from typing import Any, AsyncGenerator, Dict, Iterator, List, Optional, Tuple, Union import inspect import aiohttp import discord import requests import gui from htmldate import find_date import assets from javascriptasync import require, eval_js, eval_js_a from .metadataenums import MetadataDocType from bs4 import BeautifulSoup """This is a special loader that makes use of Mozilla's readability library.""" from utility import Timer def remove_links(markdown_text): # Regular expression pattern to match masked links # pattern = r'\[([^\]]+)\]\(([^\)]+)\)' pattern = r"\[([^\]]+)\]\([^)]+\)" # Replace the masked links with their text content no_links_string = re.sub(pattern, r"\1", markdown_text) return no_links_string async def check_readability(jsenv, html, url): myfile = await assets.JavascriptLookup.get_full_pathas( "readwebpage.js", "WEBJS", jsenv ) htmls: str = str(html) rsult = await myfile.check_read(url, htmls, timeout=45) return rsult async def read_article_direct(jsenv, html, url): myfile = await assets.JavascriptLookup.get_full_pathas( "readwebpage.js", "WEBJS", jsenv ) timeout = 30 htmls: str = str(html) pythonObject = {"var": htmls, "url": url} rsult = await myfile.read_webpage_html_direct(htmls, url, timeout=45) output = await rsult.get_a("mark") header = await rsult.get_a("orig") serial = await header.get_dict_a() simplified_text = output.strip() simplified_text = re.sub(r"(\n){4,}", "\n\n\n", simplified_text) simplified_text = re.sub(r"\n\n", "\n", simplified_text) simplified_text = re.sub(r" {3,}", " ", simplified_text) simplified_text = simplified_text.replace("\t", "") simplified_text = re.sub(r"\n+(\s*\n)*", "\n", simplified_text) return [simplified_text, serial] async def read_article_aw(jsenv, html, url): now = discord.utils.utcnow() getthread = await read_article_direct(jsenv, html, url) result = getthread text, header = result[0], result[1] return text, header def _build_metadata(soup: Any, url: str) -> dict: """Build metadata from BeautifulSoup output.""" metadata = {"source": url} if title := soup.find("title"): metadata["title"] = title.get_text() if description := soup.find("meta", attrs={"name": "description"}): metadata["description"] = description.get("content", "No description found.") if html := soup.find("html"): metadata["language"] = html.get("lang", "No language found.") metadata["dateadded"] = datetime.datetime.utcnow().timestamp() metadata["date"] = "None" try: dt = find_date(str(soup)) if dt: metadata["date"] = dt except Exception as e: gui.dprint(e) metadata["reader"] = False return metadata ScrapeResult = Tuple[str, BeautifulSoup, Dict[str, Any]] class ReadableLoader(dl.WebBaseLoader): async def _fetch_with_rate_limit( self, url: str, semaphore: asyncio.Semaphore ) -> str: # Extended from WebBaseLoader so that it will log the errors # using this app's logging system. async with semaphore: try: return await self._fetch(url) except Exception as e: await self.bot.send_error(e, title="fetching a url.", uselog=True) if self.continue_on_failure: self.bot.logs.warning( f"Error fetching {url}, skipping due to" f" continue_on_failure=True" ) return e self.bot.logs.exception( f"Error fetching {url} and aborting, use continue_on_failure=True " "to continue loading urls after encountering an error." ) raise e async def scrape_all( self, urls: List[Tuple[int, str]], parser: Union[str, None] = None ) -> AsyncGenerator[Tuple[int, int, Union[ScrapeResult, Exception]], None]: """Fetch all urls, then return soups for all results. This function is an asyncronous generator.""" regular_urls = [] for e, url in urls: regular_urls.append(url) with Timer() as timer: results = await self.fetch_all(regular_urls) elapsed_time = timer.get_time() print(f"READ: Took {elapsed_time:.4f} seconds to gather {len(urls)}.") for i, result in enumerate(results): if isinstance(result, Exception): yield i, urls[i][0], result continue url = regular_urls[i] if parser is None: if url.endswith(".xml"): parser = "xml" else: parser = self.default_parser self._check_parser(parser) souped = BeautifulSoup(result, parser) clean_html = re.sub( r"<script\b[^<]*(?:(?!<\/script>)<[^<]*)*<\/script>", "", result ) print("attempting read of ", urls[i][0], "length is", len(clean_html)) readable = await check_readability(self.jsenv, clean_html, url) if not readable: gui.dprint("Not readable link.") try: with Timer() as timer: text, header = await read_article_aw(self.jsenv, clean_html, url) elapsed_time = timer.get_time() print( f"READABILITY: Took {elapsed_time:.4f} seconds to convert {urls[i][0]} to readable." ) # YIELD THIS: out = (remove_links(text), souped, header) yield i, urls[i][0], out except Exception as e: gui.dprint(e) text = souped.get_text(**self.bs_get_text_kwargs) # YIELD THIS: out = (remove_links(text), souped, None) yield i, urls[i][0], out # return final_results def _scrape(self, url: str, parser: Union[str, None] = None) -> Any: from bs4 import BeautifulSoup if parser is None: if url.endswith(".xml"): parser = "xml" else: parser = self.default_parser self._check_parser(parser) html_doc = self.session.get(url, **self.requests_kwargs) if self.raise_for_status: html_doc.raise_for_status() html_doc.encoding = html_doc.apparent_encoding return BeautifulSoup(html_doc.text, parser) def scrape(self, parser: Union[str, None] = None) -> Any: """Scrape data from webpage and return it in BeautifulSoup format.""" if parser is None: parser = self.default_parser return self._scrape(self.web_path, parser) def lazy_load(self) -> Iterator[Document]: """Lazy load text from the url(s) in web_path.""" for path in self.web_paths: soup = self._scrape(path) text = soup.get_text(**self.bs_get_text_kwargs) metadata = _build_metadata(soup, path) yield Document(page_content=text, metadata=metadata) def load(self) -> List[Document]: """Load text from the url(s) in web_path.""" return list(self.lazy_load()) async def aload( self, bot ) -> AsyncGenerator[Tuple[Union[List[Document], Exception], int, int], None]: """Load text from the urls in web_path async into Documents.""" self.jsenv = bot.jsenv self.bot = bot self.continue_on_failure = True docs, typev = [], -1 # e is the original fetched url position. # i is the position in the self.web_paths list. # Result is either a tuple or exception. async for i, e, result in self.scrape_all(self.web_paths): if isinstance(result, Exception): yield result, e, -5 else: try: text, soup, header = result metadata = _build_metadata(soup, self.web_paths[i][1]) typev = MetadataDocType.htmltext if not "title" in metadata: metadata["title"] = "No Title" if header is not None: if "byline" in header: metadata["authors"] = header["byline"] metadata["website"] = header.get("siteName", "siteunknown") metadata["title"] = header.get("title") typev = MetadataDocType.readertext metadata["type"] = int(typev) metadata["sum"] = "source" yield Document(page_content=text, metadata=metadata), e, typev except Exception as err: gui.dprint(str(err)) yield err, e, -5

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import streamlit as st import os # Utils import time from typing import List # Langchain import langchain from pydantic import BaseModel from vertexai.language_models import TextGenerationModel # Vertex AI from langchain.llms import VertexAI from llm_experiments.utils import here os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = str(here() / 'motorway-genai-ccebd34bd403.json') # LLM model llm = VertexAI( model_name="text-bison@001", max_output_tokens=1024, temperature=0.3, top_p=0.8, top_k=40, verbose=True, ) def form_assistant_prompt(seller_email): return f"""Your task is to assist a customer service agent. Step 1: Summarise the following email from a customer who is trying to sell their vehicle and the agent. Use 2-5 bullet points. Step 2: Estimate the customer's emotional state in no more than 3 words. This might be distressed, angry, upset, happy, excited etc. Step 3: Output the recommended action for the agent. Step 3: Draft a response for the agent, in a polite but semi-formal and friendly tone (suitable for a start up). The email will be delimited with #### Format your response like this: **** Summary: - <bullet 1> - <bullet 2> - <bullet 3> **** Customer's emotional state: <examples: distressed, angry, upset> **** Recommended Action: <Action> **** Draft Response: <Draft response> **** Customer email below: #### {seller_email} #### Response: """ def form_validation_prompt(summary_and_context, proposed_response): return f"""Your task is to assist a customer service agent. You will receive an email from a customer and a proposed response from a customer support agent. You will also receive a summary, an estimate of the customer's emotional state, the recommended action and then the draft response. You should conduct the following actions: Step 1: Identify if the proposed response meets the needs of the customer and is of the appropriate tone to match the customer's emotional state. Respond with a "Yes, this meets the customer's needs" or "No, this doesn't meet the customer's needs because <give reason>" Step 2: Recommend a better response that meets the customer's needs more closely. The information will be delimited with #### Format your response like this: **** Validation: <response> **** Improved response: <response> **** Context: #### {summary_and_context} #### Proposed Response: #### {proposed_response} #### Response: """ # Streamlit code starts here st.set_page_config(page_title='Customer Email Assistant 📧🤖', layout='wide') st.title('Customer Email Assistant 📧🤖') seller_email = st.text_area('Seller Email', """Good morning Unfortunately the dealer who came to collect the car decided that they only wanted to offer me ¬£9800 when they arrived to collect the car and i was not willing to let it go for that amount. There was 3 men who cam to collect it and it felt really intimidating and pressured.The car has not been collected.""") if 'result' not in st.session_state: st.session_state['result'] = None if st.button('Generate Response') or st.session_state.result is not None: st.session_state.result = llm(form_assistant_prompt(seller_email)) sections = st.session_state.result.split('****') for section in sections[1:]: title, _, rest = section.partition(':') st.subheader(title) st.session_state.text_box = st.text_area('', rest.strip()) st.divider() st.subheader('Check Response:') draft_response = st.text_area('Draft your response here') if st.button('Validate Response'): summary_and_context, _, _ = st.session_state.result.rpartition('****') response = llm(form_validation_prompt(summary_and_context, draft_response)) title, _, rest = response.partition(':') st.subheader(title) st.text_area('', rest)

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#Multi-agent decentralized speaker selection: ''' This notebook showcases how to implement a multi-agent simulation without a fixed schedule for who speaks when. Instead the agents decide for themselves who speaks. We can implement this by having each agent bid to speak. Whichever agent’s bid is the highest gets to speak. We will show how to do this in the example below that showcases a fictitious presidential debate. ''' import os os.environ["OPENAI_API_KEY"] ="your_api_key" serpapi_key="your_serpapi_key" #Import LangChain related modules from langchain import PromptTemplate import re import tenacity from typing import List, Dict, Callable from langchain.chat_models import ChatOpenAI from langchain.output_parsers import RegexParser from langchain.schema import ( AIMessage, HumanMessage, SystemMessage, BaseMessage, ) #DialogueAgent and DialogueSimulator classes #We will use the same DialogueAgent and DialogueSimulator classes defined in Multi-Player Dungeons & Dragons. def dec_speaker_selection(): class DialogueAgent: def __init__( self, name: str, system_message: SystemMessage, model: ChatOpenAI, ) -> None: self.name = name self.system_message = system_message self.model = model self.prefix = f"{self.name}: " self.reset() def reset(self): self.message_history = ["Here is the conversation so far."] def send(self) -> str: """ Applies the chatmodel to the message history and returns the message string """ message = self.model( [ self.system_message, HumanMessage(content="\n".join(self.message_history + [self.prefix])), ] ) return message.content def receive(self, name: str, message: str) -> None: """ Concatenates {message} spoken by {name} into message history """ self.message_history.append(f"{name}: {message}") class DialogueSimulator: def __init__( self, agents: List[DialogueAgent], selection_function: Callable[[int, List[DialogueAgent]], int], ) -> None: self.agents = agents self._step = 0 self.select_next_speaker = selection_function def reset(self): for agent in self.agents: agent.reset() def inject(self, name: str, message: str): """ Initiates the conversation with a {message} from {name} """ for agent in self.agents: agent.receive(name, message) # increment time self._step += 1 def step(self) -> tuple[str, str]: # 1. choose the next speaker speaker_idx = self.select_next_speaker(self._step, self.agents) speaker = self.agents[speaker_idx] # 2. next speaker sends message message = speaker.send() # 3. everyone receives message for receiver in self.agents: receiver.receive(speaker.name, message) # 4. increment time self._step += 1 return speaker.name, message #BiddingDialogueAgent class #We define a subclass of DialogueAgent that has a bid() method that produces a bid given the message history and the most recent message. class BiddingDialogueAgent(DialogueAgent): def __init__( self, name, system_message: SystemMessage, bidding_template: PromptTemplate, model: ChatOpenAI, ) -> None: super().__init__(name, system_message, model) self.bidding_template = bidding_template def bid(self) -> str: """ Asks the chat model to output a bid to speak """ prompt = PromptTemplate( input_variables=['message_history', 'recent_message'], template = self.bidding_template ).format( message_history='\n'.join(self.message_history), recent_message=self.message_history[-1]) bid_string = self.model([SystemMessage(content=prompt)]).content return bid_string #Define participants and debate topic character_names = ["Donald Trump", "Kanye West", "Elizabeth Warren"] topic = "transcontinental high speed rail" word_limit = 50 #Generate system messages game_description = f"""Here is the topic for the presidential debate: {topic}. The presidential candidates are: {', '.join(character_names)}.""" player_descriptor_system_message = SystemMessage( content="You can add detail to the description of each presidential candidate.") def generate_character_description(character_name): character_specifier_prompt = [ player_descriptor_system_message, HumanMessage(content= f"""{game_description} Please reply with a creative description of the presidential candidate, {character_name}, in {word_limit} words or less, that emphasizes their personalities. Speak directly to {character_name}. Do not add anything else.""" ) ] character_description = ChatOpenAI(temperature=1.0)(character_specifier_prompt).content return character_description def generate_character_header(character_name, character_description): return f"""{game_description} Your name is {character_name}. You are a presidential candidate. Your description is as follows: {character_description} You are debating the topic: {topic}. Your goal is to be as creative as possible and make the voters think you are the best candidate. """ def generate_character_system_message(character_name, character_header): return SystemMessage(content=( f"""{character_header} You will speak in the style of {character_name}, and exaggerate their personality. You will come up with creative ideas related to {topic}. Do not say the same things over and over again. Speak in the first person from the perspective of {character_name} For describing your own body movements, wrap your description in '*'. Do not change roles! Do not speak from the perspective of anyone else. Speak only from the perspective of {character_name}. Stop speaking the moment you finish speaking from your perspective. Never forget to keep your response to {word_limit} words! Do not add anything else. """ )) character_descriptions = [generate_character_description(character_name) for character_name in character_names] character_headers = [generate_character_header(character_name, character_description) for character_name, character_description in zip(character_names, character_descriptions)] character_system_messages = [generate_character_system_message(character_name, character_headers) for character_name, character_headers in zip(character_names, character_headers)] for character_name, character_description, character_header, character_system_message in zip(character_names, character_descriptions, character_headers, character_system_messages): print(f'\n\n{character_name} Description:') print(f'\n{character_description}') print(f'\n{character_header}') print(f'\n{character_system_message.content}') dec_speaker_selection()

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import torch from transformers import BitsAndBytesConfig from langchain import HuggingFacePipeline from langchain import PromptTemplate, LLMChain from pathlib import Path import langchain import json import chromadb from chromadb.config import Settings from langchain.llms import HuggingFacePipeline from langchain.document_loaders import TextLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import HuggingFaceEmbeddings from langchain.chains import RetrievalQA from langchain.vectorstores import Chroma from langchain.document_loaders import DirectoryLoader from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline from sentence_transformers import SentenceTransformer, util from sklearn.metrics.pairwise import cosine_similarity import utils with open("config.json", "r") as content: config = json.load(content) with open("book_config.json", "r") as content: book_config = json.load(content) book = config.get("book") output_json = "output_json" dir_path = rf"books/{book}/part" rootdir = rf"books/{book}/" wiki_url = book_config.get(book).get("wikipedia_link") person = book.replace("_", " ") print(book) Path(rf"{output_json}/{book}").mkdir(parents=True, exist_ok=True) loader = DirectoryLoader(rootdir, glob="**/*.txt", loader_cls=TextLoader, loader_kwargs={"encoding": "utf-8"}) data=loader.load() ####################### text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) all_splits = text_splitter.split_documents(data) model_name = "sentence-transformers/all-mpnet-base-v2" model_kwargs = {"device": "cuda"} embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs) ####################### vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory="chroma_db") quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, ) model_id = "mistralai/Mistral-7B-Instruct-v0.1" model_4bit = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto",quantization_config=quantization_config, ) tokenizer = AutoTokenizer.from_pretrained(model_id) pipe = pipeline( "text-generation", model=model_4bit, tokenizer=tokenizer, use_cache=True, device_map="auto", max_length=2000, do_sample=True, top_k=5, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, ) llm = HuggingFacePipeline(pipeline=pipe) # Retriving Top n Chunks most Similar to query. retriever = vectordb.as_retriever(search_kwargs={"k": 2}) ####################### qa = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", # chain_type="map_reduce", retriever=retriever, verbose=True ) def run_my_rag(qa, query): # print(f"Query: {query}\n") result = qa.run(query) # print("\nResult: ", result) return result def format_docs(docs): return "\n\n".join(doc.page_content for doc in docs) wikipedia_content = utils.get_wikipedia_content(wiki_url) generated_content = {} for section_name, section_content in wikipedia_content.items(): query_stage_1 = f"""You are an AI assistant in writing Wikipedia articles on personalities and your task is to expand the existing content of the given Wikipedia section about the personality: "{person}" from the source documents. Can you add 3-4 most relevant sentences to the existing content? DO NOT use any external information. Existing content: "{section_content}" New relevant sentences: """ result = qa({"query": query_stage_1}) ## Using the retrieved document as context to query the LLM context = format_docs(result.get("source_documents", [])) query_stage_2 = f"""You are an AI assistant in writing Wikipedia articles on personalities and your task is to expand the existing content of the given Wikipedia section about the personality: "{person}" from the given context. Using the context generate a coherent, insightful and neutral expansion of the existing content. STRCTLY Do not generate more than 4 sentences. If it is not possible to expand the content from the context, say so. context: "{context}" Existing content: "{section_name}: {section_content}" Expanded content: """ generated_content[section_name] = llm(query_stage_2) # print("="*50) # print(section_content) # print("*"*25) # print(llm(query_stage_2)) old_wikipedia_content = " ".join(wikipedia_content.values()) text = "" for section_name, text in generated_content.items(): print(text) print("="*20) if "not possible" not in text.lower(): wikipedia_content[section_name] += text with open(f"{output_json}/{book}/e2e_RAG_generated_content.json", "w") as content: json.dump(generated_content, content) updated_wikipedia_content = " ".join(wikipedia_content.values()) old_score = utils.calculate_quality(old_wikipedia_content) updated_score = utils.calculate_quality(updated_wikipedia_content) # Calculate the difference between corresponding values difference_dict = {key: updated_score[key] - old_score[key] for key in old_score} print(difference_dict)

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#!/usr/bin/env python # -*- coding: utf-8 -*- # 配置环境变量 import os from LangChain_study.common import ChatParam os.environ["OPENAI_API_KEY"] = ChatParam.OPENAI_API_KEY os.environ["OPENAI_API_BASE"] = ChatParam.OPENAI_API_BASE # 初始化LLM模型 import langchain from langchain.llms import OpenAI llm = OpenAI(model_name="text-davinci-002", n=2, best_of=2) # 利用内存缓存返回结果:结果会缓存到内存中，在此问相同的问题会直接从内存中读取结果 from langchain.cache import InMemoryCache langchain.llm_cache = InMemoryCache() print(llm("Tell me a joke")) print("----------- 在此提问 ————————————————") print(llm("Tell me a joke")) # 支持的缓存方式：SQLite 、Redis（支持语义缓存） 、GPTCache（精确匹配缓存或基于语义相似性缓存）、Momento缓存、SQLAlchemy 缓存

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import pandas as pd from langchain.document_loaders.word_document import Docx2txtLoader # this does not work, some how, I can not install some of its requirement libs. from langchain.document_loaders.word_document import UnstructuredWordDocumentLoader # from langchain.text_splitter import CharacterTextSplitter import langchain.text_splitter as ts from langchain.embeddings import HuggingFaceInstructEmbeddings # from langchain.vectorstores import FAISS import re loader = Docx2txtLoader( "../../data/raw/6. HR.03.V3.2023. Nội quy Lao động_Review by Labor Department - Final.DOCX") doc = loader.load()[0].page_content.lower() doc = doc.replace("\t", "") doc = re.sub(r"\n+", r".\n\n ", doc) replace_mapping = { # this is for replacing some unwanted characters "..\n": ".\n", ". .\n": ".\n", "?.\n": "?\n", "? .\n": "?\n", ":.\n": ":\n", ": .\n": ":\n", ";.\n": ",\n", ": .\n": ";\n" } for pattern, replacement in replace_mapping.items(): doc = doc.replace(pattern, replacement) print(doc) # splitting into chunks char_splt = ts.CharacterTextSplitter(separator='.', chunk_size=1000) doc_chunks = char_splt.split_text(text=doc) # ------ Segmenting vietnamese # this model is for segmenting vietnamese text before tokenizing. import py_vncorenlp model = py_vncorenlp.VnCoreNLP(save_dir="../../models/VnCoreNLP") # model.word_segment(doc) # this chunked the text into pieces already segmented_chunks = [] for i in doc_chunks: i = model.word_segment(i) segmented_chunks.append(i) segmented_chunks2=[] # now we have to rejoin each element of the list for chunks_list in segmented_chunks: chunks_string="\n".join(chunks_list) segmented_chunks2.append(chunks_string) # type(segmented_chunks2[0]) # def plotting_len_text_list(tex_list): # len_series = pd.Series([len(i) for i in tex_list]) # len_series.plot(kind='barh', figsize=(5, len(len_series)/3)) # plotting_len_text_list(segmented_chunks2) # ------ import torch from transformers import AutoModel, AutoTokenizer # from langchain.text_splitter import TextSplitter phobert = AutoModel.from_pretrained("vinai/phobert-base-v2") tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base-v2") # With TensorFlow 2.0+: # from transformers import TFAutoModel # phobert = TFAutoModel.from_pretrained("vinai/phobert-base") # # reading the text # file = open('../../data/processed/6. HR.03.V3.2023. Nội quy Lao động_Review by Labor Department - Final.txt', 'r') # input_txt = file.read() # file.close() token_tensors=[] # this stores the token_tensor of word tokenization for each segmented text_chunk for chunk in segmented_chunks2: input_ids = torch.tensor([tokenizer.encode(chunk)]) token_tensors.append(input_ids) # vocab_dict=tokenizer.get_vocab() # get the vocab_dict of the tokenizer # for id in input_ids[0].tolist(): # print out the index ot the word after getting tokenized # for key, val in vocab_dict.items(): # if val==id: # print(id, key) features_list=[] for tensor in token_tensors[:10]: with torch.no_grad(): features = phobert(tensor).pooler_output # Models outputs are now tuples features_list.append(features) # I think this works, but not with text_chunks which is to big, the model will not process. # with torch.no_grad(): # features = phobert(token_tensors[0]) """___note___ features.keys()=odict_keys(['last_hidden_state', 'pooler_output']) features.last_hidden_state is the tensor that store vectors for word-embedding. if the input_ids has 37 tokens, features.last_hidden_state has 37 vectors length 768. features.pooler_output is the tensor that store vectors for sentence-embedding contains only 1 vector length 768. """ len(features.last_hidden_state[0]) word_vectors_list=features.last_hidden_state[0].tolist() for index, vector in enumerate(word_vectors_list): print(index, len(vector)) len(features.pooler_output[0]) # # ---- # def get_text_chunks(text): # text_splitter = CharacterTextSplitter( # separator="\n", # chunk_size=1000, # chunk_overlap=200, # length_function=len # ) # chunks = text_splitter.split_text(text) # return chunks # chunks=get_text_chunks(text=input_txt.lower()) # for i in chunks: # print() # print(len(i), "\n", i) # def get_vectorstore(text_chunks): # embeddings = HuggingFaceInstructEmbeddings(model_name="vinai/phobert-base-v2") # vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) # return vectorstore # vector_store = get_vectorstore(text_chunks=chunks) # this does not work # vector_store = get_vectorstore(text_chunks=segmented_chunks2[:1]) # this does not work

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"""Create a ConversationalRetrievalChain for question/answering.""" import imp import logging import sys from typing import Union from langchain.callbacks.base import BaseCallbackManager, BaseCallbackHandler from langchain.callbacks.tracers import LangChainTracer from langchain.chains import ConversationalRetrievalChain from langchain.chains.llm import LLMChain from langchain.chains.question_answering import load_qa_chain from langchain.vectorstores.base import VectorStore from base_bot.prompts import QA_PROMPT, REPHRASE_PROMPT from . import config # dynamic import def dynamic_imp(name): # find_module() method is used # to find the module and return # its description and path try: fp, path, desc = imp.find_module(name, [".", "base_bot/llm"]) except ImportError as e: logging.error("module not found: " + name + " " + str(e)) try: # load_modules loads the module # dynamically and takes the filepath # module and description as parameter return imp.load_module(name, fp, path, desc) except Exception as e: logging.error("error loading module: " + name + " " + str(e)) def get_chain( vectorstore: Union[VectorStore, any], rephrase_handler: BaseCallbackHandler, stream_handler: BaseCallbackHandler, tracing: bool = False ) -> ConversationalRetrievalChain: _vectorstore = vectorstore() if callable(vectorstore) else vectorstore manager = BaseCallbackManager([]) rephrase_manager = BaseCallbackManager([rephrase_handler]) stream_manager = BaseCallbackManager([stream_handler]) if tracing: tracer = LangChainTracer() tracer.load_default_session() manager.add_handler(tracer) rephrase_manager.add_handler(tracer) stream_manager.add_handler(tracer) llm_package = dynamic_imp(config.LLM_MODULE) rephrase_generator_llm = llm_package.getLLM( model=config.LLM_REPRHASING_MODEL, temperature=config.LLM_REPHRASING_TEMPERATURE, verbose=config.LLM_REPHRASING_VERBOSE, callback_manager=rephrase_manager, ) streaming_llm = llm_package.getLLM( streaming=True, callback_manager=stream_manager, verbose=config.LLM_STREAMING_VERBOSE, temperature=config.LLM_STREAMING_TEMPERATURE, model=config.LLM_STREAMING_MODEL, ) rephrase_generator = LLMChain( llm=rephrase_generator_llm, prompt=REPHRASE_PROMPT, callback_manager=manager ) doc_chain = load_qa_chain( streaming_llm, chain_type="stuff", prompt=QA_PROMPT, callback_manager=manager ) qa = ConversationalRetrievalChain( retriever=_vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=rephrase_generator, callback_manager=manager, ) return qa

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# Drive Imports import yaml import asyncio from deferred_imports import langchain, imports_done import webbrowser # Global Variables dictionaries_folder_path="" structure_dictionary_path="" information_dictionary_path="" folder_dictionary_path="" # Information Mapping async def a_update_mapping(your_dictionary,override=False): tasks=[] for key in list(your_dictionary.keys()): if override or 'mappedDate' not in your_dictionary[key] or not your_dictionary[key]['mappedDate'] or your_dictionary[key]['modifiedDate'] > your_dictionary[key]['mappedDate']: tasks.append(a_generate_mapping(your_dictionary[key]['content'],your_dictionary[key]['title'],your_dictionary[key]['path'],key)) results=await asyncio.gather(*tasks) for item in results: id=list(item.keys())[0] your_dictionary[id]['mapping'] = item[id] your_dictionary[id]['mapped']=True your_dictionary[id]['mappedDate'] = your_dictionary[id]['modifiedDate'] return your_dictionary # Information Mapping async def a_generate_mapping(content,title,parent,id): imports_done.wait() from langchain.chat_models import ChatOpenAI from langchain.prompts.chat import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ) # Define the templates system_template="I want you to give a summary of the of the document the user gives you as if you were describing it and what it is for, the user will also tell you the path of the parent directory of the document and its title which you should use to understand what the summary should be, for example a 'book summaries' document's summary should include that it is a summary of books and not simply the contents of the books." human_template="Here is my document title-{title}, with parent directory-{parent}, and here is content of the document:\n{content}" system_template2="""You are to provide a useful summary and description so that someone reading your description would know what you are refering to. Use the context of the title and parent directory to understand what the description and summary should be. Think before begin to describe the document. Break down the path into sections splitting with each \\ and think about them out loud, tell me what the meaning of each directory is in your interpretation. After you are finished thinking give your description . Your response should follow this template: \"'Thoughts: 'what your thoughts are on the meaning of the document are and what it is for with relation to its title and parent directory' Description: 'your description and summary based on your thoughts so someone would know what this is for'\"""" # Create the prompt system_message=SystemMessagePromptTemplate.from_template(system_template) human_message=HumanMessagePromptTemplate.from_template(human_template) system_message2=SystemMessagePromptTemplate.from_template(system_template2) message_list=[system_message,human_message,system_message2] chat_prompt=ChatPromptTemplate.from_messages(message_list) # Generate the mapping formated_prompt=chat_prompt.format_prompt(content=content, title=title, parent=parent).to_messages() raw_string_prompt="" for item in formated_prompt: raw_string_prompt+=item.type+": "+item.content+"\n\n" if len(raw_string_prompt)>9000: model_name="gpt-3.5-turbo-16k" else: model_name="gpt-3.5-turbo" chat=ChatOpenAI(model=model_name,temperature=0.3) chat_response=await chat.agenerate([formated_prompt]) print(title+" "+parent+" "+chat_response.generations[0][0].text+"\n\n") output_string=chat_response.generations[0][0].text # Parse the mapping mapped_result="" mapped_result=(output_string).split("Description: ")[-1] if mapped_result=="": mapped_result=(output_string).split("Description:")[-1] return {id:mapped_result} # Folder Mapping async def a_update_folder_mapping(folder_dictionary,information_dictionary,override=False): finished_folders=[] length_folders=len(list(folder_dictionary.keys())) results=[] while(length_folders>len(finished_folders)): tasks=[] print("finished folders: "+str(len(finished_folders))+"/"+str(length_folders)) for key in list(folder_dictionary.keys()): #check if the key is already mapped if not override and 'mappedDate' in folder_dictionary[key] and folder_dictionary[key]['mappedDate'] and folder_dictionary[key]['modifiedDate'] <= folder_dictionary[key]['mappedDate']: finished_folders.append(key) print("Already done: "+key) else: print("Not done: "+key, override, 'mappedDate' in folder_dictionary[key], folder_dictionary[key]['mappedDate'], folder_dictionary[key]['modifiedDate'] <= folder_dictionary[key]['mappedDate']) if key not in finished_folders: if folder_dictionary[key]["contained_folder_ids"]==[]: #Create task contents="" for file_id in folder_dictionary[key]["contained_file_ids"]: contents+=(information_dictionary[file_id]["mapping"])+"\n" for folder_id in folder_dictionary[key]["contained_folder_ids"]: contents+=(folder_dictionary[folder_id]["mapping"])+"\n" tasks.append(a_generate_folder_mapping(contents,folder_dictionary[key]['title'],folder_dictionary[key]['path'],key)) finished_folders.append(key) else: all_completed=True for cf in folder_dictionary[key]["contained_folder_ids"]: if cf not in finished_folders: all_completed=False if all_completed: #Create task contents="" for file_id in folder_dictionary[key]["contained_file_ids"]: contents+=(information_dictionary[file_id]["mapping"])+"\n" for folder_id in folder_dictionary[key]["contained_folder_ids"]: contents+=(folder_dictionary[folder_id]["mapping"])+"\n" tasks.append(a_generate_folder_mapping(contents,folder_dictionary[key]['title'],folder_dictionary[key]['path'],key)) finished_folders.append(key) results.append(await asyncio.gather(*tasks)) for result in results: for item in result: id=list(item.keys())[0] folder_dictionary[id]['mapping'] = item[id] folder_dictionary[id]['mapped']=True folder_dictionary[id]['mappedDate'] = folder_dictionary[id]['modifiedDate'] return(folder_dictionary) # Folder Mapping async def a_generate_folder_mapping(contents,title,parent,id): # Setup imports imports_done.wait() from langchain.chat_models import ChatOpenAI from langchain.prompts.chat import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ) # Define the templates system_template="I want you to give a summary of the of the folder the user gives you as if you were describing it and what it is for, the user will also tell you the path of the parent directory of the folder, the folder title, and the descriptions of the contents of the files or folders the folder contains which you should use to understand what the description should be." human_template="Here is my folder title-{title}, with parent directory-{parent}, and here are the contents of the folder:\n{contents}" system_template2="""You are to provide a useful summary and description so that someone reading your description would know what you are refering to. Use the context of the title and parent directory to understand what the description and summary should be. Think before you begin to describe the document. Break down the path into sections splitting with each \\ and think about them out loud, tell me what the meaning of each directory is in your interpretation. After you are finished thinking give your description . Your response should follow this template: \"'Thoughts: 'what your thoughts are on the meaning of the folder are and what it is for with relation to its title and parent directory' Description: 'your description and summary based on your thoughts so someone would know what this is for'\"""" # Create the prompt system_message=SystemMessagePromptTemplate.from_template(system_template) human_message=HumanMessagePromptTemplate.from_template(human_template) system_message2=SystemMessagePromptTemplate.from_template(system_template2) message_list=[system_message,human_message,system_message2] chat_prompt=ChatPromptTemplate.from_messages(message_list) # Get the response of the mapping for the item formated_prompt=chat_prompt.format_prompt(contents=contents, title=title, parent=parent).to_messages() raw_string_prompt="" for item in formated_prompt: raw_string_prompt+=item.type+": "+item.content+"\n\n" if len(raw_string_prompt)>9000: model_name="gpt-3.5-turbo-16k" else: model_name="gpt-3.5-turbo" chat=ChatOpenAI(model=model_name,temperature=0.3) chat_response=await chat.agenerate([formated_prompt]) print(title+" "+parent+" "+chat_response.generations[0][0].text+"\n\n") output_string=chat_response.generations[0][0].text # Parse the mapping mapped_result=(output_string).split("Description: ")[-1] if mapped_result=="": mapped_result=(output_string).split("Description:")[-1] return {id:mapped_result} # Generate Mappings def map(override=False): # Setup dictionary paths global dictionaries_folder_path, structure_dictionary_path, information_dictionary_path, folder_dictionary_path # map information dictionary with open(information_dictionary_path, "r") as file: information_dict = yaml.load(file, Loader=yaml.FullLoader) information_dict=asyncio.run(a_update_mapping(information_dict,override=override)) with open(information_dictionary_path, 'w') as outfile: yaml.dump(information_dict, outfile) # Map the folder dictionary with open(information_dictionary_path, "r") as file: information_dict = yaml.load(file, Loader=yaml.FullLoader) with open(folder_dictionary_path, "r") as file: folder_dictionary = yaml.load(file, Loader=yaml.FullLoader) folder_dictionary=asyncio.run(a_update_folder_mapping(folder_dictionary,information_dict,override=False)) with open(folder_dictionary_path, 'w') as outfile: yaml.dump(folder_dictionary, outfile) print("Done mapping") # Update Database def update_vectordb(persist_directory,finish_que): # Setup imports imports_done.wait() from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings # Create custom Document class class Document: def __init__(self, page_content="",source="",dict_id="",mimeType="",title=""): self.page_content = page_content self.metadata={'source': source, 'id': dict_id, "mimeType":mimeType,"title":title} def __repr__(self): attributes = ", ".join(f"{k}={v!r}" for k, v in vars(self).items()) return f"Document({attributes})" # Read from information dictionary global dictionaries_folder_path, structure_dictionary_path, information_dictionary_path, folder_dictionary_path if "information" in persist_directory: base_dict_file_name=information_dictionary_path elif "folder" in persist_directory: base_dict_file_name=folder_dictionary_path with open(base_dict_file_name) as f: base_dict = yaml.load(f, Loader=yaml.FullLoader) # Create list of documents my_documents = [] for key in list(base_dict.keys()): if base_dict[key]["path"]=="": my_documents.append(Document(base_dict[key]["mapping"],source=base_dict[key]["path"]+"none id:"+base_dict[key]["id"]+":mimeType:"+base_dict[key]["mimeType"], dict_id=base_dict[key]["id"],mimeType=base_dict[key]["mimeType"],title=base_dict[key]["title"])) else: my_documents.append(Document(base_dict[key]["mapping"],source=base_dict[key]["path"]+" id:"+base_dict[key]["id"]+":mimeType:"+base_dict[key]["mimeType"], dict_id=base_dict[key]["id"],mimeType=base_dict[key]["mimeType"],title=base_dict[key]["title"])) # Delete and regenerate the database embedding = OpenAIEmbeddings(model="text-embedding-ada-002") vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding) try: vectordb.delete_collection() vectordb.persist() except Exception as e: print(e) vectordb = Chroma.from_documents( documents=my_documents, embedding=embedding, persist_directory=persist_directory ) vectordb.persist() vectordb = None # Depricated queue usage finish_que.put(True) # Make Vector Database with files and folders def combine_vectordb(persist_directory): #Setup imports and paths imports_done.wait() from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings global dictionaries_folder_path, structure_dictionary_path, information_dictionary_path, folder_dictionary_path # Create custom Document class class Document: def __init__(self, page_content="",source="",dict_id="",mimeType="",title=""): self.page_content = page_content self.metadata={'source': source, 'id': dict_id, "mimeType":mimeType,"title":title} def __repr__(self): attributes = ", ".join(f"{k}={v!r}" for k, v in vars(self).items()) return f"Document({attributes})" # Dicitonary to list of documents function def add_documents(base_dict): my_documents = [] for key in list(base_dict.keys()): if base_dict[key]["path"]=="": my_documents.append(Document(base_dict[key]["mapping"],source=base_dict[key]["path"]+"none id:"+base_dict[key]["id"]+":mimeType:"+base_dict[key]["mimeType"], dict_id=base_dict[key]["id"],mimeType=base_dict[key]["mimeType"],title=base_dict[key]["title"])) else: my_documents.append(Document(base_dict[key]["mapping"],source=base_dict[key]["path"]+" id:"+base_dict[key]["id"]+":mimeType:"+base_dict[key]["mimeType"], dict_id=base_dict[key]["id"],mimeType=base_dict[key]["mimeType"],title=base_dict[key]["title"])) return(my_documents) # Read from information and folder dictionaries with open(information_dictionary_path) as f: information_dict = yaml.load(f, Loader=yaml.FullLoader) with open(folder_dictionary_path) as f: folder_dict = yaml.load(f, Loader=yaml.FullLoader) # Turn dictionaries into document list my_documents=add_documents(information_dict)+add_documents(folder_dict) # Delete and regenerate the combined_db database embedding = OpenAIEmbeddings(model="text-embedding-ada-002") vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding) try: vectordb.delete_collection() vectordb.persist() except Exception as e: print(e) vectordb = Chroma.from_documents( documents=my_documents, embedding=embedding, persist_directory=persist_directory ) vectordb.persist() vectordb = None print("Finished combining databases") # Retrieve From Information def retrieve_from_information(user_question,return_que): # Setup imports imports_done.wait() from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings # Get vectordb persist_directory = 'combined_db' embedding = OpenAIEmbeddings(model="text-embedding-ada-002") vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding) # Retrive documents docs_and_scores = vectordb.similarity_search_with_score(user_question) # Get docs from docs_and_scores docs=[] scores=[] for item in docs_and_scores: docs.append(item[0]) scores.append(item[1]) print(scores) # Open the website for the first doc open_website(docs[0]) # Pass the docs to the main app return_que.put(docs) # Retrieve From Folder def retrieve_from_folder(user_question): # Setup imports imports_done.wait() from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings # Get vectordb persist_directory = 'folder_db' embedding = OpenAIEmbeddings(model="text-embedding-ada-002") vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding) # Retrive documents docs_and_scores = vectordb.similarity_search_with_score(user_question) # Get docs from docs_and_scores docs=[] scores=[] for item in docs_and_scores: docs.append(item[0]) scores.append(item[1]) print(scores) # Open the website for the first doc open_website(docs[0]) # Opens website of a document def open_website(doc): if doc==None: print("No documents found") else: url=None if "spreadsheet" in doc.metadata["mimeType"]: url = "https://docs.google.com/spreadsheets/d/"+doc.metadata["id"] elif "document" in doc.metadata["mimeType"]: url = "https://docs.google.com/document/d/"+doc.metadata["id"] elif "folder" in doc.metadata["mimeType"]: url = "https://drive.google.com/drive/folders/"+doc.metadata["id"] print(url) if url != None: webbrowser.open(url)

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import tensorflow import dotenv import transformers from tensorflow import keras from dotenv import find_dotenv, load_dotenv from transformers import pipeline import langchain from langchain import PromptTemplate, LLMChain, OpenAI import requests import os import openai import streamlit as st HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") openai.api_key = os.getenv("OPENAI_API_KEY") load_dotenv(find_dotenv()) # img to text def img2text(data): image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base") text = image_to_text(data)[0]["generated_text"] print(text) return text # llm def generate_story(scenario): template = """ You are a story teller; You can generate a short story based on a simple narrative, the story should be no more than 20 words; CONTEXT: {scenario} STORY: """ prompt = PromptTemplate(template=template, input_variables=["scenario"]) story_llm = LLMChain(llm=OpenAI(model_name='gpt-3.5-turbo', temperature=1), prompt=prompt, verbose=True) story = story_llm.predict(scenario=scenario) print(story) return story # txt to speech def text2speech(message): API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits" headers = {"Authorization": f"Bearer {HUGGINGFACEHUB_API_TOKEN}"} payload = { "inputs": message } response = requests.post(API_URL, headers=headers, json=payload) with open('audio.flac', 'wb') as file: file.write(response.content) def main(): st.set_page_config(page_title="Image to Speech", page_icon="🤗") uploaded_file = st.file_uploader("Choose an image...", type="jpg") if uploaded_file is not None: print(uploaded_file) bytes_data = uploaded_file.getvalue() with open(uploaded_file.name, 'wb') as file: file.write(bytes_data) st.image(bytes_data, caption='Uploaded Image.', use_column_width=True) scenario = img2text(uploaded_file.name) story = generate_story(scenario) text2speech(story) with st.expander("scenario"): st.write(scenario) with st.expander("story"): st.write(story) st.audio('audio.flac') if __name__ == '__main__': main()

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from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings from langchain.chains import RetrievalQA from langchain.chat_models import ChatOpenAI from redundant_filter_retriever import RedundantFilterRetriever from dotenv import load_dotenv import langchain langchain.debug = True load_dotenv() chat = ChatOpenAI() embeddings = OpenAIEmbeddings() db = Chroma( persist_directory="emb", embedding_function=embeddings ) retriever = RedundantFilterRetriever( embeddings=embeddings, chroma=db ) chain = RetrievalQA.from_chain_type( llm=chat, retriever=retriever, chain_type="stuff" ) result = chain.run("What is an interesting fact about the English language?") print(result)

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