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""" AI Module This module provides an AI class that interfaces with language models to perform various tasks such as starting a conversation, advancing the conversation, and handling message serialization. It also includes backoff strategies for handling rate limit errors from the OpenAI API. Classes: AI: A class that interfaces with language models for conversation management and message serialization. Functions: serialize_messages(messages: List[Message]) -> str Serialize a list of messages to a JSON string. """ from __future__ import annotations import json import logging import os from pathlib import Path from typing import List, Optional, Union import backoff import openai import pyperclip from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chat_models.base import BaseChatModel from langchain.schema import ( AIMessage, HumanMessage, SystemMessage, messages_from_dict, messages_to_dict, ) from langchain_anthropic import ChatAnthropic from langchain_openai import AzureChatOpenAI, ChatOpenAI from gpt_engineer.core.token_usage import TokenUsageLog # Type hint for a chat message Message = Union[AIMessage, HumanMessage, SystemMessage] # Set up logging logger = logging.getLogger(__name__) class AI: """ A class that interfaces with language models for conversation management and message serialization. This class provides methods to start and advance conversations, handle message serialization, and implement backoff strategies for rate limit errors when interacting with the OpenAI API. Attributes ---------- temperature : float The temperature setting for the language model. azure_endpoint : str The endpoint URL for the Azure-hosted language model. model_name : str The name of the language model to use. streaming : bool A flag indicating whether to use streaming for the language model. llm : BaseChatModel The language model instance for conversation management. token_usage_log : TokenUsageLog A log for tracking token usage during conversations. Methods ------- start(system: str, user: str, step_name: str) -> List[Message] Start the conversation with a system message and a user message. next(messages: List[Message], prompt: Optional[str], step_name: str) -> List[Message] Advances the conversation by sending message history to LLM and updating with the response. backoff_inference(messages: List[Message]) -> Any Perform inference using the language model with an exponential backoff strategy. serialize_messages(messages: List[Message]) -> str Serialize a list of messages to a JSON string. deserialize_messages(jsondictstr: str) -> List[Message] Deserialize a JSON string to a list of messages. _create_chat_model() -> BaseChatModel Create a chat model with the specified model name and temperature. """ def __init__( self, model_name="gpt-4-1106-preview", temperature=0.1, azure_endpoint="", streaming=True, ): """ Initialize the AI class. Parameters ---------- model_name : str, optional The name of the model to use, by default "gpt-4". temperature : float, optional The temperature to use for the model, by default 0.1. """ self.temperature = temperature self.azure_endpoint = azure_endpoint self.model_name = model_name self.streaming = streaming self.llm = self._create_chat_model() self.token_usage_log = TokenUsageLog(model_name) logger.debug(f"Using model {self.model_name}") def start(self, system: str, user: str, step_name: str) -> List[Message]: """ Start the conversation with a system message and a user message. Parameters ---------- system : str The content of the system message. user : str The content of the user message. step_name : str The name of the step. Returns ------- List[Message] The list of messages in the conversation. """ messages: List[Message] = [ SystemMessage(content=system), HumanMessage(content=user), ] return self.next(messages, step_name=step_name) def next( self, messages: List[Message], prompt: Optional[str] = None, *, step_name: str, ) -> List[Message]: """ Advances the conversation by sending message history to LLM and updating with the response. Parameters ---------- messages : List[Message] The list of messages in the conversation. prompt : Optional[str], optional The prompt to use, by default None. step_name : str The name of the step. Returns ------- List[Message] The updated list of messages in the conversation. """ if prompt: messages.append(HumanMessage(content=prompt)) logger.debug(f"Creating a new chat completion: {messages}") messages = self._collapse_messages(messages) response = self.backoff_inference(messages) self.token_usage_log.update_log( messages=messages, answer=response.content, step_name=step_name ) messages.append(response) logger.debug(f"Chat completion finished: {messages}") return messages def _collapse_messages(self, messages: List[Message]): """ Combine consecutive messages of the same type into a single message. This method iterates through the list of messages, combining consecutive messages of the same type by joining their content with a newline character. This reduces the number of messages and simplifies the conversation for processing. Parameters ---------- messages : List[Message] The list of messages to collapse. Returns ------- List[Message] The list of messages after collapsing consecutive messages of the same type. """ collapsed_messages = [] if not messages: return collapsed_messages previous_message = messages[0] combined_content = previous_message.content for current_message in messages[1:]: if current_message.type == previous_message.type: combined_content += "\n\n" + current_message.content else: collapsed_messages.append( previous_message.__class__(content=combined_content) ) previous_message = current_message combined_content = current_message.content collapsed_messages.append(previous_message.__class__(content=combined_content)) return collapsed_messages @backoff.on_exception(backoff.expo, openai.RateLimitError, max_tries=7, max_time=45) def backoff_inference(self, messages): """ Perform inference using the language model while implementing an exponential backoff strategy. This function will retry the inference in case of a rate limit error from the OpenAI API. It uses an exponential backoff strategy, meaning the wait time between retries increases exponentially. The function will attempt to retry up to 7 times within a span of 45 seconds. Parameters ---------- messages : List[Message] A list of chat messages which will be passed to the language model for processing. callbacks : List[Callable] A list of callback functions that are triggered after each inference. These functions can be used for logging, monitoring, or other auxiliary tasks. Returns ------- Any The output from the language model after processing the provided messages. Raises ------ openai.error.RateLimitError If the number of retries exceeds the maximum or if the rate limit persists beyond the allotted time, the function will ultimately raise a RateLimitError. Example ------- >>> messages = [SystemMessage(content="Hello"), HumanMessage(content="How's the weather?")] >>> response = backoff_inference(messages) """ return self.llm.invoke(messages) # type: ignore @staticmethod def serialize_messages(messages: List[Message]) -> str: """ Serialize a list of messages to a JSON string. Parameters ---------- messages : List[Message] The list of messages to serialize. Returns ------- str The serialized messages as a JSON string. """ return json.dumps(messages_to_dict(messages)) @staticmethod def deserialize_messages(jsondictstr: str) -> List[Message]: """ Deserialize a JSON string to a list of messages. Parameters ---------- jsondictstr : str The JSON string to deserialize. Returns ------- List[Message] The deserialized list of messages. """ data = json.loads(jsondictstr) # Modify implicit is_chunk property to ALWAYS false # since Langchain's Message schema is stricter prevalidated_data = [ {**item, "tools": {**item.get("tools", {}), "is_chunk": False}} for item in data ] return list(messages_from_dict(prevalidated_data)) # type: ignore def _create_chat_model(self) -> BaseChatModel: """ Create a chat model with the specified model name and temperature. Parameters ---------- model : str The name of the model to create. temperature : float The temperature to use for the model. Returns ------- BaseChatModel The created chat model. """ if self.azure_endpoint: return AzureChatOpenAI( azure_endpoint=self.azure_endpoint, openai_api_version=os.getenv("OPENAI_API_VERSION", "2023-05-15"), deployment_name=self.model_name, openai_api_type="azure", streaming=self.streaming, callbacks=[StreamingStdOutCallbackHandler()], ) if "claude" in self.model_name: return ChatAnthropic( model=self.model_name, temperature=self.temperature, callbacks=[StreamingStdOutCallbackHandler()], max_tokens_to_sample=4096, ) return ChatOpenAI( model=self.model_name, temperature=self.temperature, streaming=self.streaming, callbacks=[StreamingStdOutCallbackHandler()], ) def serialize_messages(messages: List[Message]) -> str: return AI.serialize_messages(messages) class ClipboardAI(AI): # Ignore not init superclass def __init__(self, **_): # type: ignore pass @staticmethod def serialize_messages(messages: List[Message]) -> str: return "\n\n".join([f"{m.type}:\n{m.content}" for m in messages]) @staticmethod def multiline_input(): print("Enter/Paste your content. Ctrl-D or Ctrl-Z ( windows ) to save it.") content = [] while True: try: line = input() except EOFError: break content.append(line) return "\n".join(content) def next( self, messages: List[Message], prompt: Optional[str] = None, *, step_name: str, ) -> List[Message]: """ Not yet fully supported """ if prompt: messages.append(HumanMessage(content=prompt)) logger.debug(f"Creating a new chat completion: {messages}") msgs = self.serialize_messages(messages) pyperclip.copy(msgs) Path("clipboard.txt").write_text(msgs) print( "Messages copied to clipboard and written to clipboard.txt,", len(msgs), "characters in total", ) response = self.multiline_input() messages.append(AIMessage(content=response)) logger.debug(f"Chat completion finished: {messages}") return messages

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from fastapi import Body from sse_starlette.sse import EventSourceResponse from configs import LLM_MODELS, TEMPERATURE from server.utils import wrap_done, get_OpenAI from langchain.chains import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from typing import AsyncIterable, Optional import asyncio from langchain.prompts import PromptTemplate from server.utils import get_prompt_template async def completion(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]), stream: bool = Body(False, description="流式输出"), echo: bool = Body(False, description="除了输出之外，还回显输入"), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"), # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0), prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), ): #todo 因ApiModelWorker 默认是按chat处理的，会对params["prompt"] 解析为messages，因此ApiModelWorker 使用时需要有相应处理 async def completion_iterator(query: str, model_name: str = LLM_MODELS[0], prompt_name: str = prompt_name, echo: bool = echo, ) -> AsyncIterable[str]: nonlocal max_tokens callback = AsyncIteratorCallbackHandler() if isinstance(max_tokens, int) and max_tokens <= 0: max_tokens = None model = get_OpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[callback], echo=echo ) prompt_template = get_prompt_template("completion", prompt_name) prompt = PromptTemplate.from_template(prompt_template) chain = LLMChain(prompt=prompt, llm=model) # Begin a task that runs in the background. task = asyncio.create_task(wrap_done( chain.acall({"input": query}), callback.done), ) if stream: async for token in callback.aiter(): # Use server-sent-events to stream the response yield token else: answer = "" async for token in callback.aiter(): answer += token yield answer await task return EventSourceResponse(completion_iterator(query=query, model_name=model_name, prompt_name=prompt_name), )

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# — coding: utf-8 – import openai import json import logging import sys import argparse from langchain.chat_models import ChatOpenAI from langchain.prompts import ( ChatPromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, HumanMessagePromptTemplate ) from langchain import LLMChain import numpy as np import requests import os import subprocess import re import importlib.util from sklearn.metrics.pairwise import cosine_similarity import pickle from util import * from tqdm import tqdm openai.api_key = os.environ["OPENAI_API_KEY"] def get_last_processed_index(progress_file): """Retrieve the last processed index from the progress file.""" if os.path.exists(progress_file): with open(progress_file, 'r', encoding='utf-8') as f: last_index = f.read().strip() return int(last_index) if last_index else 0 else: return 0 def update_progress(progress_file, index): """Update the last processed index in the progress file.""" with open(progress_file, 'w', encoding='utf-8') as f: f.write(str(index)) def task_decompose(question, Tool_dic, model_name): chat = ChatOpenAI(model_name=model_name) template = "You are a helpful assistant." system_message_prompt = SystemMessagePromptTemplate.from_template(template) human_message_prompt = HumanMessagePromptTemplate.from_template( "We have spotify database and the following tools:\n" "{Tool_dic}" "You need to decompose a complex user's question into some simple subtasks and let the model execute it step by step with these tools.\n" "Please note that: \n" "1. you should break down tasks into appropriate subtasks to use the tools mentioned above.\n" "2. You should not only list the subtask, but also list the ID of the tool used to solve this subtask.\n" "3. If you think you do not need to use the tool to solve the subtask, just leave it as {{\"ID\": -1}}\n" "4. You must consider the logical connections, order and constraints among the tools to achieve a correct tool path." "5. You must ONLY output the ID of the tool you chose in a parsible JSON format. Two examples output look like:\n" "'''\n" "Question: Pause the player" "Example 1: [{{\"Task\":\"Get information about the user’s current playback state\", \"ID\":15}}, {{\"Task\":\"Pause playback on the user's account\", \"ID\":19}}]\n" "'''\n" "This is the user's question: {question}\n" "Output:" ) chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) chain = LLMChain(llm=chat, prompt=chat_prompt) ind = 0 while True: try: result = chain.run(question=question, Tool_dic=Tool_dic) result = eval(result.split('\n\n')[0]) break except Exception as e: print(f"task decompose fails: {e}") if ind > 10: return -1 ind += 1 continue return result def task_execution( Tool_dic, dic_tool, test_data, progress_file, start_index, total_files, retrieval_num, ind, model_name): with tqdm(total=total_files, desc="Processing files", initial=start_index) as pbar: for i, data in enumerate(test_data[start_index:], start=start_index): question = data["query"] print(question) task_path = task_decompose(question, Tool_dic, model_name) tool_choice_ls = [] for task in task_path: if isinstance(task["ID"], list): for ele in task["ID"]: tool_choice_ls.append(dic_tool[ele]['tool_usage']) elif int(task["ID"]) in dic_tool.keys(): tool_choice_ls.append(dic_tool[task["ID"]]['tool_usage']) ind = ind + 1 with open(f"restbench_{model_name}_Easytool.jsonl", 'a+', encoding='utf-8') as f: line = json.dumps({ "ID": ind, "question": question, "task_path": task_path, "tool_choice_ls": tool_choice_ls }, ensure_ascii=False) f.write(line + '\n') print(tool_choice_ls) update_progress(progress_file, i + 1) pbar.update(1)

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from langchain.llms import Ollama input = input("What is your question?") llm = Ollama(model="llama2") res = llm.predict(input) print (res)

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import os from pathlib import Path from typing import Union import cloudpickle import yaml from mlflow.exceptions import MlflowException from mlflow.langchain.utils import ( _BASE_LOAD_KEY, _CONFIG_LOAD_KEY, _MODEL_DATA_FOLDER_NAME, _MODEL_DATA_KEY, _MODEL_DATA_PKL_FILE_NAME, _MODEL_DATA_YAML_FILE_NAME, _MODEL_LOAD_KEY, _MODEL_TYPE_KEY, _RUNNABLE_LOAD_KEY, _UNSUPPORTED_MODEL_ERROR_MESSAGE, _load_base_lcs, _load_from_json, _load_from_pickle, _load_from_yaml, _save_base_lcs, _validate_and_wrap_lc_model, base_lc_types, custom_type_to_loader_dict, lc_runnable_assign_types, lc_runnable_branch_types, lc_runnable_with_steps_types, lc_runnables_types, picklable_runnable_types, ) _STEPS_FOLDER_NAME = "steps" _RUNNABLE_STEPS_FILE_NAME = "steps.yaml" _BRANCHES_FOLDER_NAME = "branches" _MAPPER_FOLDER_NAME = "mapper" _RUNNABLE_BRANCHES_FILE_NAME = "branches.yaml" _DEFAULT_BRANCH_NAME = "default" def _load_model_from_config(path, model_config): from langchain.chains.loading import type_to_loader_dict as chains_type_to_loader_dict from langchain.llms import get_type_to_cls_dict as llms_get_type_to_cls_dict try: from langchain.prompts.loading import type_to_loader_dict as prompts_types except ImportError: prompts_types = {"prompt", "few_shot_prompt"} config_path = os.path.join(path, model_config.get(_MODEL_DATA_KEY, _MODEL_DATA_YAML_FILE_NAME)) # Load runnables from config file if config_path.endswith(".yaml"): config = _load_from_yaml(config_path) elif config_path.endswith(".json"): config = _load_from_json(config_path) else: raise MlflowException( f"Cannot load runnable without a config file. Got path {config_path}." ) _type = config.get("_type") if _type in chains_type_to_loader_dict: from langchain.chains.loading import load_chain return load_chain(config_path) elif _type in prompts_types: from langchain.prompts.loading import load_prompt return load_prompt(config_path) elif _type in llms_get_type_to_cls_dict(): from langchain.llms.loading import load_llm return load_llm(config_path) elif _type in custom_type_to_loader_dict(): return custom_type_to_loader_dict()[_type](config) raise MlflowException(f"Unsupported type {_type} for loading.") def _load_model_from_path(path: str, model_config=None): model_load_fn = model_config.get(_MODEL_LOAD_KEY) if model_load_fn == _RUNNABLE_LOAD_KEY: return _load_runnables(path, model_config) if model_load_fn == _BASE_LOAD_KEY: return _load_base_lcs(path, model_config) if model_load_fn == _CONFIG_LOAD_KEY: return _load_model_from_config(path, model_config) raise MlflowException(f"Unsupported model load key {model_load_fn}") def _load_runnable_with_steps(file_path: Union[Path, str], model_type: str): """Load the model Args: file_path: Path to file to load the model from. model_type: Type of the model to load. """ from langchain.schema.runnable import RunnableParallel, RunnableSequence # Convert file to Path object. load_path = Path(file_path) if not load_path.exists() or not load_path.is_dir(): raise MlflowException( f"File {load_path} must exist and must be a directory " "in order to load runnable with steps." ) steps_conf_file = load_path / _RUNNABLE_STEPS_FILE_NAME if not steps_conf_file.exists(): raise MlflowException( f"File {steps_conf_file} must exist in order to load runnable with steps." ) steps_conf = _load_from_yaml(steps_conf_file) steps_path = load_path / _STEPS_FOLDER_NAME if not steps_path.exists() or not steps_path.is_dir(): raise MlflowException( f"Folder {steps_path} must exist and must be a directory " "in order to load runnable with steps." ) steps = {} # ignore hidden files for step in (f for f in os.listdir(steps_path) if not f.startswith(".")): config = steps_conf.get(step) # load model from the folder of the step runnable = _load_model_from_path(os.path.join(steps_path, step), config) steps[step] = runnable if model_type == RunnableSequence.__name__: steps = [value for _, value in sorted(steps.items(), key=lambda item: int(item[0]))] return runnable_sequence_from_steps(steps) if model_type == RunnableParallel.__name__: return RunnableParallel(steps) def runnable_sequence_from_steps(steps): """Construct a RunnableSequence from steps. Args: steps: List of steps to construct the RunnableSequence from. """ from langchain.schema.runnable import RunnableSequence if len(steps) < 2: raise ValueError(f"RunnableSequence must have at least 2 steps, got {len(steps)}.") first, *middle, last = steps return RunnableSequence(first=first, middle=middle, last=last) def _load_runnable_branch(file_path: Union[Path, str]): """Load the model Args: file_path: Path to file to load the model from. """ from langchain.schema.runnable import RunnableBranch # Convert file to Path object. load_path = Path(file_path) if not load_path.exists() or not load_path.is_dir(): raise MlflowException( f"File {load_path} must exist and must be a directory " "in order to load runnable with steps." ) branches_conf_file = load_path / _RUNNABLE_BRANCHES_FILE_NAME if not branches_conf_file.exists(): raise MlflowException( f"File {branches_conf_file} must exist in order to load runnable with steps." ) branches_conf = _load_from_yaml(branches_conf_file) branches_path = load_path / _BRANCHES_FOLDER_NAME if not branches_path.exists() or not branches_path.is_dir(): raise MlflowException( f"Folder {branches_path} must exist and must be a directory " "in order to load runnable with steps." ) branches = [] for branch in os.listdir(branches_path): # load model from the folder of the branch if branch == _DEFAULT_BRANCH_NAME: default_branch_path = branches_path / _DEFAULT_BRANCH_NAME default = _load_model_from_path( default_branch_path, branches_conf.get(_DEFAULT_BRANCH_NAME) ) else: branch_tuple = [] for i in range(2): config = branches_conf.get(f"{branch}-{i}") runnable = _load_model_from_path( os.path.join(branches_path, branch, str(i)), config ) branch_tuple.append(runnable) branches.append(tuple(branch_tuple)) # default branch must be the last branch branches.append(default) return RunnableBranch(*branches) def _load_runnable_assign(file_path: Union[Path, str]): """Load the model Args: file_path: Path to file to load the model from. """ from langchain.schema.runnable.passthrough import RunnableAssign # Convert file to Path object. load_path = Path(file_path) if not load_path.exists() or not load_path.is_dir(): raise MlflowException( f"File {load_path} must exist and must be a directory in order to load runnable." ) mapper_file = load_path / _MAPPER_FOLDER_NAME if not mapper_file.exists() or not mapper_file.is_dir(): raise MlflowException( f"Folder {mapper_file} must exist and must be a directory " "in order to load runnable assign with mapper." ) mapper = _load_runnable_with_steps(mapper_file, "RunnableParallel") return RunnableAssign(mapper) def _save_internal_runnables(runnable, path, loader_fn, persist_dir): conf = {} if isinstance(runnable, lc_runnables_types()): conf[_MODEL_TYPE_KEY] = runnable.__class__.__name__ conf.update(_save_runnables(runnable, path, loader_fn, persist_dir)) elif isinstance(runnable, base_lc_types()): lc_model = _validate_and_wrap_lc_model(runnable, loader_fn) conf[_MODEL_TYPE_KEY] = lc_model.__class__.__name__ conf.update(_save_base_lcs(lc_model, path, loader_fn, persist_dir)) else: conf = { _MODEL_TYPE_KEY: runnable.__class__.__name__, _MODEL_DATA_KEY: _MODEL_DATA_YAML_FILE_NAME, _MODEL_LOAD_KEY: _CONFIG_LOAD_KEY, } path = path / _MODEL_DATA_YAML_FILE_NAME # Save some simple runnables that langchain natively supports. if hasattr(runnable, "save"): runnable.save(path) # TODO: check if `dict` is enough to load it back elif hasattr(runnable, "dict"): runnable_dict = runnable.dict() with open(path, "w") as f: yaml.dump(runnable_dict, f, default_flow_style=False) else: return return conf def _save_runnable_with_steps(model, file_path: Union[Path, str], loader_fn=None, persist_dir=None): """Save the model with steps. Currently it supports saving RunnableSequence and RunnableParallel. If saving a RunnableSequence, steps is a list of Runnable objects. We save each step to the subfolder named by the step index. e.g. - model - steps - 0 - model.yaml - 1 - model.pkl - steps.yaml If saving a RunnableParallel, steps is a dictionary of key-Runnable pairs. We save each step to the subfolder named by the key. e.g. - model - steps - context - model.yaml - question - model.pkl - steps.yaml We save steps.yaml file to the model folder. It contains each step's model's configuration. Args: model: Runnable to be saved. file_path: Path to file to save the model to. """ # Convert file to Path object. save_path = Path(file_path) save_path.mkdir(parents=True, exist_ok=True) # Save steps into a folder steps_path = save_path / _STEPS_FOLDER_NAME steps_path.mkdir() steps = model.steps if isinstance(steps, list): generator = enumerate(steps) elif isinstance(steps, dict): generator = steps.items() else: raise MlflowException( f"Runnable {model} steps attribute must be either a list or a dictionary. " f"Got {type(steps).__name__}." ) unsaved_runnables = {} steps_conf = {} for key, runnable in generator: step = str(key) # Save each step into a subfolder named by step save_runnable_path = steps_path / step save_runnable_path.mkdir() if result := _save_internal_runnables(runnable, save_runnable_path, loader_fn, persist_dir): steps_conf[step] = result else: unsaved_runnables[step] = str(runnable) if unsaved_runnables: raise MlflowException( f"Failed to save runnable sequence: {unsaved_runnables}. " "Runnable must have either `save` or `dict` method." ) # save steps configs with save_path.joinpath(_RUNNABLE_STEPS_FILE_NAME).open("w") as f: yaml.dump(steps_conf, f, default_flow_style=False) def _save_runnable_branch(model, file_path, loader_fn, persist_dir): """ Save runnable branch in to path. """ save_path = Path(file_path) save_path.mkdir(parents=True, exist_ok=True) # save branches into a folder branches_path = save_path / _BRANCHES_FOLDER_NAME branches_path.mkdir() unsaved_runnables = {} branches_conf = {} for index, branch_tuple in enumerate(model.branches): # Save each branch into a subfolder named by index # and save condition and runnable into subfolder for i, runnable in enumerate(branch_tuple): save_runnable_path = branches_path / str(index) / str(i) save_runnable_path.mkdir(parents=True) branches_conf[f"{index}-{i}"] = {} if result := _save_internal_runnables( runnable, save_runnable_path, loader_fn, persist_dir ): branches_conf[f"{index}-{i}"] = result else: unsaved_runnables[f"{index}-{i}"] = str(runnable) # save default branch default_branch_path = branches_path / _DEFAULT_BRANCH_NAME default_branch_path.mkdir() if result := _save_internal_runnables( model.default, default_branch_path, loader_fn, persist_dir ): branches_conf[_DEFAULT_BRANCH_NAME] = result else: unsaved_runnables[_DEFAULT_BRANCH_NAME] = str(model.default) if unsaved_runnables: raise MlflowException( f"Failed to save runnable branch: {unsaved_runnables}. " "Runnable must have either `save` or `dict` method." ) # save branches configs with save_path.joinpath(_RUNNABLE_BRANCHES_FILE_NAME).open("w") as f: yaml.dump(branches_conf, f, default_flow_style=False) def _save_runnable_assign(model, file_path, loader_fn=None, persist_dir=None): from langchain.schema.runnable import RunnableParallel save_path = Path(file_path) save_path.mkdir(parents=True, exist_ok=True) # save mapper into a folder mapper_path = save_path / _MAPPER_FOLDER_NAME mapper_path.mkdir() if not isinstance(model.mapper, RunnableParallel): raise MlflowException( f"Failed to save model {model} with type {model.__class__.__name__}. " "RunnableAssign's mapper must be a RunnableParallel." ) _save_runnable_with_steps(model.mapper, mapper_path, loader_fn, persist_dir) def _save_picklable_runnable(model, path): if not path.endswith(".pkl"): raise ValueError(f"File path must end with .pkl, got {path}.") with open(path, "wb") as f: cloudpickle.dump(model, f) def _save_runnables(model, path, loader_fn=None, persist_dir=None): model_data_kwargs = {_MODEL_LOAD_KEY: _RUNNABLE_LOAD_KEY} if isinstance(model, lc_runnable_with_steps_types()): model_data_path = _MODEL_DATA_FOLDER_NAME _save_runnable_with_steps( model, os.path.join(path, model_data_path), loader_fn, persist_dir ) elif isinstance(model, picklable_runnable_types()): model_data_path = _MODEL_DATA_PKL_FILE_NAME _save_picklable_runnable(model, os.path.join(path, model_data_path)) elif isinstance(model, lc_runnable_branch_types()): model_data_path = _MODEL_DATA_FOLDER_NAME _save_runnable_branch(model, os.path.join(path, model_data_path), loader_fn, persist_dir) elif isinstance(model, lc_runnable_assign_types()): model_data_path = _MODEL_DATA_FOLDER_NAME _save_runnable_assign(model, os.path.join(path, model_data_path), loader_fn, persist_dir) else: raise MlflowException.invalid_parameter_value( _UNSUPPORTED_MODEL_ERROR_MESSAGE.format(instance_type=type(model).__name__) ) model_data_kwargs.update({_MODEL_DATA_KEY: model_data_path}) return model_data_kwargs def _load_runnables(path, conf): model_type = conf.get(_MODEL_TYPE_KEY) model_data = conf.get(_MODEL_DATA_KEY, _MODEL_DATA_YAML_FILE_NAME) if model_type in (x.__name__ for x in lc_runnable_with_steps_types()): return _load_runnable_with_steps(os.path.join(path, model_data), model_type) if ( model_type in (x.__name__ for x in picklable_runnable_types()) or model_data == _MODEL_DATA_PKL_FILE_NAME ): return _load_from_pickle(os.path.join(path, model_data)) if model_type in (x.__name__ for x in lc_runnable_branch_types()): return _load_runnable_branch(os.path.join(path, model_data)) if model_type in (x.__name__ for x in lc_runnable_assign_types()): return _load_runnable_assign(os.path.join(path, model_data)) raise MlflowException.invalid_parameter_value( _UNSUPPORTED_MODEL_ERROR_MESSAGE.format(instance_type=model_type) )

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import json from langchain.schema import OutputParserException def parse_json_markdown(json_string: str) -> dict: # Remove the triple backticks if present json_string = json_string.strip() start_index = json_string.find("```json") end_index = json_string.find("```", start_index + len("```json")) if start_index != -1 and end_index != -1: extracted_content = json_string[start_index + len("```json"):end_index].strip() # Parse the JSON string into a Python dictionary parsed = json.loads(extracted_content) elif start_index != -1 and end_index == -1 and json_string.endswith("``"): end_index = json_string.find("``", start_index + len("```json")) extracted_content = json_string[start_index + len("```json"):end_index].strip() # Parse the JSON string into a Python dictionary parsed = json.loads(extracted_content) elif json_string.startswith("{"): # Parse the JSON string into a Python dictionary parsed = json.loads(json_string) else: raise Exception("Could not find JSON block in the output.") return parsed def parse_and_check_json_markdown(text: str, expected_keys: list[str]) -> dict: try: json_obj = parse_json_markdown(text) except json.JSONDecodeError as e: raise OutputParserException(f"Got invalid JSON object. Error: {e}") for key in expected_keys: if key not in json_obj: raise OutputParserException( f"Got invalid return object. Expected key `{key}` " f"to be present, but got {json_obj}" ) return json_obj

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import os import uuid from typing import Any, Dict, List, Optional, Tuple from langchain.agents.agent import RunnableAgent from langchain.agents.tools import tool as LangChainTool from langchain.memory import ConversationSummaryMemory from langchain.tools.render import render_text_description from langchain_core.agents import AgentAction from langchain_core.callbacks import BaseCallbackHandler from langchain_openai import ChatOpenAI from pydantic import ( UUID4, BaseModel, ConfigDict, Field, InstanceOf, PrivateAttr, field_validator, model_validator, ) from pydantic_core import PydanticCustomError from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler from crewai.utilities import I18N, Logger, Prompts, RPMController from crewai.utilities.token_counter_callback import TokenCalcHandler, TokenProcess class Agent(BaseModel): """Represents an agent in a system. Each agent has a role, a goal, a backstory, and an optional language model (llm). The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents. Attributes: agent_executor: An instance of the CrewAgentExecutor class. role: The role of the agent. goal: The objective of the agent. backstory: The backstory of the agent. config: Dict representation of agent configuration. llm: The language model that will run the agent. function_calling_llm: The language model that will the tool calling for this agent, it overrides the crew function_calling_llm. max_iter: Maximum number of iterations for an agent to execute a task. memory: Whether the agent should have memory or not. max_rpm: Maximum number of requests per minute for the agent execution to be respected. verbose: Whether the agent execution should be in verbose mode. allow_delegation: Whether the agent is allowed to delegate tasks to other agents. tools: Tools at agents disposal step_callback: Callback to be executed after each step of the agent execution. callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process """ __hash__ = object.__hash__ # type: ignore _logger: Logger = PrivateAttr() _rpm_controller: RPMController = PrivateAttr(default=None) _request_within_rpm_limit: Any = PrivateAttr(default=None) _token_process: TokenProcess = TokenProcess() formatting_errors: int = 0 model_config = ConfigDict(arbitrary_types_allowed=True) id: UUID4 = Field( default_factory=uuid.uuid4, frozen=True, description="Unique identifier for the object, not set by user.", ) role: str = Field(description="Role of the agent") goal: str = Field(description="Objective of the agent") backstory: str = Field(description="Backstory of the agent") config: Optional[Dict[str, Any]] = Field( description="Configuration for the agent", default=None, ) max_rpm: Optional[int] = Field( default=None, description="Maximum number of requests per minute for the agent execution to be respected.", ) memory: bool = Field( default=False, description="Whether the agent should have memory or not" ) verbose: bool = Field( default=False, description="Verbose mode for the Agent Execution" ) allow_delegation: bool = Field( default=True, description="Allow delegation of tasks to agents" ) tools: Optional[List[Any]] = Field( default_factory=list, description="Tools at agents disposal" ) max_iter: Optional[int] = Field( default=15, description="Maximum iterations for an agent to execute a task" ) agent_executor: InstanceOf[CrewAgentExecutor] = Field( default=None, description="An instance of the CrewAgentExecutor class." ) tools_handler: InstanceOf[ToolsHandler] = Field( default=None, description="An instance of the ToolsHandler class." ) cache_handler: InstanceOf[CacheHandler] = Field( default=CacheHandler(), description="An instance of the CacheHandler class." ) step_callback: Optional[Any] = Field( default=None, description="Callback to be executed after each step of the agent execution.", ) i18n: I18N = Field(default=I18N(), description="Internationalization settings.") llm: Any = Field( default_factory=lambda: ChatOpenAI( model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4") ), description="Language model that will run the agent.", ) function_calling_llm: Optional[Any] = Field( description="Language model that will run the agent.", default=None ) callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field( default=None, description="Callback to be executed" ) def __init__(__pydantic_self__, **data): config = data.pop("config", {}) super().__init__(**config, **data) @field_validator("id", mode="before") @classmethod def _deny_user_set_id(cls, v: Optional[UUID4]) -> None: if v: raise PydanticCustomError( "may_not_set_field", "This field is not to be set by the user.", {} ) @model_validator(mode="after") def set_attributes_based_on_config(self) -> "Agent": """Set attributes based on the agent configuration.""" if self.config: for key, value in self.config.items(): setattr(self, key, value) return self @model_validator(mode="after") def set_private_attrs(self): """Set private attributes.""" self._logger = Logger(self.verbose) if self.max_rpm and not self._rpm_controller: self._rpm_controller = RPMController( max_rpm=self.max_rpm, logger=self._logger ) return self @model_validator(mode="after") def set_agent_executor(self) -> "Agent": """set agent executor is set.""" if hasattr(self.llm, "model_name"): self.llm.callbacks = [ TokenCalcHandler(self.llm.model_name, self._token_process) ] if not self.agent_executor: self.set_cache_handler(self.cache_handler) return self def execute_task( self, task: Any, context: Optional[str] = None, tools: Optional[List[Any]] = None, ) -> str: """Execute a task with the agent. Args: task: Task to execute. context: Context to execute the task in. tools: Tools to use for the task. Returns: Output of the agent """ self.tools_handler.last_used_tool = {} task_prompt = task.prompt() if context: task_prompt = self.i18n.slice("task_with_context").format( task=task_prompt, context=context ) tools = self._parse_tools(tools or self.tools) self.create_agent_executor(tools=tools) self.agent_executor.tools = tools self.agent_executor.task = task self.agent_executor.tools_description = render_text_description(tools) self.agent_executor.tools_names = self.__tools_names(tools) result = self.agent_executor.invoke( { "input": task_prompt, "tool_names": self.agent_executor.tools_names, "tools": self.agent_executor.tools_description, } )["output"] if self.max_rpm: self._rpm_controller.stop_rpm_counter() return result def set_cache_handler(self, cache_handler: CacheHandler) -> None: """Set the cache handler for the agent. Args: cache_handler: An instance of the CacheHandler class. """ self.cache_handler = cache_handler self.tools_handler = ToolsHandler(cache=self.cache_handler) self.create_agent_executor() def set_rpm_controller(self, rpm_controller: RPMController) -> None: """Set the rpm controller for the agent. Args: rpm_controller: An instance of the RPMController class. """ if not self._rpm_controller: self._rpm_controller = rpm_controller self.create_agent_executor() def create_agent_executor(self, tools=None) -> None: """Create an agent executor for the agent. Returns: An instance of the CrewAgentExecutor class. """ tools = tools or self.tools agent_args = { "input": lambda x: x["input"], "tools": lambda x: x["tools"], "tool_names": lambda x: x["tool_names"], "agent_scratchpad": lambda x: self.format_log_to_str( x["intermediate_steps"] ), } executor_args = { "llm": self.llm, "i18n": self.i18n, "tools": self._parse_tools(tools), "verbose": self.verbose, "handle_parsing_errors": True, "max_iterations": self.max_iter, "step_callback": self.step_callback, "tools_handler": self.tools_handler, "function_calling_llm": self.function_calling_llm, "callbacks": self.callbacks, } if self._rpm_controller: executor_args[ "request_within_rpm_limit" ] = self._rpm_controller.check_or_wait if self.memory: summary_memory = ConversationSummaryMemory( llm=self.llm, input_key="input", memory_key="chat_history" ) executor_args["memory"] = summary_memory agent_args["chat_history"] = lambda x: x["chat_history"] prompt = Prompts(i18n=self.i18n, tools=tools).task_execution_with_memory() else: prompt = Prompts(i18n=self.i18n, tools=tools).task_execution() execution_prompt = prompt.partial( goal=self.goal, role=self.role, backstory=self.backstory, ) bind = self.llm.bind(stop=[self.i18n.slice("observation")]) inner_agent = agent_args | execution_prompt | bind | CrewAgentParser(agent=self) self.agent_executor = CrewAgentExecutor( agent=RunnableAgent(runnable=inner_agent), **executor_args ) def interpolate_inputs(self, inputs: Dict[str, Any]) -> None: """Interpolate inputs into the agent description and backstory.""" if inputs: self.role = self.role.format(**inputs) self.goal = self.goal.format(**inputs) self.backstory = self.backstory.format(**inputs) def increment_formatting_errors(self) -> None: """Count the formatting errors of the agent.""" self.formatting_errors += 1 def format_log_to_str( self, intermediate_steps: List[Tuple[AgentAction, str]], observation_prefix: str = "Observation: ", llm_prefix: str = "", ) -> str: """Construct the scratchpad that lets the agent continue its thought process.""" thoughts = "" for action, observation in intermediate_steps: thoughts += action.log thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}" return thoughts def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]: """Parse tools to be used for the task.""" # tentatively try to import from crewai_tools import BaseTool as CrewAITool tools_list = [] try: from crewai_tools import BaseTool as CrewAITool for tool in tools: if isinstance(tool, CrewAITool): tools_list.append(tool.to_langchain()) else: tools_list.append(tool) except ModuleNotFoundError: for tool in tools: tools_list.append(tool) return tools_list @staticmethod def __tools_names(tools) -> str: return ", ".join([t.name for t in tools]) def __repr__(self): return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"

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import os import logging import hashlib import PyPDF2 from tqdm import tqdm from modules.presets import * from modules.utils import * from modules.config import local_embedding def get_documents(file_src): from langchain.schema import Document from langchain.text_splitter import TokenTextSplitter text_splitter = TokenTextSplitter(chunk_size=500, chunk_overlap=30) documents = [] logging.debug("Loading documents...") logging.debug(f"file_src: {file_src}") for file in file_src: filepath = file.name filename = os.path.basename(filepath) file_type = os.path.splitext(filename)[1] logging.info(f"loading file: {filename}") texts = None try: if file_type == ".pdf": logging.debug("Loading PDF...") try: from modules.pdf_func import parse_pdf from modules.config import advance_docs two_column = advance_docs["pdf"].get("two_column", False) pdftext = parse_pdf(filepath, two_column).text except: pdftext = "" with open(filepath, "rb") as pdfFileObj: pdfReader = PyPDF2.PdfReader(pdfFileObj) for page in tqdm(pdfReader.pages): pdftext += page.extract_text() texts = [Document(page_content=pdftext, metadata={"source": filepath})] elif file_type == ".docx": logging.debug("Loading Word...") from langchain.document_loaders import UnstructuredWordDocumentLoader loader = UnstructuredWordDocumentLoader(filepath) texts = loader.load() elif file_type == ".pptx": logging.debug("Loading PowerPoint...") from langchain.document_loaders import UnstructuredPowerPointLoader loader = UnstructuredPowerPointLoader(filepath) texts = loader.load() elif file_type == ".epub": logging.debug("Loading EPUB...") from langchain.document_loaders import UnstructuredEPubLoader loader = UnstructuredEPubLoader(filepath) texts = loader.load() elif file_type == ".xlsx": logging.debug("Loading Excel...") text_list = excel_to_string(filepath) texts = [] for elem in text_list: texts.append(Document(page_content=elem, metadata={"source": filepath})) elif file_type in [".jpg", ".jpeg", ".png", ".heif", ".heic", ".webp", ".bmp", ".gif", ".tiff", ".tif"]: raise gr.Warning(i18n("不支持的文件: ") + filename + i18n("，请使用 .pdf, .docx, .pptx, .epub, .xlsx 等文档。")) else: logging.debug("Loading text file...") from langchain.document_loaders import TextLoader loader = TextLoader(filepath, "utf8") texts = loader.load() except Exception as e: import traceback logging.error(f"Error loading file: {filename}") traceback.print_exc() if texts is not None: texts = text_splitter.split_documents(texts) documents.extend(texts) logging.debug("Documents loaded.") return documents def construct_index( api_key, file_src, max_input_size=4096, num_outputs=5, max_chunk_overlap=20, chunk_size_limit=600, embedding_limit=None, separator=" ", load_from_cache_if_possible=True, ): from langchain.chat_models import ChatOpenAI from langchain.vectorstores import FAISS if api_key: os.environ["OPENAI_API_KEY"] = api_key else: # 由于一个依赖的愚蠢的设计，这里必须要有一个API KEY os.environ["OPENAI_API_KEY"] = "sk-xxxxxxx" logging.debug(f"api base: {os.environ.get('OPENAI_API_BASE', None)}") chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit embedding_limit = None if embedding_limit == 0 else embedding_limit separator = " " if separator == "" else separator index_name = get_file_hash(file_src) index_path = f"./index/{index_name}" if local_embedding: from langchain.embeddings.huggingface import HuggingFaceEmbeddings embeddings = HuggingFaceEmbeddings( model_name="sentence-transformers/distiluse-base-multilingual-cased-v2") else: from langchain.embeddings import OpenAIEmbeddings if os.environ.get("OPENAI_API_TYPE", "openai") == "openai": embeddings = OpenAIEmbeddings(openai_api_base=os.environ.get( "OPENAI_API_BASE", None), openai_api_key=os.environ.get("OPENAI_EMBEDDING_API_KEY", api_key)) else: embeddings = OpenAIEmbeddings(deployment=os.environ["AZURE_EMBEDDING_DEPLOYMENT_NAME"], openai_api_key=os.environ["AZURE_OPENAI_API_KEY"], model=os.environ["AZURE_EMBEDDING_MODEL_NAME"], openai_api_base=os.environ["AZURE_OPENAI_API_BASE_URL"], openai_api_type="azure") if os.path.exists(index_path) and load_from_cache_if_possible: logging.info(i18n("找到了缓存的索引文件，加载中……")) return FAISS.load_local(index_path, embeddings) else: documents = get_documents(file_src) logging.debug(i18n("构建索引中……")) if documents: with retrieve_proxy(): index = FAISS.from_documents(documents, embeddings) else: raise Exception(i18n("没有找到任何支持的文档。")) logging.debug(i18n("索引构建完成！")) os.makedirs("./index", exist_ok=True) index.save_local(index_path) logging.debug(i18n("索引已保存至本地!")) return index

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import re from typing import Union from langchain.agents.mrkl.output_parser import MRKLOutputParser from langchain.schema import AgentAction, AgentFinish, OutputParserException FORMAT_INSTRUCTIONS0 = """Use the following format and be sure to use new lines after each task. Question: the input question you must answer Thought: you should always think about what to do Action: Exactly only one word out 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""" FORMAT_INSTRUCTIONS = """List of tools, use exactly one word when choosing Action: {tool_names} Only user asks a question, not you. For example user might ask: What is the latest news? Here is an example sequence you can follow: Thought: I should search online for the latest news. Action: Search Action Input: What is the latest news? Observation: X is going away. Z is again happening. Thought: That is interesting, I should search for more information about X and Z and also search about Q. Action: Search Action Input: How is X impacting things. Why is Z happening again, and what are the consequences? Observation: X is causing Y. Z may be caused by P and will lead to H. Thought: I now know the final answer Final Answer: The latest news is: * X is going away, and this is caused by Y. * Z is happening again, and the cause is P and will lead to H. Overall, X and Z are important problems. """ FORMAT_INSTRUCTIONS_PYTHON = """List of tools, use exactly one word when choosing Action: {tool_names} Only user asks a question, not you. For example user might ask: How many rows are in the dataset? Here is an example sequence you can follow. You can repeat Thoughts, but as soon as possible you should try to answer the original user question. Once you an answer the user question, just say: Thought: I now know the final answer Thought: I should use python_repl_ast tool. Action: python_repl_ast Action Input: df.shape Observation: (25, 10) Thought: I now know the final answer Final Answer: There are 25 rows in the dataset. """ FINAL_ANSWER_ACTION = "Final Answer:" MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE = ( "Invalid Format: Missing 'Action:' after 'Thought:" ) MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE = ( "Invalid Format: Missing 'Action Input:' after 'Action:'" ) FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = ( "Parsing LLM output produced both a final answer and a parse-able action:" ) class H2OMRKLOutputParser(MRKLOutputParser): """MRKL Output parser for the chat agent.""" def get_format_instructions(self) -> str: return FORMAT_INSTRUCTIONS def parse(self, text: str) -> Union[AgentAction, AgentFinish]: includes_answer = FINAL_ANSWER_ACTION in text regex = ( r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)" ) action_match = re.search(regex, text, re.DOTALL) if includes_answer: return AgentFinish( {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text ) elif action_match: action = action_match.group(1).strip() action_input = action_match.group(2) tool_input = action_input.strip(" ") # ensure if its a well formed SQL query we don't remove any trailing " chars if tool_input.startswith("SELECT ") is False: tool_input = tool_input.strip('"') return AgentAction(action, tool_input, text) if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL): raise OutputParserException( f"Could not parse LLM output: `{text}`", observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE, llm_output=text, send_to_llm=True, ) elif not re.search( r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL ): raise OutputParserException( f"Could not parse LLM output: `{text}`", observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE, llm_output=text, send_to_llm=True, ) else: raise OutputParserException(f"Could not parse LLM output: `{text}`") @property def _type(self) -> str: return "mrkl" class H2OPythonMRKLOutputParser(H2OMRKLOutputParser): def get_format_instructions(self) -> str: return FORMAT_INSTRUCTIONS_PYTHON

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from typing import Any, Callable, Dict, TypeVar from langchain import BasePromptTemplate, LLMChain from langchain.chat_models.base import BaseChatModel from langchain.schema import BaseOutputParser, OutputParserException from openai.error import ( AuthenticationError, InvalidRequestError, RateLimitError, ServiceUnavailableError, ) from reworkd_platform.schemas.agent import ModelSettings from reworkd_platform.web.api.errors import OpenAIError T = TypeVar("T") def parse_with_handling(parser: BaseOutputParser[T], completion: str) -> T: try: return parser.parse(completion) except OutputParserException as e: raise OpenAIError( e, "There was an issue parsing the response from the AI model." ) async def openai_error_handler( func: Callable[..., Any], *args: Any, settings: ModelSettings, **kwargs: Any ) -> Any: try: return await func(*args, **kwargs) except ServiceUnavailableError as e: raise OpenAIError( e, "OpenAI is experiencing issues. Visit " "https://status.openai.com/ for more info.", should_log=not settings.custom_api_key, ) except InvalidRequestError as e: if e.user_message.startswith("The model:"): raise OpenAIError( e, f"Your API key does not have access to your current model. Please use a different model.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except AuthenticationError as e: raise OpenAIError( e, "Authentication error: Ensure a valid API key is being used.", should_log=not settings.custom_api_key, ) except RateLimitError as e: if e.user_message.startswith("You exceeded your current quota"): raise OpenAIError( e, f"Your API key exceeded your current quota, please check your plan and billing details.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except Exception as e: raise OpenAIError( e, "There was an unexpected issue getting a response from the AI model." ) async def call_model_with_handling( model: BaseChatModel, prompt: BasePromptTemplate, args: Dict[str, str], settings: ModelSettings, **kwargs: Any, ) -> str: chain = LLMChain(llm=model, prompt=prompt) return await openai_error_handler(chain.arun, args, settings=settings, **kwargs)

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import json import os.path import logging import time from langchain.vectorstores import FAISS from langchain import PromptTemplate from utils.references import References from utils.knowledge import Knowledge from utils.file_operations import make_archive, copy_templates from utils.tex_processing import create_copies from utils.gpt_interaction import GPTModel from utils.prompts import SYSTEM from utils.embeddings import EMBEDDINGS from utils.gpt_interaction import get_gpt_responses TOTAL_TOKENS = 0 TOTAL_PROMPTS_TOKENS = 0 TOTAL_COMPLETION_TOKENS = 0 def log_usage(usage, generating_target, print_out=True): global TOTAL_TOKENS global TOTAL_PROMPTS_TOKENS global TOTAL_COMPLETION_TOKENS prompts_tokens = usage['prompt_tokens'] completion_tokens = usage['completion_tokens'] total_tokens = usage['total_tokens'] TOTAL_TOKENS += total_tokens TOTAL_PROMPTS_TOKENS += prompts_tokens TOTAL_COMPLETION_TOKENS += completion_tokens message = f">>USAGE>> For generating {generating_target}, {total_tokens} tokens have been used " \ f"({prompts_tokens} for prompts; {completion_tokens} for completion). " \ f"{TOTAL_TOKENS} tokens have been used in total." if print_out: print(message) logging.info(message) def _generation_setup(title, template="Default", tldr=False, max_kw_refs=20, bib_refs=None, max_tokens_ref=2048, # generating references knowledge_database=None, max_tokens_kd=2048, query_counts=10): llm = GPTModel(model="gpt-3.5-turbo-16k") bibtex_path, destination_folder = copy_templates(template, title) logging.basicConfig(level=logging.INFO, filename=os.path.join(destination_folder, "generation.log")) #generate key words keywords, usage = llm(systems=SYSTEM["keywords"], prompts=title, return_json=True) log_usage(usage, "keywords") keywords = {keyword: max_kw_refs for keyword in keywords} print("Keywords: \n", keywords) #generate references ref = References(title, bib_refs) ref.collect_papers(keywords, tldr=tldr) references = ref.to_prompts(max_tokens=max_tokens_ref) all_paper_ids = ref.to_bibtex(bibtex_path) #product domain knowledge prompts = f"Title: {title}" preliminaries_kw, _ = llm(systems=SYSTEM["preliminaries"], prompts=prompts) # check if the database exists or not db_path = f"utils/knowledge_databases/{knowledge_database}" db_config_path = os.path.join(db_path, "db_meta.json") db_index_path = os.path.join(db_path, "faiss_index") if os.path.isdir(db_path): try: with open(db_config_path, "r", encoding="utf-8") as f: db_config = json.load(f) model_name = db_config["embedding_model"] embeddings = EMBEDDINGS[model_name] db = FAISS.load_local(db_index_path, embeddings) knowledge = Knowledge(db=db) knowledge.collect_knowledge(preliminaries_kw, max_query=query_counts) domain_knowledge = knowledge.to_prompts(max_tokens_kd) except Exception as e: domain_knowledge='' prompts = f"Title: {title}" syetem_promot = "You are an assistant designed to propose necessary components of an survey papers. Your response should follow the JSON format." components, usage = llm(systems=syetem_promot, prompts=prompts, return_json=True) log_usage(usage, "media") print(f"The paper information has been initialized. References are saved to {bibtex_path}.") paper = {} paper["title"] = title paper["references"] = references paper["bibtex"] = bibtex_path paper["components"] = components paper["domain_knowledge"] = domain_knowledge return paper, destination_folder, all_paper_ids def section_generation(paper, section, save_to_path, model, research_field="machine learning"): """ The main pipeline of generating a section. 1. Generate prompts. 2. Get responses from AI assistant. 3. Extract the section text. 4. Save the text to .tex file. :return usage """ title = paper["title"] references = paper["references"] components = paper['components'] instruction = '- Discuss three to five main related fields to this paper. For each field, select five to ten key publications from references. For each reference, analyze its strengths and weaknesses in one or two sentences. Present the related works in a logical manner, often chronologically. Consider using a taxonomy or categorization to structure the discussion. Do not use \section{...} or \subsection{...}; use \paragraph{...} to list related fields.' fundamental_subprompt = "Your task is to write the {section} section of the paper with the title '{title}'. This paper has the following content: {components}\n" instruction_subprompt = "\n" \ "Your response should follow the following instructions:\n" \ "{instruction}\n" ref_instruction_subprompt = "- Read references. " \ "Every time you use information from the references, you need to appropriately cite it (using \citep or \citet)." \ "For example of \citep, the sentence where you use information from lei2022adaptive \citep{{lei2022adaptive}}. " \ "For example of \citet, \citet{{lei2022adaptive}} claims some information.\n" \ "- Avoid citing the same reference in a same paragraph.\n" \ "\n" \ "References:\n" \ "{references}" output_subprompt = "Ensure that it can be directly compiled by LeTaX." reivew_prompts = PromptTemplate( input_variables=["title", "components", "instruction", "section", "references"], template=fundamental_subprompt + instruction_subprompt + ref_instruction_subprompt + output_subprompt) prompts = reivew_prompts.format(title=title, components=components, instruction=instruction, section=section, references=references) SECTION_GENERATION_SYSTEM = PromptTemplate(input_variables=["research_field"], template="You are an assistant designed to write academic papers in the field of {research_field} using LaTeX." ) output, usage = get_gpt_responses(SECTION_GENERATION_SYSTEM.format(research_field=research_field), prompts, model=model, temperature=0.4) output=output[25:] tex_file = os.path.join(save_to_path, f"{section}.tex") with open(tex_file, "w", encoding="utf-8") as f: f.write(output) use_md =True use_chinese = True if use_md: system_md = 'You are an translator between the LaTeX and .MD. here is a latex file where the content is: \n \n ' + output prompts_md = 'you should transfer the latex content to the .MD format seriously, and pay attention to the correctness of the citation format (use the number). you should directly output the new content without anyoter replay. you should add reference papers at the end of the paper, and add line breaks between two reference papers. The Title should be ' + paper['title'] output_md, usage_md = get_gpt_responses(system_md, prompts_md, model=model, temperature=0.4) md_file = os.path.join(save_to_path, f"{'survey'}.md") with open(md_file, "w", encoding="utf-8") as m: m.write(output_md) if use_chinese == True: system_md_chi = 'You are an translator between the english and chinese. here is a english file where the content is: \n \n ' + output prompts_md_chi = 'you should transfer the english to chinese and dont change anything others. you should directly output the new content without anyoter replay. you should keep the reference papers unchanged.' output_md_chi, usage_md_chi = get_gpt_responses(system_md_chi, prompts_md_chi, model=model, temperature=0.4) md_file_chi = os.path.join(save_to_path, f"{'survey_chinese'}.md") with open(md_file_chi, "w", encoding="utf-8") as c: c.write(output_md_chi) return usage def generate_draft(title, tldr=True, max_kw_refs=20, bib_refs=None, max_tokens_ref=2048, knowledge_database=None, max_tokens_kd=2048, query_counts=10, section='related works', model="gpt-3.5-turbo-16k", template="Default" , save_zip=None): print("================START================") paper, destination_folder, _ = _generation_setup(title, template, tldr, max_kw_refs, bib_refs, max_tokens_ref=max_tokens_ref, max_tokens_kd=max_tokens_kd, query_counts=query_counts, knowledge_database=knowledge_database) # main components print(f"================PROCESSING================") usage = section_generation(paper, section, destination_folder, model=model) log_usage(usage, section) create_copies(destination_folder) print("\nPROCESSING COMPLETE\n") return make_archive(destination_folder, title+".zip") print("draft has been generated in " + destination_folder) if __name__ == "__main__": import openai openai.api_key = "your key" openai.api_base = 'https://api.openai.com/v1' #openai.proxy = "socks5h://localhost:7890 # if use the vpn target_title = "Reinforcement Learning for Robot Control" generate_draft(target_title, knowledge_database="ml_textbook_test",max_kw_refs=20)

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import sys import os sys.path.append(os.path.dirname(os.path.realpath(__file__))) sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'NeuralSeq')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'text_to_audio/Make_An_Audio')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'audio_detection')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'mono2binaural')) import gradio as gr import matplotlib import librosa import torch from langchain.agents.initialize import initialize_agent from langchain.agents.tools import Tool from langchain.chains.conversation.memory import ConversationBufferMemory from langchain.llms.openai import OpenAI import re import uuid import soundfile from PIL import Image import numpy as np from omegaconf import OmegaConf from einops import repeat from ldm.util import instantiate_from_config from ldm.data.extract_mel_spectrogram import TRANSFORMS_16000 from vocoder.bigvgan.models import VocoderBigVGAN from ldm.models.diffusion.ddim import DDIMSampler import whisper from utils.hparams import set_hparams from utils.hparams import hparams as hp import scipy.io.wavfile as wavfile import librosa from audio_infer.utils import config as detection_config from audio_infer.pytorch.models import PVT import clip import numpy as np AUDIO_CHATGPT_PREFIX = """AudioGPT AudioGPT can not directly read audios, but it has a list of tools to finish different speech, audio, and singing voice tasks. Each audio will have a file name formed as "audio/xxx.wav". When talking about audios, AudioGPT is very strict to the file name and will never fabricate nonexistent files. AudioGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the audio content and audio file name. It will remember to provide the file name from the last tool observation, if a new audio is generated. Human may provide new audios to AudioGPT with a description. The description helps AudioGPT to understand this audio, but AudioGPT should use tools to finish following tasks, rather than directly imagine from the description. Overall, AudioGPT is a powerful audio dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. TOOLS: ------ AudioGPT has access to the following tools:""" AUDIO_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format: ``` Thought: Do I need to use a tool? Yes Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action ``` When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: ``` Thought: Do I need to use a tool? No {ai_prefix}: [your response here] ``` """ AUDIO_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if not exists. You will remember to provide the audio file name loyally if it's provided in the last tool observation. Begin! Previous conversation history: {chat_history} New input: {input} Thought: Do I need to use a tool? {agent_scratchpad}""" def cut_dialogue_history(history_memory, keep_last_n_words = 500): tokens = history_memory.split() n_tokens = len(tokens) print(f"history_memory:{history_memory}, n_tokens: {n_tokens}") if n_tokens < keep_last_n_words: return history_memory else: paragraphs = history_memory.split('\n') last_n_tokens = n_tokens while last_n_tokens >= keep_last_n_words: last_n_tokens = last_n_tokens - len(paragraphs[0].split(' ')) paragraphs = paragraphs[1:] return '\n' + '\n'.join(paragraphs) def merge_audio(audio_path_1, audio_path_2): merged_signal = [] sr_1, signal_1 = wavfile.read(audio_path_1) sr_2, signal_2 = wavfile.read(audio_path_2) merged_signal.append(signal_1) merged_signal.append(signal_2) merged_signal = np.hstack(merged_signal) merged_signal = np.asarray(merged_signal, dtype=np.int16) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, sr_2, merged_signal) return audio_filename class T2I: def __init__(self, device): from transformers import AutoModelForCausalLM, AutoTokenizer from diffusers import StableDiffusionPipeline from transformers import pipeline print("Initializing T2I to %s" % device) self.device = device self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model, tokenizer=self.text_refine_tokenizer, device=self.device) self.pipe.to(device) def inference(self, text): image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"] print(f'{text} refined to {refined_text}') image = self.pipe(refined_text).images[0] image.save(image_filename) print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}") return image_filename class ImageCaptioning: def __init__(self, device): from transformers import BlipProcessor, BlipForConditionalGeneration print("Initializing ImageCaptioning to %s" % device) self.device = device self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device) def inference(self, image_path): inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device) out = self.model.generate(**inputs) captions = self.processor.decode(out[0], skip_special_tokens=True) return captions class T2A: def __init__(self, device): print("Initializing Make-An-Audio to %s" % device) self.device = device self.sampler = self._initialize_model('text_to_audio/Make_An_Audio/configs/text_to_audio/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta40multi_epoch=000085.ckpt', device=device) self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) def _initialize_model(self, config, ckpt, device): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) model = model.to(device) model.cond_stage_model.to(model.device) model.cond_stage_model.device = model.device sampler = DDIMSampler(model) return sampler def txt2audio(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80): SAMPLE_RATE = 16000 prng = np.random.RandomState(seed) start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) uc = self.sampler.model.get_learned_conditioning(n_samples * [""]) c = self.sampler.model.get_learned_conditioning(n_samples * [text]) shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8] # (z_dim, 80//2^x, 848//2^x) samples_ddim, _ = self.sampler.sample(S = ddim_steps, conditioning = c, batch_size = n_samples, shape = shape, verbose = False, unconditional_guidance_scale = scale, unconditional_conditioning = uc, x_T = start_code) x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1] wav_list = [] for idx,spec in enumerate(x_samples_ddim): wav = self.vocoder.vocode(spec) wav_list.append((SAMPLE_RATE,wav)) best_wav = self.select_best_audio(text, wav_list) return best_wav def select_best_audio(self, prompt, wav_list): from wav_evaluation.models.CLAPWrapper import CLAPWrapper clap_model = CLAPWrapper('text_to_audio/Make_An_Audio/useful_ckpts/CLAP/CLAP_weights_2022.pth', 'text_to_audio/Make_An_Audio/useful_ckpts/CLAP/config.yml', use_cuda=torch.cuda.is_available()) text_embeddings = clap_model.get_text_embeddings([prompt]) score_list = [] for data in wav_list: sr, wav = data audio_embeddings = clap_model.get_audio_embeddings([(torch.FloatTensor(wav), sr)], resample=True) score = clap_model.compute_similarity(audio_embeddings, text_embeddings, use_logit_scale=False).squeeze().cpu().numpy() score_list.append(score) max_index = np.array(score_list).argmax() print(score_list, max_index) return wav_list[max_index] def inference(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80): melbins,mel_len = 80,624 with torch.no_grad(): result = self.txt2audio( text = text, H = melbins, W = mel_len ) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, result[1], samplerate = 16000) print(f"Processed T2I.run, text: {text}, audio_filename: {audio_filename}") return audio_filename class I2A: def __init__(self, device): print("Initializing Make-An-Audio-Image to %s" % device) self.device = device self.sampler = self._initialize_model('text_to_audio/Make_An_Audio/configs/img_to_audio/img2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta54_epoch=000216.ckpt', device=device) self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) def _initialize_model(self, config, ckpt, device): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) model = model.to(device) model.cond_stage_model.to(model.device) model.cond_stage_model.device = model.device sampler = DDIMSampler(model) return sampler def img2audio(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80): SAMPLE_RATE = 16000 n_samples = 1 # only support 1 sample prng = np.random.RandomState(seed) start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) uc = self.sampler.model.get_learned_conditioning(n_samples * [""]) #image = Image.fromarray(image) image = Image.open(image) image = self.sampler.model.cond_stage_model.preprocess(image).unsqueeze(0) image_embedding = self.sampler.model.cond_stage_model.forward_img(image) c = image_embedding.repeat(n_samples, 1, 1) shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8] # (z_dim, 80//2^x, 848//2^x) samples_ddim, _ = self.sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, x_T=start_code) x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1] wav_list = [] for idx,spec in enumerate(x_samples_ddim): wav = self.vocoder.vocode(spec) wav_list.append((SAMPLE_RATE,wav)) best_wav = wav_list[0] return best_wav def inference(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80): melbins,mel_len = 80,624 with torch.no_grad(): result = self.img2audio( image=image, H=melbins, W=mel_len ) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, result[1], samplerate = 16000) print(f"Processed I2a.run, image_filename: {image}, audio_filename: {audio_filename}") return audio_filename class TTS: def __init__(self, device=None): from inference.tts.PortaSpeech import TTSInference if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing PortaSpeech to %s" % device) self.device = device self.exp_name = 'checkpoints/ps_adv_baseline' self.set_model_hparams() self.inferencer = TTSInference(self.hp, device) def set_model_hparams(self): set_hparams(exp_name=self.exp_name, print_hparams=False) self.hp = hp def inference(self, text): self.set_model_hparams() inp = {"text": text} out = self.inferencer.infer_once(inp) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, out, samplerate=22050) return audio_filename class T2S: def __init__(self, device= None): from inference.svs.ds_e2e import DiffSingerE2EInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing DiffSinger to %s" % device) self.device = device self.exp_name = 'checkpoints/0831_opencpop_ds1000' self.config= 'NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml' self.set_model_hparams() self.pipe = DiffSingerE2EInfer(self.hp, device) self.default_inp = { 'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP', 'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest', 'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590' } def set_model_hparams(self): set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False) self.hp = hp def inference(self, inputs): self.set_model_hparams() val = inputs.split(",") key = ['text', 'notes', 'notes_duration'] try: inp = {k: v for k, v in zip(key, val)} wav = self.pipe.infer_once(inp) except: print('Error occurs. Generate default audio sample.\n') inp = self.default_inp wav = self.pipe.infer_once(inp) #if inputs == '' or len(val) < len(key): # inp = self.default_inp #else: # inp = {k:v for k,v in zip(key,val)} #wav = self.pipe.infer_once(inp) wav *= 32767 audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16)) print(f"Processed T2S.run, audio_filename: {audio_filename}") return audio_filename class t2s_VISinger: def __init__(self, device=None): from espnet2.bin.svs_inference import SingingGenerate if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing VISingere to %s" % device) tag = 'AQuarterMile/opencpop_visinger1' self.model = SingingGenerate.from_pretrained( model_tag=str_or_none(tag), device=device, ) phn_dur = [[0. , 0.219 ], [0.219 , 0.50599998], [0.50599998, 0.71399999], [0.71399999, 1.097 ], [1.097 , 1.28799999], [1.28799999, 1.98300004], [1.98300004, 7.10500002], [7.10500002, 7.60400009]] phn = ['sh', 'i', 'q', 'v', 'n', 'i', 'SP', 'AP'] score = [[0, 0.50625, 'sh_i', 58, 'sh_i'], [0.50625, 1.09728, 'q_v', 56, 'q_v'], [1.09728, 1.9832100000000001, 'n_i', 53, 'n_i'], [1.9832100000000001, 7.105360000000001, 'SP', 0, 'SP'], [7.105360000000001, 7.604390000000001, 'AP', 0, 'AP']] tempo = 70 tmp = {} tmp["label"] = phn_dur, phn tmp["score"] = tempo, score self.default_inp = tmp def inference(self, inputs): val = inputs.split(",") key = ['text', 'notes', 'notes_duration'] try: # TODO: input will be update inp = {k: v for k, v in zip(key, val)} wav = self.model(text=inp)["wav"] except: print('Error occurs. Generate default audio sample.\n') inp = self.default_inp wav = self.model(text=inp)["wav"] audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, wav, samplerate=self.model.fs) return audio_filename class TTS_OOD: def __init__(self, device): from inference.tts.GenerSpeech import GenerSpeechInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing GenerSpeech to %s" % device) self.device = device self.exp_name = 'checkpoints/GenerSpeech' self.config = 'NeuralSeq/modules/GenerSpeech/config/generspeech.yaml' self.set_model_hparams() self.pipe = GenerSpeechInfer(self.hp, device) def set_model_hparams(self): set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False) f0_stats_fn = f'{hp["binary_data_dir"]}/train_f0s_mean_std.npy' if os.path.exists(f0_stats_fn): hp['f0_mean'], hp['f0_std'] = np.load(f0_stats_fn) hp['f0_mean'] = float(hp['f0_mean']) hp['f0_std'] = float(hp['f0_std']) hp['emotion_encoder_path'] = 'checkpoints/Emotion_encoder.pt' self.hp = hp def inference(self, inputs): self.set_model_hparams() key = ['ref_audio', 'text'] val = inputs.split(",") inp = {k: v for k, v in zip(key, val)} wav = self.pipe.infer_once(inp) wav *= 32767 audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16)) print( f"Processed GenerSpeech.run. Input text:{val[1]}. Input reference audio: {val[0]}. Output Audio_filename: {audio_filename}") return audio_filename class Inpaint: def __init__(self, device): print("Initializing Make-An-Audio-inpaint to %s" % device) self.device = device self.sampler = self._initialize_model_inpaint('text_to_audio/Make_An_Audio/configs/inpaint/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/inpaint7_epoch00047.ckpt') self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) self.cmap_transform = matplotlib.cm.viridis def _initialize_model_inpaint(self, config, ckpt): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) print(model.device, device, model.cond_stage_model.device) sampler = DDIMSampler(model) return sampler def make_batch_sd(self, mel, mask, num_samples=1): mel = torch.from_numpy(mel)[None,None,...].to(dtype=torch.float32) mask = torch.from_numpy(mask)[None,None,...].to(dtype=torch.float32) masked_mel = (1 - mask) * mel mel = mel * 2 - 1 mask = mask * 2 - 1 masked_mel = masked_mel * 2 -1 batch = { "mel": repeat(mel.to(device=self.device), "1 ... -> n ...", n=num_samples), "mask": repeat(mask.to(device=self.device), "1 ... -> n ...", n=num_samples), "masked_mel": repeat(masked_mel.to(device=self.device), "1 ... -> n ...", n=num_samples), } return batch def gen_mel(self, input_audio_path): SAMPLE_RATE = 16000 sr, ori_wav = wavfile.read(input_audio_path) print("gen_mel") print(sr,ori_wav.shape,ori_wav) ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 if len(ori_wav.shape)==2:# stereo ori_wav = librosa.to_mono(ori_wav.T) print(sr,ori_wav.shape,ori_wav) ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE) mel_len,hop_size = 848,256 input_len = mel_len * hop_size if len(ori_wav) < input_len: input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0) else: input_wav = ori_wav[:input_len] mel = TRANSFORMS_16000(input_wav) return mel def gen_mel_audio(self, input_audio): SAMPLE_RATE = 16000 sr,ori_wav = input_audio print("gen_mel_audio") print(sr,ori_wav.shape,ori_wav) ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 if len(ori_wav.shape)==2:# stereo ori_wav = librosa.to_mono(ori_wav.T) print(sr,ori_wav.shape,ori_wav) ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE) mel_len,hop_size = 848,256 input_len = mel_len * hop_size if len(ori_wav) < input_len: input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0) else: input_wav = ori_wav[:input_len] mel = TRANSFORMS_16000(input_wav) return mel def show_mel_fn(self, input_audio_path): crop_len = 500 crop_mel = self.gen_mel(input_audio_path)[:,:crop_len] color_mel = self.cmap_transform(crop_mel) image = Image.fromarray((color_mel*255).astype(np.uint8)) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") image.save(image_filename) return image_filename def inpaint(self, batch, seed, ddim_steps, num_samples=1, W=512, H=512): model = self.sampler.model prng = np.random.RandomState(seed) start_code = prng.randn(num_samples, model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) c = model.get_first_stage_encoding(model.encode_first_stage(batch["masked_mel"])) cc = torch.nn.functional.interpolate(batch["mask"], size=c.shape[-2:]) c = torch.cat((c, cc), dim=1) # (b,c+1,h,w) 1 is mask shape = (c.shape[1]-1,)+c.shape[2:] samples_ddim, _ = self.sampler.sample(S=ddim_steps, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False) x_samples_ddim = model.decode_first_stage(samples_ddim) mel = torch.clamp((batch["mel"]+1.0)/2.0,min=0.0, max=1.0) mask = torch.clamp((batch["mask"]+1.0)/2.0,min=0.0, max=1.0) predicted_mel = torch.clamp((x_samples_ddim+1.0)/2.0,min=0.0, max=1.0) inpainted = (1-mask)*mel+mask*predicted_mel inpainted = inpainted.cpu().numpy().squeeze() inapint_wav = self.vocoder.vocode(inpainted) return inpainted, inapint_wav def inference(self, input_audio, mel_and_mask, seed = 55, ddim_steps = 100): SAMPLE_RATE = 16000 torch.set_grad_enabled(False) mel_img = Image.open(mel_and_mask['image']) mask_img = Image.open(mel_and_mask["mask"]) show_mel = np.array(mel_img.convert("L"))/255 mask = np.array(mask_img.convert("L"))/255 mel_bins,mel_len = 80,848 input_mel = self.gen_mel_audio(input_audio)[:,:mel_len] mask = np.pad(mask,((0,0),(0,mel_len-mask.shape[1])),mode='constant',constant_values=0) print(mask.shape,input_mel.shape) with torch.no_grad(): batch = self.make_batch_sd(input_mel,mask,num_samples=1) inpainted,gen_wav = self.inpaint( batch=batch, seed=seed, ddim_steps=ddim_steps, num_samples=1, H=mel_bins, W=mel_len ) inpainted = inpainted[:,:show_mel.shape[1]] color_mel = self.cmap_transform(inpainted) input_len = int(input_audio[1].shape[0] * SAMPLE_RATE / input_audio[0]) gen_wav = (gen_wav * 32768).astype(np.int16)[:input_len] image = Image.fromarray((color_mel*255).astype(np.uint8)) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") image.save(image_filename) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, gen_wav, samplerate = 16000) return image_filename, audio_filename class ASR: def __init__(self, device): print("Initializing Whisper to %s" % device) self.device = device self.model = whisper.load_model("base", device=device) def inference(self, audio_path): audio = whisper.load_audio(audio_path) audio = whisper.pad_or_trim(audio) mel = whisper.log_mel_spectrogram(audio).to(self.device) _, probs = self.model.detect_language(mel) options = whisper.DecodingOptions() result = whisper.decode(self.model, mel, options) return result.text def translate_english(self, audio_path): audio = self.model.transcribe(audio_path, language='English') return audio['text'] class A2T: def __init__(self, device): from audio_to_text.inference_waveform import AudioCapModel print("Initializing Audio-To-Text Model to %s" % device) self.device = device self.model = AudioCapModel("audio_to_text/audiocaps_cntrstv_cnn14rnn_trm") def inference(self, audio_path): audio = whisper.load_audio(audio_path) caption_text = self.model(audio) return caption_text[0] class GeneFace: def __init__(self, device=None): print("Initializing GeneFace model to %s" % device) from audio_to_face.GeneFace_binding import GeneFaceInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' self.device = device self.geneface_model = GeneFaceInfer(device) print("Loaded GeneFace model") def inference(self, audio_path): audio_base_name = os.path.basename(audio_path)[:-4] out_video_name = audio_path.replace("audio","video").replace(".wav", ".mp4") inp = { 'audio_source_name': audio_path, 'out_npy_name': f'geneface/tmp/{audio_base_name}.npy', 'cond_name': f'geneface/tmp/{audio_base_name}.npy', 'out_video_name': out_video_name, 'tmp_imgs_dir': f'video/tmp_imgs', } self.geneface_model.infer_once(inp) return out_video_name class SoundDetection: def __init__(self, device): self.device = device self.sample_rate = 32000 self.window_size = 1024 self.hop_size = 320 self.mel_bins = 64 self.fmin = 50 self.fmax = 14000 self.model_type = 'PVT' self.checkpoint_path = 'audio_detection/audio_infer/useful_ckpts/audio_detection.pth' self.classes_num = detection_config.classes_num self.labels = detection_config.labels self.frames_per_second = self.sample_rate // self.hop_size # Model = eval(self.model_type) self.model = PVT(sample_rate=self.sample_rate, window_size=self.window_size, hop_size=self.hop_size, mel_bins=self.mel_bins, fmin=self.fmin, fmax=self.fmax, classes_num=self.classes_num) checkpoint = torch.load(self.checkpoint_path, map_location=self.device) self.model.load_state_dict(checkpoint['model']) self.model.to(device) def inference(self, audio_path): # Forward (waveform, _) = librosa.core.load(audio_path, sr=self.sample_rate, mono=True) waveform = waveform[None, :] # (1, audio_length) waveform = torch.from_numpy(waveform) waveform = waveform.to(self.device) # Forward with torch.no_grad(): self.model.eval() batch_output_dict = self.model(waveform, None) framewise_output = batch_output_dict['framewise_output'].data.cpu().numpy()[0] """(time_steps, classes_num)""" # print('Sound event detection result (time_steps x classes_num): {}'.format( # framewise_output.shape)) import numpy as np import matplotlib.pyplot as plt sorted_indexes = np.argsort(np.max(framewise_output, axis=0))[::-1] top_k = 10 # Show top results top_result_mat = framewise_output[:, sorted_indexes[0 : top_k]] """(time_steps, top_k)""" # Plot result stft = librosa.core.stft(y=waveform[0].data.cpu().numpy(), n_fft=self.window_size, hop_length=self.hop_size, window='hann', center=True) frames_num = stft.shape[-1] fig, axs = plt.subplots(2, 1, sharex=True, figsize=(10, 4)) axs[0].matshow(np.log(np.abs(stft)), origin='lower', aspect='auto', cmap='jet') axs[0].set_ylabel('Frequency bins') axs[0].set_title('Log spectrogram') axs[1].matshow(top_result_mat.T, origin='upper', aspect='auto', cmap='jet', vmin=0, vmax=1) axs[1].xaxis.set_ticks(np.arange(0, frames_num, self.frames_per_second)) axs[1].xaxis.set_ticklabels(np.arange(0, frames_num / self.frames_per_second)) axs[1].yaxis.set_ticks(np.arange(0, top_k)) axs[1].yaxis.set_ticklabels(np.array(self.labels)[sorted_indexes[0 : top_k]]) axs[1].yaxis.grid(color='k', linestyle='solid', linewidth=0.3, alpha=0.3) axs[1].set_xlabel('Seconds') axs[1].xaxis.set_ticks_position('bottom') plt.tight_layout() image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") plt.savefig(image_filename) return image_filename class SoundExtraction: def __init__(self, device): from sound_extraction.model.LASSNet import LASSNet from sound_extraction.utils.stft import STFT import torch.nn as nn self.device = device self.model_file = 'sound_extraction/useful_ckpts/LASSNet.pt' self.stft = STFT() self.model = nn.DataParallel(LASSNet(device)).to(device) checkpoint = torch.load(self.model_file) self.model.load_state_dict(checkpoint['model']) self.model.eval() def inference(self, inputs): #key = ['ref_audio', 'text'] from sound_extraction.utils.wav_io import load_wav, save_wav val = inputs.split(",") audio_path = val[0] # audio_path, text text = val[1] waveform = load_wav(audio_path) waveform = torch.tensor(waveform).transpose(1,0) mixed_mag, mixed_phase = self.stft.transform(waveform) text_query = ['[CLS] ' + text] mixed_mag = mixed_mag.transpose(2,1).unsqueeze(0).to(self.device) est_mask = self.model(mixed_mag, text_query) est_mag = est_mask * mixed_mag est_mag = est_mag.squeeze(1) est_mag = est_mag.permute(0, 2, 1) est_wav = self.stft.inverse(est_mag.cpu().detach(), mixed_phase) est_wav = est_wav.squeeze(0).squeeze(0).numpy() #est_path = f'output/est{i}.wav' audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") print('audio_filename ', audio_filename) save_wav(est_wav, audio_filename) return audio_filename class Binaural: def __init__(self, device): from src.models import BinauralNetwork self.device = device self.model_file = 'mono2binaural/useful_ckpts/m2b/binaural_network.net' self.position_file = ['mono2binaural/useful_ckpts/m2b/tx_positions.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions2.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions3.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions4.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions5.txt'] self.net = BinauralNetwork(view_dim=7, warpnet_layers=4, warpnet_channels=64, ) self.net.load_from_file(self.model_file) self.sr = 48000 def inference(self, audio_path): mono, sr = librosa.load(path=audio_path, sr=self.sr, mono=True) mono = torch.from_numpy(mono) mono = mono.unsqueeze(0) import numpy as np import random rand_int = random.randint(0,4) view = np.loadtxt(self.position_file[rand_int]).transpose().astype(np.float32) view = torch.from_numpy(view) if not view.shape[-1] * 400 == mono.shape[-1]: mono = mono[:,:(mono.shape[-1]//400)*400] # if view.shape[1]*400 > mono.shape[1]: m_a = view.shape[1] - mono.shape[-1]//400 rand_st = random.randint(0,m_a) view = view[:,m_a:m_a+(mono.shape[-1]//400)] # # binauralize and save output self.net.eval().to(self.device) mono, view = mono.to(self.device), view.to(self.device) chunk_size = 48000 # forward in chunks of 1s rec_field = 1000 # add 1000 samples as "safe bet" since warping has undefined rec. field rec_field -= rec_field % 400 # make sure rec_field is a multiple of 400 to match audio and view frequencies chunks = [ { "mono": mono[:, max(0, i-rec_field):i+chunk_size], "view": view[:, max(0, i-rec_field)//400:(i+chunk_size)//400] } for i in range(0, mono.shape[-1], chunk_size) ] for i, chunk in enumerate(chunks): with torch.no_grad(): mono = chunk["mono"].unsqueeze(0) view = chunk["view"].unsqueeze(0) binaural = self.net(mono, view).squeeze(0) if i > 0: binaural = binaural[:, -(mono.shape[-1]-rec_field):] chunk["binaural"] = binaural binaural = torch.cat([chunk["binaural"] for chunk in chunks], dim=-1) binaural = torch.clamp(binaural, min=-1, max=1).cpu() #binaural = chunked_forwarding(net, mono, view) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") import torchaudio torchaudio.save(audio_filename, binaural, sr) #soundfile.write(audio_filename, binaural, samplerate = 48000) print(f"Processed Binaural.run, audio_filename: {audio_filename}") return audio_filename class TargetSoundDetection: def __init__(self, device): from target_sound_detection.src import models as tsd_models from target_sound_detection.src.models import event_labels self.device = device self.MEL_ARGS = { 'n_mels': 64, 'n_fft': 2048, 'hop_length': int(22050 * 20 / 1000), 'win_length': int(22050 * 40 / 1000) } self.EPS = np.spacing(1) self.clip_model, _ = clip.load("ViT-B/32", device=self.device) self.event_labels = event_labels self.id_to_event = {i : label for i, label in enumerate(self.event_labels)} config = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/run_config.pth', map_location='cpu') config_parameters = dict(config) config_parameters['tao'] = 0.6 if 'thres' not in config_parameters.keys(): config_parameters['thres'] = 0.5 if 'time_resolution' not in config_parameters.keys(): config_parameters['time_resolution'] = 125 model_parameters = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/run_model_7_loss=-0.0724.pt' , map_location=lambda storage, loc: storage) # load parameter self.model = getattr(tsd_models, config_parameters['model'])(config_parameters, inputdim=64, outputdim=2, time_resolution=config_parameters['time_resolution'], **config_parameters['model_args']) self.model.load_state_dict(model_parameters) self.model = self.model.to(self.device).eval() self.re_embeds = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/text_emb.pth') self.ref_mel = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/ref_mel.pth') def extract_feature(self, fname): import soundfile as sf y, sr = sf.read(fname, dtype='float32') print('y ', y.shape) ti = y.shape[0]/sr if y.ndim > 1: y = y.mean(1) y = librosa.resample(y, sr, 22050) lms_feature = np.log(librosa.feature.melspectrogram(y, **self.MEL_ARGS) + self.EPS).T return lms_feature,ti def build_clip(self, text): text = clip.tokenize(text).to(self.device) # ["a diagram with dog", "a dog", "a cat"] text_features = self.clip_model.encode_text(text) return text_features def cal_similarity(self, target, retrievals): ans = [] #target =torch.from_numpy(target) for name in retrievals.keys(): tmp = retrievals[name] #tmp = torch.from_numpy(tmp) s = torch.cosine_similarity(target.squeeze(), tmp.squeeze(), dim=0) ans.append(s.item()) return ans.index(max(ans)) def inference(self, text, audio_path): from target_sound_detection.src.utils import median_filter, decode_with_timestamps target_emb = self.build_clip(text) # torch type idx = self.cal_similarity(target_emb, self.re_embeds) target_event = self.id_to_event[idx] embedding = self.ref_mel[target_event] embedding = torch.from_numpy(embedding) embedding = embedding.unsqueeze(0).to(self.device).float() #print('embedding ', embedding.shape) inputs,ti = self.extract_feature(audio_path) #print('ti ', ti) inputs = torch.from_numpy(inputs) inputs = inputs.unsqueeze(0).to(self.device).float() #print('inputs ', inputs.shape) decision, decision_up, logit = self.model(inputs, embedding) pred = decision_up.detach().cpu().numpy() pred = pred[:,:,0] frame_num = decision_up.shape[1] time_ratio = ti / frame_num filtered_pred = median_filter(pred, window_size=1, threshold=0.5) #print('filtered_pred ', filtered_pred) time_predictions = [] for index_k in range(filtered_pred.shape[0]): decoded_pred = [] decoded_pred_ = decode_with_timestamps(target_event, filtered_pred[index_k,:]) if len(decoded_pred_) == 0: # neg deal decoded_pred_.append((target_event, 0, 0)) decoded_pred.append(decoded_pred_) for num_batch in range(len(decoded_pred)): # when we test our model,the batch_size is 1 cur_pred = pred[num_batch] # Save each frame output, for later visualization label_prediction = decoded_pred[num_batch] # frame predict # print(label_prediction) for event_label, onset, offset in label_prediction: time_predictions.append({ 'onset': onset*time_ratio, 'offset': offset*time_ratio,}) ans = '' for i,item in enumerate(time_predictions): ans = ans + 'segment' + str(i+1) + ' start_time: ' + str(item['onset']) + ' end_time: ' + str(item['offset']) + '\t' #print(ans) return ans # class Speech_Enh_SS_SC: # """Speech Enhancement or Separation in single-channel # Example usage: # enh_model = Speech_Enh_SS("cuda") # enh_wav = enh_model.inference("./test_chime4_audio_M05_440C0213_PED_REAL.wav") # """ # def __init__(self, device="cuda", model_name="lichenda/chime4_fasnet_dprnn_tac"): # self.model_name = model_name # self.device = device # print("Initializing ESPnet Enh to %s" % device) # self._initialize_model() # def _initialize_model(self): # from espnet_model_zoo.downloader import ModelDownloader # from espnet2.bin.enh_inference import SeparateSpeech # d = ModelDownloader() # cfg = d.download_and_unpack(self.model_name) # self.separate_speech = SeparateSpeech( # train_config=cfg["train_config"], # model_file=cfg["model_file"], # # for segment-wise process on long speech # segment_size=2.4, # hop_size=0.8, # normalize_segment_scale=False, # show_progressbar=True, # ref_channel=None, # normalize_output_wav=True, # device=self.device, # ) # def inference(self, speech_path, ref_channel=0): # speech, sr = soundfile.read(speech_path) # speech = speech[:, ref_channel] # assert speech.dim() == 1 # enh_speech = self.separate_speech(speech[None, ], fs=sr) # if len(enh_speech) == 1: # return enh_speech[0] # return enh_speech # class Speech_Enh_SS_MC: # """Speech Enhancement or Separation in multi-channel""" # def __init__(self, device="cuda", model_name=None, ref_channel=4): # self.model_name = model_name # self.ref_channel = ref_channel # self.device = device # print("Initializing ESPnet Enh to %s" % device) # self._initialize_model() # def _initialize_model(self): # from espnet_model_zoo.downloader import ModelDownloader # from espnet2.bin.enh_inference import SeparateSpeech # d = ModelDownloader() # cfg = d.download_and_unpack(self.model_name) # self.separate_speech = SeparateSpeech( # train_config=cfg["train_config"], # model_file=cfg["model_file"], # # for segment-wise process on long speech # segment_size=2.4, # hop_size=0.8, # normalize_segment_scale=False, # show_progressbar=True, # ref_channel=self.ref_channel, # normalize_output_wav=True, # device=self.device, # ) # def inference(self, speech_path): # speech, sr = soundfile.read(speech_path) # speech = speech.T # enh_speech = self.separate_speech(speech[None, ...], fs=sr) # if len(enh_speech) == 1: # return enh_speech[0] # return enh_speech class Speech_Enh_SS_SC: """Speech Enhancement or Separation in single-channel Example usage: enh_model = Speech_Enh_SS("cuda") enh_wav = enh_model.inference("./test_chime4_audio_M05_440C0213_PED_REAL.wav") """ def __init__(self, device="cuda", model_name="espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw"): self.model_name = model_name self.device = device print("Initializing ESPnet Enh to %s" % device) self._initialize_model() def _initialize_model(self): from espnet_model_zoo.downloader import ModelDownloader from espnet2.bin.enh_inference import SeparateSpeech d = ModelDownloader() cfg = d.download_and_unpack(self.model_name) self.separate_speech = SeparateSpeech( train_config=cfg["train_config"], model_file=cfg["model_file"], # for segment-wise process on long speech segment_size=2.4, hop_size=0.8, normalize_segment_scale=False, show_progressbar=True, ref_channel=None, normalize_output_wav=True, device=self.device, ) def inference(self, speech_path, ref_channel=0): speech, sr = soundfile.read(speech_path) speech = speech[:, ref_channel] # speech = torch.from_numpy(speech) # assert speech.dim() == 1 enh_speech = self.separate_speech(speech[None, ...], fs=sr) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # if len(enh_speech) == 1: soundfile.write(audio_filename, enh_speech[0].squeeze(), samplerate=sr) # return enh_speech[0] # return enh_speech # else: # print("############") # audio_filename_1 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # soundfile.write(audio_filename_1, enh_speech[0].squeeze(), samplerate=sr) # audio_filename_2 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # soundfile.write(audio_filename_2, enh_speech[1].squeeze(), samplerate=sr) # audio_filename = merge_audio(audio_filename_1, audio_filename_2) return audio_filename class Speech_SS: def __init__(self, device="cuda", model_name="lichenda/wsj0_2mix_skim_noncausal"): self.model_name = model_name self.device = device print("Initializing ESPnet SS to %s" % device) self._initialize_model() def _initialize_model(self): from espnet_model_zoo.downloader import ModelDownloader from espnet2.bin.enh_inference import SeparateSpeech d = ModelDownloader() cfg = d.download_and_unpack(self.model_name) self.separate_speech = SeparateSpeech( train_config=cfg["train_config"], model_file=cfg["model_file"], # for segment-wise process on long speech segment_size=2.4, hop_size=0.8, normalize_segment_scale=False, show_progressbar=True, ref_channel=None, normalize_output_wav=True, device=self.device, ) def inference(self, speech_path): speech, sr = soundfile.read(speech_path) enh_speech = self.separate_speech(speech[None, ...], fs=sr) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") if len(enh_speech) == 1: soundfile.write(audio_filename, enh_speech[0], samplerate=sr) else: # print("############") audio_filename_1 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename_1, enh_speech[0].squeeze(), samplerate=sr) audio_filename_2 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename_2, enh_speech[1].squeeze(), samplerate=sr) audio_filename = merge_audio(audio_filename_1, audio_filename_2) return audio_filename class ConversationBot: def __init__(self): print("Initializing AudioGPT") self.llm = OpenAI(temperature=0) self.t2i = T2I(device="cuda:1") self.i2t = ImageCaptioning(device="cuda:0") self.t2a = T2A(device="cuda:0") self.tts = TTS(device="cpu") self.t2s = T2S(device="cpu") self.i2a = I2A(device="cuda:0") self.a2t = A2T(device="cpu") self.asr = ASR(device="cuda:0") self.SE_SS_SC = Speech_Enh_SS_SC(device="cuda:0") # self.SE_SS_MC = Speech_Enh_SS_MC(device="cuda:0") self.SS = Speech_SS(device="cuda:0") self.inpaint = Inpaint(device="cuda:0") self.tts_ood = TTS_OOD(device="cpu") self.geneface = GeneFace(device="cuda:0") self.detection = SoundDetection(device="cpu") self.binaural = Binaural(device="cuda:0") self.extraction = SoundExtraction(device="cuda:0") self.TSD = TargetSoundDetection(device="cuda:0") self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output') def init_tools(self, interaction_type): if interaction_type == 'text': self.tools = [ Tool(name="Generate Image From User Input Text", func=self.t2i.inference, description="useful for when you want to generate an image from a user input text and it saved it to a file. like: generate an image of an object or something, or generate an image that includes some objects. " "The input to this tool should be a string, representing the text used to generate image. "), Tool(name="Get Photo Description", func=self.i2t.inference, description="useful for when you want to know what is inside the photo. receives image_path as input. " "The input to this tool should be a string, representing the image_path. "), Tool(name="Generate Audio From User Input Text", func=self.t2a.inference, description="useful for when you want to generate an audio from a user input text and it saved it to a file." "The input to this tool should be a string, representing the text used to generate audio."), Tool( name="Style Transfer", func= self.tts_ood.inference, description="useful for when you want to generate speech samples with styles (e.g., timbre, emotion, and prosody) derived from a reference custom voice." "Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx." "The input to this tool should be a comma seperated string of two, representing reference audio path and input text."), Tool(name="Generate Singing Voice From User Input Text, Note and Duration Sequence", func= self.t2s.inference, description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) and save it to a file." "If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence ." "If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. " "Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx." "The input to this tool should be a comma seperated string of three, representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided."), Tool(name="Synthesize Speech Given the User Input Text", func=self.tts.inference, description="useful for when you want to convert a user input text into speech audio it saved it to a file." "The input to this tool should be a string, representing the text used to be converted to speech."), # Tool(name="Speech Enhancement Or Separation In Single-Channel", func=self.SE_SS_SC.inference, # description="useful for when you want to enhance the quality of the speech signal by reducing background noise (single-channel), " # "or separate each speech from the speech mixture (single-channel), receives audio_path as input." # "The input to this tool should be a string, representing the audio_path."), Tool(name="Speech Enhancement In Single-Channel", func=self.SE_SS_SC.inference, description="useful for when you want to enhance the quality of the speech signal by reducing background noise (single-channel), receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Speech Separation In Single-Channel", func=self.SS.inference, description="useful for when you want to separate each speech from the speech mixture, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), # Tool(name="Speech Enhancement In Multi-Channel", func=self.SE_SS_MC.inference, # description="useful for when you want to enhance the quality of the speech signal by reducing background noise (multi-channel), receives audio_path as input." # "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate Audio From The Image", func=self.i2a.inference, description="useful for when you want to generate an audio based on an image." "The input to this tool should be a string, representing the image_path. "), Tool(name="Generate Text From The Audio", func=self.a2t.inference, description="useful for when you want to describe an audio in text, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Audio Inpainting", func=self.inpaint.show_mel_fn, description="useful for when you want to inpaint a mel spectrum of an audio and predict this audio, this tool will generate a mel spectrum and you can inpaint it, receives audio_path as input, " "The input to this tool should be a string, representing the audio_path."), Tool(name="Transcribe Speech", func=self.asr.inference, description="useful for when you want to know the text corresponding to a human speech, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate a talking human portrait video given a input Audio", func=self.geneface.inference, description="useful for when you want to generate a talking human portrait video given a input audio." "The input to this tool should be a string, representing the audio_path."), Tool(name="Detect The Sound Event From The Audio", func=self.detection.inference, description="useful for when you want to know what event in the audio and the sound event start or end time, this tool will generate an image of all predict events, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Sythesize Binaural Audio From A Mono Audio Input", func=self.binaural.inference, description="useful for when you want to transfer your mono audio into binaural audio, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Extract Sound Event From Mixture Audio Based On Language Description", func=self.extraction.inference, description="useful for when you extract target sound from a mixture audio, you can describe the target sound by text, receives audio_path and text as input. " "The input to this tool should be a comma seperated string of two, representing mixture audio path and input text."), Tool(name="Target Sound Detection", func=self.TSD.inference, description="useful for when you want to know when the target sound event in the audio happens. You can use language descriptions to instruct the model. receives text description and audio_path as input. " "The input to this tool should be a comma seperated string of two, representing audio path and the text description. ")] self.agent = initialize_agent( self.tools, self.llm, agent="conversational-react-description", verbose=True, memory=self.memory, return_intermediate_steps=True, agent_kwargs={'prefix': AUDIO_CHATGPT_PREFIX, 'format_instructions': AUDIO_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': AUDIO_CHATGPT_SUFFIX}, ) return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False) else: self.tools = [ Tool(name="Generate Audio From User Input Text", func=self.t2a.inference, description="useful for when you want to generate an audio from a user input text and it saved it to a file." "The input to this tool should be a string, representing the text used to generate audio."), Tool( name="Style Transfer", func= self.tts_ood.inference, description="useful for when you want to generate speech samples with styles (e.g., timbre, emotion, and prosody) derived from a reference custom voice." "Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx." "The input to this tool should be a comma seperated string of two, representing reference audio path and input text."), Tool(name="Generate Singing Voice From User Input Text, Note and Duration Sequence", func= self.t2s.inference, description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) and save it to a file." "If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence ." "If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. " "Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx." "The input to this tool should be a comma seperated string of three, representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided."), Tool(name="Synthesize Speech Given the User Input Text", func=self.tts.inference, description="useful for when you want to convert a user input text into speech audio it saved it to a file." "The input to this tool should be a string, representing the text used to be converted to speech."), Tool(name="Generate Text From The Audio", func=self.a2t.inference, description="useful for when you want to describe an audio in text, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate a talking human portrait video given a input Audio", func=self.geneface.inference, description="useful for when you want to generate a talking human portrait video given a input audio." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate Binaural Audio From A Mono Audio Input", func=self.binaural.inference, description="useful for when you want to transfer your mono audio into binaural audio, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Extract Sound Event From Mixture Audio Based On Language Description", func=self.extraction.inference, description="useful for when you extract target sound from a mixture audio, you can describe the target sound by text, receives audio_path and text as input. " "The input to this tool should be a comma seperated string of two, representing mixture audio path and input text."), Tool(name="Target Sound Detection", func=self.TSD.inference, description="useful for when you want to know when the target sound event in the audio happens. You can use language descriptions to instruct the model. receives text description and audio_path as input. " "The input to this tool should be a comma seperated string of two, representing audio path and the text description. ")] self.agent = initialize_agent( self.tools, self.llm, agent="conversational-react-description", verbose=True, memory=self.memory, return_intermediate_steps=True, agent_kwargs={'prefix': AUDIO_CHATGPT_PREFIX, 'format_instructions': AUDIO_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': AUDIO_CHATGPT_SUFFIX}, ) return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) def run_text(self, text, state): print("===============Running run_text =============") print("Inputs:", text, state) print("======>Previous memory:\n %s" % self.agent.memory) self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) res = self.agent({"input": text}) if res['intermediate_steps'] == []: print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) else: tool = res['intermediate_steps'][0][0].tool if tool == "Generate Image From User Input Text" or tool == "Generate Text From The Audio" or tool == "Target Sound Detection": print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Transcribe Speech": response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Detect The Sound Event From The Audio": image_filename = res['intermediate_steps'][0][1] response = res['output'] + f"*{image_filename}*" state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Audio Inpainting": audio_filename = res['intermediate_steps'][0][0].tool_input image_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False), gr.Image.update(value=image_filename,visible=True), gr.Button.update(visible=True) elif tool == "Generate a talking human portrait video given a input Audio": video_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(value=video_filename,visible=True), gr.Image.update(visible=False), gr.Button.update(visible=False) print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) audio_filename = res['intermediate_steps'][0][1] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) def run_image_or_audio(self, file, state, txt): file_type = file.name[-3:] if file_type == "wav": print("===============Running run_audio =============") print("Inputs:", file, state) print("======>Previous memory:\n %s" % self.agent.memory) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # audio_load = whisper.load_audio(file.name) audio_load, sr = soundfile.read(file.name) soundfile.write(audio_filename, audio_load, samplerate = sr) description = self.a2t.inference(audio_filename) Human_prompt = "\nHuman: provide an audio named {}. The description is: {}. This information helps you to understand this audio, but you should use tools to finish following tasks, " \ "rather than directly imagine from my description. If you understand, say \"Received\". \n".format(audio_filename, description) AI_prompt = "Received. " self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) #state = state + [(f"<audio src=audio_filename controls=controls></audio>*{audio_filename}*", AI_prompt)] state = state + [(f"*{audio_filename}*", AI_prompt)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False) else: print("===============Running run_image =============") print("Inputs:", file, state) print("======>Previous memory:\n %s" % self.agent.memory) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") print("======>Auto Resize Image...") img = Image.open(file.name) width, height = img.size ratio = min(512 / width, 512 / height) width_new, height_new = (round(width * ratio), round(height * ratio)) img = img.resize((width_new, height_new)) img = img.convert('RGB') img.save(image_filename, "PNG") print(f"Resize image form {width}x{height} to {width_new}x{height_new}") description = self.i2t.inference(image_filename) Human_prompt = "\nHuman: provide a figure named {}. The description is: {}. This information helps you to understand this image, but you should use tools to finish following tasks, " \ "rather than directly imagine from my description. If you understand, say \"Received\". \n".format(image_filename, description) AI_prompt = "Received. " self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) state = state + [(f"*{image_filename}*", AI_prompt)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False) def speech(self, speech_input, state): input_audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") text = self.asr.translate_english(speech_input) print("Inputs:", text, state) print("======>Previous memory:\n %s" % self.agent.memory) self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) res = self.agent({"input": text}) if res['intermediate_steps'] == []: print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] output_audio_filename = self.tts.inference(response) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) else: tool = res['intermediate_steps'][0][0].tool if tool == "Generate Image From User Input Text" or tool == "Generate Text From The Audio" or tool == "Target Sound Detection": print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) output_audio_filename = self.tts.inference(res['output']) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Transcribe Speech": print("======>Current memory:\n %s" % self.agent.memory) output_audio_filename = self.tts.inference(res['output']) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Detect The Sound Event From The Audio": print("======>Current memory:\n %s" % self.agent.memory) image_filename = res['intermediate_steps'][0][1] output_audio_filename = self.tts.inference(res['output']) response = res['output'] + f"*{image_filename}*" state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Generate a talking human portrait video given a input Audio": video_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] output_audio_filename = self.tts.inference(res['output']) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(value=video_filename,visible=True) print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) audio_filename = res['intermediate_steps'][0][1] Res = "The audio file has been generated and the audio is " output_audio_filename = merge_audio(self.tts.inference(Res), audio_filename) print(output_audio_filename) state = state + [(text, response)] response = res['output'] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) def inpainting(self, state, audio_filename, image_filename): print("===============Running inpainting =============") print("Inputs:", state) print("======>Previous memory:\n %s" % self.agent.memory) new_image_filename, new_audio_filename = self.inpaint.inference(audio_filename, image_filename) AI_prompt = "Here are the predict audio and the mel spectrum." + f"*{new_audio_filename}*" + f"*{new_image_filename}*" output_audio_filename = self.tts.inference(AI_prompt) self.agent.memory.buffer = self.agent.memory.buffer + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) state = state + [(f"Audio Inpainting", AI_prompt)] print("Outputs:", state) return state, state, gr.Image.update(visible=False), gr.Audio.update(value=new_audio_filename, visible=True), gr.Video.update(visible=False), gr.Button.update(visible=False) def clear_audio(self): return gr.Audio.update(value=None, visible=False) def clear_input_audio(self): return gr.Audio.update(value=None) def clear_image(self): return gr.Image.update(value=None, visible=False) def clear_video(self): return gr.Video.update(value=None, visible=False) def clear_button(self): return gr.Button.update(visible=False) if __name__ == '__main__': bot = ConversationBot() with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo: with gr.Row(): gr.Markdown("## AudioGPT") chatbot = gr.Chatbot(elem_id="chatbot", label="AudioGPT", visible=False) state = gr.State([]) with gr.Row() as select_raws: with gr.Column(scale=0.7): interaction_type = gr.Radio(choices=['text', 'speech'], value='text', label='Interaction Type') with gr.Column(scale=0.3, min_width=0): select = gr.Button("Select") with gr.Row(visible=False) as text_input_raws: with gr.Column(scale=0.7): txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(container=False) with gr.Column(scale=0.1, min_width=0): run = gr.Button("🏃‍♂️Run") with gr.Column(scale=0.1, min_width=0): clear_txt = gr.Button("🔄Clear️") with gr.Column(scale=0.1, min_width=0): btn = gr.UploadButton("🖼️Upload", file_types=["image","audio"]) with gr.Row(): outaudio = gr.Audio(visible=False) with gr.Row(): with gr.Column(scale=0.3, min_width=0): outvideo = gr.Video(visible=False) with gr.Row(): show_mel = gr.Image(type="filepath",tool='sketch',visible=False) with gr.Row(): run_button = gr.Button("Predict Masked Place",visible=False) with gr.Row(visible=False) as speech_input_raws: with gr.Column(scale=0.7): speech_input = gr.Audio(source="microphone", type="filepath", label="Input") with gr.Column(scale=0.15, min_width=0): submit_btn = gr.Button("🏃‍♂️Submit") with gr.Column(scale=0.15, min_width=0): clear_speech = gr.Button("🔄Clear️") with gr.Row(): speech_output = gr.Audio(label="Output",visible=False) select.click(bot.init_tools, [interaction_type], [chatbot, select_raws, text_input_raws, speech_input_raws]) txt.submit(bot.run_text, [txt, state], [chatbot, state, outaudio, outvideo, show_mel, run_button]) txt.submit(lambda: "", None, txt) run.click(bot.run_text, [txt, state], [chatbot, state, outaudio, outvideo, show_mel, run_button]) run.click(lambda: "", None, txt) btn.upload(bot.run_image_or_audio, [btn, state, txt], [chatbot, state, outaudio, outvideo]) run_button.click(bot.inpainting, [state, outaudio, show_mel], [chatbot, state, show_mel, outaudio, outvideo, run_button]) clear_txt.click(bot.memory.clear) clear_txt.click(lambda: [], None, chatbot) clear_txt.click(lambda: [], None, state) clear_txt.click(lambda:None, None, txt) clear_txt.click(bot.clear_button, None, run_button) clear_txt.click(bot.clear_image, None, show_mel) clear_txt.click(bot.clear_audio, None, outaudio) clear_txt.click(bot.clear_video, None, outvideo) submit_btn.click(bot.speech, [speech_input, state], [speech_input, speech_output, state, outvideo]) clear_speech.click(bot.clear_input_audio, None, speech_input) clear_speech.click(bot.clear_audio, None, speech_output) clear_speech.click(lambda: [], None, state) clear_speech.click(bot.clear_video, None, outvideo) demo.launch(server_name="0.0.0.0", server_port=7860, share=True)

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from langchain.indexes import VectorstoreIndexCreator from langchain_community.document_loaders import CSVLoader from langchain_community.vectorstores import FAISS loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv") docs = loader.load() index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS) index = index_creator.from_documents(docs) index.vectorstore.save_local("titanic_data")

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# ruff: noqa: E402 """Main entrypoint into package.""" import warnings from importlib import metadata from typing import Any, Optional from langchain_core._api.deprecation import surface_langchain_deprecation_warnings try: __version__ = metadata.version(__package__) except metadata.PackageNotFoundError: # Case where package metadata is not available. __version__ = "" del metadata # optional, avoids polluting the results of dir(__package__) def _warn_on_import(name: str, replacement: Optional[str] = None) -> None: """Warn on import of deprecated module.""" from langchain.utils.interactive_env import is_interactive_env if is_interactive_env(): # No warnings for interactive environments. # This is done to avoid polluting the output of interactive environments # where users rely on auto-complete and may trigger this warning # even if they are not using any deprecated modules return if replacement: warnings.warn( f"Importing {name} from langchain root module is no longer supported. " f"Please use {replacement} instead." ) else: warnings.warn( f"Importing {name} from langchain root module is no longer supported." ) # Surfaces Deprecation and Pending Deprecation warnings from langchain. surface_langchain_deprecation_warnings() def __getattr__(name: str) -> Any: if name == "MRKLChain": from langchain.agents import MRKLChain _warn_on_import(name, replacement="langchain.agents.MRKLChain") return MRKLChain elif name == "ReActChain": from langchain.agents import ReActChain _warn_on_import(name, replacement="langchain.agents.ReActChain") return ReActChain elif name == "SelfAskWithSearchChain": from langchain.agents import SelfAskWithSearchChain _warn_on_import(name, replacement="langchain.agents.SelfAskWithSearchChain") return SelfAskWithSearchChain elif name == "ConversationChain": from langchain.chains import ConversationChain _warn_on_import(name, replacement="langchain.chains.ConversationChain") return ConversationChain elif name == "LLMBashChain": raise ImportError( "This module has been moved to langchain-experimental. " "For more details: " "https://github.com/langchain-ai/langchain/discussions/11352." "To access this code, install it with `pip install langchain-experimental`." "`from langchain_experimental.llm_bash.base " "import LLMBashChain`" ) elif name == "LLMChain": from langchain.chains import LLMChain _warn_on_import(name, replacement="langchain.chains.LLMChain") return LLMChain elif name == "LLMCheckerChain": from langchain.chains import LLMCheckerChain _warn_on_import(name, replacement="langchain.chains.LLMCheckerChain") return LLMCheckerChain elif name == "LLMMathChain": from langchain.chains import LLMMathChain _warn_on_import(name, replacement="langchain.chains.LLMMathChain") return LLMMathChain elif name == "QAWithSourcesChain": from langchain.chains import QAWithSourcesChain _warn_on_import(name, replacement="langchain.chains.QAWithSourcesChain") return QAWithSourcesChain elif name == "VectorDBQA": from langchain.chains import VectorDBQA _warn_on_import(name, replacement="langchain.chains.VectorDBQA") return VectorDBQA elif name == "VectorDBQAWithSourcesChain": from langchain.chains import VectorDBQAWithSourcesChain _warn_on_import(name, replacement="langchain.chains.VectorDBQAWithSourcesChain") return VectorDBQAWithSourcesChain elif name == "InMemoryDocstore": from langchain.docstore import InMemoryDocstore _warn_on_import(name, replacement="langchain.docstore.InMemoryDocstore") return InMemoryDocstore elif name == "Wikipedia": from langchain.docstore import Wikipedia _warn_on_import(name, replacement="langchain.docstore.Wikipedia") return Wikipedia elif name == "Anthropic": from langchain_community.llms import Anthropic _warn_on_import(name, replacement="langchain_community.llms.Anthropic") return Anthropic elif name == "Banana": from langchain_community.llms import Banana _warn_on_import(name, replacement="langchain_community.llms.Banana") return Banana elif name == "CerebriumAI": from langchain_community.llms import CerebriumAI _warn_on_import(name, replacement="langchain_community.llms.CerebriumAI") return CerebriumAI elif name == "Cohere": from langchain_community.llms import Cohere _warn_on_import(name, replacement="langchain_community.llms.Cohere") return Cohere elif name == "ForefrontAI": from langchain_community.llms import ForefrontAI _warn_on_import(name, replacement="langchain_community.llms.ForefrontAI") return ForefrontAI elif name == "GooseAI": from langchain_community.llms import GooseAI _warn_on_import(name, replacement="langchain_community.llms.GooseAI") return GooseAI elif name == "HuggingFaceHub": from langchain_community.llms import HuggingFaceHub _warn_on_import(name, replacement="langchain_community.llms.HuggingFaceHub") return HuggingFaceHub elif name == "HuggingFaceTextGenInference": from langchain_community.llms import HuggingFaceTextGenInference _warn_on_import( name, replacement="langchain_community.llms.HuggingFaceTextGenInference" ) return HuggingFaceTextGenInference elif name == "LlamaCpp": from langchain_community.llms import LlamaCpp _warn_on_import(name, replacement="langchain_community.llms.LlamaCpp") return LlamaCpp elif name == "Modal": from langchain_community.llms import Modal _warn_on_import(name, replacement="langchain_community.llms.Modal") return Modal elif name == "OpenAI": from langchain_community.llms import OpenAI _warn_on_import(name, replacement="langchain_community.llms.OpenAI") return OpenAI elif name == "Petals": from langchain_community.llms import Petals _warn_on_import(name, replacement="langchain_community.llms.Petals") return Petals elif name == "PipelineAI": from langchain_community.llms import PipelineAI _warn_on_import(name, replacement="langchain_community.llms.PipelineAI") return PipelineAI elif name == "SagemakerEndpoint": from langchain_community.llms import SagemakerEndpoint _warn_on_import(name, replacement="langchain_community.llms.SagemakerEndpoint") return SagemakerEndpoint elif name == "StochasticAI": from langchain_community.llms import StochasticAI _warn_on_import(name, replacement="langchain_community.llms.StochasticAI") return StochasticAI elif name == "Writer": from langchain_community.llms import Writer _warn_on_import(name, replacement="langchain_community.llms.Writer") return Writer elif name == "HuggingFacePipeline": from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline _warn_on_import( name, replacement="langchain_community.llms.huggingface_pipeline.HuggingFacePipeline", ) return HuggingFacePipeline elif name == "FewShotPromptTemplate": from langchain_core.prompts import FewShotPromptTemplate _warn_on_import( name, replacement="langchain_core.prompts.FewShotPromptTemplate" ) return FewShotPromptTemplate elif name == "Prompt": from langchain_core.prompts import PromptTemplate _warn_on_import(name, replacement="langchain_core.prompts.PromptTemplate") # it's renamed as prompt template anyways # this is just for backwards compat return PromptTemplate elif name == "PromptTemplate": from langchain_core.prompts import PromptTemplate _warn_on_import(name, replacement="langchain_core.prompts.PromptTemplate") return PromptTemplate elif name == "BasePromptTemplate": from langchain_core.prompts import BasePromptTemplate _warn_on_import(name, replacement="langchain_core.prompts.BasePromptTemplate") return BasePromptTemplate elif name == "ArxivAPIWrapper": from langchain_community.utilities import ArxivAPIWrapper _warn_on_import( name, replacement="langchain_community.utilities.ArxivAPIWrapper" ) return ArxivAPIWrapper elif name == "GoldenQueryAPIWrapper": from langchain_community.utilities import GoldenQueryAPIWrapper _warn_on_import( name, replacement="langchain_community.utilities.GoldenQueryAPIWrapper" ) return GoldenQueryAPIWrapper elif name == "GoogleSearchAPIWrapper": from langchain_community.utilities import GoogleSearchAPIWrapper _warn_on_import( name, replacement="langchain_community.utilities.GoogleSearchAPIWrapper" ) return GoogleSearchAPIWrapper elif name == "GoogleSerperAPIWrapper": from langchain_community.utilities import GoogleSerperAPIWrapper _warn_on_import( name, replacement="langchain_community.utilities.GoogleSerperAPIWrapper" ) return GoogleSerperAPIWrapper elif name == "PowerBIDataset": from langchain_community.utilities import PowerBIDataset _warn_on_import( name, replacement="langchain_community.utilities.PowerBIDataset" ) return PowerBIDataset elif name == "SearxSearchWrapper": from langchain_community.utilities import SearxSearchWrapper _warn_on_import( name, replacement="langchain_community.utilities.SearxSearchWrapper" ) return SearxSearchWrapper elif name == "WikipediaAPIWrapper": from langchain_community.utilities import WikipediaAPIWrapper _warn_on_import( name, replacement="langchain_community.utilities.WikipediaAPIWrapper" ) return WikipediaAPIWrapper elif name == "WolframAlphaAPIWrapper": from langchain_community.utilities import WolframAlphaAPIWrapper _warn_on_import( name, replacement="langchain_community.utilities.WolframAlphaAPIWrapper" ) return WolframAlphaAPIWrapper elif name == "SQLDatabase": from langchain_community.utilities import SQLDatabase _warn_on_import(name, replacement="langchain_community.utilities.SQLDatabase") return SQLDatabase elif name == "FAISS": from langchain_community.vectorstores import FAISS _warn_on_import(name, replacement="langchain_community.vectorstores.FAISS") return FAISS elif name == "ElasticVectorSearch": from langchain_community.vectorstores import ElasticVectorSearch _warn_on_import( name, replacement="langchain_community.vectorstores.ElasticVectorSearch" ) return ElasticVectorSearch # For backwards compatibility elif name == "SerpAPIChain" or name == "SerpAPIWrapper": from langchain_community.utilities import SerpAPIWrapper _warn_on_import( name, replacement="langchain_community.utilities.SerpAPIWrapper" ) return SerpAPIWrapper elif name == "verbose": from langchain.globals import _verbose _warn_on_import( name, replacement=( "langchain.globals.set_verbose() / langchain.globals.get_verbose()" ), ) return _verbose elif name == "debug": from langchain.globals import _debug _warn_on_import( name, replacement=( "langchain.globals.set_debug() / langchain.globals.get_debug()" ), ) return _debug elif name == "llm_cache": from langchain.globals import _llm_cache _warn_on_import( name, replacement=( "langchain.globals.set_llm_cache() / langchain.globals.get_llm_cache()" ), ) return _llm_cache else: raise AttributeError(f"Could not find: {name}") __all__ = [ "LLMChain", "LLMCheckerChain", "LLMMathChain", "ArxivAPIWrapper", "GoldenQueryAPIWrapper", "SelfAskWithSearchChain", "SerpAPIWrapper", "SerpAPIChain", "SearxSearchWrapper", "GoogleSearchAPIWrapper", "GoogleSerperAPIWrapper", "WolframAlphaAPIWrapper", "WikipediaAPIWrapper", "Anthropic", "Banana", "CerebriumAI", "Cohere", "ForefrontAI", "GooseAI", "Modal", "OpenAI", "Petals", "PipelineAI", "StochasticAI", "Writer", "BasePromptTemplate", "Prompt", "FewShotPromptTemplate", "PromptTemplate", "ReActChain", "Wikipedia", "HuggingFaceHub", "SagemakerEndpoint", "HuggingFacePipeline", "SQLDatabase", "PowerBIDataset", "FAISS", "MRKLChain", "VectorDBQA", "ElasticVectorSearch", "InMemoryDocstore", "ConversationChain", "VectorDBQAWithSourcesChain", "QAWithSourcesChain", "LlamaCpp", "HuggingFaceTextGenInference", ]

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from typing import Any, Dict, List, Type, Union from langchain_community.graphs import NetworkxEntityGraph from langchain_community.graphs.networkx_graph import ( KnowledgeTriple, get_entities, parse_triples, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key class ConversationKGMemory(BaseChatMemory): """Knowledge graph conversation memory. Integrates with external knowledge graph to store and retrieve information about knowledge triples in the conversation. """ k: int = 2 human_prefix: str = "Human" ai_prefix: str = "AI" kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph) knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT llm: BaseLanguageModel summary_message_cls: Type[BaseMessage] = SystemMessage """Number of previous utterances to include in the context.""" memory_key: str = "history" #: :meta private: def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" entities = self._get_current_entities(inputs) summary_strings = [] for entity in entities: knowledge = self.kg.get_entity_knowledge(entity) if knowledge: summary = f"On {entity}: {'. '.join(knowledge)}." summary_strings.append(summary) context: Union[str, List] if not summary_strings: context = [] if self.return_messages else "" elif self.return_messages: context = [ self.summary_message_cls(content=text) for text in summary_strings ] else: context = "\n".join(summary_strings) return {self.memory_key: context} @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str: """Get the input key for the prompt.""" if self.input_key is None: return get_prompt_input_key(inputs, self.memory_variables) return self.input_key def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str: """Get the output key for the prompt.""" if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") return list(outputs.keys())[0] return self.output_key def get_current_entities(self, input_string: str) -> List[str]: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, ) return get_entities(output) def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]: """Get the current entities in the conversation.""" prompt_input_key = self._get_prompt_input_key(inputs) return self.get_current_entities(inputs[prompt_input_key]) def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]: chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, verbose=True, ) knowledge = parse_triples(output) return knowledge def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None: """Get and update knowledge graph from the conversation history.""" prompt_input_key = self._get_prompt_input_key(inputs) knowledge = self.get_knowledge_triplets(inputs[prompt_input_key]) for triple in knowledge: self.kg.add_triple(triple) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" super().save_context(inputs, outputs) self._get_and_update_kg(inputs) def clear(self) -> None: """Clear memory contents.""" super().clear() self.kg.clear()

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""" **LLM** classes provide access to the large language model (**LLM**) APIs and services. **Class hierarchy:** .. code-block:: BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI **Main helpers:** .. code-block:: LLMResult, PromptValue, CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun, CallbackManager, AsyncCallbackManager, AIMessage, BaseMessage """ # noqa: E501 import warnings from typing import Any, Callable, Dict, Type from langchain_core._api import LangChainDeprecationWarning from langchain_core.language_models.llms import BaseLLM from langchain.utils.interactive_env import is_interactive_env def _import_ai21() -> Any: from langchain_community.llms.ai21 import AI21 return AI21 def _import_aleph_alpha() -> Any: from langchain_community.llms.aleph_alpha import AlephAlpha return AlephAlpha def _import_amazon_api_gateway() -> Any: from langchain_community.llms.amazon_api_gateway import AmazonAPIGateway return AmazonAPIGateway def _import_anthropic() -> Any: from langchain_community.llms.anthropic import Anthropic return Anthropic def _import_anyscale() -> Any: from langchain_community.llms.anyscale import Anyscale return Anyscale def _import_arcee() -> Any: from langchain_community.llms.arcee import Arcee return Arcee def _import_aviary() -> Any: from langchain_community.llms.aviary import Aviary return Aviary def _import_azureml_endpoint() -> Any: from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint return AzureMLOnlineEndpoint def _import_baidu_qianfan_endpoint() -> Any: from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint return QianfanLLMEndpoint def _import_bananadev() -> Any: from langchain_community.llms.bananadev import Banana return Banana def _import_baseten() -> Any: from langchain_community.llms.baseten import Baseten return Baseten def _import_beam() -> Any: from langchain_community.llms.beam import Beam return Beam def _import_bedrock() -> Any: from langchain_community.llms.bedrock import Bedrock return Bedrock def _import_bittensor() -> Any: from langchain_community.llms.bittensor import NIBittensorLLM return NIBittensorLLM def _import_cerebriumai() -> Any: from langchain_community.llms.cerebriumai import CerebriumAI return CerebriumAI def _import_chatglm() -> Any: from langchain_community.llms.chatglm import ChatGLM return ChatGLM def _import_clarifai() -> Any: from langchain_community.llms.clarifai import Clarifai return Clarifai def _import_cohere() -> Any: from langchain_community.llms.cohere import Cohere return Cohere def _import_ctransformers() -> Any: from langchain_community.llms.ctransformers import CTransformers return CTransformers def _import_ctranslate2() -> Any: from langchain_community.llms.ctranslate2 import CTranslate2 return CTranslate2 def _import_databricks() -> Any: from langchain_community.llms.databricks import Databricks return Databricks def _import_databricks_chat() -> Any: from langchain_community.chat_models.databricks import ChatDatabricks return ChatDatabricks def _import_deepinfra() -> Any: from langchain_community.llms.deepinfra import DeepInfra return DeepInfra def _import_deepsparse() -> Any: from langchain_community.llms.deepsparse import DeepSparse return DeepSparse def _import_edenai() -> Any: from langchain_community.llms.edenai import EdenAI return EdenAI def _import_fake() -> Any: from langchain_community.llms.fake import FakeListLLM return FakeListLLM def _import_fireworks() -> Any: from langchain_community.llms.fireworks import Fireworks return Fireworks def _import_forefrontai() -> Any: from langchain_community.llms.forefrontai import ForefrontAI return ForefrontAI def _import_gigachat() -> Any: from langchain_community.llms.gigachat import GigaChat return GigaChat def _import_google_palm() -> Any: from langchain_community.llms.google_palm import GooglePalm return GooglePalm def _import_gooseai() -> Any: from langchain_community.llms.gooseai import GooseAI return GooseAI def _import_gpt4all() -> Any: from langchain_community.llms.gpt4all import GPT4All return GPT4All def _import_gradient_ai() -> Any: from langchain_community.llms.gradient_ai import GradientLLM return GradientLLM def _import_huggingface_endpoint() -> Any: from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint return HuggingFaceEndpoint def _import_huggingface_hub() -> Any: from langchain_community.llms.huggingface_hub import HuggingFaceHub return HuggingFaceHub def _import_huggingface_pipeline() -> Any: from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline return HuggingFacePipeline def _import_huggingface_text_gen_inference() -> Any: from langchain_community.llms.huggingface_text_gen_inference import ( HuggingFaceTextGenInference, ) return HuggingFaceTextGenInference def _import_human() -> Any: from langchain_community.llms.human import HumanInputLLM return HumanInputLLM def _import_javelin_ai_gateway() -> Any: from langchain_community.llms.javelin_ai_gateway import JavelinAIGateway return JavelinAIGateway def _import_koboldai() -> Any: from langchain_community.llms.koboldai import KoboldApiLLM return KoboldApiLLM def _import_llamacpp() -> Any: from langchain_community.llms.llamacpp import LlamaCpp return LlamaCpp def _import_manifest() -> Any: from langchain_community.llms.manifest import ManifestWrapper return ManifestWrapper def _import_minimax() -> Any: from langchain_community.llms.minimax import Minimax return Minimax def _import_mlflow() -> Any: from langchain_community.llms.mlflow import Mlflow return Mlflow def _import_mlflow_chat() -> Any: from langchain_community.chat_models.mlflow import ChatMlflow return ChatMlflow def _import_mlflow_ai_gateway() -> Any: from langchain_community.llms.mlflow_ai_gateway import MlflowAIGateway return MlflowAIGateway def _import_modal() -> Any: from langchain_community.llms.modal import Modal return Modal def _import_mosaicml() -> Any: from langchain_community.llms.mosaicml import MosaicML return MosaicML def _import_nlpcloud() -> Any: from langchain_community.llms.nlpcloud import NLPCloud return NLPCloud def _import_octoai_endpoint() -> Any: from langchain_community.llms.octoai_endpoint import OctoAIEndpoint return OctoAIEndpoint def _import_ollama() -> Any: from langchain_community.llms.ollama import Ollama return Ollama def _import_opaqueprompts() -> Any: from langchain_community.llms.opaqueprompts import OpaquePrompts return OpaquePrompts def _import_azure_openai() -> Any: from langchain_community.llms.openai import AzureOpenAI return AzureOpenAI def _import_openai() -> Any: from langchain_community.llms.openai import OpenAI return OpenAI def _import_openai_chat() -> Any: from langchain_community.llms.openai import OpenAIChat return OpenAIChat def _import_openllm() -> Any: from langchain_community.llms.openllm import OpenLLM return OpenLLM def _import_openlm() -> Any: from langchain_community.llms.openlm import OpenLM return OpenLM def _import_pai_eas_endpoint() -> Any: from langchain_community.llms.pai_eas_endpoint import PaiEasEndpoint return PaiEasEndpoint def _import_petals() -> Any: from langchain_community.llms.petals import Petals return Petals def _import_pipelineai() -> Any: from langchain_community.llms.pipelineai import PipelineAI return PipelineAI def _import_predibase() -> Any: from langchain_community.llms.predibase import Predibase return Predibase def _import_predictionguard() -> Any: from langchain_community.llms.predictionguard import PredictionGuard return PredictionGuard def _import_promptlayer() -> Any: from langchain_community.llms.promptlayer_openai import PromptLayerOpenAI return PromptLayerOpenAI def _import_promptlayer_chat() -> Any: from langchain_community.llms.promptlayer_openai import PromptLayerOpenAIChat return PromptLayerOpenAIChat def _import_replicate() -> Any: from langchain_community.llms.replicate import Replicate return Replicate def _import_rwkv() -> Any: from langchain_community.llms.rwkv import RWKV return RWKV def _import_sagemaker_endpoint() -> Any: from langchain_community.llms.sagemaker_endpoint import SagemakerEndpoint return SagemakerEndpoint def _import_self_hosted() -> Any: from langchain_community.llms.self_hosted import SelfHostedPipeline return SelfHostedPipeline def _import_self_hosted_hugging_face() -> Any: from langchain_community.llms.self_hosted_hugging_face import ( SelfHostedHuggingFaceLLM, ) return SelfHostedHuggingFaceLLM def _import_stochasticai() -> Any: from langchain_community.llms.stochasticai import StochasticAI return StochasticAI def _import_symblai_nebula() -> Any: from langchain_community.llms.symblai_nebula import Nebula return Nebula def _import_textgen() -> Any: from langchain_community.llms.textgen import TextGen return TextGen def _import_titan_takeoff() -> Any: from langchain_community.llms.titan_takeoff import TitanTakeoff return TitanTakeoff def _import_titan_takeoff_pro() -> Any: from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro return TitanTakeoffPro def _import_together() -> Any: from langchain_community.llms.together import Together return Together def _import_tongyi() -> Any: from langchain_community.llms.tongyi import Tongyi return Tongyi def _import_vertex() -> Any: from langchain_community.llms.vertexai import VertexAI return VertexAI def _import_vertex_model_garden() -> Any: from langchain_community.llms.vertexai import VertexAIModelGarden return VertexAIModelGarden def _import_vllm() -> Any: from langchain_community.llms.vllm import VLLM return VLLM def _import_vllm_openai() -> Any: from langchain_community.llms.vllm import VLLMOpenAI return VLLMOpenAI def _import_watsonxllm() -> Any: from langchain_community.llms.watsonxllm import WatsonxLLM return WatsonxLLM def _import_writer() -> Any: from langchain_community.llms.writer import Writer return Writer def _import_xinference() -> Any: from langchain_community.llms.xinference import Xinference return Xinference def _import_yandex_gpt() -> Any: from langchain_community.llms.yandex import YandexGPT return YandexGPT def _import_volcengine_maas() -> Any: from langchain_community.llms.volcengine_maas import VolcEngineMaasLLM return VolcEngineMaasLLM def __getattr__(name: str) -> Any: from langchain_community import llms # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing LLMs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.llms import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) if name == "type_to_cls_dict": # for backwards compatibility type_to_cls_dict: Dict[str, Type[BaseLLM]] = { k: v() for k, v in get_type_to_cls_dict().items() } return type_to_cls_dict else: return getattr(llms, name) __all__ = [ "AI21", "AlephAlpha", "AmazonAPIGateway", "Anthropic", "Anyscale", "Arcee", "Aviary", "AzureMLOnlineEndpoint", "AzureOpenAI", "Banana", "Baseten", "Beam", "Bedrock", "CTransformers", "CTranslate2", "CerebriumAI", "ChatGLM", "Clarifai", "Cohere", "Databricks", "DeepInfra", "DeepSparse", "EdenAI", "FakeListLLM", "Fireworks", "ForefrontAI", "GigaChat", "GPT4All", "GooglePalm", "GooseAI", "GradientLLM", "HuggingFaceEndpoint", "HuggingFaceHub", "HuggingFacePipeline", "HuggingFaceTextGenInference", "HumanInputLLM", "KoboldApiLLM", "LlamaCpp", "TextGen", "ManifestWrapper", "Minimax", "MlflowAIGateway", "Modal", "MosaicML", "Nebula", "NIBittensorLLM", "NLPCloud", "Ollama", "OpenAI", "OpenAIChat", "OpenLLM", "OpenLM", "PaiEasEndpoint", "Petals", "PipelineAI", "Predibase", "PredictionGuard", "PromptLayerOpenAI", "PromptLayerOpenAIChat", "OpaquePrompts", "RWKV", "Replicate", "SagemakerEndpoint", "SelfHostedHuggingFaceLLM", "SelfHostedPipeline", "StochasticAI", "TitanTakeoff", "TitanTakeoffPro", "Tongyi", "VertexAI", "VertexAIModelGarden", "VLLM", "VLLMOpenAI", "WatsonxLLM", "Writer", "OctoAIEndpoint", "Xinference", "JavelinAIGateway", "QianfanLLMEndpoint", "YandexGPT", "VolcEngineMaasLLM", ] def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]: return { "ai21": _import_ai21, "aleph_alpha": _import_aleph_alpha, "amazon_api_gateway": _import_amazon_api_gateway, "amazon_bedrock": _import_bedrock, "anthropic": _import_anthropic, "anyscale": _import_anyscale, "arcee": _import_arcee, "aviary": _import_aviary, "azure": _import_azure_openai, "azureml_endpoint": _import_azureml_endpoint, "bananadev": _import_bananadev, "baseten": _import_baseten, "beam": _import_beam, "cerebriumai": _import_cerebriumai, "chat_glm": _import_chatglm, "clarifai": _import_clarifai, "cohere": _import_cohere, "ctransformers": _import_ctransformers, "ctranslate2": _import_ctranslate2, "databricks": _import_databricks, "databricks-chat": _import_databricks_chat, "deepinfra": _import_deepinfra, "deepsparse": _import_deepsparse, "edenai": _import_edenai, "fake-list": _import_fake, "forefrontai": _import_forefrontai, "giga-chat-model": _import_gigachat, "google_palm": _import_google_palm, "gooseai": _import_gooseai, "gradient": _import_gradient_ai, "gpt4all": _import_gpt4all, "huggingface_endpoint": _import_huggingface_endpoint, "huggingface_hub": _import_huggingface_hub, "huggingface_pipeline": _import_huggingface_pipeline, "huggingface_textgen_inference": _import_huggingface_text_gen_inference, "human-input": _import_human, "koboldai": _import_koboldai, "llamacpp": _import_llamacpp, "textgen": _import_textgen, "minimax": _import_minimax, "mlflow": _import_mlflow, "mlflow-chat": _import_mlflow_chat, "mlflow-ai-gateway": _import_mlflow_ai_gateway, "modal": _import_modal, "mosaic": _import_mosaicml, "nebula": _import_symblai_nebula, "nibittensor": _import_bittensor, "nlpcloud": _import_nlpcloud, "ollama": _import_ollama, "openai": _import_openai, "openlm": _import_openlm, "pai_eas_endpoint": _import_pai_eas_endpoint, "petals": _import_petals, "pipelineai": _import_pipelineai, "predibase": _import_predibase, "opaqueprompts": _import_opaqueprompts, "replicate": _import_replicate, "rwkv": _import_rwkv, "sagemaker_endpoint": _import_sagemaker_endpoint, "self_hosted": _import_self_hosted, "self_hosted_hugging_face": _import_self_hosted_hugging_face, "stochasticai": _import_stochasticai, "together": _import_together, "tongyi": _import_tongyi, "titan_takeoff": _import_titan_takeoff, "titan_takeoff_pro": _import_titan_takeoff_pro, "vertexai": _import_vertex, "vertexai_model_garden": _import_vertex_model_garden, "openllm": _import_openllm, "openllm_client": _import_openllm, "vllm": _import_vllm, "vllm_openai": _import_vllm_openai, "watsonxllm": _import_watsonxllm, "writer": _import_writer, "xinference": _import_xinference, "javelin-ai-gateway": _import_javelin_ai_gateway, "qianfan_endpoint": _import_baidu_qianfan_endpoint, "yandex_gpt": _import_yandex_gpt, "VolcEngineMaasLLM": _import_volcengine_maas, }

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import logging from abc import ABC, abstractmethod from itertools import islice from typing import Any, Dict, Iterable, List, Optional from langchain_community.utilities.redis import get_client from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, ENTITY_SUMMARIZATION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key logger = logging.getLogger(__name__) class BaseEntityStore(BaseModel, ABC): """Abstract base class for Entity store.""" @abstractmethod def get(self, key: str, default: Optional[str] = None) -> Optional[str]: """Get entity value from store.""" pass @abstractmethod def set(self, key: str, value: Optional[str]) -> None: """Set entity value in store.""" pass @abstractmethod def delete(self, key: str) -> None: """Delete entity value from store.""" pass @abstractmethod def exists(self, key: str) -> bool: """Check if entity exists in store.""" pass @abstractmethod def clear(self) -> None: """Delete all entities from store.""" pass class InMemoryEntityStore(BaseEntityStore): """In-memory Entity store.""" store: Dict[str, Optional[str]] = {} def get(self, key: str, default: Optional[str] = None) -> Optional[str]: return self.store.get(key, default) def set(self, key: str, value: Optional[str]) -> None: self.store[key] = value def delete(self, key: str) -> None: del self.store[key] def exists(self, key: str) -> bool: return key in self.store def clear(self) -> None: return self.store.clear() class UpstashRedisEntityStore(BaseEntityStore): """Upstash Redis backed Entity store. Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back. """ def __init__( self, session_id: str = "default", url: str = "", token: str = "", key_prefix: str = "memory_store", ttl: Optional[int] = 60 * 60 * 24, recall_ttl: Optional[int] = 60 * 60 * 24 * 3, *args: Any, **kwargs: Any, ): try: from upstash_redis import Redis except ImportError: raise ImportError( "Could not import upstash_redis python package. " "Please install it with `pip install upstash_redis`." ) super().__init__(*args, **kwargs) try: self.redis_client = Redis(url=url, token=token) except Exception: logger.error("Upstash Redis instance could not be initiated.") self.session_id = session_id self.key_prefix = key_prefix self.ttl = ttl self.recall_ttl = recall_ttl or ttl @property def full_key_prefix(self) -> str: return f"{self.key_prefix}:{self.session_id}" def get(self, key: str, default: Optional[str] = None) -> Optional[str]: res = ( self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl) or default or "" ) logger.debug(f"Upstash Redis MEM get '{self.full_key_prefix}:{key}': '{res}'") return res def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl) logger.debug( f"Redis MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}" ) def delete(self, key: str) -> None: self.redis_client.delete(f"{self.full_key_prefix}:{key}") def exists(self, key: str) -> bool: return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1 def clear(self) -> None: def scan_and_delete(cursor: int) -> int: cursor, keys_to_delete = self.redis_client.scan( cursor, f"{self.full_key_prefix}:*" ) self.redis_client.delete(*keys_to_delete) return cursor cursor = scan_and_delete(0) while cursor != 0: scan_and_delete(cursor) class RedisEntityStore(BaseEntityStore): """Redis-backed Entity store. Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back. """ redis_client: Any session_id: str = "default" key_prefix: str = "memory_store" ttl: Optional[int] = 60 * 60 * 24 recall_ttl: Optional[int] = 60 * 60 * 24 * 3 def __init__( self, session_id: str = "default", url: str = "redis://localhost:6379/0", key_prefix: str = "memory_store", ttl: Optional[int] = 60 * 60 * 24, recall_ttl: Optional[int] = 60 * 60 * 24 * 3, *args: Any, **kwargs: Any, ): try: import redis except ImportError: raise ImportError( "Could not import redis python package. " "Please install it with `pip install redis`." ) super().__init__(*args, **kwargs) try: self.redis_client = get_client(redis_url=url, decode_responses=True) except redis.exceptions.ConnectionError as error: logger.error(error) self.session_id = session_id self.key_prefix = key_prefix self.ttl = ttl self.recall_ttl = recall_ttl or ttl @property def full_key_prefix(self) -> str: return f"{self.key_prefix}:{self.session_id}" def get(self, key: str, default: Optional[str] = None) -> Optional[str]: res = ( self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl) or default or "" ) logger.debug(f"REDIS MEM get '{self.full_key_prefix}:{key}': '{res}'") return res def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl) logger.debug( f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}" ) def delete(self, key: str) -> None: self.redis_client.delete(f"{self.full_key_prefix}:{key}") def exists(self, key: str) -> bool: return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1 def clear(self) -> None: # iterate a list in batches of size batch_size def batched(iterable: Iterable[Any], batch_size: int) -> Iterable[Any]: iterator = iter(iterable) while batch := list(islice(iterator, batch_size)): yield batch for keybatch in batched( self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500 ): self.redis_client.delete(*keybatch) class SQLiteEntityStore(BaseEntityStore): """SQLite-backed Entity store""" session_id: str = "default" table_name: str = "memory_store" conn: Any = None class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True def __init__( self, session_id: str = "default", db_file: str = "entities.db", table_name: str = "memory_store", *args: Any, **kwargs: Any, ): try: import sqlite3 except ImportError: raise ImportError( "Could not import sqlite3 python package. " "Please install it with `pip install sqlite3`." ) super().__init__(*args, **kwargs) self.conn = sqlite3.connect(db_file) self.session_id = session_id self.table_name = table_name self._create_table_if_not_exists() @property def full_table_name(self) -> str: return f"{self.table_name}_{self.session_id}" def _create_table_if_not_exists(self) -> None: create_table_query = f""" CREATE TABLE IF NOT EXISTS {self.full_table_name} ( key TEXT PRIMARY KEY, value TEXT ) """ with self.conn: self.conn.execute(create_table_query) def get(self, key: str, default: Optional[str] = None) -> Optional[str]: query = f""" SELECT value FROM {self.full_table_name} WHERE key = ? """ cursor = self.conn.execute(query, (key,)) result = cursor.fetchone() if result is not None: value = result[0] return value return default def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) query = f""" INSERT OR REPLACE INTO {self.full_table_name} (key, value) VALUES (?, ?) """ with self.conn: self.conn.execute(query, (key, value)) def delete(self, key: str) -> None: query = f""" DELETE FROM {self.full_table_name} WHERE key = ? """ with self.conn: self.conn.execute(query, (key,)) def exists(self, key: str) -> bool: query = f""" SELECT 1 FROM {self.full_table_name} WHERE key = ? LIMIT 1 """ cursor = self.conn.execute(query, (key,)) result = cursor.fetchone() return result is not None def clear(self) -> None: query = f""" DELETE FROM {self.full_table_name} """ with self.conn: self.conn.execute(query) class ConversationEntityMemory(BaseChatMemory): """Entity extractor & summarizer memory. Extracts named entities from the recent chat history and generates summaries. With a swappable entity store, persisting entities across conversations. Defaults to an in-memory entity store, and can be swapped out for a Redis, SQLite, or other entity store. """ human_prefix: str = "Human" ai_prefix: str = "AI" llm: BaseLanguageModel entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT # Cache of recently detected entity names, if any # It is updated when load_memory_variables is called: entity_cache: List[str] = [] # Number of recent message pairs to consider when updating entities: k: int = 3 chat_history_key: str = "history" # Store to manage entity-related data: entity_store: BaseEntityStore = Field(default_factory=InMemoryEntityStore) @property def buffer(self) -> List[BaseMessage]: """Access chat memory messages.""" return self.chat_memory.messages @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return ["entities", self.chat_history_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """ Returns chat history and all generated entities with summaries if available, and updates or clears the recent entity cache. New entity name can be found when calling this method, before the entity summaries are generated, so the entity cache values may be empty if no entity descriptions are generated yet. """ # Create an LLMChain for predicting entity names from the recent chat history: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key # Extract an arbitrary window of the last message pairs from # the chat history, where the hyperparameter k is the # number of message pairs: buffer_string = get_buffer_string( self.buffer[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) # Generates a comma-separated list of named entities, # e.g. "Jane, White House, UFO" # or "NONE" if no named entities are extracted: output = chain.predict( history=buffer_string, input=inputs[prompt_input_key], ) # If no named entities are extracted, assigns an empty list. if output.strip() == "NONE": entities = [] else: # Make a list of the extracted entities: entities = [w.strip() for w in output.split(",")] # Make a dictionary of entities with summary if exists: entity_summaries = {} for entity in entities: entity_summaries[entity] = self.entity_store.get(entity, "") # Replaces the entity name cache with the most recently discussed entities, # or if no entities were extracted, clears the cache: self.entity_cache = entities # Should we return as message objects or as a string? if self.return_messages: # Get last `k` pair of chat messages: buffer: Any = self.buffer[-self.k * 2 :] else: # Reuse the string we made earlier: buffer = buffer_string return { self.chat_history_key: buffer, "entities": entity_summaries, } def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """ Save context from this conversation history to the entity store. Generates a summary for each entity in the entity cache by prompting the model, and saves these summaries to the entity store. """ super().save_context(inputs, outputs) if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key # Extract an arbitrary window of the last message pairs from # the chat history, where the hyperparameter k is the # number of message pairs: buffer_string = get_buffer_string( self.buffer[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) input_data = inputs[prompt_input_key] # Create an LLMChain for predicting entity summarization from the context chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt) # Generate new summaries for entities and save them in the entity store for entity in self.entity_cache: # Get existing summary if it exists existing_summary = self.entity_store.get(entity, "") output = chain.predict( summary=existing_summary, entity=entity, history=buffer_string, input=input_data, ) # Save the updated summary to the entity store self.entity_store.set(entity, output.strip()) def clear(self) -> None: """Clear memory contents.""" self.chat_memory.clear() self.entity_cache.clear() self.entity_store.clear()

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"""**Tools** are classes that an Agent uses to interact with the world. Each tool has a **description**. Agent uses the description to choose the right tool for the job. **Class hierarchy:** .. code-block:: ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool <name> # Examples: BraveSearch, HumanInputRun **Main helpers:** .. code-block:: CallbackManagerForToolRun, AsyncCallbackManagerForToolRun """ import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain_core.tools import BaseTool, StructuredTool, Tool, tool from langchain.utils.interactive_env import is_interactive_env # Used for internal purposes _DEPRECATED_TOOLS = {"PythonAstREPLTool", "PythonREPLTool"} def _import_python_tool_PythonAstREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def _import_python_tool_PythonREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def __getattr__(name: str) -> Any: if name == "PythonAstREPLTool": return _import_python_tool_PythonAstREPLTool() elif name == "PythonREPLTool": return _import_python_tool_PythonREPLTool() else: from langchain_community import tools # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing tools from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.tools import {name}`.\n\n" "To install langchain-community run " "`pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(tools, name) __all__ = [ "AINAppOps", "AINOwnerOps", "AINRuleOps", "AINTransfer", "AINValueOps", "AIPluginTool", "APIOperation", "ArxivQueryRun", "AzureCogsFormRecognizerTool", "AzureCogsImageAnalysisTool", "AzureCogsSpeech2TextTool", "AzureCogsText2SpeechTool", "AzureCogsTextAnalyticsHealthTool", "BaseGraphQLTool", "BaseRequestsTool", "BaseSQLDatabaseTool", "BaseSparkSQLTool", "BaseTool", "BearlyInterpreterTool", "BingSearchResults", "BingSearchRun", "BraveSearch", "ClickTool", "CopyFileTool", "CurrentWebPageTool", "DeleteFileTool", "DuckDuckGoSearchResults", "DuckDuckGoSearchRun", "E2BDataAnalysisTool", "EdenAiExplicitImageTool", "EdenAiObjectDetectionTool", "EdenAiParsingIDTool", "EdenAiParsingInvoiceTool", "EdenAiSpeechToTextTool", "EdenAiTextModerationTool", "EdenAiTextToSpeechTool", "EdenaiTool", "ElevenLabsText2SpeechTool", "ExtractHyperlinksTool", "ExtractTextTool", "FileSearchTool", "GetElementsTool", "GmailCreateDraft", "GmailGetMessage", "GmailGetThread", "GmailSearch", "GmailSendMessage", "GoogleCloudTextToSpeechTool", "GooglePlacesTool", "GoogleSearchResults", "GoogleSearchRun", "GoogleSerperResults", "GoogleSerperRun", "SearchAPIResults", "SearchAPIRun", "HumanInputRun", "IFTTTWebhook", "InfoPowerBITool", "InfoSQLDatabaseTool", "InfoSparkSQLTool", "JiraAction", "JsonGetValueTool", "JsonListKeysTool", "ListDirectoryTool", "ListPowerBITool", "ListSQLDatabaseTool", "ListSparkSQLTool", "MerriamWebsterQueryRun", "MetaphorSearchResults", "MoveFileTool", "NasaAction", "NavigateBackTool", "NavigateTool", "O365CreateDraftMessage", "O365SearchEmails", "O365SearchEvents", "O365SendEvent", "O365SendMessage", "OpenAPISpec", "OpenWeatherMapQueryRun", "PubmedQueryRun", "RedditSearchRun", "QueryCheckerTool", "QueryPowerBITool", "QuerySQLCheckerTool", "QuerySQLDataBaseTool", "QuerySparkSQLTool", "ReadFileTool", "RequestsDeleteTool", "RequestsGetTool", "RequestsPatchTool", "RequestsPostTool", "RequestsPutTool", "SteamWebAPIQueryRun", "SceneXplainTool", "SearxSearchResults", "SearxSearchRun", "ShellTool", "SlackGetChannel", "SlackGetMessage", "SlackScheduleMessage", "SlackSendMessage", "SleepTool", "StdInInquireTool", "StackExchangeTool", "SteamshipImageGenerationTool", "StructuredTool", "Tool", "VectorStoreQATool", "VectorStoreQAWithSourcesTool", "WikipediaQueryRun", "WolframAlphaQueryRun", "WriteFileTool", "YahooFinanceNewsTool", "YouTubeSearchTool", "ZapierNLAListActions", "ZapierNLARunAction", "format_tool_to_openai_function", "tool", ]

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from functools import partial from typing import Optional from langchain_core.callbacks.manager import ( Callbacks, ) from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain.tools import Tool class RetrieverInput(BaseModel): """Input to the retriever.""" query: str = Field(description="query to look up in retriever") def _get_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = retriever.get_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) async def _aget_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = await retriever.aget_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) def create_retriever_tool( retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional[BasePromptTemplate] = None, document_separator: str = "\n\n", ) -> Tool: """Create a tool to do retrieval of documents. Args: retriever: The retriever to use for the retrieval name: The name for the tool. This will be passed to the language model, so should be unique and somewhat descriptive. description: The description for the tool. This will be passed to the language model, so should be descriptive. Returns: Tool class to pass to an agent """ document_prompt = document_prompt or PromptTemplate.from_template("{page_content}") func = partial( _get_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) afunc = partial( _aget_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) return Tool( name=name, description=description, func=func, coroutine=afunc, args_schema=RetrieverInput, )

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from typing import Any, List, Sequence, Tuple, Union from langchain_core._api import deprecated from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import Callbacks from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.chat import AIMessagePromptTemplate, ChatPromptTemplate from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.tools import BaseTool from langchain.agents.agent import BaseSingleActionAgent from langchain.agents.format_scratchpad import format_xml from langchain.agents.output_parsers import XMLAgentOutputParser from langchain.agents.xml.prompt import agent_instructions from langchain.chains.llm import LLMChain from langchain.tools.render import ToolsRenderer, render_text_description @deprecated("0.1.0", alternative="create_xml_agent", removal="0.2.0") class XMLAgent(BaseSingleActionAgent): """Agent that uses XML tags. Args: tools: list of tools the agent can choose from llm_chain: The LLMChain to call to predict the next action Examples: .. code-block:: python from langchain.agents import XMLAgent from langchain tools = ... model = """ tools: List[BaseTool] """List of tools this agent has access to.""" llm_chain: LLMChain """Chain to use to predict action.""" @property def input_keys(self) -> List[str]: return ["input"] @staticmethod def get_default_prompt() -> ChatPromptTemplate: base_prompt = ChatPromptTemplate.from_template(agent_instructions) return base_prompt + AIMessagePromptTemplate.from_template( "{intermediate_steps}" ) @staticmethod def get_default_output_parser() -> XMLAgentOutputParser: return XMLAgentOutputParser() def plan( self, intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Callbacks = None, **kwargs: Any, ) -> Union[AgentAction, AgentFinish]: log = "" for action, observation in intermediate_steps: log += ( f"<tool>{action.tool}</tool><tool_input>{action.tool_input}" f"</tool_input><observation>{observation}</observation>" ) tools = "" for tool in self.tools: tools += f"{tool.name}: {tool.description}\n" inputs = { "intermediate_steps": log, "tools": tools, "question": kwargs["input"], "stop": ["</tool_input>", "</final_answer>"], } response = self.llm_chain(inputs, callbacks=callbacks) return response[self.llm_chain.output_key] async def aplan( self, intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Callbacks = None, **kwargs: Any, ) -> Union[AgentAction, AgentFinish]: log = "" for action, observation in intermediate_steps: log += ( f"<tool>{action.tool}</tool><tool_input>{action.tool_input}" f"</tool_input><observation>{observation}</observation>" ) tools = "" for tool in self.tools: tools += f"{tool.name}: {tool.description}\n" inputs = { "intermediate_steps": log, "tools": tools, "question": kwargs["input"], "stop": ["</tool_input>", "</final_answer>"], } response = await self.llm_chain.acall(inputs, callbacks=callbacks) return response[self.llm_chain.output_key] def create_xml_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate, tools_renderer: ToolsRenderer = render_text_description, ) -> Runnable: """Create an agent that uses XML to format its logic. Args: llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use, must have input keys `tools`: contains descriptions for each tool. `agent_scratchpad`: contains previous agent actions and tool outputs. tools_renderer: This controls how the tools are converted into a string and then passed into the LLM. Default is `render_text_description`. Returns: A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does. It returns as output either an AgentAction or AgentFinish. Example: .. code-block:: python from langchain import hub from langchain_community.chat_models import ChatAnthropic from langchain.agents import AgentExecutor, create_xml_agent prompt = hub.pull("hwchase17/xml-agent-convo") model = ChatAnthropic() tools = ... agent = create_xml_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"input": "hi"}) # Use with chat history from langchain_core.messages import AIMessage, HumanMessage agent_executor.invoke( { "input": "what's my name?", # Notice that chat_history is a string # since this prompt is aimed at LLMs, not chat models "chat_history": "Human: My name is Bob\\nAI: Hello Bob!", } ) Prompt: The prompt must have input keys: * `tools`: contains descriptions for each tool. * `agent_scratchpad`: contains previous agent actions and tool outputs as an XML string. Here's an example: .. code-block:: python from langchain_core.prompts import PromptTemplate template = '''You are a helpful assistant. Help the user answer any questions. You have access to the following tools: {tools} In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation> For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond: <tool>search</tool><tool_input>weather in SF</tool_input> <observation>64 degrees</observation> When you are done, respond with a final answer between <final_answer></final_answer>. For example: <final_answer>The weather in SF is 64 degrees</final_answer> Begin! Previous Conversation: {chat_history} Question: {input} {agent_scratchpad}''' prompt = PromptTemplate.from_template(template) """ # noqa: E501 missing_vars = {"tools", "agent_scratchpad"}.difference(prompt.input_variables) if missing_vars: raise ValueError(f"Prompt missing required variables: {missing_vars}") prompt = prompt.partial( tools=tools_renderer(list(tools)), ) llm_with_stop = llm.bind(stop=["</tool_input>"]) agent = ( RunnablePassthrough.assign( agent_scratchpad=lambda x: format_xml(x["intermediate_steps"]), ) | prompt | llm_with_stop | XMLAgentOutputParser() ) return agent

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"""**Graphs** provide a natural language interface to graph databases.""" import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain.utils.interactive_env import is_interactive_env def __getattr__(name: str) -> Any: from langchain_community import graphs # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing graphs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.graphs import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(graphs, name) __all__ = [ "MemgraphGraph", "NetworkxEntityGraph", "Neo4jGraph", "NebulaGraph", "NeptuneGraph", "KuzuGraph", "HugeGraph", "RdfGraph", "ArangoGraph", "FalkorDBGraph", ]

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"""Chain that makes API calls and summarizes the responses to answer a question.""" from __future__ import annotations from typing import Any, Dict, List, Optional, Sequence, Tuple from urllib.parse import urlparse from langchain_community.utilities.requests import TextRequestsWrapper from langchain_core.callbacks import ( AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field, root_validator from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT from langchain.chains.base import Chain from langchain.chains.llm import LLMChain def _extract_scheme_and_domain(url: str) -> Tuple[str, str]: """Extract the scheme + domain from a given URL. Args: url (str): The input URL. Returns: return a 2-tuple of scheme and domain """ parsed_uri = urlparse(url) return parsed_uri.scheme, parsed_uri.netloc def _check_in_allowed_domain(url: str, limit_to_domains: Sequence[str]) -> bool: """Check if a URL is in the allowed domains. Args: url (str): The input URL. limit_to_domains (Sequence[str]): The allowed domains. Returns: bool: True if the URL is in the allowed domains, False otherwise. """ scheme, domain = _extract_scheme_and_domain(url) for allowed_domain in limit_to_domains: allowed_scheme, allowed_domain = _extract_scheme_and_domain(allowed_domain) if scheme == allowed_scheme and domain == allowed_domain: return True return False class APIChain(Chain): """Chain that makes API calls and summarizes the responses to answer a question. *Security Note*: This API chain uses the requests toolkit to make GET, POST, PATCH, PUT, and DELETE requests to an API. Exercise care in who is allowed to use this chain. If exposing to end users, consider that users will be able to make arbitrary requests on behalf of the server hosting the code. For example, users could ask the server to make a request to a private API that is only accessible from the server. Control access to who can submit issue requests using this toolkit and what network access it has. See https://python.langchain.com/docs/security for more information. """ api_request_chain: LLMChain api_answer_chain: LLMChain requests_wrapper: TextRequestsWrapper = Field(exclude=True) api_docs: str question_key: str = "question" #: :meta private: output_key: str = "output" #: :meta private: limit_to_domains: Optional[Sequence[str]] """Use to limit the domains that can be accessed by the API chain. * For example, to limit to just the domain `https://www.example.com`, set `limit_to_domains=["https://www.example.com"]`. * The default value is an empty tuple, which means that no domains are allowed by default. By design this will raise an error on instantiation. * Use a None if you want to allow all domains by default -- this is not recommended for security reasons, as it would allow malicious users to make requests to arbitrary URLS including internal APIs accessible from the server. """ @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.question_key] @property def output_keys(self) -> List[str]: """Expect output key. :meta private: """ return [self.output_key] @root_validator(pre=True) def validate_api_request_prompt(cls, values: Dict) -> Dict: """Check that api request prompt expects the right variables.""" input_vars = values["api_request_chain"].prompt.input_variables expected_vars = {"question", "api_docs"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values @root_validator(pre=True) def validate_limit_to_domains(cls, values: Dict) -> Dict: """Check that allowed domains are valid.""" if "limit_to_domains" not in values: raise ValueError( "You must specify a list of domains to limit access using " "`limit_to_domains`" ) if not values["limit_to_domains"] and values["limit_to_domains"] is not None: raise ValueError( "Please provide a list of domains to limit access using " "`limit_to_domains`." ) return values @root_validator(pre=True) def validate_api_answer_prompt(cls, values: Dict) -> Dict: """Check that api answer prompt expects the right variables.""" input_vars = values["api_answer_chain"].prompt.input_variables expected_vars = {"question", "api_docs", "api_url", "api_response"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = self.api_request_chain.predict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) _run_manager.on_text(api_url, color="green", end="\n", verbose=self.verbose) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = self.requests_wrapper.get(api_url) _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = self.api_answer_chain.predict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = await self.api_request_chain.apredict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) await _run_manager.on_text( api_url, color="green", end="\n", verbose=self.verbose ) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = await self.requests_wrapper.aget(api_url) await _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = await self.api_answer_chain.apredict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} @classmethod def from_llm_and_api_docs( cls, llm: BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: BasePromptTemplate = API_URL_PROMPT, api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT, limit_to_domains: Optional[Sequence[str]] = tuple(), **kwargs: Any, ) -> APIChain: """Load chain from just an LLM and the api docs.""" get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt) requests_wrapper = TextRequestsWrapper(headers=headers) get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt) return cls( api_request_chain=get_request_chain, api_answer_chain=get_answer_chain, requests_wrapper=requests_wrapper, api_docs=api_docs, limit_to_domains=limit_to_domains, **kwargs, ) @property def _chain_type(self) -> str: return "api_chain"

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"""Hypothetical Document Embeddings. https://arxiv.org/abs/2212.10496 """ from __future__ import annotations from typing import Any, Dict, List, Optional import numpy as np from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Extra from langchain.chains.base import Chain from langchain.chains.hyde.prompts import PROMPT_MAP from langchain.chains.llm import LLMChain class HypotheticalDocumentEmbedder(Chain, Embeddings): """Generate hypothetical document for query, and then embed that. Based on https://arxiv.org/abs/2212.10496 """ base_embeddings: Embeddings llm_chain: LLMChain class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Input keys for Hyde's LLM chain.""" return self.llm_chain.input_keys @property def output_keys(self) -> List[str]: """Output keys for Hyde's LLM chain.""" return self.llm_chain.output_keys def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call the base embeddings.""" return self.base_embeddings.embed_documents(texts) def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]: """Combine embeddings into final embeddings.""" return list(np.array(embeddings).mean(axis=0)) def embed_query(self, text: str) -> List[float]: """Generate a hypothetical document and embedded it.""" var_name = self.llm_chain.input_keys[0] result = self.llm_chain.generate([{var_name: text}]) documents = [generation.text for generation in result.generations[0]] embeddings = self.embed_documents(documents) return self.combine_embeddings(embeddings) def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: """Call the internal llm chain.""" _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() return self.llm_chain(inputs, callbacks=_run_manager.get_child()) @classmethod def from_llm( cls, llm: BaseLanguageModel, base_embeddings: Embeddings, prompt_key: Optional[str] = None, custom_prompt: Optional[BasePromptTemplate] = None, **kwargs: Any, ) -> HypotheticalDocumentEmbedder: """Load and use LLMChain with either a specific prompt key or custom prompt.""" if custom_prompt is not None: prompt = custom_prompt elif prompt_key is not None and prompt_key in PROMPT_MAP: prompt = PROMPT_MAP[prompt_key] else: raise ValueError( f"Must specify prompt_key if custom_prompt not provided. Should be one " f"of {list(PROMPT_MAP.keys())}." ) llm_chain = LLMChain(llm=llm, prompt=prompt) return cls(base_embeddings=base_embeddings, llm_chain=llm_chain, **kwargs) @property def _chain_type(self) -> str: return "hyde_chain"

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"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf.""" from __future__ import annotations from typing import Any, Callable, List, NamedTuple, Optional, Sequence from langchain_core._api import deprecated from langchain_core.callbacks import BaseCallbackManager from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import PromptTemplate from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser from langchain.agents.agent_types import AgentType from langchain.agents.mrkl.output_parser import MRKLOutputParser from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX from langchain.agents.tools import Tool from langchain.agents.utils import validate_tools_single_input from langchain.chains import LLMChain from langchain.tools.render import render_text_description class ChainConfig(NamedTuple): """Configuration for chain to use in MRKL system. Args: action_name: Name of the action. action: Action function to call. action_description: Description of the action. """ action_name: str action: Callable action_description: str @deprecated("0.1.0", alternative="create_react_agent", removal="0.2.0") class ZeroShotAgent(Agent): """Agent for the MRKL chain.""" output_parser: AgentOutputParser = Field(default_factory=MRKLOutputParser) @classmethod def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser: return MRKLOutputParser() @property def _agent_type(self) -> str: """Return Identifier of agent type.""" return AgentType.ZERO_SHOT_REACT_DESCRIPTION @property def observation_prefix(self) -> str: """Prefix to append the observation with.""" return "Observation: " @property def llm_prefix(self) -> str: """Prefix to append the llm call with.""" return "Thought:" @classmethod def create_prompt( cls, tools: Sequence[BaseTool], prefix: str = PREFIX, suffix: str = SUFFIX, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, ) -> PromptTemplate: """Create prompt in the style of the zero shot agent. Args: tools: List of tools the agent will have access to, used to format the prompt. prefix: String to put before the list of tools. suffix: String to put after the list of tools. input_variables: List of input variables the final prompt will expect. Returns: A PromptTemplate with the template assembled from the pieces here. """ tool_strings = render_text_description(list(tools)) tool_names = ", ".join([tool.name for tool in tools]) format_instructions = format_instructions.format(tool_names=tool_names) template = "\n\n".join([prefix, tool_strings, format_instructions, suffix]) if input_variables: return PromptTemplate(template=template, input_variables=input_variables) return PromptTemplate.from_template(template) @classmethod def from_llm_and_tools( cls, llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, prefix: str = PREFIX, suffix: str = SUFFIX, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, **kwargs: Any, ) -> Agent: """Construct an agent from an LLM and tools.""" cls._validate_tools(tools) prompt = cls.create_prompt( tools, prefix=prefix, suffix=suffix, format_instructions=format_instructions, input_variables=input_variables, ) llm_chain = LLMChain( llm=llm, prompt=prompt, callback_manager=callback_manager, ) tool_names = [tool.name for tool in tools] _output_parser = output_parser or cls._get_default_output_parser() return cls( llm_chain=llm_chain, allowed_tools=tool_names, output_parser=_output_parser, **kwargs, ) @classmethod def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: validate_tools_single_input(cls.__name__, tools) if len(tools) == 0: raise ValueError( f"Got no tools for {cls.__name__}. At least one tool must be provided." ) for tool in tools: if tool.description is None: raise ValueError( f"Got a tool {tool.name} without a description. For this agent, " f"a description must always be provided." ) super()._validate_tools(tools) @deprecated("0.1.0", removal="0.2.0") class MRKLChain(AgentExecutor): """[Deprecated] Chain that implements the MRKL system.""" @classmethod def from_chains( cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any ) -> AgentExecutor: """User friendly way to initialize the MRKL chain. This is intended to be an easy way to get up and running with the MRKL chain. Args: llm: The LLM to use as the agent LLM. chains: The chains the MRKL system has access to. **kwargs: parameters to be passed to initialization. Returns: An initialized MRKL chain. """ tools = [ Tool( name=c.action_name, func=c.action, description=c.action_description, ) for c in chains ] agent = ZeroShotAgent.from_llm_and_tools(llm, tools) return cls(agent=agent, tools=tools, **kwargs)

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import base64 import io import os import uuid from io import BytesIO from pathlib import Path from langchain.retrievers.multi_vector import MultiVectorRetriever from langchain.storage import LocalFileStore from langchain_community.chat_models import ChatOllama from langchain_community.embeddings import OllamaEmbeddings from langchain_community.vectorstores import Chroma from langchain_core.documents import Document from langchain_core.messages import HumanMessage from PIL import Image def image_summarize(img_base64, prompt): """ Make image summary :param img_base64: Base64 encoded string for image :param prompt: Text prompt for summarizatiomn :return: Image summarization prompt """ chat = ChatOllama(model="bakllava", temperature=0) msg = chat.invoke( [ HumanMessage( content=[ {"type": "text", "text": prompt}, { "type": "image_url", "image_url": f"data:image/jpeg;base64,{img_base64}", }, ] ) ] ) return msg.content def generate_img_summaries(img_base64_list): """ Generate summaries for images :param img_base64_list: Base64 encoded images :return: List of image summaries and processed images """ # Store image summaries image_summaries = [] processed_images = [] # Prompt prompt = """Give a detailed summary of the image.""" # Apply summarization to images for i, base64_image in enumerate(img_base64_list): try: image_summaries.append(image_summarize(base64_image, prompt)) processed_images.append(base64_image) except Exception as e: print(f"Error with image {i+1}: {e}") # noqa: T201 return image_summaries, processed_images def get_images(img_path): """ Extract images. :param img_path: A string representing the path to the images. """ # Get image URIs pil_images = [ Image.open(os.path.join(img_path, image_name)) for image_name in os.listdir(img_path) if image_name.endswith(".jpg") ] return pil_images def resize_base64_image(base64_string, size=(128, 128)): """ Resize an image encoded as a Base64 string :param base64_string: Base64 string :param size: Image size :return: Re-sized Base64 string """ # Decode the Base64 string img_data = base64.b64decode(base64_string) img = Image.open(io.BytesIO(img_data)) # Resize the image resized_img = img.resize(size, Image.LANCZOS) # Save the resized image to a bytes buffer buffered = io.BytesIO() resized_img.save(buffered, format=img.format) # Encode the resized image to Base64 return base64.b64encode(buffered.getvalue()).decode("utf-8") def convert_to_base64(pil_image): """ Convert PIL images to Base64 encoded strings :param pil_image: PIL image :return: Re-sized Base64 string """ buffered = BytesIO() pil_image.save(buffered, format="JPEG") # You can change the format if needed img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") # img_str = resize_base64_image(img_str, size=(831,623)) return img_str def create_multi_vector_retriever(vectorstore, image_summaries, images): """ Create retriever that indexes summaries, but returns raw images or texts :param vectorstore: Vectorstore to store embedded image sumamries :param image_summaries: Image summaries :param images: Base64 encoded images :return: Retriever """ # Initialize the storage layer for images store = LocalFileStore( str(Path(__file__).parent / "multi_vector_retriever_metadata") ) id_key = "doc_id" # Create the multi-vector retriever retriever = MultiVectorRetriever( vectorstore=vectorstore, byte_store=store, id_key=id_key, ) # Helper function to add documents to the vectorstore and docstore def add_documents(retriever, doc_summaries, doc_contents): doc_ids = [str(uuid.uuid4()) for _ in doc_contents] summary_docs = [ Document(page_content=s, metadata={id_key: doc_ids[i]}) for i, s in enumerate(doc_summaries) ] retriever.vectorstore.add_documents(summary_docs) retriever.docstore.mset(list(zip(doc_ids, doc_contents))) add_documents(retriever, image_summaries, images) return retriever # Load images doc_path = Path(__file__).parent / "docs/" rel_doc_path = doc_path.relative_to(Path.cwd()) print("Read images") # noqa: T201 pil_images = get_images(rel_doc_path) # Convert to b64 images_base_64 = [convert_to_base64(i) for i in pil_images] # Image summaries print("Generate image summaries") # noqa: T201 image_summaries, images_base_64_processed = generate_img_summaries(images_base_64) # The vectorstore to use to index the images summaries vectorstore_mvr = Chroma( collection_name="image_summaries", persist_directory=str(Path(__file__).parent / "chroma_db_multi_modal"), embedding_function=OllamaEmbeddings(model="llama2:7b"), ) # Create documents images_base_64_processed_documents = [ Document(page_content=i) for i in images_base_64_processed ] # Create retriever retriever_multi_vector_img = create_multi_vector_retriever( vectorstore_mvr, image_summaries, images_base_64_processed_documents, )

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from fastapi import Body from sse_starlette.sse import EventSourceResponse from configs import LLM_MODELS, TEMPERATURE from server.utils import wrap_done, get_OpenAI from langchain.chains import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from typing import AsyncIterable, Optional import asyncio from langchain.prompts import PromptTemplate from server.utils import get_prompt_template async def completion(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]), stream: bool = Body(False, description="流式输出"), echo: bool = Body(False, description="除了输出之外，还回显输入"), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"), # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0), prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), ): #todo 因ApiModelWorker 默认是按chat处理的，会对params["prompt"] 解析为messages，因此ApiModelWorker 使用时需要有相应处理 async def completion_iterator(query: str, model_name: str = LLM_MODELS[0], prompt_name: str = prompt_name, echo: bool = echo, ) -> AsyncIterable[str]: nonlocal max_tokens callback = AsyncIteratorCallbackHandler() if isinstance(max_tokens, int) and max_tokens <= 0: max_tokens = None model = get_OpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[callback], echo=echo ) prompt_template = get_prompt_template("completion", prompt_name) prompt = PromptTemplate.from_template(prompt_template) chain = LLMChain(prompt=prompt, llm=model) # Begin a task that runs in the background. task = asyncio.create_task(wrap_done( chain.acall({"input": query}), callback.done), ) if stream: async for token in callback.aiter(): # Use server-sent-events to stream the response yield token else: answer = "" async for token in callback.aiter(): answer += token yield answer await task return EventSourceResponse(completion_iterator(query=query, model_name=model_name, prompt_name=prompt_name), )

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from fastapi import Body from sse_starlette.sse import EventSourceResponse from configs import LLM_MODELS, TEMPERATURE from server.utils import wrap_done, get_OpenAI from langchain.chains import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from typing import AsyncIterable, Optional import asyncio from langchain.prompts import PromptTemplate from server.utils import get_prompt_template async def completion(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]), stream: bool = Body(False, description="流式输出"), echo: bool = Body(False, description="除了输出之外，还回显输入"), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"), # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0), prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), ): #todo 因ApiModelWorker 默认是按chat处理的，会对params["prompt"] 解析为messages，因此ApiModelWorker 使用时需要有相应处理 async def completion_iterator(query: str, model_name: str = LLM_MODELS[0], prompt_name: str = prompt_name, echo: bool = echo, ) -> AsyncIterable[str]: nonlocal max_tokens callback = AsyncIteratorCallbackHandler() if isinstance(max_tokens, int) and max_tokens <= 0: max_tokens = None model = get_OpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[callback], echo=echo ) prompt_template = get_prompt_template("completion", prompt_name) prompt = PromptTemplate.from_template(prompt_template) chain = LLMChain(prompt=prompt, llm=model) # Begin a task that runs in the background. task = asyncio.create_task(wrap_done( chain.acall({"input": query}), callback.done), ) if stream: async for token in callback.aiter(): # Use server-sent-events to stream the response yield token else: answer = "" async for token in callback.aiter(): answer += token yield answer await task return EventSourceResponse(completion_iterator(query=query, model_name=model_name, prompt_name=prompt_name), )

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from fastapi import Body from sse_starlette.sse import EventSourceResponse from configs import LLM_MODELS, TEMPERATURE from server.utils import wrap_done, get_OpenAI from langchain.chains import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from typing import AsyncIterable, Optional import asyncio from langchain.prompts import PromptTemplate from server.utils import get_prompt_template async def completion(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]), stream: bool = Body(False, description="流式输出"), echo: bool = Body(False, description="除了输出之外，还回显输入"), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"), # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0), prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), ): #todo 因ApiModelWorker 默认是按chat处理的，会对params["prompt"] 解析为messages，因此ApiModelWorker 使用时需要有相应处理 async def completion_iterator(query: str, model_name: str = LLM_MODELS[0], prompt_name: str = prompt_name, echo: bool = echo, ) -> AsyncIterable[str]: nonlocal max_tokens callback = AsyncIteratorCallbackHandler() if isinstance(max_tokens, int) and max_tokens <= 0: max_tokens = None model = get_OpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[callback], echo=echo ) prompt_template = get_prompt_template("completion", prompt_name) prompt = PromptTemplate.from_template(prompt_template) chain = LLMChain(prompt=prompt, llm=model) # Begin a task that runs in the background. task = asyncio.create_task(wrap_done( chain.acall({"input": query}), callback.done), ) if stream: async for token in callback.aiter(): # Use server-sent-events to stream the response yield token else: answer = "" async for token in callback.aiter(): answer += token yield answer await task return EventSourceResponse(completion_iterator(query=query, model_name=model_name, prompt_name=prompt_name), )

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from fastapi import Body from sse_starlette.sse import EventSourceResponse from configs import LLM_MODELS, TEMPERATURE from server.utils import wrap_done, get_OpenAI from langchain.chains import LLMChain from langchain.callbacks import AsyncIteratorCallbackHandler from typing import AsyncIterable, Optional import asyncio from langchain.prompts import PromptTemplate from server.utils import get_prompt_template async def completion(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]), stream: bool = Body(False, description="流式输出"), echo: bool = Body(False, description="除了输出之外，还回显输入"), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"), # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0), prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), ): #todo 因ApiModelWorker 默认是按chat处理的，会对params["prompt"] 解析为messages，因此ApiModelWorker 使用时需要有相应处理 async def completion_iterator(query: str, model_name: str = LLM_MODELS[0], prompt_name: str = prompt_name, echo: bool = echo, ) -> AsyncIterable[str]: nonlocal max_tokens callback = AsyncIteratorCallbackHandler() if isinstance(max_tokens, int) and max_tokens <= 0: max_tokens = None model = get_OpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, callbacks=[callback], echo=echo ) prompt_template = get_prompt_template("completion", prompt_name) prompt = PromptTemplate.from_template(prompt_template) chain = LLMChain(prompt=prompt, llm=model) # Begin a task that runs in the background. task = asyncio.create_task(wrap_done( chain.acall({"input": query}), callback.done), ) if stream: async for token in callback.aiter(): # Use server-sent-events to stream the response yield token else: answer = "" async for token in callback.aiter(): answer += token yield answer await task return EventSourceResponse(completion_iterator(query=query, model_name=model_name, prompt_name=prompt_name), )

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from langchain.llms import Ollama input = input("What is your question?") llm = Ollama(model="llama2") res = llm.predict(input) print (res)

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from langchain.llms import Ollama input = input("What is your question?") llm = Ollama(model="llama2") res = llm.predict(input) print (res)

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import os import tempfile from typing import List, Union import streamlit as st import tiktoken from langchain.text_splitter import ( CharacterTextSplitter, RecursiveCharacterTextSplitter, ) from langchain.text_splitter import ( TextSplitter as LCSplitter, ) from langchain.text_splitter import TokenTextSplitter as LCTokenTextSplitter from llama_index import SimpleDirectoryReader from llama_index.node_parser.interface import TextSplitter from llama_index.schema import Document from llama_index.text_splitter import CodeSplitter, SentenceSplitter, TokenTextSplitter from streamlit.runtime.uploaded_file_manager import UploadedFile DEFAULT_TEXT = "The quick brown fox jumps over the lazy dog." text = st.sidebar.text_area("Enter text", value=DEFAULT_TEXT) uploaded_files = st.sidebar.file_uploader("Upload file", accept_multiple_files=True) type = st.sidebar.radio("Document Type", options=["Text", "Code"]) n_cols = st.sidebar.number_input("Columns", value=2, min_value=1, max_value=3) assert isinstance(n_cols, int) @st.cache_resource(ttl=3600) def load_document(uploaded_files: List[UploadedFile]) -> List[Document]: # Read documents temp_dir = tempfile.TemporaryDirectory() for file in uploaded_files: temp_filepath = os.path.join(temp_dir.name, file.name) with open(temp_filepath, "wb") as f: f.write(file.getvalue()) reader = SimpleDirectoryReader(input_dir=temp_dir.name) return reader.load_data() if uploaded_files: if text != DEFAULT_TEXT: st.warning("Text will be ignored when uploading files") docs = load_document(uploaded_files) text = "\n".join([doc.text for doc in docs]) chunk_size = st.slider( "Chunk Size", value=512, min_value=1, max_value=4096, ) chunk_overlap = st.slider( "Chunk Overlap", value=0, min_value=0, max_value=4096, ) cols = st.columns(n_cols) for ind, col in enumerate(cols): if type == "Text": text_splitter_cls = col.selectbox( "Text Splitter", options=[ "TokenTextSplitter", "SentenceSplitter", "LC:RecursiveCharacterTextSplitter", "LC:CharacterTextSplitter", "LC:TokenTextSplitter", ], index=ind, key=f"splitter_cls_{ind}", ) text_splitter: Union[TextSplitter, LCSplitter] if text_splitter_cls == "TokenTextSplitter": text_splitter = TokenTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) elif text_splitter_cls == "SentenceSplitter": text_splitter = SentenceSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) elif text_splitter_cls == "LC:RecursiveCharacterTextSplitter": text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) elif text_splitter_cls == "LC:CharacterTextSplitter": text_splitter = CharacterTextSplitter.from_tiktoken_encoder( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) elif text_splitter_cls == "LC:TokenTextSplitter": text_splitter = LCTokenTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) else: raise ValueError("Unknown text splitter") elif type == "Code": text_splitter_cls = col.selectbox("Text Splitter", options=["CodeSplitter"]) if text_splitter_cls == "CodeSplitter": language = col.text_input("Language", value="python") max_chars = col.slider("Max Chars", value=1500) text_splitter = CodeSplitter(language=language, max_chars=max_chars) else: raise ValueError("Unknown text splitter") chunks = text_splitter.split_text(text) tokenizer = tiktoken.get_encoding("gpt2").encode for chunk_ind, chunk in enumerate(chunks): n_tokens = len(tokenizer(chunk)) n_chars = len(chunk) col.text_area( f"Chunk {chunk_ind} - {n_tokens} tokens - {n_chars} chars", chunk, key=f"text_area_{ind}_{chunk_ind}", height=500, )

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import os import tempfile from typing import List, Union import streamlit as st import tiktoken from langchain.text_splitter import ( CharacterTextSplitter, RecursiveCharacterTextSplitter, ) from langchain.text_splitter import ( TextSplitter as LCSplitter, ) from langchain.text_splitter import TokenTextSplitter as LCTokenTextSplitter from llama_index import SimpleDirectoryReader from llama_index.node_parser.interface import TextSplitter from llama_index.schema import Document from llama_index.text_splitter import CodeSplitter, SentenceSplitter, TokenTextSplitter from streamlit.runtime.uploaded_file_manager import UploadedFile DEFAULT_TEXT = "The quick brown fox jumps over the lazy dog." text = st.sidebar.text_area("Enter text", value=DEFAULT_TEXT) uploaded_files = st.sidebar.file_uploader("Upload file", accept_multiple_files=True) type = st.sidebar.radio("Document Type", options=["Text", "Code"]) n_cols = st.sidebar.number_input("Columns", value=2, min_value=1, max_value=3) assert isinstance(n_cols, int) @st.cache_resource(ttl=3600) def load_document(uploaded_files: List[UploadedFile]) -> List[Document]: # Read documents temp_dir = tempfile.TemporaryDirectory() for file in uploaded_files: temp_filepath = os.path.join(temp_dir.name, file.name) with open(temp_filepath, "wb") as f: f.write(file.getvalue()) reader = SimpleDirectoryReader(input_dir=temp_dir.name) return reader.load_data() if uploaded_files: if text != DEFAULT_TEXT: st.warning("Text will be ignored when uploading files") docs = load_document(uploaded_files) text = "\n".join([doc.text for doc in docs]) chunk_size = st.slider( "Chunk Size", value=512, min_value=1, max_value=4096, ) chunk_overlap = st.slider( "Chunk Overlap", value=0, min_value=0, max_value=4096, ) cols = st.columns(n_cols) for ind, col in enumerate(cols): if type == "Text": text_splitter_cls = col.selectbox( "Text Splitter", options=[ "TokenTextSplitter", "SentenceSplitter", "LC:RecursiveCharacterTextSplitter", "LC:CharacterTextSplitter", "LC:TokenTextSplitter", ], index=ind, key=f"splitter_cls_{ind}", ) text_splitter: Union[TextSplitter, LCSplitter] if text_splitter_cls == "TokenTextSplitter": text_splitter = TokenTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) elif text_splitter_cls == "SentenceSplitter": text_splitter = SentenceSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) elif text_splitter_cls == "LC:RecursiveCharacterTextSplitter": text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) elif text_splitter_cls == "LC:CharacterTextSplitter": text_splitter = CharacterTextSplitter.from_tiktoken_encoder( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) elif text_splitter_cls == "LC:TokenTextSplitter": text_splitter = LCTokenTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) else: raise ValueError("Unknown text splitter") elif type == "Code": text_splitter_cls = col.selectbox("Text Splitter", options=["CodeSplitter"]) if text_splitter_cls == "CodeSplitter": language = col.text_input("Language", value="python") max_chars = col.slider("Max Chars", value=1500) text_splitter = CodeSplitter(language=language, max_chars=max_chars) else: raise ValueError("Unknown text splitter") chunks = text_splitter.split_text(text) tokenizer = tiktoken.get_encoding("gpt2").encode for chunk_ind, chunk in enumerate(chunks): n_tokens = len(tokenizer(chunk)) n_chars = len(chunk) col.text_area( f"Chunk {chunk_ind} - {n_tokens} tokens - {n_chars} chars", chunk, key=f"text_area_{ind}_{chunk_ind}", height=500, )

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import json from langchain.schema import OutputParserException def parse_json_markdown(json_string: str) -> dict: # Remove the triple backticks if present json_string = json_string.strip() start_index = json_string.find("```json") end_index = json_string.find("```", start_index + len("```json")) if start_index != -1 and end_index != -1: extracted_content = json_string[start_index + len("```json"):end_index].strip() # Parse the JSON string into a Python dictionary parsed = json.loads(extracted_content) elif start_index != -1 and end_index == -1 and json_string.endswith("``"): end_index = json_string.find("``", start_index + len("```json")) extracted_content = json_string[start_index + len("```json"):end_index].strip() # Parse the JSON string into a Python dictionary parsed = json.loads(extracted_content) elif json_string.startswith("{"): # Parse the JSON string into a Python dictionary parsed = json.loads(json_string) else: raise Exception("Could not find JSON block in the output.") return parsed def parse_and_check_json_markdown(text: str, expected_keys: list[str]) -> dict: try: json_obj = parse_json_markdown(text) except json.JSONDecodeError as e: raise OutputParserException(f"Got invalid JSON object. Error: {e}") for key in expected_keys: if key not in json_obj: raise OutputParserException( f"Got invalid return object. Expected key `{key}` " f"to be present, but got {json_obj}" ) return json_obj

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# From project chatglm-langchain from langchain.document_loaders import UnstructuredFileLoader from langchain.text_splitter import CharacterTextSplitter import re from typing import List class ChineseTextSplitter(CharacterTextSplitter): def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs): super().__init__(**kwargs) self.pdf = pdf self.sentence_size = sentence_size def split_text1(self, text: str) -> List[str]: if self.pdf: text = re.sub(r"\n{3,}", "\n", text) text = re.sub('\s', ' ', text) text = text.replace("\n\n", "") sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') # del ：； sent_list = [] for ele in sent_sep_pattern.split(text): if sent_sep_pattern.match(ele) and sent_list: sent_list[-1] += ele elif ele: sent_list.append(ele) return sent_list def split_text(self, text: str) -> List[str]: ##此处需要进一步优化逻辑 if self.pdf: text = re.sub(r"\n{3,}", r"\n", text) text = re.sub('\s', " ", text) text = re.sub("\n\n", "", text) text = re.sub(r'([;；.!?。！？\?])([^”’])', r"\1\n\2", text) # 单字符断句符 text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text) # 英文省略号 text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text) # 中文省略号 text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r'\1\n\2', text) # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号 text = text.rstrip() # 段尾如果有多余的\n就去掉它 # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。 ls = [i for i in text.split("\n") if i] for ele in ls: if len(ele) > self.sentence_size: ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r'\1\n\2', ele) ele1_ls = ele1.split("\n") for ele_ele1 in ele1_ls: if len(ele_ele1) > self.sentence_size: ele_ele2 = re.sub(r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r'\1\n\2', ele_ele1) ele2_ls = ele_ele2.split("\n") for ele_ele2 in ele2_ls: if len(ele_ele2) > self.sentence_size: ele_ele3 = re.sub('( ["’”」』]{0,2})([^ ])', r'\1\n\2', ele_ele2) ele2_id = ele2_ls.index(ele_ele2) ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split("\n") if i] + ele2_ls[ ele2_id + 1:] ele_id = ele1_ls.index(ele_ele1) ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:] id = ls.index(ele) ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:] return ls def load_file(filepath, sentence_size): loader = UnstructuredFileLoader(filepath, mode="elements") textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) docs = loader.load_and_split(text_splitter=textsplitter) # write_check_file(filepath, docs) return docs

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import os import uuid from typing import Any, Dict, List, Optional, Tuple from langchain.agents.agent import RunnableAgent from langchain.agents.tools import tool as LangChainTool from langchain.memory import ConversationSummaryMemory from langchain.tools.render import render_text_description from langchain_core.agents import AgentAction from langchain_core.callbacks import BaseCallbackHandler from langchain_openai import ChatOpenAI from pydantic import ( UUID4, BaseModel, ConfigDict, Field, InstanceOf, PrivateAttr, field_validator, model_validator, ) from pydantic_core import PydanticCustomError from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler from crewai.utilities import I18N, Logger, Prompts, RPMController from crewai.utilities.token_counter_callback import TokenCalcHandler, TokenProcess class Agent(BaseModel): """Represents an agent in a system. Each agent has a role, a goal, a backstory, and an optional language model (llm). The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents. Attributes: agent_executor: An instance of the CrewAgentExecutor class. role: The role of the agent. goal: The objective of the agent. backstory: The backstory of the agent. config: Dict representation of agent configuration. llm: The language model that will run the agent. function_calling_llm: The language model that will the tool calling for this agent, it overrides the crew function_calling_llm. max_iter: Maximum number of iterations for an agent to execute a task. memory: Whether the agent should have memory or not. max_rpm: Maximum number of requests per minute for the agent execution to be respected. verbose: Whether the agent execution should be in verbose mode. allow_delegation: Whether the agent is allowed to delegate tasks to other agents. tools: Tools at agents disposal step_callback: Callback to be executed after each step of the agent execution. callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process """ __hash__ = object.__hash__ # type: ignore _logger: Logger = PrivateAttr() _rpm_controller: RPMController = PrivateAttr(default=None) _request_within_rpm_limit: Any = PrivateAttr(default=None) _token_process: TokenProcess = TokenProcess() formatting_errors: int = 0 model_config = ConfigDict(arbitrary_types_allowed=True) id: UUID4 = Field( default_factory=uuid.uuid4, frozen=True, description="Unique identifier for the object, not set by user.", ) role: str = Field(description="Role of the agent") goal: str = Field(description="Objective of the agent") backstory: str = Field(description="Backstory of the agent") config: Optional[Dict[str, Any]] = Field( description="Configuration for the agent", default=None, ) max_rpm: Optional[int] = Field( default=None, description="Maximum number of requests per minute for the agent execution to be respected.", ) memory: bool = Field( default=False, description="Whether the agent should have memory or not" ) verbose: bool = Field( default=False, description="Verbose mode for the Agent Execution" ) allow_delegation: bool = Field( default=True, description="Allow delegation of tasks to agents" ) tools: Optional[List[Any]] = Field( default_factory=list, description="Tools at agents disposal" ) max_iter: Optional[int] = Field( default=15, description="Maximum iterations for an agent to execute a task" ) agent_executor: InstanceOf[CrewAgentExecutor] = Field( default=None, description="An instance of the CrewAgentExecutor class." ) tools_handler: InstanceOf[ToolsHandler] = Field( default=None, description="An instance of the ToolsHandler class." ) cache_handler: InstanceOf[CacheHandler] = Field( default=CacheHandler(), description="An instance of the CacheHandler class." ) step_callback: Optional[Any] = Field( default=None, description="Callback to be executed after each step of the agent execution.", ) i18n: I18N = Field(default=I18N(), description="Internationalization settings.") llm: Any = Field( default_factory=lambda: ChatOpenAI( model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4") ), description="Language model that will run the agent.", ) function_calling_llm: Optional[Any] = Field( description="Language model that will run the agent.", default=None ) callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field( default=None, description="Callback to be executed" ) def __init__(__pydantic_self__, **data): config = data.pop("config", {}) super().__init__(**config, **data) @field_validator("id", mode="before") @classmethod def _deny_user_set_id(cls, v: Optional[UUID4]) -> None: if v: raise PydanticCustomError( "may_not_set_field", "This field is not to be set by the user.", {} ) @model_validator(mode="after") def set_attributes_based_on_config(self) -> "Agent": """Set attributes based on the agent configuration.""" if self.config: for key, value in self.config.items(): setattr(self, key, value) return self @model_validator(mode="after") def set_private_attrs(self): """Set private attributes.""" self._logger = Logger(self.verbose) if self.max_rpm and not self._rpm_controller: self._rpm_controller = RPMController( max_rpm=self.max_rpm, logger=self._logger ) return self @model_validator(mode="after") def set_agent_executor(self) -> "Agent": """set agent executor is set.""" if hasattr(self.llm, "model_name"): self.llm.callbacks = [ TokenCalcHandler(self.llm.model_name, self._token_process) ] if not self.agent_executor: self.set_cache_handler(self.cache_handler) return self def execute_task( self, task: Any, context: Optional[str] = None, tools: Optional[List[Any]] = None, ) -> str: """Execute a task with the agent. Args: task: Task to execute. context: Context to execute the task in. tools: Tools to use for the task. Returns: Output of the agent """ self.tools_handler.last_used_tool = {} task_prompt = task.prompt() if context: task_prompt = self.i18n.slice("task_with_context").format( task=task_prompt, context=context ) tools = self._parse_tools(tools or self.tools) self.create_agent_executor(tools=tools) self.agent_executor.tools = tools self.agent_executor.task = task self.agent_executor.tools_description = render_text_description(tools) self.agent_executor.tools_names = self.__tools_names(tools) result = self.agent_executor.invoke( { "input": task_prompt, "tool_names": self.agent_executor.tools_names, "tools": self.agent_executor.tools_description, } )["output"] if self.max_rpm: self._rpm_controller.stop_rpm_counter() return result def set_cache_handler(self, cache_handler: CacheHandler) -> None: """Set the cache handler for the agent. Args: cache_handler: An instance of the CacheHandler class. """ self.cache_handler = cache_handler self.tools_handler = ToolsHandler(cache=self.cache_handler) self.create_agent_executor() def set_rpm_controller(self, rpm_controller: RPMController) -> None: """Set the rpm controller for the agent. Args: rpm_controller: An instance of the RPMController class. """ if not self._rpm_controller: self._rpm_controller = rpm_controller self.create_agent_executor() def create_agent_executor(self, tools=None) -> None: """Create an agent executor for the agent. Returns: An instance of the CrewAgentExecutor class. """ tools = tools or self.tools agent_args = { "input": lambda x: x["input"], "tools": lambda x: x["tools"], "tool_names": lambda x: x["tool_names"], "agent_scratchpad": lambda x: self.format_log_to_str( x["intermediate_steps"] ), } executor_args = { "llm": self.llm, "i18n": self.i18n, "tools": self._parse_tools(tools), "verbose": self.verbose, "handle_parsing_errors": True, "max_iterations": self.max_iter, "step_callback": self.step_callback, "tools_handler": self.tools_handler, "function_calling_llm": self.function_calling_llm, "callbacks": self.callbacks, } if self._rpm_controller: executor_args[ "request_within_rpm_limit" ] = self._rpm_controller.check_or_wait if self.memory: summary_memory = ConversationSummaryMemory( llm=self.llm, input_key="input", memory_key="chat_history" ) executor_args["memory"] = summary_memory agent_args["chat_history"] = lambda x: x["chat_history"] prompt = Prompts(i18n=self.i18n, tools=tools).task_execution_with_memory() else: prompt = Prompts(i18n=self.i18n, tools=tools).task_execution() execution_prompt = prompt.partial( goal=self.goal, role=self.role, backstory=self.backstory, ) bind = self.llm.bind(stop=[self.i18n.slice("observation")]) inner_agent = agent_args | execution_prompt | bind | CrewAgentParser(agent=self) self.agent_executor = CrewAgentExecutor( agent=RunnableAgent(runnable=inner_agent), **executor_args ) def interpolate_inputs(self, inputs: Dict[str, Any]) -> None: """Interpolate inputs into the agent description and backstory.""" if inputs: self.role = self.role.format(**inputs) self.goal = self.goal.format(**inputs) self.backstory = self.backstory.format(**inputs) def increment_formatting_errors(self) -> None: """Count the formatting errors of the agent.""" self.formatting_errors += 1 def format_log_to_str( self, intermediate_steps: List[Tuple[AgentAction, str]], observation_prefix: str = "Observation: ", llm_prefix: str = "", ) -> str: """Construct the scratchpad that lets the agent continue its thought process.""" thoughts = "" for action, observation in intermediate_steps: thoughts += action.log thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}" return thoughts def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]: """Parse tools to be used for the task.""" # tentatively try to import from crewai_tools import BaseTool as CrewAITool tools_list = [] try: from crewai_tools import BaseTool as CrewAITool for tool in tools: if isinstance(tool, CrewAITool): tools_list.append(tool.to_langchain()) else: tools_list.append(tool) except ModuleNotFoundError: for tool in tools: tools_list.append(tool) return tools_list @staticmethod def __tools_names(tools) -> str: return ", ".join([t.name for t in tools]) def __repr__(self): return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"

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import re from typing import Union from langchain.agents.mrkl.output_parser import MRKLOutputParser from langchain.schema import AgentAction, AgentFinish, OutputParserException FORMAT_INSTRUCTIONS0 = """Use the following format and be sure to use new lines after each task. Question: the input question you must answer Thought: you should always think about what to do Action: Exactly only one word out 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""" FORMAT_INSTRUCTIONS = """List of tools, use exactly one word when choosing Action: {tool_names} Only user asks a question, not you. For example user might ask: What is the latest news? Here is an example sequence you can follow: Thought: I should search online for the latest news. Action: Search Action Input: What is the latest news? Observation: X is going away. Z is again happening. Thought: That is interesting, I should search for more information about X and Z and also search about Q. Action: Search Action Input: How is X impacting things. Why is Z happening again, and what are the consequences? Observation: X is causing Y. Z may be caused by P and will lead to H. Thought: I now know the final answer Final Answer: The latest news is: * X is going away, and this is caused by Y. * Z is happening again, and the cause is P and will lead to H. Overall, X and Z are important problems. """ FORMAT_INSTRUCTIONS_PYTHON = """List of tools, use exactly one word when choosing Action: {tool_names} Only user asks a question, not you. For example user might ask: How many rows are in the dataset? Here is an example sequence you can follow. You can repeat Thoughts, but as soon as possible you should try to answer the original user question. Once you an answer the user question, just say: Thought: I now know the final answer Thought: I should use python_repl_ast tool. Action: python_repl_ast Action Input: df.shape Observation: (25, 10) Thought: I now know the final answer Final Answer: There are 25 rows in the dataset. """ FINAL_ANSWER_ACTION = "Final Answer:" MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE = ( "Invalid Format: Missing 'Action:' after 'Thought:" ) MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE = ( "Invalid Format: Missing 'Action Input:' after 'Action:'" ) FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = ( "Parsing LLM output produced both a final answer and a parse-able action:" ) class H2OMRKLOutputParser(MRKLOutputParser): """MRKL Output parser for the chat agent.""" def get_format_instructions(self) -> str: return FORMAT_INSTRUCTIONS def parse(self, text: str) -> Union[AgentAction, AgentFinish]: includes_answer = FINAL_ANSWER_ACTION in text regex = ( r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)" ) action_match = re.search(regex, text, re.DOTALL) if includes_answer: return AgentFinish( {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text ) elif action_match: action = action_match.group(1).strip() action_input = action_match.group(2) tool_input = action_input.strip(" ") # ensure if its a well formed SQL query we don't remove any trailing " chars if tool_input.startswith("SELECT ") is False: tool_input = tool_input.strip('"') return AgentAction(action, tool_input, text) if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL): raise OutputParserException( f"Could not parse LLM output: `{text}`", observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE, llm_output=text, send_to_llm=True, ) elif not re.search( r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL ): raise OutputParserException( f"Could not parse LLM output: `{text}`", observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE, llm_output=text, send_to_llm=True, ) else: raise OutputParserException(f"Could not parse LLM output: `{text}`") @property def _type(self) -> str: return "mrkl" class H2OPythonMRKLOutputParser(H2OMRKLOutputParser): def get_format_instructions(self) -> str: return FORMAT_INSTRUCTIONS_PYTHON

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import os import re import uuid import cv2 import torch import requests import io, base64 import numpy as np import gradio as gr from PIL import Image from omegaconf import OmegaConf from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPSegProcessor, CLIPSegForImageSegmentation from langchain.agents.initialize import initialize_agent from langchain.agents.tools import Tool from langchain.chains.conversation.memory import ConversationBufferMemory from langchain.llms.openai import OpenAI VISUAL_CHATGPT_PREFIX = """Visual ChatGPT is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. Visual ChatGPT is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand. Visual ChatGPT is able to process and understand large amounts of text and images. As a language model, Visual ChatGPT can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "image/xxx.png", and Visual ChatGPT can invoke different tools to indirectly understand pictures. When talking about images, Visual ChatGPT is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image files, Visual ChatGPT is also known that the image may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. Visual ChatGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name. It will remember to provide the file name from the last tool observation, if a new image is generated. Human may provide new figures to Visual ChatGPT with a description. The description helps Visual ChatGPT to understand this image, but Visual ChatGPT should use tools to finish following tasks, rather than directly imagine from the description. Overall, Visual ChatGPT is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. TOOLS: ------ Visual ChatGPT has access to the following tools:""" VISUAL_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format: ``` Thought: Do I need to use a tool? Yes Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action ``` When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: ``` Thought: Do I need to use a tool? No {ai_prefix}: [your response here] ``` """ VISUAL_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if it does not exist. You will remember to provide the image file name loyally if it's provided in the last tool observation. Begin! Previous conversation history: {chat_history} New input: {input} Since Visual ChatGPT is a text language model, Visual ChatGPT must use tools to observe images rather than imagination. The thoughts and observations are only visible for Visual ChatGPT, Visual ChatGPT should remember to repeat important information in the final response for Human. Thought: Do I need to use a tool? {agent_scratchpad}""" ENDPOINT = "http://localhost:7860" T2IAPI = ENDPOINT + "/controlnet/txt2img" DETECTAPI = ENDPOINT + "/controlnet/detect" MODELLIST = ENDPOINT + "/controlnet/model_list" device = "cpu" if torch.cuda.is_available(): device = "cuda" def readImage(path): img = cv2.imread(path) retval, buffer = cv2.imencode('.jpg', img) b64img = base64.b64encode(buffer).decode("utf-8") return b64img def get_model(pattern='^control_canny.*'): r = requests.get(MODELLIST) result = r.json()["model_list"] for item in result: if re.match(pattern, item): return item def do_webui_request(url=T2IAPI, **kwargs): reqbody = { "prompt": "best quality, extremely detailed", "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", "seed": -1, "subseed": -1, "subseed_strength": 0, "batch_size": 1, "n_iter": 1, "steps": 15, "cfg_scale": 7, "width": 512, "height": 768, "restore_faces": True, "eta": 0, "sampler_index": "Euler a", "controlnet_input_images": [], "controlnet_module": 'canny', "controlnet_model": 'control_canny-fp16 [e3fe7712]', "controlnet_guidance": 1.0, } reqbody.update(kwargs) r = requests.post(url, json=reqbody) return r.json() def cut_dialogue_history(history_memory, keep_last_n_words=500): tokens = history_memory.split() n_tokens = len(tokens) print(f"hitory_memory:{history_memory}, n_tokens: {n_tokens}") if n_tokens < keep_last_n_words: return history_memory else: paragraphs = history_memory.split('\n') last_n_tokens = n_tokens while last_n_tokens >= keep_last_n_words: last_n_tokens = last_n_tokens - len(paragraphs[0].split(' ')) paragraphs = paragraphs[1:] return '\n' + '\n'.join(paragraphs) def get_new_image_name(org_img_name, func_name="update"): head_tail = os.path.split(org_img_name) head = head_tail[0] tail = head_tail[1] name_split = tail.split('.')[0].split('_') this_new_uuid = str(uuid.uuid4())[0:4] if len(name_split) == 1: most_org_file_name = name_split[0] recent_prev_file_name = name_split[0] new_file_name = '{}_{}_{}_{}.png'.format(this_new_uuid, func_name, recent_prev_file_name, most_org_file_name) else: assert len(name_split) == 4 most_org_file_name = name_split[3] recent_prev_file_name = name_split[0] new_file_name = '{}_{}_{}_{}.png'.format(this_new_uuid, func_name, recent_prev_file_name, most_org_file_name) return os.path.join(head, new_file_name) class MaskFormer: def __init__(self, device): self.device = device self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") self.model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined").to(device) def inference(self, image_path, text): threshold = 0.5 min_area = 0.02 padding = 20 original_image = Image.open(image_path) image = original_image.resize((512, 512)) inputs = self.processor(text=text, images=image, padding="max_length", return_tensors="pt",).to(self.device) with torch.no_grad(): outputs = self.model(**inputs) mask = torch.sigmoid(outputs[0]).squeeze().cpu().numpy() > threshold area_ratio = len(np.argwhere(mask)) / (mask.shape[0] * mask.shape[1]) if area_ratio < min_area: return None true_indices = np.argwhere(mask) mask_array = np.zeros_like(mask, dtype=bool) for idx in true_indices: padded_slice = tuple(slice(max(0, i - padding), i + padding + 1) for i in idx) mask_array[padded_slice] = True visual_mask = (mask_array * 255).astype(np.uint8) image_mask = Image.fromarray(visual_mask) return image_mask.resize(image.size) # class ImageEditing: # def __init__(self, device): # print("Initializing StableDiffusionInpaint to %s" % device) # self.device = device # self.mask_former = MaskFormer(device=self.device) # # self.inpainting = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting",).to(device) # def remove_part_of_image(self, input): # image_path, to_be_removed_txt = input.split(",") # print(f'remove_part_of_image: to_be_removed {to_be_removed_txt}') # return self.replace_part_of_image(f"{image_path},{to_be_removed_txt},background") # def replace_part_of_image(self, input): # image_path, to_be_replaced_txt, replace_with_txt = input.split(",") # print(f'replace_part_of_image: replace_with_txt {replace_with_txt}') # mask_image = self.mask_former.inference(image_path, to_be_replaced_txt) # buffered = io.BytesIO() # mask_image.save(buffered, format="JPEG") # resp = do_webui_request( # url=ENDPOINT + "/sdapi/v1/img2img", # init_images=[readImage(image_path)], # mask=base64.b64encode(buffered.getvalue()).decode("utf-8"), # prompt=replace_with_txt, # ) # updated_image_path = get_new_image_name(image_path, func_name="replace-something") # with open(updated_image_path, 'wb') as f: # f.write(base64.b64decode(resp['images'][0])) # return updated_image_path # class Pix2Pix: # def __init__(self, device): # print("Initializing Pix2Pix to %s" % device) # self.device = device # self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix", torch_dtype=torch.float16, safety_checker=None).to(device) # self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config) # def inference(self, inputs): # """Change style of image.""" # print("===>Starting Pix2Pix Inference") # image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) # original_image = Image.open(image_path) # image = self.pipe(instruct_text,image=original_image,num_inference_steps=40,image_guidance_scale=1.2,).images[0] # updated_image_path = get_new_image_name(image_path, func_name="pix2pix") # image.save(updated_image_path) # return updated_image_path class T2I: def __init__(self, device): print("Initializing T2I to %s" % device) self.device = device self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model, tokenizer=self.text_refine_tokenizer, device=self.device) def inference(self, text): image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"] print(f'{text} refined to {refined_text}') resp = do_webui_request( url=ENDPOINT + "/sdapi/v1/txt2img", prompt=refined_text, ) with open(image_filename, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}") return image_filename class ImageCaptioning: def __init__(self, device): print("Initializing ImageCaptioning to %s" % device) self.device = device self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device) def inference(self, image_path): inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device) out = self.model.generate(**inputs) captions = self.processor.decode(out[0], skip_special_tokens=True) return captions class image2canny: def inference(self, inputs): print("===>Starting image2canny Inference") resp = do_webui_request( url=DETECTAPI, controlnet_input_images=[readImage(inputs)], controlnet_module="segmentation", ) updated_image_path = get_new_image_name(inputs, func_name="edge") image.save(updated_image_path) return updated_image_path class canny2image: def inference(self, inputs): print("===>Starting canny2image Inference") image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) resp = do_webui_request( prompt=instruct_text, controlnet_input_images=[readImage(image_path)], controlnet_module="none", controlnet_model=get_model(pattern='^control_canny.*'), ) updated_image_path = get_new_image_name(image_path, func_name="canny2image") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class image2line: def inference(self, inputs): print("===>Starting image2hough Inference") resp = do_webui_request( url=DETECTAPI, controlnet_input_images=[readImage(inputs)], controlnet_module="mlsd", ) updated_image_path = get_new_image_name(inputs, func_name="line-of") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class line2image: def inference(self, inputs): print("===>Starting line2image Inference") image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) resp = do_webui_request( prompt=instruct_text, controlnet_input_images=[readImage(image_path)], controlnet_module="none", controlnet_model=get_model(pattern='^control_mlsd.*'), ) updated_image_path = get_new_image_name(image_path, func_name="line2image") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class image2hed: def inference(self, inputs): print("===>Starting image2hed Inference") resp = do_webui_request( url=DETECTAPI, controlnet_input_images=[readImage(inputs)], controlnet_module="hed", ) updated_image_path = get_new_image_name(inputs, func_name="hed-boundary") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class hed2image: def inference(self, inputs): print("===>Starting hed2image Inference") resp = do_webui_request( prompt=instruct_text, controlnet_input_images=[readImage(image_path)], controlnet_module="none", controlnet_model=get_model(pattern='^control_hed.*'), ) updated_image_path = get_new_image_name(image_path, func_name="hed2image") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class image2scribble: def inference(self, inputs): print("===>Starting image2scribble Inference") resp = do_webui_request( url=DETECTAPI, controlnet_input_images=[readImage(inputs)], controlnet_module="scribble", ) updated_image_path = get_new_image_name(inputs, func_name="scribble") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class scribble2image: def inference(self, inputs): print("===>Starting seg2image Inference") image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) resp = do_webui_request( prompt=instruct_text, controlnet_input_images=[readImage(image_path)], controlnet_module="none", controlnet_model=get_model(pattern='^control_scribble.*'), ) updated_image_path = get_new_image_name(image_path, func_name="scribble2image") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class image2pose: def inference(self, inputs): print("===>Starting image2pose Inference") resp = do_webui_request( url=DETECTAPI, controlnet_input_images=[readImage(inputs)], controlnet_module="openpose", ) updated_image_path = get_new_image_name(inputs, func_name="human-pose") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class pose2image: def inference(self, inputs): print("===>Starting pose2image Inference") image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) resp = do_webui_request( prompt=instruct_text, controlnet_input_images=[readImage(image_path)], controlnet_module="none", controlnet_model=get_model(pattern='^control_openpose.*'), ) updated_image_path = get_new_image_name(image_path, func_name="pose2image") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class image2seg: def inference(self, inputs): print("===>Starting image2seg Inference") resp = do_webui_request( url=DETECTAPI, controlnet_input_images=[readImage(inputs)], controlnet_module="segmentation", ) updated_image_path = get_new_image_name(inputs, func_name="segmentation") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class seg2image: def inference(self, inputs): print("===>Starting seg2image Inference") image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) resp = do_webui_request( prompt=instruct_text, controlnet_input_images=[readImage(image_path)], controlnet_module="none", controlnet_model=get_model(pattern='^control_seg.*'), ) updated_image_path = get_new_image_name(image_path, func_name="segment2image") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class image2depth: def inference(self, inputs): print("===>Starting image2depth Inference") resp = do_webui_request( url=DETECTAPI, controlnet_input_images=[readImage(inputs)], controlnet_module="depth", ) updated_image_path = get_new_image_name(inputs, func_name="depth") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class depth2image: def inference(self, inputs): print("===>Starting depth2image Inference") image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) resp = do_webui_request( prompt=instruct_text, controlnet_input_images=[readImage(image_path)], controlnet_module="depth", controlnet_model=get_model(pattern='^control_depth.*'), ) updated_image_path = get_new_image_name(image_path, func_name="depth2image") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class image2normal: def inference(self, inputs): print("===>Starting image2 normal Inference") resp = do_webui_request( url=DETECTAPI, controlnet_input_images=[readImage(inputs)], controlnet_module="normal", ) updated_image_path = get_new_image_name(inputs, func_name="normal-map") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class normal2image: def inference(self, inputs): print("===>Starting normal2image Inference") image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) resp = do_webui_request( prompt=instruct_text, controlnet_input_images=[readImage(image_path)], controlnet_module="normal", controlnet_model=get_model(pattern='^control_normal.*'), ) updated_image_path = get_new_image_name(image_path, func_name="normal2image") with open(updated_image_path, 'wb') as f: f.write(base64.b64decode(resp['images'][0])) return updated_image_path class BLIPVQA: def __init__(self, device): print("Initializing BLIP VQA to %s" % device) self.device = device self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") self.model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(self.device) def get_answer_from_question_and_image(self, inputs): image_path, question = inputs.split(",") raw_image = Image.open(image_path).convert('RGB') print(F'BLIPVQA :question :{question}') inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device) out = self.model.generate(**inputs) answer = self.processor.decode(out[0], skip_special_tokens=True) return answer class ConversationBot: def __init__(self): print("Initializing VisualChatGPT") # self.edit = ImageEditing(device=device) self.i2t = ImageCaptioning(device=device) self.t2i = T2I(device=device) self.image2canny = image2canny() self.canny2image = canny2image() self.image2line = image2line() self.line2image = line2image() self.image2hed = image2hed() self.hed2image = hed2image() self.image2scribble = image2scribble() self.scribble2image = scribble2image() self.image2pose = image2pose() self.pose2image = pose2image() self.BLIPVQA = BLIPVQA(device=device) self.image2seg = image2seg() self.seg2image = seg2image() self.image2depth = image2depth() self.depth2image = depth2image() self.image2normal = image2normal() self.normal2image = normal2image() # self.pix2pix = Pix2Pix(device="cuda:3") self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output') self.tools = [ Tool(name="Get Photo Description", func=self.i2t.inference, description="useful when you want to know what is inside the photo. receives image_path as input. " "The input to this tool should be a string, representing the image_path. "), Tool(name="Generate Image From User Input Text", func=self.t2i.inference, description="useful when you want to generate an image from a user input text and save it to a file. like: generate an image of an object or something, or generate an image that includes some objects. " "The input to this tool should be a string, representing the text used to generate image. "), # Tool(name="Remove Something From The Photo", func=self.edit.remove_part_of_image, # description="useful when you want to remove and object or something from the photo from its description or location. " # "The input to this tool should be a comma seperated string of two, representing the image_path and the object need to be removed. "), # Tool(name="Replace Something From The Photo", func=self.edit.replace_part_of_image, # description="useful when you want to replace an object from the object description or location with another object from its description. " # "The input to this tool should be a comma seperated string of three, representing the image_path, the object to be replaced, the object to be replaced with "), # Tool(name="Instruct Image Using Text", func=self.pix2pix.inference, # description="useful when you want to the style of the image to be like the text. like: make it look like a painting. or make it like a robot. " # "The input to this tool should be a comma seperated string of two, representing the image_path and the text. "), Tool(name="Answer Question About The Image", func=self.BLIPVQA.get_answer_from_question_and_image, description="useful when you need an answer for a question based on an image. like: what is the background color of the last image, how many cats in this figure, what is in this figure. " "The input to this tool should be a comma seperated string of two, representing the image_path and the question"), Tool(name="Edge Detection On Image", func=self.image2canny.inference, description="useful when you want to detect the edge of the image. like: detect the edges of this image, or canny detection on image, or peform edge detection on this image, or detect the canny image of this image. " "The input to this tool should be a string, representing the image_path"), Tool(name="Generate Image Condition On Canny Image", func=self.canny2image.inference, description="useful when you want to generate a new real image from both the user desciption and a canny image. like: generate a real image of a object or something from this canny image, or generate a new real image of a object or something from this edge image. " "The input to this tool should be a comma seperated string of two, representing the image_path and the user description. "), Tool(name="Line Detection On Image", func=self.image2line.inference, description="useful when you want to detect the straight line of the image. like: detect the straight lines of this image, or straight line detection on image, or peform straight line detection on this image, or detect the straight line image of this image. " "The input to this tool should be a string, representing the image_path"), Tool(name="Generate Image Condition On Line Image", func=self.line2image.inference, description="useful when you want to generate a new real image from both the user desciption and a straight line image. like: generate a real image of a object or something from this straight line image, or generate a new real image of a object or something from this straight lines. " "The input to this tool should be a comma seperated string of two, representing the image_path and the user description. "), Tool(name="Hed Detection On Image", func=self.image2hed.inference, description="useful when you want to detect the soft hed boundary of the image. like: detect the soft hed boundary of this image, or hed boundary detection on image, or peform hed boundary detection on this image, or detect soft hed boundary image of this image. " "The input to this tool should be a string, representing the image_path"), Tool(name="Generate Image Condition On Soft Hed Boundary Image", func=self.hed2image.inference, description="useful when you want to generate a new real image from both the user desciption and a soft hed boundary image. like: generate a real image of a object or something from this soft hed boundary image, or generate a new real image of a object or something from this hed boundary. " "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"), Tool(name="Segmentation On Image", func=self.image2seg.inference, description="useful when you want to detect segmentations of the image. like: segment this image, or generate segmentations on this image, or peform segmentation on this image. " "The input to this tool should be a string, representing the image_path"), Tool(name="Generate Image Condition On Segmentations", func=self.seg2image.inference, description="useful when you want to generate a new real image from both the user desciption and segmentations. like: generate a real image of a object or something from this segmentation image, or generate a new real image of a object or something from these segmentations. " "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"), Tool(name="Predict Depth On Image", func=self.image2depth.inference, description="useful when you want to detect depth of the image. like: generate the depth from this image, or detect the depth map on this image, or predict the depth for this image. " "The input to this tool should be a string, representing the image_path"), Tool(name="Generate Image Condition On Depth", func=self.depth2image.inference, description="useful when you want to generate a new real image from both the user desciption and depth image. like: generate a real image of a object or something from this depth image, or generate a new real image of a object or something from the depth map. " "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"), Tool(name="Predict Normal Map On Image", func=self.image2normal.inference, description="useful when you want to detect norm map of the image. like: generate normal map from this image, or predict normal map of this image. " "The input to this tool should be a string, representing the image_path"), Tool(name="Generate Image Condition On Normal Map", func=self.normal2image.inference, description="useful when you want to generate a new real image from both the user desciption and normal map. like: generate a real image of a object or something from this normal map, or generate a new real image of a object or something from the normal map. " "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"), Tool(name="Sketch Detection On Image", func=self.image2scribble.inference, description="useful when you want to generate a scribble of the image. like: generate a scribble of this image, or generate a sketch from this image, detect the sketch from this image. " "The input to this tool should be a string, representing the image_path"), Tool(name="Generate Image Condition On Sketch Image", func=self.scribble2image.inference, description="useful when you want to generate a new real image from both the user desciption and a scribble image or a sketch image. " "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"), Tool(name="Pose Detection On Image", func=self.image2pose.inference, description="useful when you want to detect the human pose of the image. like: generate human poses of this image, or generate a pose image from this image. " "The input to this tool should be a string, representing the image_path"), Tool(name="Generate Image Condition On Pose Image", func=self.pose2image.inference, description="useful when you want to generate a new real image from both the user desciption and a human pose image. like: generate a real image of a human from this human pose image, or generate a new real image of a human from this pose. " "The input to this tool should be a comma seperated string of two, representing the image_path and the user description")] def init_langchain(self, openai_api_key): self.llm = OpenAI(temperature=0, openai_api_key=openai_api_key) self.agent = initialize_agent( self.tools, self.llm, agent="conversational-react-description", verbose=True, memory=self.memory, return_intermediate_steps=True, agent_kwargs={'prefix': VISUAL_CHATGPT_PREFIX, 'format_instructions': VISUAL_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': VISUAL_CHATGPT_SUFFIX} ) def run_text(self, openai_api_key, text, state): if not hasattr(self, "agent"): self.init_langchain(openai_api_key) print("===============Running run_text =============") print("Inputs:", text, state) print("======>Previous memory:\n %s" % self.agent.memory) self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) res = self.agent({"input": text}) print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) state = state + [(text, response)] print("Outputs:", state) return state, state def run_image(self, openai_api_key, image, state, txt): if not hasattr(self, "agent"): self.init_langchain(openai_api_key) print("===============Running run_image =============") print("Inputs:", image, state) print("======>Previous memory:\n %s" % self.agent.memory) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") print("======>Auto Resize Image...") img = Image.open(image.name) width, height = img.size ratio = min(512 / width, 512 / height) width_new, height_new = (round(width * ratio), round(height * ratio)) img = img.resize((width_new, height_new)) img = img.convert('RGB') img.save(image_filename, "PNG") print(f"Resize image form {width}x{height} to {width_new}x{height_new}") description = self.i2t.inference(image_filename) Human_prompt = "\nHuman: provide a figure named {}. The description is: {}. This information helps you to understand this image, but you should use tools to finish following tasks, " \ "rather than directly imagine from my description. If you understand, say \"Received\". \n".format(image_filename, description) AI_prompt = "Received. " self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) state = state + [(f"*{image_filename}*", AI_prompt)] print("Outputs:", state) return state, state, txt + ' ' + image_filename + ' ' if __name__ == '__main__': os.makedirs("image/", exist_ok=True) bot = ConversationBot() with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo: openai_api_key = gr.Textbox(type="password", label="Enter your OpenAI API key here") chatbot = gr.Chatbot(elem_id="chatbot", label="Visual ChatGPT") state = gr.State([]) with gr.Row(): with gr.Column(scale=0.7): txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(container=False) with gr.Column(scale=0.15, min_width=0): clear = gr.Button("Clear️") with gr.Column(scale=0.15, min_width=0): btn = gr.UploadButton("Upload", file_types=["image"]) txt.submit(bot.run_text, [openai_api_key, txt, state], [chatbot, state]) txt.submit(lambda: "", None, txt) btn.upload(bot.run_image, [openai_api_key, btn, state, txt], [chatbot, state, txt]) clear.click(bot.memory.clear) clear.click(lambda: [], None, chatbot) clear.click(lambda: [], None, state) demo.launch(server_name="0.0.0.0", server_port=7864)

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The input to this tool should be a string, representing the image_path'\n )\n", (28325, 28650), False, 'from langchain.agents.tools import Tool\n'), ((28699, 29195), 'langchain.agents.tools.Tool', 'Tool', ([], {'name': '"""Generate Image Condition On Segmentations"""', 'func': 'self.seg2image.inference', 'description': '"""useful when you want to generate a new real image from both the user desciption and segmentations. like: generate a real image of a object or something from this segmentation image, or generate a new real image of a object or something from these segmentations. The input to this tool should be a comma seperated string of two, representing the image_path and the user description"""'}), "(name='Generate Image Condition On Segmentations', func=self.seg2image.\n inference, description=\n 'useful when you want to generate a new real image from both the user desciption and segmentations. like: generate a real image of a object or something from this segmentation image, or generate a new real image of a object or something from these segmentations. 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The input to this tool should be a comma seperated string of two, representing the image_path and the user description"""'}), "(name='Generate Image Condition On Depth', func=self.depth2image.\n inference, description=\n 'useful when you want to generate a new real image from both the user desciption and depth image. like: generate a real image of a object or something from this depth image, or generate a new real image of a object or something from the depth map. The input to this tool should be a comma seperated string of two, representing the image_path and the user description'\n )\n", (29633, 30104), False, 'from langchain.agents.tools import Tool\n'), ((30153, 30461), 'langchain.agents.tools.Tool', 'Tool', ([], {'name': '"""Predict Normal Map On Image"""', 'func': 'self.image2normal.inference', 'description': '"""useful when you want to detect norm map of the image. like: generate normal map from this image, or predict normal map of this image. The input to this tool should be a string, representing the image_path"""'}), "(name='Predict Normal Map On Image', func=self.image2normal.inference,\n description=\n 'useful when you want to detect norm map of the image. like: generate normal map from this image, or predict normal map of this image. The input to this tool should be a string, representing the image_path'\n )\n", (30157, 30461), False, 'from langchain.agents.tools import Tool\n'), ((30510, 30990), 'langchain.agents.tools.Tool', 'Tool', ([], {'name': '"""Generate Image Condition On Normal Map"""', 'func': 'self.normal2image.inference', 'description': '"""useful when you want to generate a new real image from both the user desciption and normal map. like: generate a real image of a object or something from this normal map, or generate a new real image of a object or something from the normal map. 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from typing import Any, Callable, Dict, TypeVar from langchain import BasePromptTemplate, LLMChain from langchain.chat_models.base import BaseChatModel from langchain.schema import BaseOutputParser, OutputParserException from openai.error import ( AuthenticationError, InvalidRequestError, RateLimitError, ServiceUnavailableError, ) from reworkd_platform.schemas.agent import ModelSettings from reworkd_platform.web.api.errors import OpenAIError T = TypeVar("T") def parse_with_handling(parser: BaseOutputParser[T], completion: str) -> T: try: return parser.parse(completion) except OutputParserException as e: raise OpenAIError( e, "There was an issue parsing the response from the AI model." ) async def openai_error_handler( func: Callable[..., Any], *args: Any, settings: ModelSettings, **kwargs: Any ) -> Any: try: return await func(*args, **kwargs) except ServiceUnavailableError as e: raise OpenAIError( e, "OpenAI is experiencing issues. Visit " "https://status.openai.com/ for more info.", should_log=not settings.custom_api_key, ) except InvalidRequestError as e: if e.user_message.startswith("The model:"): raise OpenAIError( e, f"Your API key does not have access to your current model. Please use a different model.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except AuthenticationError as e: raise OpenAIError( e, "Authentication error: Ensure a valid API key is being used.", should_log=not settings.custom_api_key, ) except RateLimitError as e: if e.user_message.startswith("You exceeded your current quota"): raise OpenAIError( e, f"Your API key exceeded your current quota, please check your plan and billing details.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except Exception as e: raise OpenAIError( e, "There was an unexpected issue getting a response from the AI model." ) async def call_model_with_handling( model: BaseChatModel, prompt: BasePromptTemplate, args: Dict[str, str], settings: ModelSettings, **kwargs: Any, ) -> str: chain = LLMChain(llm=model, prompt=prompt) return await openai_error_handler(chain.arun, args, settings=settings, **kwargs)

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from typing import Any, Callable, Dict, TypeVar from langchain import BasePromptTemplate, LLMChain from langchain.chat_models.base import BaseChatModel from langchain.schema import BaseOutputParser, OutputParserException from openai.error import ( AuthenticationError, InvalidRequestError, RateLimitError, ServiceUnavailableError, ) from reworkd_platform.schemas.agent import ModelSettings from reworkd_platform.web.api.errors import OpenAIError T = TypeVar("T") def parse_with_handling(parser: BaseOutputParser[T], completion: str) -> T: try: return parser.parse(completion) except OutputParserException as e: raise OpenAIError( e, "There was an issue parsing the response from the AI model." ) async def openai_error_handler( func: Callable[..., Any], *args: Any, settings: ModelSettings, **kwargs: Any ) -> Any: try: return await func(*args, **kwargs) except ServiceUnavailableError as e: raise OpenAIError( e, "OpenAI is experiencing issues. Visit " "https://status.openai.com/ for more info.", should_log=not settings.custom_api_key, ) except InvalidRequestError as e: if e.user_message.startswith("The model:"): raise OpenAIError( e, f"Your API key does not have access to your current model. Please use a different model.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except AuthenticationError as e: raise OpenAIError( e, "Authentication error: Ensure a valid API key is being used.", should_log=not settings.custom_api_key, ) except RateLimitError as e: if e.user_message.startswith("You exceeded your current quota"): raise OpenAIError( e, f"Your API key exceeded your current quota, please check your plan and billing details.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except Exception as e: raise OpenAIError( e, "There was an unexpected issue getting a response from the AI model." ) async def call_model_with_handling( model: BaseChatModel, prompt: BasePromptTemplate, args: Dict[str, str], settings: ModelSettings, **kwargs: Any, ) -> str: chain = LLMChain(llm=model, prompt=prompt) return await openai_error_handler(chain.arun, args, settings=settings, **kwargs)

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from typing import Any, Callable, Dict, TypeVar from langchain import BasePromptTemplate, LLMChain from langchain.chat_models.base import BaseChatModel from langchain.schema import BaseOutputParser, OutputParserException from openai.error import ( AuthenticationError, InvalidRequestError, RateLimitError, ServiceUnavailableError, ) from reworkd_platform.schemas.agent import ModelSettings from reworkd_platform.web.api.errors import OpenAIError T = TypeVar("T") def parse_with_handling(parser: BaseOutputParser[T], completion: str) -> T: try: return parser.parse(completion) except OutputParserException as e: raise OpenAIError( e, "There was an issue parsing the response from the AI model." ) async def openai_error_handler( func: Callable[..., Any], *args: Any, settings: ModelSettings, **kwargs: Any ) -> Any: try: return await func(*args, **kwargs) except ServiceUnavailableError as e: raise OpenAIError( e, "OpenAI is experiencing issues. Visit " "https://status.openai.com/ for more info.", should_log=not settings.custom_api_key, ) except InvalidRequestError as e: if e.user_message.startswith("The model:"): raise OpenAIError( e, f"Your API key does not have access to your current model. Please use a different model.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except AuthenticationError as e: raise OpenAIError( e, "Authentication error: Ensure a valid API key is being used.", should_log=not settings.custom_api_key, ) except RateLimitError as e: if e.user_message.startswith("You exceeded your current quota"): raise OpenAIError( e, f"Your API key exceeded your current quota, please check your plan and billing details.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except Exception as e: raise OpenAIError( e, "There was an unexpected issue getting a response from the AI model." ) async def call_model_with_handling( model: BaseChatModel, prompt: BasePromptTemplate, args: Dict[str, str], settings: ModelSettings, **kwargs: Any, ) -> str: chain = LLMChain(llm=model, prompt=prompt) return await openai_error_handler(chain.arun, args, settings=settings, **kwargs)

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from typing import Any, Callable, Dict, TypeVar from langchain import BasePromptTemplate, LLMChain from langchain.chat_models.base import BaseChatModel from langchain.schema import BaseOutputParser, OutputParserException from openai.error import ( AuthenticationError, InvalidRequestError, RateLimitError, ServiceUnavailableError, ) from reworkd_platform.schemas.agent import ModelSettings from reworkd_platform.web.api.errors import OpenAIError T = TypeVar("T") def parse_with_handling(parser: BaseOutputParser[T], completion: str) -> T: try: return parser.parse(completion) except OutputParserException as e: raise OpenAIError( e, "There was an issue parsing the response from the AI model." ) async def openai_error_handler( func: Callable[..., Any], *args: Any, settings: ModelSettings, **kwargs: Any ) -> Any: try: return await func(*args, **kwargs) except ServiceUnavailableError as e: raise OpenAIError( e, "OpenAI is experiencing issues. Visit " "https://status.openai.com/ for more info.", should_log=not settings.custom_api_key, ) except InvalidRequestError as e: if e.user_message.startswith("The model:"): raise OpenAIError( e, f"Your API key does not have access to your current model. Please use a different model.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except AuthenticationError as e: raise OpenAIError( e, "Authentication error: Ensure a valid API key is being used.", should_log=not settings.custom_api_key, ) except RateLimitError as e: if e.user_message.startswith("You exceeded your current quota"): raise OpenAIError( e, f"Your API key exceeded your current quota, please check your plan and billing details.", should_log=not settings.custom_api_key, ) raise OpenAIError(e, e.user_message) except Exception as e: raise OpenAIError( e, "There was an unexpected issue getting a response from the AI model." ) async def call_model_with_handling( model: BaseChatModel, prompt: BasePromptTemplate, args: Dict[str, str], settings: ModelSettings, **kwargs: Any, ) -> str: chain = LLMChain(llm=model, prompt=prompt) return await openai_error_handler(chain.arun, args, settings=settings, **kwargs)

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import json import os.path import logging import time from langchain.vectorstores import FAISS from langchain import PromptTemplate from utils.references import References from utils.knowledge import Knowledge from utils.file_operations import make_archive, copy_templates from utils.tex_processing import create_copies from utils.gpt_interaction import GPTModel from utils.prompts import SYSTEM from utils.embeddings import EMBEDDINGS from utils.gpt_interaction import get_gpt_responses TOTAL_TOKENS = 0 TOTAL_PROMPTS_TOKENS = 0 TOTAL_COMPLETION_TOKENS = 0 def log_usage(usage, generating_target, print_out=True): global TOTAL_TOKENS global TOTAL_PROMPTS_TOKENS global TOTAL_COMPLETION_TOKENS prompts_tokens = usage['prompt_tokens'] completion_tokens = usage['completion_tokens'] total_tokens = usage['total_tokens'] TOTAL_TOKENS += total_tokens TOTAL_PROMPTS_TOKENS += prompts_tokens TOTAL_COMPLETION_TOKENS += completion_tokens message = f">>USAGE>> For generating {generating_target}, {total_tokens} tokens have been used " \ f"({prompts_tokens} for prompts; {completion_tokens} for completion). " \ f"{TOTAL_TOKENS} tokens have been used in total." if print_out: print(message) logging.info(message) def _generation_setup(title, template="Default", tldr=False, max_kw_refs=20, bib_refs=None, max_tokens_ref=2048, # generating references knowledge_database=None, max_tokens_kd=2048, query_counts=10): llm = GPTModel(model="gpt-3.5-turbo-16k") bibtex_path, destination_folder = copy_templates(template, title) logging.basicConfig(level=logging.INFO, filename=os.path.join(destination_folder, "generation.log")) #generate key words keywords, usage = llm(systems=SYSTEM["keywords"], prompts=title, return_json=True) log_usage(usage, "keywords") keywords = {keyword: max_kw_refs for keyword in keywords} print("Keywords: \n", keywords) #generate references ref = References(title, bib_refs) ref.collect_papers(keywords, tldr=tldr) references = ref.to_prompts(max_tokens=max_tokens_ref) all_paper_ids = ref.to_bibtex(bibtex_path) #product domain knowledge prompts = f"Title: {title}" preliminaries_kw, _ = llm(systems=SYSTEM["preliminaries"], prompts=prompts) # check if the database exists or not db_path = f"utils/knowledge_databases/{knowledge_database}" db_config_path = os.path.join(db_path, "db_meta.json") db_index_path = os.path.join(db_path, "faiss_index") if os.path.isdir(db_path): try: with open(db_config_path, "r", encoding="utf-8") as f: db_config = json.load(f) model_name = db_config["embedding_model"] embeddings = EMBEDDINGS[model_name] db = FAISS.load_local(db_index_path, embeddings) knowledge = Knowledge(db=db) knowledge.collect_knowledge(preliminaries_kw, max_query=query_counts) domain_knowledge = knowledge.to_prompts(max_tokens_kd) except Exception as e: domain_knowledge='' prompts = f"Title: {title}" syetem_promot = "You are an assistant designed to propose necessary components of an survey papers. Your response should follow the JSON format." components, usage = llm(systems=syetem_promot, prompts=prompts, return_json=True) log_usage(usage, "media") print(f"The paper information has been initialized. References are saved to {bibtex_path}.") paper = {} paper["title"] = title paper["references"] = references paper["bibtex"] = bibtex_path paper["components"] = components paper["domain_knowledge"] = domain_knowledge return paper, destination_folder, all_paper_ids def section_generation(paper, section, save_to_path, model, research_field="machine learning"): """ The main pipeline of generating a section. 1. Generate prompts. 2. Get responses from AI assistant. 3. Extract the section text. 4. Save the text to .tex file. :return usage """ title = paper["title"] references = paper["references"] components = paper['components'] instruction = '- Discuss three to five main related fields to this paper. For each field, select five to ten key publications from references. For each reference, analyze its strengths and weaknesses in one or two sentences. Present the related works in a logical manner, often chronologically. Consider using a taxonomy or categorization to structure the discussion. Do not use \section{...} or \subsection{...}; use \paragraph{...} to list related fields.' fundamental_subprompt = "Your task is to write the {section} section of the paper with the title '{title}'. This paper has the following content: {components}\n" instruction_subprompt = "\n" \ "Your response should follow the following instructions:\n" \ "{instruction}\n" ref_instruction_subprompt = "- Read references. " \ "Every time you use information from the references, you need to appropriately cite it (using \citep or \citet)." \ "For example of \citep, the sentence where you use information from lei2022adaptive \citep{{lei2022adaptive}}. " \ "For example of \citet, \citet{{lei2022adaptive}} claims some information.\n" \ "- Avoid citing the same reference in a same paragraph.\n" \ "\n" \ "References:\n" \ "{references}" output_subprompt = "Ensure that it can be directly compiled by LeTaX." reivew_prompts = PromptTemplate( input_variables=["title", "components", "instruction", "section", "references"], template=fundamental_subprompt + instruction_subprompt + ref_instruction_subprompt + output_subprompt) prompts = reivew_prompts.format(title=title, components=components, instruction=instruction, section=section, references=references) SECTION_GENERATION_SYSTEM = PromptTemplate(input_variables=["research_field"], template="You are an assistant designed to write academic papers in the field of {research_field} using LaTeX." ) output, usage = get_gpt_responses(SECTION_GENERATION_SYSTEM.format(research_field=research_field), prompts, model=model, temperature=0.4) output=output[25:] tex_file = os.path.join(save_to_path, f"{section}.tex") with open(tex_file, "w", encoding="utf-8") as f: f.write(output) use_md =True use_chinese = True if use_md: system_md = 'You are an translator between the LaTeX and .MD. here is a latex file where the content is: \n \n ' + output prompts_md = 'you should transfer the latex content to the .MD format seriously, and pay attention to the correctness of the citation format (use the number). you should directly output the new content without anyoter replay. you should add reference papers at the end of the paper, and add line breaks between two reference papers. The Title should be ' + paper['title'] output_md, usage_md = get_gpt_responses(system_md, prompts_md, model=model, temperature=0.4) md_file = os.path.join(save_to_path, f"{'survey'}.md") with open(md_file, "w", encoding="utf-8") as m: m.write(output_md) if use_chinese == True: system_md_chi = 'You are an translator between the english and chinese. here is a english file where the content is: \n \n ' + output prompts_md_chi = 'you should transfer the english to chinese and dont change anything others. you should directly output the new content without anyoter replay. you should keep the reference papers unchanged.' output_md_chi, usage_md_chi = get_gpt_responses(system_md_chi, prompts_md_chi, model=model, temperature=0.4) md_file_chi = os.path.join(save_to_path, f"{'survey_chinese'}.md") with open(md_file_chi, "w", encoding="utf-8") as c: c.write(output_md_chi) return usage def generate_draft(title, tldr=True, max_kw_refs=20, bib_refs=None, max_tokens_ref=2048, knowledge_database=None, max_tokens_kd=2048, query_counts=10, section='related works', model="gpt-3.5-turbo-16k", template="Default" , save_zip=None): print("================START================") paper, destination_folder, _ = _generation_setup(title, template, tldr, max_kw_refs, bib_refs, max_tokens_ref=max_tokens_ref, max_tokens_kd=max_tokens_kd, query_counts=query_counts, knowledge_database=knowledge_database) # main components print(f"================PROCESSING================") usage = section_generation(paper, section, destination_folder, model=model) log_usage(usage, section) create_copies(destination_folder) print("\nPROCESSING COMPLETE\n") return make_archive(destination_folder, title+".zip") print("draft has been generated in " + destination_folder) if __name__ == "__main__": import openai openai.api_key = "your key" openai.api_base = 'https://api.openai.com/v1' #openai.proxy = "socks5h://localhost:7890 # if use the vpn target_title = "Reinforcement Learning for Robot Control" generate_draft(target_title, knowledge_database="ml_textbook_test",max_kw_refs=20)

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import json import os.path import logging import time from langchain.vectorstores import FAISS from langchain import PromptTemplate from utils.references import References from utils.knowledge import Knowledge from utils.file_operations import make_archive, copy_templates from utils.tex_processing import create_copies from utils.gpt_interaction import GPTModel from utils.prompts import SYSTEM from utils.embeddings import EMBEDDINGS from utils.gpt_interaction import get_gpt_responses TOTAL_TOKENS = 0 TOTAL_PROMPTS_TOKENS = 0 TOTAL_COMPLETION_TOKENS = 0 def log_usage(usage, generating_target, print_out=True): global TOTAL_TOKENS global TOTAL_PROMPTS_TOKENS global TOTAL_COMPLETION_TOKENS prompts_tokens = usage['prompt_tokens'] completion_tokens = usage['completion_tokens'] total_tokens = usage['total_tokens'] TOTAL_TOKENS += total_tokens TOTAL_PROMPTS_TOKENS += prompts_tokens TOTAL_COMPLETION_TOKENS += completion_tokens message = f">>USAGE>> For generating {generating_target}, {total_tokens} tokens have been used " \ f"({prompts_tokens} for prompts; {completion_tokens} for completion). " \ f"{TOTAL_TOKENS} tokens have been used in total." if print_out: print(message) logging.info(message) def _generation_setup(title, template="Default", tldr=False, max_kw_refs=20, bib_refs=None, max_tokens_ref=2048, # generating references knowledge_database=None, max_tokens_kd=2048, query_counts=10): llm = GPTModel(model="gpt-3.5-turbo-16k") bibtex_path, destination_folder = copy_templates(template, title) logging.basicConfig(level=logging.INFO, filename=os.path.join(destination_folder, "generation.log")) #generate key words keywords, usage = llm(systems=SYSTEM["keywords"], prompts=title, return_json=True) log_usage(usage, "keywords") keywords = {keyword: max_kw_refs for keyword in keywords} print("Keywords: \n", keywords) #generate references ref = References(title, bib_refs) ref.collect_papers(keywords, tldr=tldr) references = ref.to_prompts(max_tokens=max_tokens_ref) all_paper_ids = ref.to_bibtex(bibtex_path) #product domain knowledge prompts = f"Title: {title}" preliminaries_kw, _ = llm(systems=SYSTEM["preliminaries"], prompts=prompts) # check if the database exists or not db_path = f"utils/knowledge_databases/{knowledge_database}" db_config_path = os.path.join(db_path, "db_meta.json") db_index_path = os.path.join(db_path, "faiss_index") if os.path.isdir(db_path): try: with open(db_config_path, "r", encoding="utf-8") as f: db_config = json.load(f) model_name = db_config["embedding_model"] embeddings = EMBEDDINGS[model_name] db = FAISS.load_local(db_index_path, embeddings) knowledge = Knowledge(db=db) knowledge.collect_knowledge(preliminaries_kw, max_query=query_counts) domain_knowledge = knowledge.to_prompts(max_tokens_kd) except Exception as e: domain_knowledge='' prompts = f"Title: {title}" syetem_promot = "You are an assistant designed to propose necessary components of an survey papers. Your response should follow the JSON format." components, usage = llm(systems=syetem_promot, prompts=prompts, return_json=True) log_usage(usage, "media") print(f"The paper information has been initialized. References are saved to {bibtex_path}.") paper = {} paper["title"] = title paper["references"] = references paper["bibtex"] = bibtex_path paper["components"] = components paper["domain_knowledge"] = domain_knowledge return paper, destination_folder, all_paper_ids def section_generation(paper, section, save_to_path, model, research_field="machine learning"): """ The main pipeline of generating a section. 1. Generate prompts. 2. Get responses from AI assistant. 3. Extract the section text. 4. Save the text to .tex file. :return usage """ title = paper["title"] references = paper["references"] components = paper['components'] instruction = '- Discuss three to five main related fields to this paper. For each field, select five to ten key publications from references. For each reference, analyze its strengths and weaknesses in one or two sentences. Present the related works in a logical manner, often chronologically. Consider using a taxonomy or categorization to structure the discussion. Do not use \section{...} or \subsection{...}; use \paragraph{...} to list related fields.' fundamental_subprompt = "Your task is to write the {section} section of the paper with the title '{title}'. This paper has the following content: {components}\n" instruction_subprompt = "\n" \ "Your response should follow the following instructions:\n" \ "{instruction}\n" ref_instruction_subprompt = "- Read references. " \ "Every time you use information from the references, you need to appropriately cite it (using \citep or \citet)." \ "For example of \citep, the sentence where you use information from lei2022adaptive \citep{{lei2022adaptive}}. " \ "For example of \citet, \citet{{lei2022adaptive}} claims some information.\n" \ "- Avoid citing the same reference in a same paragraph.\n" \ "\n" \ "References:\n" \ "{references}" output_subprompt = "Ensure that it can be directly compiled by LeTaX." reivew_prompts = PromptTemplate( input_variables=["title", "components", "instruction", "section", "references"], template=fundamental_subprompt + instruction_subprompt + ref_instruction_subprompt + output_subprompt) prompts = reivew_prompts.format(title=title, components=components, instruction=instruction, section=section, references=references) SECTION_GENERATION_SYSTEM = PromptTemplate(input_variables=["research_field"], template="You are an assistant designed to write academic papers in the field of {research_field} using LaTeX." ) output, usage = get_gpt_responses(SECTION_GENERATION_SYSTEM.format(research_field=research_field), prompts, model=model, temperature=0.4) output=output[25:] tex_file = os.path.join(save_to_path, f"{section}.tex") with open(tex_file, "w", encoding="utf-8") as f: f.write(output) use_md =True use_chinese = True if use_md: system_md = 'You are an translator between the LaTeX and .MD. here is a latex file where the content is: \n \n ' + output prompts_md = 'you should transfer the latex content to the .MD format seriously, and pay attention to the correctness of the citation format (use the number). you should directly output the new content without anyoter replay. you should add reference papers at the end of the paper, and add line breaks between two reference papers. The Title should be ' + paper['title'] output_md, usage_md = get_gpt_responses(system_md, prompts_md, model=model, temperature=0.4) md_file = os.path.join(save_to_path, f"{'survey'}.md") with open(md_file, "w", encoding="utf-8") as m: m.write(output_md) if use_chinese == True: system_md_chi = 'You are an translator between the english and chinese. here is a english file where the content is: \n \n ' + output prompts_md_chi = 'you should transfer the english to chinese and dont change anything others. you should directly output the new content without anyoter replay. you should keep the reference papers unchanged.' output_md_chi, usage_md_chi = get_gpt_responses(system_md_chi, prompts_md_chi, model=model, temperature=0.4) md_file_chi = os.path.join(save_to_path, f"{'survey_chinese'}.md") with open(md_file_chi, "w", encoding="utf-8") as c: c.write(output_md_chi) return usage def generate_draft(title, tldr=True, max_kw_refs=20, bib_refs=None, max_tokens_ref=2048, knowledge_database=None, max_tokens_kd=2048, query_counts=10, section='related works', model="gpt-3.5-turbo-16k", template="Default" , save_zip=None): print("================START================") paper, destination_folder, _ = _generation_setup(title, template, tldr, max_kw_refs, bib_refs, max_tokens_ref=max_tokens_ref, max_tokens_kd=max_tokens_kd, query_counts=query_counts, knowledge_database=knowledge_database) # main components print(f"================PROCESSING================") usage = section_generation(paper, section, destination_folder, model=model) log_usage(usage, section) create_copies(destination_folder) print("\nPROCESSING COMPLETE\n") return make_archive(destination_folder, title+".zip") print("draft has been generated in " + destination_folder) if __name__ == "__main__": import openai openai.api_key = "your key" openai.api_base = 'https://api.openai.com/v1' #openai.proxy = "socks5h://localhost:7890 # if use the vpn target_title = "Reinforcement Learning for Robot Control" generate_draft(target_title, knowledge_database="ml_textbook_test",max_kw_refs=20)

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import sys import os sys.path.append(os.path.dirname(os.path.realpath(__file__))) sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'NeuralSeq')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'text_to_audio/Make_An_Audio')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'audio_detection')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'mono2binaural')) import gradio as gr import matplotlib import librosa import torch from langchain.agents.initialize import initialize_agent from langchain.agents.tools import Tool from langchain.chains.conversation.memory import ConversationBufferMemory from langchain.llms.openai import OpenAI import re import uuid import soundfile from PIL import Image import numpy as np from omegaconf import OmegaConf from einops import repeat from ldm.util import instantiate_from_config from ldm.data.extract_mel_spectrogram import TRANSFORMS_16000 from vocoder.bigvgan.models import VocoderBigVGAN from ldm.models.diffusion.ddim import DDIMSampler import whisper from utils.hparams import set_hparams from utils.hparams import hparams as hp import scipy.io.wavfile as wavfile import librosa from audio_infer.utils import config as detection_config from audio_infer.pytorch.models import PVT import clip import numpy as np AUDIO_CHATGPT_PREFIX = """AudioGPT AudioGPT can not directly read audios, but it has a list of tools to finish different speech, audio, and singing voice tasks. Each audio will have a file name formed as "audio/xxx.wav". When talking about audios, AudioGPT is very strict to the file name and will never fabricate nonexistent files. AudioGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the audio content and audio file name. It will remember to provide the file name from the last tool observation, if a new audio is generated. Human may provide new audios to AudioGPT with a description. The description helps AudioGPT to understand this audio, but AudioGPT should use tools to finish following tasks, rather than directly imagine from the description. Overall, AudioGPT is a powerful audio dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. TOOLS: ------ AudioGPT has access to the following tools:""" AUDIO_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format: ``` Thought: Do I need to use a tool? Yes Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action ``` When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: ``` Thought: Do I need to use a tool? No {ai_prefix}: [your response here] ``` """ AUDIO_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if not exists. You will remember to provide the audio file name loyally if it's provided in the last tool observation. Begin! Previous conversation history: {chat_history} New input: {input} Thought: Do I need to use a tool? {agent_scratchpad}""" def cut_dialogue_history(history_memory, keep_last_n_words = 500): tokens = history_memory.split() n_tokens = len(tokens) print(f"history_memory:{history_memory}, n_tokens: {n_tokens}") if n_tokens < keep_last_n_words: return history_memory else: paragraphs = history_memory.split('\n') last_n_tokens = n_tokens while last_n_tokens >= keep_last_n_words: last_n_tokens = last_n_tokens - len(paragraphs[0].split(' ')) paragraphs = paragraphs[1:] return '\n' + '\n'.join(paragraphs) def merge_audio(audio_path_1, audio_path_2): merged_signal = [] sr_1, signal_1 = wavfile.read(audio_path_1) sr_2, signal_2 = wavfile.read(audio_path_2) merged_signal.append(signal_1) merged_signal.append(signal_2) merged_signal = np.hstack(merged_signal) merged_signal = np.asarray(merged_signal, dtype=np.int16) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, sr_2, merged_signal) return audio_filename class T2I: def __init__(self, device): from transformers import AutoModelForCausalLM, AutoTokenizer from diffusers import StableDiffusionPipeline from transformers import pipeline print("Initializing T2I to %s" % device) self.device = device self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model, tokenizer=self.text_refine_tokenizer, device=self.device) self.pipe.to(device) def inference(self, text): image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"] print(f'{text} refined to {refined_text}') image = self.pipe(refined_text).images[0] image.save(image_filename) print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}") return image_filename class ImageCaptioning: def __init__(self, device): from transformers import BlipProcessor, BlipForConditionalGeneration print("Initializing ImageCaptioning to %s" % device) self.device = device self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device) def inference(self, image_path): inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device) out = self.model.generate(**inputs) captions = self.processor.decode(out[0], skip_special_tokens=True) return captions class T2A: def __init__(self, device): print("Initializing Make-An-Audio to %s" % device) self.device = device self.sampler = self._initialize_model('text_to_audio/Make_An_Audio/configs/text_to_audio/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta40multi_epoch=000085.ckpt', device=device) self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) def _initialize_model(self, config, ckpt, device): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) model = model.to(device) model.cond_stage_model.to(model.device) model.cond_stage_model.device = model.device sampler = DDIMSampler(model) return sampler def txt2audio(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80): SAMPLE_RATE = 16000 prng = np.random.RandomState(seed) start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) uc = self.sampler.model.get_learned_conditioning(n_samples * [""]) c = self.sampler.model.get_learned_conditioning(n_samples * [text]) shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8] # (z_dim, 80//2^x, 848//2^x) samples_ddim, _ = self.sampler.sample(S = ddim_steps, conditioning = c, batch_size = n_samples, shape = shape, verbose = False, unconditional_guidance_scale = scale, unconditional_conditioning = uc, x_T = start_code) x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1] wav_list = [] for idx,spec in enumerate(x_samples_ddim): wav = self.vocoder.vocode(spec) wav_list.append((SAMPLE_RATE,wav)) best_wav = self.select_best_audio(text, wav_list) return best_wav def select_best_audio(self, prompt, wav_list): from wav_evaluation.models.CLAPWrapper import CLAPWrapper clap_model = CLAPWrapper('text_to_audio/Make_An_Audio/useful_ckpts/CLAP/CLAP_weights_2022.pth', 'text_to_audio/Make_An_Audio/useful_ckpts/CLAP/config.yml', use_cuda=torch.cuda.is_available()) text_embeddings = clap_model.get_text_embeddings([prompt]) score_list = [] for data in wav_list: sr, wav = data audio_embeddings = clap_model.get_audio_embeddings([(torch.FloatTensor(wav), sr)], resample=True) score = clap_model.compute_similarity(audio_embeddings, text_embeddings, use_logit_scale=False).squeeze().cpu().numpy() score_list.append(score) max_index = np.array(score_list).argmax() print(score_list, max_index) return wav_list[max_index] def inference(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80): melbins,mel_len = 80,624 with torch.no_grad(): result = self.txt2audio( text = text, H = melbins, W = mel_len ) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, result[1], samplerate = 16000) print(f"Processed T2I.run, text: {text}, audio_filename: {audio_filename}") return audio_filename class I2A: def __init__(self, device): print("Initializing Make-An-Audio-Image to %s" % device) self.device = device self.sampler = self._initialize_model('text_to_audio/Make_An_Audio/configs/img_to_audio/img2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta54_epoch=000216.ckpt', device=device) self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) def _initialize_model(self, config, ckpt, device): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) model = model.to(device) model.cond_stage_model.to(model.device) model.cond_stage_model.device = model.device sampler = DDIMSampler(model) return sampler def img2audio(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80): SAMPLE_RATE = 16000 n_samples = 1 # only support 1 sample prng = np.random.RandomState(seed) start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) uc = self.sampler.model.get_learned_conditioning(n_samples * [""]) #image = Image.fromarray(image) image = Image.open(image) image = self.sampler.model.cond_stage_model.preprocess(image).unsqueeze(0) image_embedding = self.sampler.model.cond_stage_model.forward_img(image) c = image_embedding.repeat(n_samples, 1, 1) shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8] # (z_dim, 80//2^x, 848//2^x) samples_ddim, _ = self.sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=scale, unconditional_conditioning=uc, x_T=start_code) x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1] wav_list = [] for idx,spec in enumerate(x_samples_ddim): wav = self.vocoder.vocode(spec) wav_list.append((SAMPLE_RATE,wav)) best_wav = wav_list[0] return best_wav def inference(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80): melbins,mel_len = 80,624 with torch.no_grad(): result = self.img2audio( image=image, H=melbins, W=mel_len ) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, result[1], samplerate = 16000) print(f"Processed I2a.run, image_filename: {image}, audio_filename: {audio_filename}") return audio_filename class TTS: def __init__(self, device=None): from inference.tts.PortaSpeech import TTSInference if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing PortaSpeech to %s" % device) self.device = device self.exp_name = 'checkpoints/ps_adv_baseline' self.set_model_hparams() self.inferencer = TTSInference(self.hp, device) def set_model_hparams(self): set_hparams(exp_name=self.exp_name, print_hparams=False) self.hp = hp def inference(self, text): self.set_model_hparams() inp = {"text": text} out = self.inferencer.infer_once(inp) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, out, samplerate=22050) return audio_filename class T2S: def __init__(self, device= None): from inference.svs.ds_e2e import DiffSingerE2EInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing DiffSinger to %s" % device) self.device = device self.exp_name = 'checkpoints/0831_opencpop_ds1000' self.config= 'NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml' self.set_model_hparams() self.pipe = DiffSingerE2EInfer(self.hp, device) self.default_inp = { 'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP', 'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest', 'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590' } def set_model_hparams(self): set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False) self.hp = hp def inference(self, inputs): self.set_model_hparams() val = inputs.split(",") key = ['text', 'notes', 'notes_duration'] try: inp = {k: v for k, v in zip(key, val)} wav = self.pipe.infer_once(inp) except: print('Error occurs. Generate default audio sample.\n') inp = self.default_inp wav = self.pipe.infer_once(inp) #if inputs == '' or len(val) < len(key): # inp = self.default_inp #else: # inp = {k:v for k,v in zip(key,val)} #wav = self.pipe.infer_once(inp) wav *= 32767 audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16)) print(f"Processed T2S.run, audio_filename: {audio_filename}") return audio_filename class t2s_VISinger: def __init__(self, device=None): from espnet2.bin.svs_inference import SingingGenerate if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing VISingere to %s" % device) tag = 'AQuarterMile/opencpop_visinger1' self.model = SingingGenerate.from_pretrained( model_tag=str_or_none(tag), device=device, ) phn_dur = [[0. , 0.219 ], [0.219 , 0.50599998], [0.50599998, 0.71399999], [0.71399999, 1.097 ], [1.097 , 1.28799999], [1.28799999, 1.98300004], [1.98300004, 7.10500002], [7.10500002, 7.60400009]] phn = ['sh', 'i', 'q', 'v', 'n', 'i', 'SP', 'AP'] score = [[0, 0.50625, 'sh_i', 58, 'sh_i'], [0.50625, 1.09728, 'q_v', 56, 'q_v'], [1.09728, 1.9832100000000001, 'n_i', 53, 'n_i'], [1.9832100000000001, 7.105360000000001, 'SP', 0, 'SP'], [7.105360000000001, 7.604390000000001, 'AP', 0, 'AP']] tempo = 70 tmp = {} tmp["label"] = phn_dur, phn tmp["score"] = tempo, score self.default_inp = tmp def inference(self, inputs): val = inputs.split(",") key = ['text', 'notes', 'notes_duration'] try: # TODO: input will be update inp = {k: v for k, v in zip(key, val)} wav = self.model(text=inp)["wav"] except: print('Error occurs. Generate default audio sample.\n') inp = self.default_inp wav = self.model(text=inp)["wav"] audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, wav, samplerate=self.model.fs) return audio_filename class TTS_OOD: def __init__(self, device): from inference.tts.GenerSpeech import GenerSpeechInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' print("Initializing GenerSpeech to %s" % device) self.device = device self.exp_name = 'checkpoints/GenerSpeech' self.config = 'NeuralSeq/modules/GenerSpeech/config/generspeech.yaml' self.set_model_hparams() self.pipe = GenerSpeechInfer(self.hp, device) def set_model_hparams(self): set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False) f0_stats_fn = f'{hp["binary_data_dir"]}/train_f0s_mean_std.npy' if os.path.exists(f0_stats_fn): hp['f0_mean'], hp['f0_std'] = np.load(f0_stats_fn) hp['f0_mean'] = float(hp['f0_mean']) hp['f0_std'] = float(hp['f0_std']) hp['emotion_encoder_path'] = 'checkpoints/Emotion_encoder.pt' self.hp = hp def inference(self, inputs): self.set_model_hparams() key = ['ref_audio', 'text'] val = inputs.split(",") inp = {k: v for k, v in zip(key, val)} wav = self.pipe.infer_once(inp) wav *= 32767 audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16)) print( f"Processed GenerSpeech.run. Input text:{val[1]}. Input reference audio: {val[0]}. Output Audio_filename: {audio_filename}") return audio_filename class Inpaint: def __init__(self, device): print("Initializing Make-An-Audio-inpaint to %s" % device) self.device = device self.sampler = self._initialize_model_inpaint('text_to_audio/Make_An_Audio/configs/inpaint/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/inpaint7_epoch00047.ckpt') self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device) self.cmap_transform = matplotlib.cm.viridis def _initialize_model_inpaint(self, config, ckpt): config = OmegaConf.load(config) model = instantiate_from_config(config.model) model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model = model.to(device) print(model.device, device, model.cond_stage_model.device) sampler = DDIMSampler(model) return sampler def make_batch_sd(self, mel, mask, num_samples=1): mel = torch.from_numpy(mel)[None,None,...].to(dtype=torch.float32) mask = torch.from_numpy(mask)[None,None,...].to(dtype=torch.float32) masked_mel = (1 - mask) * mel mel = mel * 2 - 1 mask = mask * 2 - 1 masked_mel = masked_mel * 2 -1 batch = { "mel": repeat(mel.to(device=self.device), "1 ... -> n ...", n=num_samples), "mask": repeat(mask.to(device=self.device), "1 ... -> n ...", n=num_samples), "masked_mel": repeat(masked_mel.to(device=self.device), "1 ... -> n ...", n=num_samples), } return batch def gen_mel(self, input_audio_path): SAMPLE_RATE = 16000 sr, ori_wav = wavfile.read(input_audio_path) print("gen_mel") print(sr,ori_wav.shape,ori_wav) ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 if len(ori_wav.shape)==2:# stereo ori_wav = librosa.to_mono(ori_wav.T) print(sr,ori_wav.shape,ori_wav) ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE) mel_len,hop_size = 848,256 input_len = mel_len * hop_size if len(ori_wav) < input_len: input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0) else: input_wav = ori_wav[:input_len] mel = TRANSFORMS_16000(input_wav) return mel def gen_mel_audio(self, input_audio): SAMPLE_RATE = 16000 sr,ori_wav = input_audio print("gen_mel_audio") print(sr,ori_wav.shape,ori_wav) ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 if len(ori_wav.shape)==2:# stereo ori_wav = librosa.to_mono(ori_wav.T) print(sr,ori_wav.shape,ori_wav) ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE) mel_len,hop_size = 848,256 input_len = mel_len * hop_size if len(ori_wav) < input_len: input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0) else: input_wav = ori_wav[:input_len] mel = TRANSFORMS_16000(input_wav) return mel def show_mel_fn(self, input_audio_path): crop_len = 500 crop_mel = self.gen_mel(input_audio_path)[:,:crop_len] color_mel = self.cmap_transform(crop_mel) image = Image.fromarray((color_mel*255).astype(np.uint8)) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") image.save(image_filename) return image_filename def inpaint(self, batch, seed, ddim_steps, num_samples=1, W=512, H=512): model = self.sampler.model prng = np.random.RandomState(seed) start_code = prng.randn(num_samples, model.first_stage_model.embed_dim, H // 8, W // 8) start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32) c = model.get_first_stage_encoding(model.encode_first_stage(batch["masked_mel"])) cc = torch.nn.functional.interpolate(batch["mask"], size=c.shape[-2:]) c = torch.cat((c, cc), dim=1) # (b,c+1,h,w) 1 is mask shape = (c.shape[1]-1,)+c.shape[2:] samples_ddim, _ = self.sampler.sample(S=ddim_steps, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False) x_samples_ddim = model.decode_first_stage(samples_ddim) mel = torch.clamp((batch["mel"]+1.0)/2.0,min=0.0, max=1.0) mask = torch.clamp((batch["mask"]+1.0)/2.0,min=0.0, max=1.0) predicted_mel = torch.clamp((x_samples_ddim+1.0)/2.0,min=0.0, max=1.0) inpainted = (1-mask)*mel+mask*predicted_mel inpainted = inpainted.cpu().numpy().squeeze() inapint_wav = self.vocoder.vocode(inpainted) return inpainted, inapint_wav def inference(self, input_audio, mel_and_mask, seed = 55, ddim_steps = 100): SAMPLE_RATE = 16000 torch.set_grad_enabled(False) mel_img = Image.open(mel_and_mask['image']) mask_img = Image.open(mel_and_mask["mask"]) show_mel = np.array(mel_img.convert("L"))/255 mask = np.array(mask_img.convert("L"))/255 mel_bins,mel_len = 80,848 input_mel = self.gen_mel_audio(input_audio)[:,:mel_len] mask = np.pad(mask,((0,0),(0,mel_len-mask.shape[1])),mode='constant',constant_values=0) print(mask.shape,input_mel.shape) with torch.no_grad(): batch = self.make_batch_sd(input_mel,mask,num_samples=1) inpainted,gen_wav = self.inpaint( batch=batch, seed=seed, ddim_steps=ddim_steps, num_samples=1, H=mel_bins, W=mel_len ) inpainted = inpainted[:,:show_mel.shape[1]] color_mel = self.cmap_transform(inpainted) input_len = int(input_audio[1].shape[0] * SAMPLE_RATE / input_audio[0]) gen_wav = (gen_wav * 32768).astype(np.int16)[:input_len] image = Image.fromarray((color_mel*255).astype(np.uint8)) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") image.save(image_filename) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename, gen_wav, samplerate = 16000) return image_filename, audio_filename class ASR: def __init__(self, device): print("Initializing Whisper to %s" % device) self.device = device self.model = whisper.load_model("base", device=device) def inference(self, audio_path): audio = whisper.load_audio(audio_path) audio = whisper.pad_or_trim(audio) mel = whisper.log_mel_spectrogram(audio).to(self.device) _, probs = self.model.detect_language(mel) options = whisper.DecodingOptions() result = whisper.decode(self.model, mel, options) return result.text def translate_english(self, audio_path): audio = self.model.transcribe(audio_path, language='English') return audio['text'] class A2T: def __init__(self, device): from audio_to_text.inference_waveform import AudioCapModel print("Initializing Audio-To-Text Model to %s" % device) self.device = device self.model = AudioCapModel("audio_to_text/audiocaps_cntrstv_cnn14rnn_trm") def inference(self, audio_path): audio = whisper.load_audio(audio_path) caption_text = self.model(audio) return caption_text[0] class GeneFace: def __init__(self, device=None): print("Initializing GeneFace model to %s" % device) from audio_to_face.GeneFace_binding import GeneFaceInfer if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' self.device = device self.geneface_model = GeneFaceInfer(device) print("Loaded GeneFace model") def inference(self, audio_path): audio_base_name = os.path.basename(audio_path)[:-4] out_video_name = audio_path.replace("audio","video").replace(".wav", ".mp4") inp = { 'audio_source_name': audio_path, 'out_npy_name': f'geneface/tmp/{audio_base_name}.npy', 'cond_name': f'geneface/tmp/{audio_base_name}.npy', 'out_video_name': out_video_name, 'tmp_imgs_dir': f'video/tmp_imgs', } self.geneface_model.infer_once(inp) return out_video_name class SoundDetection: def __init__(self, device): self.device = device self.sample_rate = 32000 self.window_size = 1024 self.hop_size = 320 self.mel_bins = 64 self.fmin = 50 self.fmax = 14000 self.model_type = 'PVT' self.checkpoint_path = 'audio_detection/audio_infer/useful_ckpts/audio_detection.pth' self.classes_num = detection_config.classes_num self.labels = detection_config.labels self.frames_per_second = self.sample_rate // self.hop_size # Model = eval(self.model_type) self.model = PVT(sample_rate=self.sample_rate, window_size=self.window_size, hop_size=self.hop_size, mel_bins=self.mel_bins, fmin=self.fmin, fmax=self.fmax, classes_num=self.classes_num) checkpoint = torch.load(self.checkpoint_path, map_location=self.device) self.model.load_state_dict(checkpoint['model']) self.model.to(device) def inference(self, audio_path): # Forward (waveform, _) = librosa.core.load(audio_path, sr=self.sample_rate, mono=True) waveform = waveform[None, :] # (1, audio_length) waveform = torch.from_numpy(waveform) waveform = waveform.to(self.device) # Forward with torch.no_grad(): self.model.eval() batch_output_dict = self.model(waveform, None) framewise_output = batch_output_dict['framewise_output'].data.cpu().numpy()[0] """(time_steps, classes_num)""" # print('Sound event detection result (time_steps x classes_num): {}'.format( # framewise_output.shape)) import numpy as np import matplotlib.pyplot as plt sorted_indexes = np.argsort(np.max(framewise_output, axis=0))[::-1] top_k = 10 # Show top results top_result_mat = framewise_output[:, sorted_indexes[0 : top_k]] """(time_steps, top_k)""" # Plot result stft = librosa.core.stft(y=waveform[0].data.cpu().numpy(), n_fft=self.window_size, hop_length=self.hop_size, window='hann', center=True) frames_num = stft.shape[-1] fig, axs = plt.subplots(2, 1, sharex=True, figsize=(10, 4)) axs[0].matshow(np.log(np.abs(stft)), origin='lower', aspect='auto', cmap='jet') axs[0].set_ylabel('Frequency bins') axs[0].set_title('Log spectrogram') axs[1].matshow(top_result_mat.T, origin='upper', aspect='auto', cmap='jet', vmin=0, vmax=1) axs[1].xaxis.set_ticks(np.arange(0, frames_num, self.frames_per_second)) axs[1].xaxis.set_ticklabels(np.arange(0, frames_num / self.frames_per_second)) axs[1].yaxis.set_ticks(np.arange(0, top_k)) axs[1].yaxis.set_ticklabels(np.array(self.labels)[sorted_indexes[0 : top_k]]) axs[1].yaxis.grid(color='k', linestyle='solid', linewidth=0.3, alpha=0.3) axs[1].set_xlabel('Seconds') axs[1].xaxis.set_ticks_position('bottom') plt.tight_layout() image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") plt.savefig(image_filename) return image_filename class SoundExtraction: def __init__(self, device): from sound_extraction.model.LASSNet import LASSNet from sound_extraction.utils.stft import STFT import torch.nn as nn self.device = device self.model_file = 'sound_extraction/useful_ckpts/LASSNet.pt' self.stft = STFT() self.model = nn.DataParallel(LASSNet(device)).to(device) checkpoint = torch.load(self.model_file) self.model.load_state_dict(checkpoint['model']) self.model.eval() def inference(self, inputs): #key = ['ref_audio', 'text'] from sound_extraction.utils.wav_io import load_wav, save_wav val = inputs.split(",") audio_path = val[0] # audio_path, text text = val[1] waveform = load_wav(audio_path) waveform = torch.tensor(waveform).transpose(1,0) mixed_mag, mixed_phase = self.stft.transform(waveform) text_query = ['[CLS] ' + text] mixed_mag = mixed_mag.transpose(2,1).unsqueeze(0).to(self.device) est_mask = self.model(mixed_mag, text_query) est_mag = est_mask * mixed_mag est_mag = est_mag.squeeze(1) est_mag = est_mag.permute(0, 2, 1) est_wav = self.stft.inverse(est_mag.cpu().detach(), mixed_phase) est_wav = est_wav.squeeze(0).squeeze(0).numpy() #est_path = f'output/est{i}.wav' audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") print('audio_filename ', audio_filename) save_wav(est_wav, audio_filename) return audio_filename class Binaural: def __init__(self, device): from src.models import BinauralNetwork self.device = device self.model_file = 'mono2binaural/useful_ckpts/m2b/binaural_network.net' self.position_file = ['mono2binaural/useful_ckpts/m2b/tx_positions.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions2.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions3.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions4.txt', 'mono2binaural/useful_ckpts/m2b/tx_positions5.txt'] self.net = BinauralNetwork(view_dim=7, warpnet_layers=4, warpnet_channels=64, ) self.net.load_from_file(self.model_file) self.sr = 48000 def inference(self, audio_path): mono, sr = librosa.load(path=audio_path, sr=self.sr, mono=True) mono = torch.from_numpy(mono) mono = mono.unsqueeze(0) import numpy as np import random rand_int = random.randint(0,4) view = np.loadtxt(self.position_file[rand_int]).transpose().astype(np.float32) view = torch.from_numpy(view) if not view.shape[-1] * 400 == mono.shape[-1]: mono = mono[:,:(mono.shape[-1]//400)*400] # if view.shape[1]*400 > mono.shape[1]: m_a = view.shape[1] - mono.shape[-1]//400 rand_st = random.randint(0,m_a) view = view[:,m_a:m_a+(mono.shape[-1]//400)] # # binauralize and save output self.net.eval().to(self.device) mono, view = mono.to(self.device), view.to(self.device) chunk_size = 48000 # forward in chunks of 1s rec_field = 1000 # add 1000 samples as "safe bet" since warping has undefined rec. field rec_field -= rec_field % 400 # make sure rec_field is a multiple of 400 to match audio and view frequencies chunks = [ { "mono": mono[:, max(0, i-rec_field):i+chunk_size], "view": view[:, max(0, i-rec_field)//400:(i+chunk_size)//400] } for i in range(0, mono.shape[-1], chunk_size) ] for i, chunk in enumerate(chunks): with torch.no_grad(): mono = chunk["mono"].unsqueeze(0) view = chunk["view"].unsqueeze(0) binaural = self.net(mono, view).squeeze(0) if i > 0: binaural = binaural[:, -(mono.shape[-1]-rec_field):] chunk["binaural"] = binaural binaural = torch.cat([chunk["binaural"] for chunk in chunks], dim=-1) binaural = torch.clamp(binaural, min=-1, max=1).cpu() #binaural = chunked_forwarding(net, mono, view) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") import torchaudio torchaudio.save(audio_filename, binaural, sr) #soundfile.write(audio_filename, binaural, samplerate = 48000) print(f"Processed Binaural.run, audio_filename: {audio_filename}") return audio_filename class TargetSoundDetection: def __init__(self, device): from target_sound_detection.src import models as tsd_models from target_sound_detection.src.models import event_labels self.device = device self.MEL_ARGS = { 'n_mels': 64, 'n_fft': 2048, 'hop_length': int(22050 * 20 / 1000), 'win_length': int(22050 * 40 / 1000) } self.EPS = np.spacing(1) self.clip_model, _ = clip.load("ViT-B/32", device=self.device) self.event_labels = event_labels self.id_to_event = {i : label for i, label in enumerate(self.event_labels)} config = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/run_config.pth', map_location='cpu') config_parameters = dict(config) config_parameters['tao'] = 0.6 if 'thres' not in config_parameters.keys(): config_parameters['thres'] = 0.5 if 'time_resolution' not in config_parameters.keys(): config_parameters['time_resolution'] = 125 model_parameters = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/run_model_7_loss=-0.0724.pt' , map_location=lambda storage, loc: storage) # load parameter self.model = getattr(tsd_models, config_parameters['model'])(config_parameters, inputdim=64, outputdim=2, time_resolution=config_parameters['time_resolution'], **config_parameters['model_args']) self.model.load_state_dict(model_parameters) self.model = self.model.to(self.device).eval() self.re_embeds = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/text_emb.pth') self.ref_mel = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/ref_mel.pth') def extract_feature(self, fname): import soundfile as sf y, sr = sf.read(fname, dtype='float32') print('y ', y.shape) ti = y.shape[0]/sr if y.ndim > 1: y = y.mean(1) y = librosa.resample(y, sr, 22050) lms_feature = np.log(librosa.feature.melspectrogram(y, **self.MEL_ARGS) + self.EPS).T return lms_feature,ti def build_clip(self, text): text = clip.tokenize(text).to(self.device) # ["a diagram with dog", "a dog", "a cat"] text_features = self.clip_model.encode_text(text) return text_features def cal_similarity(self, target, retrievals): ans = [] #target =torch.from_numpy(target) for name in retrievals.keys(): tmp = retrievals[name] #tmp = torch.from_numpy(tmp) s = torch.cosine_similarity(target.squeeze(), tmp.squeeze(), dim=0) ans.append(s.item()) return ans.index(max(ans)) def inference(self, text, audio_path): from target_sound_detection.src.utils import median_filter, decode_with_timestamps target_emb = self.build_clip(text) # torch type idx = self.cal_similarity(target_emb, self.re_embeds) target_event = self.id_to_event[idx] embedding = self.ref_mel[target_event] embedding = torch.from_numpy(embedding) embedding = embedding.unsqueeze(0).to(self.device).float() #print('embedding ', embedding.shape) inputs,ti = self.extract_feature(audio_path) #print('ti ', ti) inputs = torch.from_numpy(inputs) inputs = inputs.unsqueeze(0).to(self.device).float() #print('inputs ', inputs.shape) decision, decision_up, logit = self.model(inputs, embedding) pred = decision_up.detach().cpu().numpy() pred = pred[:,:,0] frame_num = decision_up.shape[1] time_ratio = ti / frame_num filtered_pred = median_filter(pred, window_size=1, threshold=0.5) #print('filtered_pred ', filtered_pred) time_predictions = [] for index_k in range(filtered_pred.shape[0]): decoded_pred = [] decoded_pred_ = decode_with_timestamps(target_event, filtered_pred[index_k,:]) if len(decoded_pred_) == 0: # neg deal decoded_pred_.append((target_event, 0, 0)) decoded_pred.append(decoded_pred_) for num_batch in range(len(decoded_pred)): # when we test our model,the batch_size is 1 cur_pred = pred[num_batch] # Save each frame output, for later visualization label_prediction = decoded_pred[num_batch] # frame predict # print(label_prediction) for event_label, onset, offset in label_prediction: time_predictions.append({ 'onset': onset*time_ratio, 'offset': offset*time_ratio,}) ans = '' for i,item in enumerate(time_predictions): ans = ans + 'segment' + str(i+1) + ' start_time: ' + str(item['onset']) + ' end_time: ' + str(item['offset']) + '\t' #print(ans) return ans # class Speech_Enh_SS_SC: # """Speech Enhancement or Separation in single-channel # Example usage: # enh_model = Speech_Enh_SS("cuda") # enh_wav = enh_model.inference("./test_chime4_audio_M05_440C0213_PED_REAL.wav") # """ # def __init__(self, device="cuda", model_name="lichenda/chime4_fasnet_dprnn_tac"): # self.model_name = model_name # self.device = device # print("Initializing ESPnet Enh to %s" % device) # self._initialize_model() # def _initialize_model(self): # from espnet_model_zoo.downloader import ModelDownloader # from espnet2.bin.enh_inference import SeparateSpeech # d = ModelDownloader() # cfg = d.download_and_unpack(self.model_name) # self.separate_speech = SeparateSpeech( # train_config=cfg["train_config"], # model_file=cfg["model_file"], # # for segment-wise process on long speech # segment_size=2.4, # hop_size=0.8, # normalize_segment_scale=False, # show_progressbar=True, # ref_channel=None, # normalize_output_wav=True, # device=self.device, # ) # def inference(self, speech_path, ref_channel=0): # speech, sr = soundfile.read(speech_path) # speech = speech[:, ref_channel] # assert speech.dim() == 1 # enh_speech = self.separate_speech(speech[None, ], fs=sr) # if len(enh_speech) == 1: # return enh_speech[0] # return enh_speech # class Speech_Enh_SS_MC: # """Speech Enhancement or Separation in multi-channel""" # def __init__(self, device="cuda", model_name=None, ref_channel=4): # self.model_name = model_name # self.ref_channel = ref_channel # self.device = device # print("Initializing ESPnet Enh to %s" % device) # self._initialize_model() # def _initialize_model(self): # from espnet_model_zoo.downloader import ModelDownloader # from espnet2.bin.enh_inference import SeparateSpeech # d = ModelDownloader() # cfg = d.download_and_unpack(self.model_name) # self.separate_speech = SeparateSpeech( # train_config=cfg["train_config"], # model_file=cfg["model_file"], # # for segment-wise process on long speech # segment_size=2.4, # hop_size=0.8, # normalize_segment_scale=False, # show_progressbar=True, # ref_channel=self.ref_channel, # normalize_output_wav=True, # device=self.device, # ) # def inference(self, speech_path): # speech, sr = soundfile.read(speech_path) # speech = speech.T # enh_speech = self.separate_speech(speech[None, ...], fs=sr) # if len(enh_speech) == 1: # return enh_speech[0] # return enh_speech class Speech_Enh_SS_SC: """Speech Enhancement or Separation in single-channel Example usage: enh_model = Speech_Enh_SS("cuda") enh_wav = enh_model.inference("./test_chime4_audio_M05_440C0213_PED_REAL.wav") """ def __init__(self, device="cuda", model_name="espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw"): self.model_name = model_name self.device = device print("Initializing ESPnet Enh to %s" % device) self._initialize_model() def _initialize_model(self): from espnet_model_zoo.downloader import ModelDownloader from espnet2.bin.enh_inference import SeparateSpeech d = ModelDownloader() cfg = d.download_and_unpack(self.model_name) self.separate_speech = SeparateSpeech( train_config=cfg["train_config"], model_file=cfg["model_file"], # for segment-wise process on long speech segment_size=2.4, hop_size=0.8, normalize_segment_scale=False, show_progressbar=True, ref_channel=None, normalize_output_wav=True, device=self.device, ) def inference(self, speech_path, ref_channel=0): speech, sr = soundfile.read(speech_path) speech = speech[:, ref_channel] # speech = torch.from_numpy(speech) # assert speech.dim() == 1 enh_speech = self.separate_speech(speech[None, ...], fs=sr) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # if len(enh_speech) == 1: soundfile.write(audio_filename, enh_speech[0].squeeze(), samplerate=sr) # return enh_speech[0] # return enh_speech # else: # print("############") # audio_filename_1 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # soundfile.write(audio_filename_1, enh_speech[0].squeeze(), samplerate=sr) # audio_filename_2 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # soundfile.write(audio_filename_2, enh_speech[1].squeeze(), samplerate=sr) # audio_filename = merge_audio(audio_filename_1, audio_filename_2) return audio_filename class Speech_SS: def __init__(self, device="cuda", model_name="lichenda/wsj0_2mix_skim_noncausal"): self.model_name = model_name self.device = device print("Initializing ESPnet SS to %s" % device) self._initialize_model() def _initialize_model(self): from espnet_model_zoo.downloader import ModelDownloader from espnet2.bin.enh_inference import SeparateSpeech d = ModelDownloader() cfg = d.download_and_unpack(self.model_name) self.separate_speech = SeparateSpeech( train_config=cfg["train_config"], model_file=cfg["model_file"], # for segment-wise process on long speech segment_size=2.4, hop_size=0.8, normalize_segment_scale=False, show_progressbar=True, ref_channel=None, normalize_output_wav=True, device=self.device, ) def inference(self, speech_path): speech, sr = soundfile.read(speech_path) enh_speech = self.separate_speech(speech[None, ...], fs=sr) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") if len(enh_speech) == 1: soundfile.write(audio_filename, enh_speech[0], samplerate=sr) else: # print("############") audio_filename_1 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename_1, enh_speech[0].squeeze(), samplerate=sr) audio_filename_2 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") soundfile.write(audio_filename_2, enh_speech[1].squeeze(), samplerate=sr) audio_filename = merge_audio(audio_filename_1, audio_filename_2) return audio_filename class ConversationBot: def __init__(self): print("Initializing AudioGPT") self.llm = OpenAI(temperature=0) self.t2i = T2I(device="cuda:1") self.i2t = ImageCaptioning(device="cuda:0") self.t2a = T2A(device="cuda:0") self.tts = TTS(device="cpu") self.t2s = T2S(device="cpu") self.i2a = I2A(device="cuda:0") self.a2t = A2T(device="cpu") self.asr = ASR(device="cuda:0") self.SE_SS_SC = Speech_Enh_SS_SC(device="cuda:0") # self.SE_SS_MC = Speech_Enh_SS_MC(device="cuda:0") self.SS = Speech_SS(device="cuda:0") self.inpaint = Inpaint(device="cuda:0") self.tts_ood = TTS_OOD(device="cpu") self.geneface = GeneFace(device="cuda:0") self.detection = SoundDetection(device="cpu") self.binaural = Binaural(device="cuda:0") self.extraction = SoundExtraction(device="cuda:0") self.TSD = TargetSoundDetection(device="cuda:0") self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output') def init_tools(self, interaction_type): if interaction_type == 'text': self.tools = [ Tool(name="Generate Image From User Input Text", func=self.t2i.inference, description="useful for when you want to generate an image from a user input text and it saved it to a file. like: generate an image of an object or something, or generate an image that includes some objects. " "The input to this tool should be a string, representing the text used to generate image. "), Tool(name="Get Photo Description", func=self.i2t.inference, description="useful for when you want to know what is inside the photo. receives image_path as input. " "The input to this tool should be a string, representing the image_path. "), Tool(name="Generate Audio From User Input Text", func=self.t2a.inference, description="useful for when you want to generate an audio from a user input text and it saved it to a file." "The input to this tool should be a string, representing the text used to generate audio."), Tool( name="Style Transfer", func= self.tts_ood.inference, description="useful for when you want to generate speech samples with styles (e.g., timbre, emotion, and prosody) derived from a reference custom voice." "Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx." "The input to this tool should be a comma seperated string of two, representing reference audio path and input text."), Tool(name="Generate Singing Voice From User Input Text, Note and Duration Sequence", func= self.t2s.inference, description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) and save it to a file." "If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence ." "If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. " "Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx." "The input to this tool should be a comma seperated string of three, representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided."), Tool(name="Synthesize Speech Given the User Input Text", func=self.tts.inference, description="useful for when you want to convert a user input text into speech audio it saved it to a file." "The input to this tool should be a string, representing the text used to be converted to speech."), # Tool(name="Speech Enhancement Or Separation In Single-Channel", func=self.SE_SS_SC.inference, # description="useful for when you want to enhance the quality of the speech signal by reducing background noise (single-channel), " # "or separate each speech from the speech mixture (single-channel), receives audio_path as input." # "The input to this tool should be a string, representing the audio_path."), Tool(name="Speech Enhancement In Single-Channel", func=self.SE_SS_SC.inference, description="useful for when you want to enhance the quality of the speech signal by reducing background noise (single-channel), receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Speech Separation In Single-Channel", func=self.SS.inference, description="useful for when you want to separate each speech from the speech mixture, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), # Tool(name="Speech Enhancement In Multi-Channel", func=self.SE_SS_MC.inference, # description="useful for when you want to enhance the quality of the speech signal by reducing background noise (multi-channel), receives audio_path as input." # "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate Audio From The Image", func=self.i2a.inference, description="useful for when you want to generate an audio based on an image." "The input to this tool should be a string, representing the image_path. "), Tool(name="Generate Text From The Audio", func=self.a2t.inference, description="useful for when you want to describe an audio in text, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Audio Inpainting", func=self.inpaint.show_mel_fn, description="useful for when you want to inpaint a mel spectrum of an audio and predict this audio, this tool will generate a mel spectrum and you can inpaint it, receives audio_path as input, " "The input to this tool should be a string, representing the audio_path."), Tool(name="Transcribe Speech", func=self.asr.inference, description="useful for when you want to know the text corresponding to a human speech, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate a talking human portrait video given a input Audio", func=self.geneface.inference, description="useful for when you want to generate a talking human portrait video given a input audio." "The input to this tool should be a string, representing the audio_path."), Tool(name="Detect The Sound Event From The Audio", func=self.detection.inference, description="useful for when you want to know what event in the audio and the sound event start or end time, this tool will generate an image of all predict events, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Sythesize Binaural Audio From A Mono Audio Input", func=self.binaural.inference, description="useful for when you want to transfer your mono audio into binaural audio, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Extract Sound Event From Mixture Audio Based On Language Description", func=self.extraction.inference, description="useful for when you extract target sound from a mixture audio, you can describe the target sound by text, receives audio_path and text as input. " "The input to this tool should be a comma seperated string of two, representing mixture audio path and input text."), Tool(name="Target Sound Detection", func=self.TSD.inference, description="useful for when you want to know when the target sound event in the audio happens. You can use language descriptions to instruct the model. receives text description and audio_path as input. " "The input to this tool should be a comma seperated string of two, representing audio path and the text description. ")] self.agent = initialize_agent( self.tools, self.llm, agent="conversational-react-description", verbose=True, memory=self.memory, return_intermediate_steps=True, agent_kwargs={'prefix': AUDIO_CHATGPT_PREFIX, 'format_instructions': AUDIO_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': AUDIO_CHATGPT_SUFFIX}, ) return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False) else: self.tools = [ Tool(name="Generate Audio From User Input Text", func=self.t2a.inference, description="useful for when you want to generate an audio from a user input text and it saved it to a file." "The input to this tool should be a string, representing the text used to generate audio."), Tool( name="Style Transfer", func= self.tts_ood.inference, description="useful for when you want to generate speech samples with styles (e.g., timbre, emotion, and prosody) derived from a reference custom voice." "Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx." "The input to this tool should be a comma seperated string of two, representing reference audio path and input text."), Tool(name="Generate Singing Voice From User Input Text, Note and Duration Sequence", func= self.t2s.inference, description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) and save it to a file." "If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence ." "If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. " "Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx." "The input to this tool should be a comma seperated string of three, representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided."), Tool(name="Synthesize Speech Given the User Input Text", func=self.tts.inference, description="useful for when you want to convert a user input text into speech audio it saved it to a file." "The input to this tool should be a string, representing the text used to be converted to speech."), Tool(name="Generate Text From The Audio", func=self.a2t.inference, description="useful for when you want to describe an audio in text, receives audio_path as input." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate a talking human portrait video given a input Audio", func=self.geneface.inference, description="useful for when you want to generate a talking human portrait video given a input audio." "The input to this tool should be a string, representing the audio_path."), Tool(name="Generate Binaural Audio From A Mono Audio Input", func=self.binaural.inference, description="useful for when you want to transfer your mono audio into binaural audio, receives audio_path as input. " "The input to this tool should be a string, representing the audio_path. "), Tool(name="Extract Sound Event From Mixture Audio Based On Language Description", func=self.extraction.inference, description="useful for when you extract target sound from a mixture audio, you can describe the target sound by text, receives audio_path and text as input. " "The input to this tool should be a comma seperated string of two, representing mixture audio path and input text."), Tool(name="Target Sound Detection", func=self.TSD.inference, description="useful for when you want to know when the target sound event in the audio happens. You can use language descriptions to instruct the model. receives text description and audio_path as input. " "The input to this tool should be a comma seperated string of two, representing audio path and the text description. ")] self.agent = initialize_agent( self.tools, self.llm, agent="conversational-react-description", verbose=True, memory=self.memory, return_intermediate_steps=True, agent_kwargs={'prefix': AUDIO_CHATGPT_PREFIX, 'format_instructions': AUDIO_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': AUDIO_CHATGPT_SUFFIX}, ) return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) def run_text(self, text, state): print("===============Running run_text =============") print("Inputs:", text, state) print("======>Previous memory:\n %s" % self.agent.memory) self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) res = self.agent({"input": text}) if res['intermediate_steps'] == []: print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) else: tool = res['intermediate_steps'][0][0].tool if tool == "Generate Image From User Input Text" or tool == "Generate Text From The Audio" or tool == "Target Sound Detection": print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Transcribe Speech": response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Detect The Sound Event From The Audio": image_filename = res['intermediate_steps'][0][1] response = res['output'] + f"*{image_filename}*" state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) elif tool == "Audio Inpainting": audio_filename = res['intermediate_steps'][0][0].tool_input image_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False), gr.Image.update(value=image_filename,visible=True), gr.Button.update(visible=True) elif tool == "Generate a talking human portrait video given a input Audio": video_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(value=video_filename,visible=True), gr.Image.update(visible=False), gr.Button.update(visible=False) print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) audio_filename = res['intermediate_steps'][0][1] state = state + [(text, response)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False), gr.Image.update(visible=False), gr.Button.update(visible=False) def run_image_or_audio(self, file, state, txt): file_type = file.name[-3:] if file_type == "wav": print("===============Running run_audio =============") print("Inputs:", file, state) print("======>Previous memory:\n %s" % self.agent.memory) audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") # audio_load = whisper.load_audio(file.name) audio_load, sr = soundfile.read(file.name) soundfile.write(audio_filename, audio_load, samplerate = sr) description = self.a2t.inference(audio_filename) Human_prompt = "\nHuman: provide an audio named {}. The description is: {}. This information helps you to understand this audio, but you should use tools to finish following tasks, " \ "rather than directly imagine from my description. If you understand, say \"Received\". \n".format(audio_filename, description) AI_prompt = "Received. " self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) #state = state + [(f"<audio src=audio_filename controls=controls></audio>*{audio_filename}*", AI_prompt)] state = state + [(f"*{audio_filename}*", AI_prompt)] print("Outputs:", state) return state, state, gr.Audio.update(value=audio_filename,visible=True), gr.Video.update(visible=False) else: print("===============Running run_image =============") print("Inputs:", file, state) print("======>Previous memory:\n %s" % self.agent.memory) image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png") print("======>Auto Resize Image...") img = Image.open(file.name) width, height = img.size ratio = min(512 / width, 512 / height) width_new, height_new = (round(width * ratio), round(height * ratio)) img = img.resize((width_new, height_new)) img = img.convert('RGB') img.save(image_filename, "PNG") print(f"Resize image form {width}x{height} to {width_new}x{height_new}") description = self.i2t.inference(image_filename) Human_prompt = "\nHuman: provide a figure named {}. The description is: {}. This information helps you to understand this image, but you should use tools to finish following tasks, " \ "rather than directly imagine from my description. If you understand, say \"Received\". \n".format(image_filename, description) AI_prompt = "Received. " self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) state = state + [(f"*{image_filename}*", AI_prompt)] print("Outputs:", state) return state, state, gr.Audio.update(visible=False), gr.Video.update(visible=False) def speech(self, speech_input, state): input_audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav") text = self.asr.translate_english(speech_input) print("Inputs:", text, state) print("======>Previous memory:\n %s" % self.agent.memory) self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500) res = self.agent({"input": text}) if res['intermediate_steps'] == []: print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] output_audio_filename = self.tts.inference(response) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) else: tool = res['intermediate_steps'][0][0].tool if tool == "Generate Image From User Input Text" or tool == "Generate Text From The Audio" or tool == "Target Sound Detection": print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) output_audio_filename = self.tts.inference(res['output']) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Transcribe Speech": print("======>Current memory:\n %s" % self.agent.memory) output_audio_filename = self.tts.inference(res['output']) response = res['output'] state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Detect The Sound Event From The Audio": print("======>Current memory:\n %s" % self.agent.memory) image_filename = res['intermediate_steps'][0][1] output_audio_filename = self.tts.inference(res['output']) response = res['output'] + f"*{image_filename}*" state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) elif tool == "Generate a talking human portrait video given a input Audio": video_filename = res['intermediate_steps'][0][1] print("======>Current memory:\n %s" % self.agent.memory) response = res['output'] output_audio_filename = self.tts.inference(res['output']) state = state + [(text, response)] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(value=video_filename,visible=True) print("======>Current memory:\n %s" % self.agent.memory) response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output']) audio_filename = res['intermediate_steps'][0][1] Res = "The audio file has been generated and the audio is " output_audio_filename = merge_audio(self.tts.inference(Res), audio_filename) print(output_audio_filename) state = state + [(text, response)] response = res['output'] print("Outputs:", state) return gr.Audio.update(value=None), gr.Audio.update(value=output_audio_filename,visible=True), state, gr.Video.update(visible=False) def inpainting(self, state, audio_filename, image_filename): print("===============Running inpainting =============") print("Inputs:", state) print("======>Previous memory:\n %s" % self.agent.memory) new_image_filename, new_audio_filename = self.inpaint.inference(audio_filename, image_filename) AI_prompt = "Here are the predict audio and the mel spectrum." + f"*{new_audio_filename}*" + f"*{new_image_filename}*" output_audio_filename = self.tts.inference(AI_prompt) self.agent.memory.buffer = self.agent.memory.buffer + 'AI: ' + AI_prompt print("======>Current memory:\n %s" % self.agent.memory) state = state + [(f"Audio Inpainting", AI_prompt)] print("Outputs:", state) return state, state, gr.Image.update(visible=False), gr.Audio.update(value=new_audio_filename, visible=True), gr.Video.update(visible=False), gr.Button.update(visible=False) def clear_audio(self): return gr.Audio.update(value=None, visible=False) def clear_input_audio(self): return gr.Audio.update(value=None) def clear_image(self): return gr.Image.update(value=None, visible=False) def clear_video(self): return gr.Video.update(value=None, visible=False) def clear_button(self): return gr.Button.update(visible=False) if __name__ == '__main__': bot = ConversationBot() with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo: with gr.Row(): gr.Markdown("## AudioGPT") chatbot = gr.Chatbot(elem_id="chatbot", label="AudioGPT", visible=False) state = gr.State([]) with gr.Row() as select_raws: with gr.Column(scale=0.7): interaction_type = gr.Radio(choices=['text', 'speech'], value='text', label='Interaction Type') with gr.Column(scale=0.3, min_width=0): select = gr.Button("Select") with gr.Row(visible=False) as text_input_raws: with gr.Column(scale=0.7): txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(container=False) with gr.Column(scale=0.1, min_width=0): run = gr.Button("🏃‍♂️Run") with gr.Column(scale=0.1, min_width=0): clear_txt = gr.Button("🔄Clear️") with gr.Column(scale=0.1, min_width=0): btn = gr.UploadButton("🖼️Upload", file_types=["image","audio"]) with gr.Row(): outaudio = gr.Audio(visible=False) with gr.Row(): with gr.Column(scale=0.3, min_width=0): outvideo = gr.Video(visible=False) with gr.Row(): show_mel = gr.Image(type="filepath",tool='sketch',visible=False) with gr.Row(): run_button = gr.Button("Predict Masked Place",visible=False) with gr.Row(visible=False) as speech_input_raws: with gr.Column(scale=0.7): speech_input = gr.Audio(source="microphone", type="filepath", label="Input") with gr.Column(scale=0.15, min_width=0): submit_btn = gr.Button("🏃‍♂️Submit") with gr.Column(scale=0.15, min_width=0): clear_speech = gr.Button("🔄Clear️") with gr.Row(): speech_output = gr.Audio(label="Output",visible=False) select.click(bot.init_tools, [interaction_type], [chatbot, select_raws, text_input_raws, speech_input_raws]) txt.submit(bot.run_text, [txt, state], [chatbot, state, outaudio, outvideo, show_mel, run_button]) txt.submit(lambda: "", None, txt) run.click(bot.run_text, [txt, state], [chatbot, state, outaudio, outvideo, show_mel, run_button]) run.click(lambda: "", None, txt) btn.upload(bot.run_image_or_audio, [btn, state, txt], [chatbot, state, outaudio, outvideo]) run_button.click(bot.inpainting, [state, outaudio, show_mel], [chatbot, state, show_mel, outaudio, outvideo, run_button]) clear_txt.click(bot.memory.clear) clear_txt.click(lambda: [], None, chatbot) clear_txt.click(lambda: [], None, state) clear_txt.click(lambda:None, None, txt) clear_txt.click(bot.clear_button, None, run_button) clear_txt.click(bot.clear_image, None, show_mel) clear_txt.click(bot.clear_audio, None, outaudio) clear_txt.click(bot.clear_video, None, outvideo) submit_btn.click(bot.speech, [speech_input, state], [speech_input, speech_output, state, outvideo]) clear_speech.click(bot.clear_input_audio, None, speech_input) clear_speech.click(bot.clear_audio, None, speech_output) clear_speech.click(lambda: [], None, state) clear_speech.click(bot.clear_video, None, outvideo) demo.launch(server_name="0.0.0.0", server_port=7860, share=True)

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from langchain.indexes import VectorstoreIndexCreator from langchain_community.document_loaders import CSVLoader from langchain_community.vectorstores import FAISS loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv") docs = loader.load() index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS) index = index_creator.from_documents(docs) index.vectorstore.save_local("titanic_data")

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from langchain.indexes import VectorstoreIndexCreator from langchain_community.document_loaders import CSVLoader from langchain_community.vectorstores import FAISS loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv") docs = loader.load() index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS) index = index_creator.from_documents(docs) index.vectorstore.save_local("titanic_data")

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from langchain.indexes import VectorstoreIndexCreator from langchain_community.document_loaders import CSVLoader from langchain_community.vectorstores import FAISS loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv") docs = loader.load() index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS) index = index_creator.from_documents(docs) index.vectorstore.save_local("titanic_data")

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from langchain.indexes import VectorstoreIndexCreator from langchain_community.document_loaders import CSVLoader from langchain_community.vectorstores import FAISS loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv") docs = loader.load() index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS) index = index_creator.from_documents(docs) index.vectorstore.save_local("titanic_data")

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from typing import Any, Dict, List, Type, Union from langchain_community.graphs import NetworkxEntityGraph from langchain_community.graphs.networkx_graph import ( KnowledgeTriple, get_entities, parse_triples, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key class ConversationKGMemory(BaseChatMemory): """Knowledge graph conversation memory. Integrates with external knowledge graph to store and retrieve information about knowledge triples in the conversation. """ k: int = 2 human_prefix: str = "Human" ai_prefix: str = "AI" kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph) knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT llm: BaseLanguageModel summary_message_cls: Type[BaseMessage] = SystemMessage """Number of previous utterances to include in the context.""" memory_key: str = "history" #: :meta private: def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" entities = self._get_current_entities(inputs) summary_strings = [] for entity in entities: knowledge = self.kg.get_entity_knowledge(entity) if knowledge: summary = f"On {entity}: {'. '.join(knowledge)}." summary_strings.append(summary) context: Union[str, List] if not summary_strings: context = [] if self.return_messages else "" elif self.return_messages: context = [ self.summary_message_cls(content=text) for text in summary_strings ] else: context = "\n".join(summary_strings) return {self.memory_key: context} @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str: """Get the input key for the prompt.""" if self.input_key is None: return get_prompt_input_key(inputs, self.memory_variables) return self.input_key def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str: """Get the output key for the prompt.""" if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") return list(outputs.keys())[0] return self.output_key def get_current_entities(self, input_string: str) -> List[str]: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, ) return get_entities(output) def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]: """Get the current entities in the conversation.""" prompt_input_key = self._get_prompt_input_key(inputs) return self.get_current_entities(inputs[prompt_input_key]) def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]: chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, verbose=True, ) knowledge = parse_triples(output) return knowledge def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None: """Get and update knowledge graph from the conversation history.""" prompt_input_key = self._get_prompt_input_key(inputs) knowledge = self.get_knowledge_triplets(inputs[prompt_input_key]) for triple in knowledge: self.kg.add_triple(triple) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" super().save_context(inputs, outputs) self._get_and_update_kg(inputs) def clear(self) -> None: """Clear memory contents.""" super().clear() self.kg.clear()

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from typing import Any, Dict, List, Type, Union from langchain_community.graphs import NetworkxEntityGraph from langchain_community.graphs.networkx_graph import ( KnowledgeTriple, get_entities, parse_triples, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key class ConversationKGMemory(BaseChatMemory): """Knowledge graph conversation memory. Integrates with external knowledge graph to store and retrieve information about knowledge triples in the conversation. """ k: int = 2 human_prefix: str = "Human" ai_prefix: str = "AI" kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph) knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT llm: BaseLanguageModel summary_message_cls: Type[BaseMessage] = SystemMessage """Number of previous utterances to include in the context.""" memory_key: str = "history" #: :meta private: def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" entities = self._get_current_entities(inputs) summary_strings = [] for entity in entities: knowledge = self.kg.get_entity_knowledge(entity) if knowledge: summary = f"On {entity}: {'. '.join(knowledge)}." summary_strings.append(summary) context: Union[str, List] if not summary_strings: context = [] if self.return_messages else "" elif self.return_messages: context = [ self.summary_message_cls(content=text) for text in summary_strings ] else: context = "\n".join(summary_strings) return {self.memory_key: context} @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str: """Get the input key for the prompt.""" if self.input_key is None: return get_prompt_input_key(inputs, self.memory_variables) return self.input_key def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str: """Get the output key for the prompt.""" if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") return list(outputs.keys())[0] return self.output_key def get_current_entities(self, input_string: str) -> List[str]: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, ) return get_entities(output) def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]: """Get the current entities in the conversation.""" prompt_input_key = self._get_prompt_input_key(inputs) return self.get_current_entities(inputs[prompt_input_key]) def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]: chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, verbose=True, ) knowledge = parse_triples(output) return knowledge def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None: """Get and update knowledge graph from the conversation history.""" prompt_input_key = self._get_prompt_input_key(inputs) knowledge = self.get_knowledge_triplets(inputs[prompt_input_key]) for triple in knowledge: self.kg.add_triple(triple) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" super().save_context(inputs, outputs) self._get_and_update_kg(inputs) def clear(self) -> None: """Clear memory contents.""" super().clear() self.kg.clear()

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from typing import Any, Dict, List, Type, Union from langchain_community.graphs import NetworkxEntityGraph from langchain_community.graphs.networkx_graph import ( KnowledgeTriple, get_entities, parse_triples, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key class ConversationKGMemory(BaseChatMemory): """Knowledge graph conversation memory. Integrates with external knowledge graph to store and retrieve information about knowledge triples in the conversation. """ k: int = 2 human_prefix: str = "Human" ai_prefix: str = "AI" kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph) knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT llm: BaseLanguageModel summary_message_cls: Type[BaseMessage] = SystemMessage """Number of previous utterances to include in the context.""" memory_key: str = "history" #: :meta private: def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" entities = self._get_current_entities(inputs) summary_strings = [] for entity in entities: knowledge = self.kg.get_entity_knowledge(entity) if knowledge: summary = f"On {entity}: {'. '.join(knowledge)}." summary_strings.append(summary) context: Union[str, List] if not summary_strings: context = [] if self.return_messages else "" elif self.return_messages: context = [ self.summary_message_cls(content=text) for text in summary_strings ] else: context = "\n".join(summary_strings) return {self.memory_key: context} @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str: """Get the input key for the prompt.""" if self.input_key is None: return get_prompt_input_key(inputs, self.memory_variables) return self.input_key def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str: """Get the output key for the prompt.""" if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") return list(outputs.keys())[0] return self.output_key def get_current_entities(self, input_string: str) -> List[str]: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, ) return get_entities(output) def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]: """Get the current entities in the conversation.""" prompt_input_key = self._get_prompt_input_key(inputs) return self.get_current_entities(inputs[prompt_input_key]) def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]: chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, verbose=True, ) knowledge = parse_triples(output) return knowledge def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None: """Get and update knowledge graph from the conversation history.""" prompt_input_key = self._get_prompt_input_key(inputs) knowledge = self.get_knowledge_triplets(inputs[prompt_input_key]) for triple in knowledge: self.kg.add_triple(triple) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" super().save_context(inputs, outputs) self._get_and_update_kg(inputs) def clear(self) -> None: """Clear memory contents.""" super().clear() self.kg.clear()

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from typing import Any, Dict, List, Type, Union from langchain_community.graphs import NetworkxEntityGraph from langchain_community.graphs.networkx_graph import ( KnowledgeTriple, get_entities, parse_triples, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key class ConversationKGMemory(BaseChatMemory): """Knowledge graph conversation memory. Integrates with external knowledge graph to store and retrieve information about knowledge triples in the conversation. """ k: int = 2 human_prefix: str = "Human" ai_prefix: str = "AI" kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph) knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT llm: BaseLanguageModel summary_message_cls: Type[BaseMessage] = SystemMessage """Number of previous utterances to include in the context.""" memory_key: str = "history" #: :meta private: def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" entities = self._get_current_entities(inputs) summary_strings = [] for entity in entities: knowledge = self.kg.get_entity_knowledge(entity) if knowledge: summary = f"On {entity}: {'. '.join(knowledge)}." summary_strings.append(summary) context: Union[str, List] if not summary_strings: context = [] if self.return_messages else "" elif self.return_messages: context = [ self.summary_message_cls(content=text) for text in summary_strings ] else: context = "\n".join(summary_strings) return {self.memory_key: context} @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str: """Get the input key for the prompt.""" if self.input_key is None: return get_prompt_input_key(inputs, self.memory_variables) return self.input_key def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str: """Get the output key for the prompt.""" if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") return list(outputs.keys())[0] return self.output_key def get_current_entities(self, input_string: str) -> List[str]: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, ) return get_entities(output) def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]: """Get the current entities in the conversation.""" prompt_input_key = self._get_prompt_input_key(inputs) return self.get_current_entities(inputs[prompt_input_key]) def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]: chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt) buffer_string = get_buffer_string( self.chat_memory.messages[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) output = chain.predict( history=buffer_string, input=input_string, verbose=True, ) knowledge = parse_triples(output) return knowledge def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None: """Get and update knowledge graph from the conversation history.""" prompt_input_key = self._get_prompt_input_key(inputs) knowledge = self.get_knowledge_triplets(inputs[prompt_input_key]) for triple in knowledge: self.kg.add_triple(triple) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" super().save_context(inputs, outputs) self._get_and_update_kg(inputs) def clear(self) -> None: """Clear memory contents.""" super().clear() self.kg.clear()

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""" **LLM** classes provide access to the large language model (**LLM**) APIs and services. **Class hierarchy:** .. code-block:: BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI **Main helpers:** .. code-block:: LLMResult, PromptValue, CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun, CallbackManager, AsyncCallbackManager, AIMessage, BaseMessage """ # noqa: E501 import warnings from typing import Any, Callable, Dict, Type from langchain_core._api import LangChainDeprecationWarning from langchain_core.language_models.llms import BaseLLM from langchain.utils.interactive_env import is_interactive_env def _import_ai21() -> Any: from langchain_community.llms.ai21 import AI21 return AI21 def _import_aleph_alpha() -> Any: from langchain_community.llms.aleph_alpha import AlephAlpha return AlephAlpha def _import_amazon_api_gateway() -> Any: from langchain_community.llms.amazon_api_gateway import AmazonAPIGateway return AmazonAPIGateway def _import_anthropic() -> Any: from langchain_community.llms.anthropic import Anthropic return Anthropic def _import_anyscale() -> Any: from langchain_community.llms.anyscale import Anyscale return Anyscale def _import_arcee() -> Any: from langchain_community.llms.arcee import Arcee return Arcee def _import_aviary() -> Any: from langchain_community.llms.aviary import Aviary return Aviary def _import_azureml_endpoint() -> Any: from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint return AzureMLOnlineEndpoint def _import_baidu_qianfan_endpoint() -> Any: from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint return QianfanLLMEndpoint def _import_bananadev() -> Any: from langchain_community.llms.bananadev import Banana return Banana def _import_baseten() -> Any: from langchain_community.llms.baseten import Baseten return Baseten def _import_beam() -> Any: from langchain_community.llms.beam import Beam return Beam def _import_bedrock() -> Any: from langchain_community.llms.bedrock import Bedrock return Bedrock def _import_bittensor() -> Any: from langchain_community.llms.bittensor import NIBittensorLLM return NIBittensorLLM def _import_cerebriumai() -> Any: from langchain_community.llms.cerebriumai import CerebriumAI return CerebriumAI def _import_chatglm() -> Any: from langchain_community.llms.chatglm import ChatGLM return ChatGLM def _import_clarifai() -> Any: from langchain_community.llms.clarifai import Clarifai return Clarifai def _import_cohere() -> Any: from langchain_community.llms.cohere import Cohere return Cohere def _import_ctransformers() -> Any: from langchain_community.llms.ctransformers import CTransformers return CTransformers def _import_ctranslate2() -> Any: from langchain_community.llms.ctranslate2 import CTranslate2 return CTranslate2 def _import_databricks() -> Any: from langchain_community.llms.databricks import Databricks return Databricks def _import_databricks_chat() -> Any: from langchain_community.chat_models.databricks import ChatDatabricks return ChatDatabricks def _import_deepinfra() -> Any: from langchain_community.llms.deepinfra import DeepInfra return DeepInfra def _import_deepsparse() -> Any: from langchain_community.llms.deepsparse import DeepSparse return DeepSparse def _import_edenai() -> Any: from langchain_community.llms.edenai import EdenAI return EdenAI def _import_fake() -> Any: from langchain_community.llms.fake import FakeListLLM return FakeListLLM def _import_fireworks() -> Any: from langchain_community.llms.fireworks import Fireworks return Fireworks def _import_forefrontai() -> Any: from langchain_community.llms.forefrontai import ForefrontAI return ForefrontAI def _import_gigachat() -> Any: from langchain_community.llms.gigachat import GigaChat return GigaChat def _import_google_palm() -> Any: from langchain_community.llms.google_palm import GooglePalm return GooglePalm def _import_gooseai() -> Any: from langchain_community.llms.gooseai import GooseAI return GooseAI def _import_gpt4all() -> Any: from langchain_community.llms.gpt4all import GPT4All return GPT4All def _import_gradient_ai() -> Any: from langchain_community.llms.gradient_ai import GradientLLM return GradientLLM def _import_huggingface_endpoint() -> Any: from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint return HuggingFaceEndpoint def _import_huggingface_hub() -> Any: from langchain_community.llms.huggingface_hub import HuggingFaceHub return HuggingFaceHub def _import_huggingface_pipeline() -> Any: from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline return HuggingFacePipeline def _import_huggingface_text_gen_inference() -> Any: from langchain_community.llms.huggingface_text_gen_inference import ( HuggingFaceTextGenInference, ) return HuggingFaceTextGenInference def _import_human() -> Any: from langchain_community.llms.human import HumanInputLLM return HumanInputLLM def _import_javelin_ai_gateway() -> Any: from langchain_community.llms.javelin_ai_gateway import JavelinAIGateway return JavelinAIGateway def _import_koboldai() -> Any: from langchain_community.llms.koboldai import KoboldApiLLM return KoboldApiLLM def _import_llamacpp() -> Any: from langchain_community.llms.llamacpp import LlamaCpp return LlamaCpp def _import_manifest() -> Any: from langchain_community.llms.manifest import ManifestWrapper return ManifestWrapper def _import_minimax() -> Any: from langchain_community.llms.minimax import Minimax return Minimax def _import_mlflow() -> Any: from langchain_community.llms.mlflow import Mlflow return Mlflow def _import_mlflow_chat() -> Any: from langchain_community.chat_models.mlflow import ChatMlflow return ChatMlflow def _import_mlflow_ai_gateway() -> Any: from langchain_community.llms.mlflow_ai_gateway import MlflowAIGateway return MlflowAIGateway def _import_modal() -> Any: from langchain_community.llms.modal import Modal return Modal def _import_mosaicml() -> Any: from langchain_community.llms.mosaicml import MosaicML return MosaicML def _import_nlpcloud() -> Any: from langchain_community.llms.nlpcloud import NLPCloud return NLPCloud def _import_octoai_endpoint() -> Any: from langchain_community.llms.octoai_endpoint import OctoAIEndpoint return OctoAIEndpoint def _import_ollama() -> Any: from langchain_community.llms.ollama import Ollama return Ollama def _import_opaqueprompts() -> Any: from langchain_community.llms.opaqueprompts import OpaquePrompts return OpaquePrompts def _import_azure_openai() -> Any: from langchain_community.llms.openai import AzureOpenAI return AzureOpenAI def _import_openai() -> Any: from langchain_community.llms.openai import OpenAI return OpenAI def _import_openai_chat() -> Any: from langchain_community.llms.openai import OpenAIChat return OpenAIChat def _import_openllm() -> Any: from langchain_community.llms.openllm import OpenLLM return OpenLLM def _import_openlm() -> Any: from langchain_community.llms.openlm import OpenLM return OpenLM def _import_pai_eas_endpoint() -> Any: from langchain_community.llms.pai_eas_endpoint import PaiEasEndpoint return PaiEasEndpoint def _import_petals() -> Any: from langchain_community.llms.petals import Petals return Petals def _import_pipelineai() -> Any: from langchain_community.llms.pipelineai import PipelineAI return PipelineAI def _import_predibase() -> Any: from langchain_community.llms.predibase import Predibase return Predibase def _import_predictionguard() -> Any: from langchain_community.llms.predictionguard import PredictionGuard return PredictionGuard def _import_promptlayer() -> Any: from langchain_community.llms.promptlayer_openai import PromptLayerOpenAI return PromptLayerOpenAI def _import_promptlayer_chat() -> Any: from langchain_community.llms.promptlayer_openai import PromptLayerOpenAIChat return PromptLayerOpenAIChat def _import_replicate() -> Any: from langchain_community.llms.replicate import Replicate return Replicate def _import_rwkv() -> Any: from langchain_community.llms.rwkv import RWKV return RWKV def _import_sagemaker_endpoint() -> Any: from langchain_community.llms.sagemaker_endpoint import SagemakerEndpoint return SagemakerEndpoint def _import_self_hosted() -> Any: from langchain_community.llms.self_hosted import SelfHostedPipeline return SelfHostedPipeline def _import_self_hosted_hugging_face() -> Any: from langchain_community.llms.self_hosted_hugging_face import ( SelfHostedHuggingFaceLLM, ) return SelfHostedHuggingFaceLLM def _import_stochasticai() -> Any: from langchain_community.llms.stochasticai import StochasticAI return StochasticAI def _import_symblai_nebula() -> Any: from langchain_community.llms.symblai_nebula import Nebula return Nebula def _import_textgen() -> Any: from langchain_community.llms.textgen import TextGen return TextGen def _import_titan_takeoff() -> Any: from langchain_community.llms.titan_takeoff import TitanTakeoff return TitanTakeoff def _import_titan_takeoff_pro() -> Any: from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro return TitanTakeoffPro def _import_together() -> Any: from langchain_community.llms.together import Together return Together def _import_tongyi() -> Any: from langchain_community.llms.tongyi import Tongyi return Tongyi def _import_vertex() -> Any: from langchain_community.llms.vertexai import VertexAI return VertexAI def _import_vertex_model_garden() -> Any: from langchain_community.llms.vertexai import VertexAIModelGarden return VertexAIModelGarden def _import_vllm() -> Any: from langchain_community.llms.vllm import VLLM return VLLM def _import_vllm_openai() -> Any: from langchain_community.llms.vllm import VLLMOpenAI return VLLMOpenAI def _import_watsonxllm() -> Any: from langchain_community.llms.watsonxllm import WatsonxLLM return WatsonxLLM def _import_writer() -> Any: from langchain_community.llms.writer import Writer return Writer def _import_xinference() -> Any: from langchain_community.llms.xinference import Xinference return Xinference def _import_yandex_gpt() -> Any: from langchain_community.llms.yandex import YandexGPT return YandexGPT def _import_volcengine_maas() -> Any: from langchain_community.llms.volcengine_maas import VolcEngineMaasLLM return VolcEngineMaasLLM def __getattr__(name: str) -> Any: from langchain_community import llms # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing LLMs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.llms import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) if name == "type_to_cls_dict": # for backwards compatibility type_to_cls_dict: Dict[str, Type[BaseLLM]] = { k: v() for k, v in get_type_to_cls_dict().items() } return type_to_cls_dict else: return getattr(llms, name) __all__ = [ "AI21", "AlephAlpha", "AmazonAPIGateway", "Anthropic", "Anyscale", "Arcee", "Aviary", "AzureMLOnlineEndpoint", "AzureOpenAI", "Banana", "Baseten", "Beam", "Bedrock", "CTransformers", "CTranslate2", "CerebriumAI", "ChatGLM", "Clarifai", "Cohere", "Databricks", "DeepInfra", "DeepSparse", "EdenAI", "FakeListLLM", "Fireworks", "ForefrontAI", "GigaChat", "GPT4All", "GooglePalm", "GooseAI", "GradientLLM", "HuggingFaceEndpoint", "HuggingFaceHub", "HuggingFacePipeline", "HuggingFaceTextGenInference", "HumanInputLLM", "KoboldApiLLM", "LlamaCpp", "TextGen", "ManifestWrapper", "Minimax", "MlflowAIGateway", "Modal", "MosaicML", "Nebula", "NIBittensorLLM", "NLPCloud", "Ollama", "OpenAI", "OpenAIChat", "OpenLLM", "OpenLM", "PaiEasEndpoint", "Petals", "PipelineAI", "Predibase", "PredictionGuard", "PromptLayerOpenAI", "PromptLayerOpenAIChat", "OpaquePrompts", "RWKV", "Replicate", "SagemakerEndpoint", "SelfHostedHuggingFaceLLM", "SelfHostedPipeline", "StochasticAI", "TitanTakeoff", "TitanTakeoffPro", "Tongyi", "VertexAI", "VertexAIModelGarden", "VLLM", "VLLMOpenAI", "WatsonxLLM", "Writer", "OctoAIEndpoint", "Xinference", "JavelinAIGateway", "QianfanLLMEndpoint", "YandexGPT", "VolcEngineMaasLLM", ] def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]: return { "ai21": _import_ai21, "aleph_alpha": _import_aleph_alpha, "amazon_api_gateway": _import_amazon_api_gateway, "amazon_bedrock": _import_bedrock, "anthropic": _import_anthropic, "anyscale": _import_anyscale, "arcee": _import_arcee, "aviary": _import_aviary, "azure": _import_azure_openai, "azureml_endpoint": _import_azureml_endpoint, "bananadev": _import_bananadev, "baseten": _import_baseten, "beam": _import_beam, "cerebriumai": _import_cerebriumai, "chat_glm": _import_chatglm, "clarifai": _import_clarifai, "cohere": _import_cohere, "ctransformers": _import_ctransformers, "ctranslate2": _import_ctranslate2, "databricks": _import_databricks, "databricks-chat": _import_databricks_chat, "deepinfra": _import_deepinfra, "deepsparse": _import_deepsparse, "edenai": _import_edenai, "fake-list": _import_fake, "forefrontai": _import_forefrontai, "giga-chat-model": _import_gigachat, "google_palm": _import_google_palm, "gooseai": _import_gooseai, "gradient": _import_gradient_ai, "gpt4all": _import_gpt4all, "huggingface_endpoint": _import_huggingface_endpoint, "huggingface_hub": _import_huggingface_hub, "huggingface_pipeline": _import_huggingface_pipeline, "huggingface_textgen_inference": _import_huggingface_text_gen_inference, "human-input": _import_human, "koboldai": _import_koboldai, "llamacpp": _import_llamacpp, "textgen": _import_textgen, "minimax": _import_minimax, "mlflow": _import_mlflow, "mlflow-chat": _import_mlflow_chat, "mlflow-ai-gateway": _import_mlflow_ai_gateway, "modal": _import_modal, "mosaic": _import_mosaicml, "nebula": _import_symblai_nebula, "nibittensor": _import_bittensor, "nlpcloud": _import_nlpcloud, "ollama": _import_ollama, "openai": _import_openai, "openlm": _import_openlm, "pai_eas_endpoint": _import_pai_eas_endpoint, "petals": _import_petals, "pipelineai": _import_pipelineai, "predibase": _import_predibase, "opaqueprompts": _import_opaqueprompts, "replicate": _import_replicate, "rwkv": _import_rwkv, "sagemaker_endpoint": _import_sagemaker_endpoint, "self_hosted": _import_self_hosted, "self_hosted_hugging_face": _import_self_hosted_hugging_face, "stochasticai": _import_stochasticai, "together": _import_together, "tongyi": _import_tongyi, "titan_takeoff": _import_titan_takeoff, "titan_takeoff_pro": _import_titan_takeoff_pro, "vertexai": _import_vertex, "vertexai_model_garden": _import_vertex_model_garden, "openllm": _import_openllm, "openllm_client": _import_openllm, "vllm": _import_vllm, "vllm_openai": _import_vllm_openai, "watsonxllm": _import_watsonxllm, "writer": _import_writer, "xinference": _import_xinference, "javelin-ai-gateway": _import_javelin_ai_gateway, "qianfan_endpoint": _import_baidu_qianfan_endpoint, "yandex_gpt": _import_yandex_gpt, "VolcEngineMaasLLM": _import_volcengine_maas, }

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""" **LLM** classes provide access to the large language model (**LLM**) APIs and services. **Class hierarchy:** .. code-block:: BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI **Main helpers:** .. code-block:: LLMResult, PromptValue, CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun, CallbackManager, AsyncCallbackManager, AIMessage, BaseMessage """ # noqa: E501 import warnings from typing import Any, Callable, Dict, Type from langchain_core._api import LangChainDeprecationWarning from langchain_core.language_models.llms import BaseLLM from langchain.utils.interactive_env import is_interactive_env def _import_ai21() -> Any: from langchain_community.llms.ai21 import AI21 return AI21 def _import_aleph_alpha() -> Any: from langchain_community.llms.aleph_alpha import AlephAlpha return AlephAlpha def _import_amazon_api_gateway() -> Any: from langchain_community.llms.amazon_api_gateway import AmazonAPIGateway return AmazonAPIGateway def _import_anthropic() -> Any: from langchain_community.llms.anthropic import Anthropic return Anthropic def _import_anyscale() -> Any: from langchain_community.llms.anyscale import Anyscale return Anyscale def _import_arcee() -> Any: from langchain_community.llms.arcee import Arcee return Arcee def _import_aviary() -> Any: from langchain_community.llms.aviary import Aviary return Aviary def _import_azureml_endpoint() -> Any: from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint return AzureMLOnlineEndpoint def _import_baidu_qianfan_endpoint() -> Any: from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint return QianfanLLMEndpoint def _import_bananadev() -> Any: from langchain_community.llms.bananadev import Banana return Banana def _import_baseten() -> Any: from langchain_community.llms.baseten import Baseten return Baseten def _import_beam() -> Any: from langchain_community.llms.beam import Beam return Beam def _import_bedrock() -> Any: from langchain_community.llms.bedrock import Bedrock return Bedrock def _import_bittensor() -> Any: from langchain_community.llms.bittensor import NIBittensorLLM return NIBittensorLLM def _import_cerebriumai() -> Any: from langchain_community.llms.cerebriumai import CerebriumAI return CerebriumAI def _import_chatglm() -> Any: from langchain_community.llms.chatglm import ChatGLM return ChatGLM def _import_clarifai() -> Any: from langchain_community.llms.clarifai import Clarifai return Clarifai def _import_cohere() -> Any: from langchain_community.llms.cohere import Cohere return Cohere def _import_ctransformers() -> Any: from langchain_community.llms.ctransformers import CTransformers return CTransformers def _import_ctranslate2() -> Any: from langchain_community.llms.ctranslate2 import CTranslate2 return CTranslate2 def _import_databricks() -> Any: from langchain_community.llms.databricks import Databricks return Databricks def _import_databricks_chat() -> Any: from langchain_community.chat_models.databricks import ChatDatabricks return ChatDatabricks def _import_deepinfra() -> Any: from langchain_community.llms.deepinfra import DeepInfra return DeepInfra def _import_deepsparse() -> Any: from langchain_community.llms.deepsparse import DeepSparse return DeepSparse def _import_edenai() -> Any: from langchain_community.llms.edenai import EdenAI return EdenAI def _import_fake() -> Any: from langchain_community.llms.fake import FakeListLLM return FakeListLLM def _import_fireworks() -> Any: from langchain_community.llms.fireworks import Fireworks return Fireworks def _import_forefrontai() -> Any: from langchain_community.llms.forefrontai import ForefrontAI return ForefrontAI def _import_gigachat() -> Any: from langchain_community.llms.gigachat import GigaChat return GigaChat def _import_google_palm() -> Any: from langchain_community.llms.google_palm import GooglePalm return GooglePalm def _import_gooseai() -> Any: from langchain_community.llms.gooseai import GooseAI return GooseAI def _import_gpt4all() -> Any: from langchain_community.llms.gpt4all import GPT4All return GPT4All def _import_gradient_ai() -> Any: from langchain_community.llms.gradient_ai import GradientLLM return GradientLLM def _import_huggingface_endpoint() -> Any: from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint return HuggingFaceEndpoint def _import_huggingface_hub() -> Any: from langchain_community.llms.huggingface_hub import HuggingFaceHub return HuggingFaceHub def _import_huggingface_pipeline() -> Any: from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline return HuggingFacePipeline def _import_huggingface_text_gen_inference() -> Any: from langchain_community.llms.huggingface_text_gen_inference import ( HuggingFaceTextGenInference, ) return HuggingFaceTextGenInference def _import_human() -> Any: from langchain_community.llms.human import HumanInputLLM return HumanInputLLM def _import_javelin_ai_gateway() -> Any: from langchain_community.llms.javelin_ai_gateway import JavelinAIGateway return JavelinAIGateway def _import_koboldai() -> Any: from langchain_community.llms.koboldai import KoboldApiLLM return KoboldApiLLM def _import_llamacpp() -> Any: from langchain_community.llms.llamacpp import LlamaCpp return LlamaCpp def _import_manifest() -> Any: from langchain_community.llms.manifest import ManifestWrapper return ManifestWrapper def _import_minimax() -> Any: from langchain_community.llms.minimax import Minimax return Minimax def _import_mlflow() -> Any: from langchain_community.llms.mlflow import Mlflow return Mlflow def _import_mlflow_chat() -> Any: from langchain_community.chat_models.mlflow import ChatMlflow return ChatMlflow def _import_mlflow_ai_gateway() -> Any: from langchain_community.llms.mlflow_ai_gateway import MlflowAIGateway return MlflowAIGateway def _import_modal() -> Any: from langchain_community.llms.modal import Modal return Modal def _import_mosaicml() -> Any: from langchain_community.llms.mosaicml import MosaicML return MosaicML def _import_nlpcloud() -> Any: from langchain_community.llms.nlpcloud import NLPCloud return NLPCloud def _import_octoai_endpoint() -> Any: from langchain_community.llms.octoai_endpoint import OctoAIEndpoint return OctoAIEndpoint def _import_ollama() -> Any: from langchain_community.llms.ollama import Ollama return Ollama def _import_opaqueprompts() -> Any: from langchain_community.llms.opaqueprompts import OpaquePrompts return OpaquePrompts def _import_azure_openai() -> Any: from langchain_community.llms.openai import AzureOpenAI return AzureOpenAI def _import_openai() -> Any: from langchain_community.llms.openai import OpenAI return OpenAI def _import_openai_chat() -> Any: from langchain_community.llms.openai import OpenAIChat return OpenAIChat def _import_openllm() -> Any: from langchain_community.llms.openllm import OpenLLM return OpenLLM def _import_openlm() -> Any: from langchain_community.llms.openlm import OpenLM return OpenLM def _import_pai_eas_endpoint() -> Any: from langchain_community.llms.pai_eas_endpoint import PaiEasEndpoint return PaiEasEndpoint def _import_petals() -> Any: from langchain_community.llms.petals import Petals return Petals def _import_pipelineai() -> Any: from langchain_community.llms.pipelineai import PipelineAI return PipelineAI def _import_predibase() -> Any: from langchain_community.llms.predibase import Predibase return Predibase def _import_predictionguard() -> Any: from langchain_community.llms.predictionguard import PredictionGuard return PredictionGuard def _import_promptlayer() -> Any: from langchain_community.llms.promptlayer_openai import PromptLayerOpenAI return PromptLayerOpenAI def _import_promptlayer_chat() -> Any: from langchain_community.llms.promptlayer_openai import PromptLayerOpenAIChat return PromptLayerOpenAIChat def _import_replicate() -> Any: from langchain_community.llms.replicate import Replicate return Replicate def _import_rwkv() -> Any: from langchain_community.llms.rwkv import RWKV return RWKV def _import_sagemaker_endpoint() -> Any: from langchain_community.llms.sagemaker_endpoint import SagemakerEndpoint return SagemakerEndpoint def _import_self_hosted() -> Any: from langchain_community.llms.self_hosted import SelfHostedPipeline return SelfHostedPipeline def _import_self_hosted_hugging_face() -> Any: from langchain_community.llms.self_hosted_hugging_face import ( SelfHostedHuggingFaceLLM, ) return SelfHostedHuggingFaceLLM def _import_stochasticai() -> Any: from langchain_community.llms.stochasticai import StochasticAI return StochasticAI def _import_symblai_nebula() -> Any: from langchain_community.llms.symblai_nebula import Nebula return Nebula def _import_textgen() -> Any: from langchain_community.llms.textgen import TextGen return TextGen def _import_titan_takeoff() -> Any: from langchain_community.llms.titan_takeoff import TitanTakeoff return TitanTakeoff def _import_titan_takeoff_pro() -> Any: from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro return TitanTakeoffPro def _import_together() -> Any: from langchain_community.llms.together import Together return Together def _import_tongyi() -> Any: from langchain_community.llms.tongyi import Tongyi return Tongyi def _import_vertex() -> Any: from langchain_community.llms.vertexai import VertexAI return VertexAI def _import_vertex_model_garden() -> Any: from langchain_community.llms.vertexai import VertexAIModelGarden return VertexAIModelGarden def _import_vllm() -> Any: from langchain_community.llms.vllm import VLLM return VLLM def _import_vllm_openai() -> Any: from langchain_community.llms.vllm import VLLMOpenAI return VLLMOpenAI def _import_watsonxllm() -> Any: from langchain_community.llms.watsonxllm import WatsonxLLM return WatsonxLLM def _import_writer() -> Any: from langchain_community.llms.writer import Writer return Writer def _import_xinference() -> Any: from langchain_community.llms.xinference import Xinference return Xinference def _import_yandex_gpt() -> Any: from langchain_community.llms.yandex import YandexGPT return YandexGPT def _import_volcengine_maas() -> Any: from langchain_community.llms.volcengine_maas import VolcEngineMaasLLM return VolcEngineMaasLLM def __getattr__(name: str) -> Any: from langchain_community import llms # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing LLMs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.llms import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) if name == "type_to_cls_dict": # for backwards compatibility type_to_cls_dict: Dict[str, Type[BaseLLM]] = { k: v() for k, v in get_type_to_cls_dict().items() } return type_to_cls_dict else: return getattr(llms, name) __all__ = [ "AI21", "AlephAlpha", "AmazonAPIGateway", "Anthropic", "Anyscale", "Arcee", "Aviary", "AzureMLOnlineEndpoint", "AzureOpenAI", "Banana", "Baseten", "Beam", "Bedrock", "CTransformers", "CTranslate2", "CerebriumAI", "ChatGLM", "Clarifai", "Cohere", "Databricks", "DeepInfra", "DeepSparse", "EdenAI", "FakeListLLM", "Fireworks", "ForefrontAI", "GigaChat", "GPT4All", "GooglePalm", "GooseAI", "GradientLLM", "HuggingFaceEndpoint", "HuggingFaceHub", "HuggingFacePipeline", "HuggingFaceTextGenInference", "HumanInputLLM", "KoboldApiLLM", "LlamaCpp", "TextGen", "ManifestWrapper", "Minimax", "MlflowAIGateway", "Modal", "MosaicML", "Nebula", "NIBittensorLLM", "NLPCloud", "Ollama", "OpenAI", "OpenAIChat", "OpenLLM", "OpenLM", "PaiEasEndpoint", "Petals", "PipelineAI", "Predibase", "PredictionGuard", "PromptLayerOpenAI", "PromptLayerOpenAIChat", "OpaquePrompts", "RWKV", "Replicate", "SagemakerEndpoint", "SelfHostedHuggingFaceLLM", "SelfHostedPipeline", "StochasticAI", "TitanTakeoff", "TitanTakeoffPro", "Tongyi", "VertexAI", "VertexAIModelGarden", "VLLM", "VLLMOpenAI", "WatsonxLLM", "Writer", "OctoAIEndpoint", "Xinference", "JavelinAIGateway", "QianfanLLMEndpoint", "YandexGPT", "VolcEngineMaasLLM", ] def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]: return { "ai21": _import_ai21, "aleph_alpha": _import_aleph_alpha, "amazon_api_gateway": _import_amazon_api_gateway, "amazon_bedrock": _import_bedrock, "anthropic": _import_anthropic, "anyscale": _import_anyscale, "arcee": _import_arcee, "aviary": _import_aviary, "azure": _import_azure_openai, "azureml_endpoint": _import_azureml_endpoint, "bananadev": _import_bananadev, "baseten": _import_baseten, "beam": _import_beam, "cerebriumai": _import_cerebriumai, "chat_glm": _import_chatglm, "clarifai": _import_clarifai, "cohere": _import_cohere, "ctransformers": _import_ctransformers, "ctranslate2": _import_ctranslate2, "databricks": _import_databricks, "databricks-chat": _import_databricks_chat, "deepinfra": _import_deepinfra, "deepsparse": _import_deepsparse, "edenai": _import_edenai, "fake-list": _import_fake, "forefrontai": _import_forefrontai, "giga-chat-model": _import_gigachat, "google_palm": _import_google_palm, "gooseai": _import_gooseai, "gradient": _import_gradient_ai, "gpt4all": _import_gpt4all, "huggingface_endpoint": _import_huggingface_endpoint, "huggingface_hub": _import_huggingface_hub, "huggingface_pipeline": _import_huggingface_pipeline, "huggingface_textgen_inference": _import_huggingface_text_gen_inference, "human-input": _import_human, "koboldai": _import_koboldai, "llamacpp": _import_llamacpp, "textgen": _import_textgen, "minimax": _import_minimax, "mlflow": _import_mlflow, "mlflow-chat": _import_mlflow_chat, "mlflow-ai-gateway": _import_mlflow_ai_gateway, "modal": _import_modal, "mosaic": _import_mosaicml, "nebula": _import_symblai_nebula, "nibittensor": _import_bittensor, "nlpcloud": _import_nlpcloud, "ollama": _import_ollama, "openai": _import_openai, "openlm": _import_openlm, "pai_eas_endpoint": _import_pai_eas_endpoint, "petals": _import_petals, "pipelineai": _import_pipelineai, "predibase": _import_predibase, "opaqueprompts": _import_opaqueprompts, "replicate": _import_replicate, "rwkv": _import_rwkv, "sagemaker_endpoint": _import_sagemaker_endpoint, "self_hosted": _import_self_hosted, "self_hosted_hugging_face": _import_self_hosted_hugging_face, "stochasticai": _import_stochasticai, "together": _import_together, "tongyi": _import_tongyi, "titan_takeoff": _import_titan_takeoff, "titan_takeoff_pro": _import_titan_takeoff_pro, "vertexai": _import_vertex, "vertexai_model_garden": _import_vertex_model_garden, "openllm": _import_openllm, "openllm_client": _import_openllm, "vllm": _import_vllm, "vllm_openai": _import_vllm_openai, "watsonxllm": _import_watsonxllm, "writer": _import_writer, "xinference": _import_xinference, "javelin-ai-gateway": _import_javelin_ai_gateway, "qianfan_endpoint": _import_baidu_qianfan_endpoint, "yandex_gpt": _import_yandex_gpt, "VolcEngineMaasLLM": _import_volcengine_maas, }

[((11338, 11358), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (11356, 11358), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((11368, 11729), 'warnings.warn', 'warnings.warn', (['f"""Importing LLMs from langchain is deprecated. Importing from langchain will no longer be supported as of langchain==0.2.0. Please import from langchain-community instead:\n\n`from langchain_community.llms import {name}`.\n\nTo install langchain-community run `pip install -U langchain-community`."""'], {'category': 'LangChainDeprecationWarning'}), '(\n f"""Importing LLMs from langchain is deprecated. Importing from langchain will no longer be supported as of langchain==0.2.0. Please import from langchain-community instead:\n\n`from langchain_community.llms import {name}`.\n\nTo install langchain-community run `pip install -U langchain-community`."""\n , category=LangChainDeprecationWarning)\n', (11381, 11729), False, 'import warnings\n')]

""" **LLM** classes provide access to the large language model (**LLM**) APIs and services. **Class hierarchy:** .. code-block:: BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI **Main helpers:** .. code-block:: LLMResult, PromptValue, CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun, CallbackManager, AsyncCallbackManager, AIMessage, BaseMessage """ # noqa: E501 import warnings from typing import Any, Callable, Dict, Type from langchain_core._api import LangChainDeprecationWarning from langchain_core.language_models.llms import BaseLLM from langchain.utils.interactive_env import is_interactive_env def _import_ai21() -> Any: from langchain_community.llms.ai21 import AI21 return AI21 def _import_aleph_alpha() -> Any: from langchain_community.llms.aleph_alpha import AlephAlpha return AlephAlpha def _import_amazon_api_gateway() -> Any: from langchain_community.llms.amazon_api_gateway import AmazonAPIGateway return AmazonAPIGateway def _import_anthropic() -> Any: from langchain_community.llms.anthropic import Anthropic return Anthropic def _import_anyscale() -> Any: from langchain_community.llms.anyscale import Anyscale return Anyscale def _import_arcee() -> Any: from langchain_community.llms.arcee import Arcee return Arcee def _import_aviary() -> Any: from langchain_community.llms.aviary import Aviary return Aviary def _import_azureml_endpoint() -> Any: from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint return AzureMLOnlineEndpoint def _import_baidu_qianfan_endpoint() -> Any: from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint return QianfanLLMEndpoint def _import_bananadev() -> Any: from langchain_community.llms.bananadev import Banana return Banana def _import_baseten() -> Any: from langchain_community.llms.baseten import Baseten return Baseten def _import_beam() -> Any: from langchain_community.llms.beam import Beam return Beam def _import_bedrock() -> Any: from langchain_community.llms.bedrock import Bedrock return Bedrock def _import_bittensor() -> Any: from langchain_community.llms.bittensor import NIBittensorLLM return NIBittensorLLM def _import_cerebriumai() -> Any: from langchain_community.llms.cerebriumai import CerebriumAI return CerebriumAI def _import_chatglm() -> Any: from langchain_community.llms.chatglm import ChatGLM return ChatGLM def _import_clarifai() -> Any: from langchain_community.llms.clarifai import Clarifai return Clarifai def _import_cohere() -> Any: from langchain_community.llms.cohere import Cohere return Cohere def _import_ctransformers() -> Any: from langchain_community.llms.ctransformers import CTransformers return CTransformers def _import_ctranslate2() -> Any: from langchain_community.llms.ctranslate2 import CTranslate2 return CTranslate2 def _import_databricks() -> Any: from langchain_community.llms.databricks import Databricks return Databricks def _import_databricks_chat() -> Any: from langchain_community.chat_models.databricks import ChatDatabricks return ChatDatabricks def _import_deepinfra() -> Any: from langchain_community.llms.deepinfra import DeepInfra return DeepInfra def _import_deepsparse() -> Any: from langchain_community.llms.deepsparse import DeepSparse return DeepSparse def _import_edenai() -> Any: from langchain_community.llms.edenai import EdenAI return EdenAI def _import_fake() -> Any: from langchain_community.llms.fake import FakeListLLM return FakeListLLM def _import_fireworks() -> Any: from langchain_community.llms.fireworks import Fireworks return Fireworks def _import_forefrontai() -> Any: from langchain_community.llms.forefrontai import ForefrontAI return ForefrontAI def _import_gigachat() -> Any: from langchain_community.llms.gigachat import GigaChat return GigaChat def _import_google_palm() -> Any: from langchain_community.llms.google_palm import GooglePalm return GooglePalm def _import_gooseai() -> Any: from langchain_community.llms.gooseai import GooseAI return GooseAI def _import_gpt4all() -> Any: from langchain_community.llms.gpt4all import GPT4All return GPT4All def _import_gradient_ai() -> Any: from langchain_community.llms.gradient_ai import GradientLLM return GradientLLM def _import_huggingface_endpoint() -> Any: from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint return HuggingFaceEndpoint def _import_huggingface_hub() -> Any: from langchain_community.llms.huggingface_hub import HuggingFaceHub return HuggingFaceHub def _import_huggingface_pipeline() -> Any: from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline return HuggingFacePipeline def _import_huggingface_text_gen_inference() -> Any: from langchain_community.llms.huggingface_text_gen_inference import ( HuggingFaceTextGenInference, ) return HuggingFaceTextGenInference def _import_human() -> Any: from langchain_community.llms.human import HumanInputLLM return HumanInputLLM def _import_javelin_ai_gateway() -> Any: from langchain_community.llms.javelin_ai_gateway import JavelinAIGateway return JavelinAIGateway def _import_koboldai() -> Any: from langchain_community.llms.koboldai import KoboldApiLLM return KoboldApiLLM def _import_llamacpp() -> Any: from langchain_community.llms.llamacpp import LlamaCpp return LlamaCpp def _import_manifest() -> Any: from langchain_community.llms.manifest import ManifestWrapper return ManifestWrapper def _import_minimax() -> Any: from langchain_community.llms.minimax import Minimax return Minimax def _import_mlflow() -> Any: from langchain_community.llms.mlflow import Mlflow return Mlflow def _import_mlflow_chat() -> Any: from langchain_community.chat_models.mlflow import ChatMlflow return ChatMlflow def _import_mlflow_ai_gateway() -> Any: from langchain_community.llms.mlflow_ai_gateway import MlflowAIGateway return MlflowAIGateway def _import_modal() -> Any: from langchain_community.llms.modal import Modal return Modal def _import_mosaicml() -> Any: from langchain_community.llms.mosaicml import MosaicML return MosaicML def _import_nlpcloud() -> Any: from langchain_community.llms.nlpcloud import NLPCloud return NLPCloud def _import_octoai_endpoint() -> Any: from langchain_community.llms.octoai_endpoint import OctoAIEndpoint return OctoAIEndpoint def _import_ollama() -> Any: from langchain_community.llms.ollama import Ollama return Ollama def _import_opaqueprompts() -> Any: from langchain_community.llms.opaqueprompts import OpaquePrompts return OpaquePrompts def _import_azure_openai() -> Any: from langchain_community.llms.openai import AzureOpenAI return AzureOpenAI def _import_openai() -> Any: from langchain_community.llms.openai import OpenAI return OpenAI def _import_openai_chat() -> Any: from langchain_community.llms.openai import OpenAIChat return OpenAIChat def _import_openllm() -> Any: from langchain_community.llms.openllm import OpenLLM return OpenLLM def _import_openlm() -> Any: from langchain_community.llms.openlm import OpenLM return OpenLM def _import_pai_eas_endpoint() -> Any: from langchain_community.llms.pai_eas_endpoint import PaiEasEndpoint return PaiEasEndpoint def _import_petals() -> Any: from langchain_community.llms.petals import Petals return Petals def _import_pipelineai() -> Any: from langchain_community.llms.pipelineai import PipelineAI return PipelineAI def _import_predibase() -> Any: from langchain_community.llms.predibase import Predibase return Predibase def _import_predictionguard() -> Any: from langchain_community.llms.predictionguard import PredictionGuard return PredictionGuard def _import_promptlayer() -> Any: from langchain_community.llms.promptlayer_openai import PromptLayerOpenAI return PromptLayerOpenAI def _import_promptlayer_chat() -> Any: from langchain_community.llms.promptlayer_openai import PromptLayerOpenAIChat return PromptLayerOpenAIChat def _import_replicate() -> Any: from langchain_community.llms.replicate import Replicate return Replicate def _import_rwkv() -> Any: from langchain_community.llms.rwkv import RWKV return RWKV def _import_sagemaker_endpoint() -> Any: from langchain_community.llms.sagemaker_endpoint import SagemakerEndpoint return SagemakerEndpoint def _import_self_hosted() -> Any: from langchain_community.llms.self_hosted import SelfHostedPipeline return SelfHostedPipeline def _import_self_hosted_hugging_face() -> Any: from langchain_community.llms.self_hosted_hugging_face import ( SelfHostedHuggingFaceLLM, ) return SelfHostedHuggingFaceLLM def _import_stochasticai() -> Any: from langchain_community.llms.stochasticai import StochasticAI return StochasticAI def _import_symblai_nebula() -> Any: from langchain_community.llms.symblai_nebula import Nebula return Nebula def _import_textgen() -> Any: from langchain_community.llms.textgen import TextGen return TextGen def _import_titan_takeoff() -> Any: from langchain_community.llms.titan_takeoff import TitanTakeoff return TitanTakeoff def _import_titan_takeoff_pro() -> Any: from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro return TitanTakeoffPro def _import_together() -> Any: from langchain_community.llms.together import Together return Together def _import_tongyi() -> Any: from langchain_community.llms.tongyi import Tongyi return Tongyi def _import_vertex() -> Any: from langchain_community.llms.vertexai import VertexAI return VertexAI def _import_vertex_model_garden() -> Any: from langchain_community.llms.vertexai import VertexAIModelGarden return VertexAIModelGarden def _import_vllm() -> Any: from langchain_community.llms.vllm import VLLM return VLLM def _import_vllm_openai() -> Any: from langchain_community.llms.vllm import VLLMOpenAI return VLLMOpenAI def _import_watsonxllm() -> Any: from langchain_community.llms.watsonxllm import WatsonxLLM return WatsonxLLM def _import_writer() -> Any: from langchain_community.llms.writer import Writer return Writer def _import_xinference() -> Any: from langchain_community.llms.xinference import Xinference return Xinference def _import_yandex_gpt() -> Any: from langchain_community.llms.yandex import YandexGPT return YandexGPT def _import_volcengine_maas() -> Any: from langchain_community.llms.volcengine_maas import VolcEngineMaasLLM return VolcEngineMaasLLM def __getattr__(name: str) -> Any: from langchain_community import llms # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing LLMs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.llms import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) if name == "type_to_cls_dict": # for backwards compatibility type_to_cls_dict: Dict[str, Type[BaseLLM]] = { k: v() for k, v in get_type_to_cls_dict().items() } return type_to_cls_dict else: return getattr(llms, name) __all__ = [ "AI21", "AlephAlpha", "AmazonAPIGateway", "Anthropic", "Anyscale", "Arcee", "Aviary", "AzureMLOnlineEndpoint", "AzureOpenAI", "Banana", "Baseten", "Beam", "Bedrock", "CTransformers", "CTranslate2", "CerebriumAI", "ChatGLM", "Clarifai", "Cohere", "Databricks", "DeepInfra", "DeepSparse", "EdenAI", "FakeListLLM", "Fireworks", "ForefrontAI", "GigaChat", "GPT4All", "GooglePalm", "GooseAI", "GradientLLM", "HuggingFaceEndpoint", "HuggingFaceHub", "HuggingFacePipeline", "HuggingFaceTextGenInference", "HumanInputLLM", "KoboldApiLLM", "LlamaCpp", "TextGen", "ManifestWrapper", "Minimax", "MlflowAIGateway", "Modal", "MosaicML", "Nebula", "NIBittensorLLM", "NLPCloud", "Ollama", "OpenAI", "OpenAIChat", "OpenLLM", "OpenLM", "PaiEasEndpoint", "Petals", "PipelineAI", "Predibase", "PredictionGuard", "PromptLayerOpenAI", "PromptLayerOpenAIChat", "OpaquePrompts", "RWKV", "Replicate", "SagemakerEndpoint", "SelfHostedHuggingFaceLLM", "SelfHostedPipeline", "StochasticAI", "TitanTakeoff", "TitanTakeoffPro", "Tongyi", "VertexAI", "VertexAIModelGarden", "VLLM", "VLLMOpenAI", "WatsonxLLM", "Writer", "OctoAIEndpoint", "Xinference", "JavelinAIGateway", "QianfanLLMEndpoint", "YandexGPT", "VolcEngineMaasLLM", ] def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]: return { "ai21": _import_ai21, "aleph_alpha": _import_aleph_alpha, "amazon_api_gateway": _import_amazon_api_gateway, "amazon_bedrock": _import_bedrock, "anthropic": _import_anthropic, "anyscale": _import_anyscale, "arcee": _import_arcee, "aviary": _import_aviary, "azure": _import_azure_openai, "azureml_endpoint": _import_azureml_endpoint, "bananadev": _import_bananadev, "baseten": _import_baseten, "beam": _import_beam, "cerebriumai": _import_cerebriumai, "chat_glm": _import_chatglm, "clarifai": _import_clarifai, "cohere": _import_cohere, "ctransformers": _import_ctransformers, "ctranslate2": _import_ctranslate2, "databricks": _import_databricks, "databricks-chat": _import_databricks_chat, "deepinfra": _import_deepinfra, "deepsparse": _import_deepsparse, "edenai": _import_edenai, "fake-list": _import_fake, "forefrontai": _import_forefrontai, "giga-chat-model": _import_gigachat, "google_palm": _import_google_palm, "gooseai": _import_gooseai, "gradient": _import_gradient_ai, "gpt4all": _import_gpt4all, "huggingface_endpoint": _import_huggingface_endpoint, "huggingface_hub": _import_huggingface_hub, "huggingface_pipeline": _import_huggingface_pipeline, "huggingface_textgen_inference": _import_huggingface_text_gen_inference, "human-input": _import_human, "koboldai": _import_koboldai, "llamacpp": _import_llamacpp, "textgen": _import_textgen, "minimax": _import_minimax, "mlflow": _import_mlflow, "mlflow-chat": _import_mlflow_chat, "mlflow-ai-gateway": _import_mlflow_ai_gateway, "modal": _import_modal, "mosaic": _import_mosaicml, "nebula": _import_symblai_nebula, "nibittensor": _import_bittensor, "nlpcloud": _import_nlpcloud, "ollama": _import_ollama, "openai": _import_openai, "openlm": _import_openlm, "pai_eas_endpoint": _import_pai_eas_endpoint, "petals": _import_petals, "pipelineai": _import_pipelineai, "predibase": _import_predibase, "opaqueprompts": _import_opaqueprompts, "replicate": _import_replicate, "rwkv": _import_rwkv, "sagemaker_endpoint": _import_sagemaker_endpoint, "self_hosted": _import_self_hosted, "self_hosted_hugging_face": _import_self_hosted_hugging_face, "stochasticai": _import_stochasticai, "together": _import_together, "tongyi": _import_tongyi, "titan_takeoff": _import_titan_takeoff, "titan_takeoff_pro": _import_titan_takeoff_pro, "vertexai": _import_vertex, "vertexai_model_garden": _import_vertex_model_garden, "openllm": _import_openllm, "openllm_client": _import_openllm, "vllm": _import_vllm, "vllm_openai": _import_vllm_openai, "watsonxllm": _import_watsonxllm, "writer": _import_writer, "xinference": _import_xinference, "javelin-ai-gateway": _import_javelin_ai_gateway, "qianfan_endpoint": _import_baidu_qianfan_endpoint, "yandex_gpt": _import_yandex_gpt, "VolcEngineMaasLLM": _import_volcengine_maas, }

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import logging from abc import ABC, abstractmethod from itertools import islice from typing import Any, Dict, Iterable, List, Optional from langchain_community.utilities.redis import get_client from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, ENTITY_SUMMARIZATION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key logger = logging.getLogger(__name__) class BaseEntityStore(BaseModel, ABC): """Abstract base class for Entity store.""" @abstractmethod def get(self, key: str, default: Optional[str] = None) -> Optional[str]: """Get entity value from store.""" pass @abstractmethod def set(self, key: str, value: Optional[str]) -> None: """Set entity value in store.""" pass @abstractmethod def delete(self, key: str) -> None: """Delete entity value from store.""" pass @abstractmethod def exists(self, key: str) -> bool: """Check if entity exists in store.""" pass @abstractmethod def clear(self) -> None: """Delete all entities from store.""" pass class InMemoryEntityStore(BaseEntityStore): """In-memory Entity store.""" store: Dict[str, Optional[str]] = {} def get(self, key: str, default: Optional[str] = None) -> Optional[str]: return self.store.get(key, default) def set(self, key: str, value: Optional[str]) -> None: self.store[key] = value def delete(self, key: str) -> None: del self.store[key] def exists(self, key: str) -> bool: return key in self.store def clear(self) -> None: return self.store.clear() class UpstashRedisEntityStore(BaseEntityStore): """Upstash Redis backed Entity store. Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back. """ def __init__( self, session_id: str = "default", url: str = "", token: str = "", key_prefix: str = "memory_store", ttl: Optional[int] = 60 * 60 * 24, recall_ttl: Optional[int] = 60 * 60 * 24 * 3, *args: Any, **kwargs: Any, ): try: from upstash_redis import Redis except ImportError: raise ImportError( "Could not import upstash_redis python package. " "Please install it with `pip install upstash_redis`." ) super().__init__(*args, **kwargs) try: self.redis_client = Redis(url=url, token=token) except Exception: logger.error("Upstash Redis instance could not be initiated.") self.session_id = session_id self.key_prefix = key_prefix self.ttl = ttl self.recall_ttl = recall_ttl or ttl @property def full_key_prefix(self) -> str: return f"{self.key_prefix}:{self.session_id}" def get(self, key: str, default: Optional[str] = None) -> Optional[str]: res = ( self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl) or default or "" ) logger.debug(f"Upstash Redis MEM get '{self.full_key_prefix}:{key}': '{res}'") return res def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl) logger.debug( f"Redis MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}" ) def delete(self, key: str) -> None: self.redis_client.delete(f"{self.full_key_prefix}:{key}") def exists(self, key: str) -> bool: return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1 def clear(self) -> None: def scan_and_delete(cursor: int) -> int: cursor, keys_to_delete = self.redis_client.scan( cursor, f"{self.full_key_prefix}:*" ) self.redis_client.delete(*keys_to_delete) return cursor cursor = scan_and_delete(0) while cursor != 0: scan_and_delete(cursor) class RedisEntityStore(BaseEntityStore): """Redis-backed Entity store. Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back. """ redis_client: Any session_id: str = "default" key_prefix: str = "memory_store" ttl: Optional[int] = 60 * 60 * 24 recall_ttl: Optional[int] = 60 * 60 * 24 * 3 def __init__( self, session_id: str = "default", url: str = "redis://localhost:6379/0", key_prefix: str = "memory_store", ttl: Optional[int] = 60 * 60 * 24, recall_ttl: Optional[int] = 60 * 60 * 24 * 3, *args: Any, **kwargs: Any, ): try: import redis except ImportError: raise ImportError( "Could not import redis python package. " "Please install it with `pip install redis`." ) super().__init__(*args, **kwargs) try: self.redis_client = get_client(redis_url=url, decode_responses=True) except redis.exceptions.ConnectionError as error: logger.error(error) self.session_id = session_id self.key_prefix = key_prefix self.ttl = ttl self.recall_ttl = recall_ttl or ttl @property def full_key_prefix(self) -> str: return f"{self.key_prefix}:{self.session_id}" def get(self, key: str, default: Optional[str] = None) -> Optional[str]: res = ( self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl) or default or "" ) logger.debug(f"REDIS MEM get '{self.full_key_prefix}:{key}': '{res}'") return res def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl) logger.debug( f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}" ) def delete(self, key: str) -> None: self.redis_client.delete(f"{self.full_key_prefix}:{key}") def exists(self, key: str) -> bool: return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1 def clear(self) -> None: # iterate a list in batches of size batch_size def batched(iterable: Iterable[Any], batch_size: int) -> Iterable[Any]: iterator = iter(iterable) while batch := list(islice(iterator, batch_size)): yield batch for keybatch in batched( self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500 ): self.redis_client.delete(*keybatch) class SQLiteEntityStore(BaseEntityStore): """SQLite-backed Entity store""" session_id: str = "default" table_name: str = "memory_store" conn: Any = None class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True def __init__( self, session_id: str = "default", db_file: str = "entities.db", table_name: str = "memory_store", *args: Any, **kwargs: Any, ): try: import sqlite3 except ImportError: raise ImportError( "Could not import sqlite3 python package. " "Please install it with `pip install sqlite3`." ) super().__init__(*args, **kwargs) self.conn = sqlite3.connect(db_file) self.session_id = session_id self.table_name = table_name self._create_table_if_not_exists() @property def full_table_name(self) -> str: return f"{self.table_name}_{self.session_id}" def _create_table_if_not_exists(self) -> None: create_table_query = f""" CREATE TABLE IF NOT EXISTS {self.full_table_name} ( key TEXT PRIMARY KEY, value TEXT ) """ with self.conn: self.conn.execute(create_table_query) def get(self, key: str, default: Optional[str] = None) -> Optional[str]: query = f""" SELECT value FROM {self.full_table_name} WHERE key = ? """ cursor = self.conn.execute(query, (key,)) result = cursor.fetchone() if result is not None: value = result[0] return value return default def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) query = f""" INSERT OR REPLACE INTO {self.full_table_name} (key, value) VALUES (?, ?) """ with self.conn: self.conn.execute(query, (key, value)) def delete(self, key: str) -> None: query = f""" DELETE FROM {self.full_table_name} WHERE key = ? """ with self.conn: self.conn.execute(query, (key,)) def exists(self, key: str) -> bool: query = f""" SELECT 1 FROM {self.full_table_name} WHERE key = ? LIMIT 1 """ cursor = self.conn.execute(query, (key,)) result = cursor.fetchone() return result is not None def clear(self) -> None: query = f""" DELETE FROM {self.full_table_name} """ with self.conn: self.conn.execute(query) class ConversationEntityMemory(BaseChatMemory): """Entity extractor & summarizer memory. Extracts named entities from the recent chat history and generates summaries. With a swappable entity store, persisting entities across conversations. Defaults to an in-memory entity store, and can be swapped out for a Redis, SQLite, or other entity store. """ human_prefix: str = "Human" ai_prefix: str = "AI" llm: BaseLanguageModel entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT # Cache of recently detected entity names, if any # It is updated when load_memory_variables is called: entity_cache: List[str] = [] # Number of recent message pairs to consider when updating entities: k: int = 3 chat_history_key: str = "history" # Store to manage entity-related data: entity_store: BaseEntityStore = Field(default_factory=InMemoryEntityStore) @property def buffer(self) -> List[BaseMessage]: """Access chat memory messages.""" return self.chat_memory.messages @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return ["entities", self.chat_history_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """ Returns chat history and all generated entities with summaries if available, and updates or clears the recent entity cache. New entity name can be found when calling this method, before the entity summaries are generated, so the entity cache values may be empty if no entity descriptions are generated yet. """ # Create an LLMChain for predicting entity names from the recent chat history: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key # Extract an arbitrary window of the last message pairs from # the chat history, where the hyperparameter k is the # number of message pairs: buffer_string = get_buffer_string( self.buffer[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) # Generates a comma-separated list of named entities, # e.g. "Jane, White House, UFO" # or "NONE" if no named entities are extracted: output = chain.predict( history=buffer_string, input=inputs[prompt_input_key], ) # If no named entities are extracted, assigns an empty list. if output.strip() == "NONE": entities = [] else: # Make a list of the extracted entities: entities = [w.strip() for w in output.split(",")] # Make a dictionary of entities with summary if exists: entity_summaries = {} for entity in entities: entity_summaries[entity] = self.entity_store.get(entity, "") # Replaces the entity name cache with the most recently discussed entities, # or if no entities were extracted, clears the cache: self.entity_cache = entities # Should we return as message objects or as a string? if self.return_messages: # Get last `k` pair of chat messages: buffer: Any = self.buffer[-self.k * 2 :] else: # Reuse the string we made earlier: buffer = buffer_string return { self.chat_history_key: buffer, "entities": entity_summaries, } def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """ Save context from this conversation history to the entity store. Generates a summary for each entity in the entity cache by prompting the model, and saves these summaries to the entity store. """ super().save_context(inputs, outputs) if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key # Extract an arbitrary window of the last message pairs from # the chat history, where the hyperparameter k is the # number of message pairs: buffer_string = get_buffer_string( self.buffer[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) input_data = inputs[prompt_input_key] # Create an LLMChain for predicting entity summarization from the context chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt) # Generate new summaries for entities and save them in the entity store for entity in self.entity_cache: # Get existing summary if it exists existing_summary = self.entity_store.get(entity, "") output = chain.predict( summary=existing_summary, entity=entity, history=buffer_string, input=input_data, ) # Save the updated summary to the entity store self.entity_store.set(entity, output.strip()) def clear(self) -> None: """Clear memory contents.""" self.chat_memory.clear() self.entity_cache.clear() self.entity_store.clear()

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import logging from abc import ABC, abstractmethod from itertools import islice from typing import Any, Dict, Iterable, List, Optional from langchain_community.utilities.redis import get_client from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, ENTITY_SUMMARIZATION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key logger = logging.getLogger(__name__) class BaseEntityStore(BaseModel, ABC): """Abstract base class for Entity store.""" @abstractmethod def get(self, key: str, default: Optional[str] = None) -> Optional[str]: """Get entity value from store.""" pass @abstractmethod def set(self, key: str, value: Optional[str]) -> None: """Set entity value in store.""" pass @abstractmethod def delete(self, key: str) -> None: """Delete entity value from store.""" pass @abstractmethod def exists(self, key: str) -> bool: """Check if entity exists in store.""" pass @abstractmethod def clear(self) -> None: """Delete all entities from store.""" pass class InMemoryEntityStore(BaseEntityStore): """In-memory Entity store.""" store: Dict[str, Optional[str]] = {} def get(self, key: str, default: Optional[str] = None) -> Optional[str]: return self.store.get(key, default) def set(self, key: str, value: Optional[str]) -> None: self.store[key] = value def delete(self, key: str) -> None: del self.store[key] def exists(self, key: str) -> bool: return key in self.store def clear(self) -> None: return self.store.clear() class UpstashRedisEntityStore(BaseEntityStore): """Upstash Redis backed Entity store. Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back. """ def __init__( self, session_id: str = "default", url: str = "", token: str = "", key_prefix: str = "memory_store", ttl: Optional[int] = 60 * 60 * 24, recall_ttl: Optional[int] = 60 * 60 * 24 * 3, *args: Any, **kwargs: Any, ): try: from upstash_redis import Redis except ImportError: raise ImportError( "Could not import upstash_redis python package. " "Please install it with `pip install upstash_redis`." ) super().__init__(*args, **kwargs) try: self.redis_client = Redis(url=url, token=token) except Exception: logger.error("Upstash Redis instance could not be initiated.") self.session_id = session_id self.key_prefix = key_prefix self.ttl = ttl self.recall_ttl = recall_ttl or ttl @property def full_key_prefix(self) -> str: return f"{self.key_prefix}:{self.session_id}" def get(self, key: str, default: Optional[str] = None) -> Optional[str]: res = ( self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl) or default or "" ) logger.debug(f"Upstash Redis MEM get '{self.full_key_prefix}:{key}': '{res}'") return res def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl) logger.debug( f"Redis MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}" ) def delete(self, key: str) -> None: self.redis_client.delete(f"{self.full_key_prefix}:{key}") def exists(self, key: str) -> bool: return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1 def clear(self) -> None: def scan_and_delete(cursor: int) -> int: cursor, keys_to_delete = self.redis_client.scan( cursor, f"{self.full_key_prefix}:*" ) self.redis_client.delete(*keys_to_delete) return cursor cursor = scan_and_delete(0) while cursor != 0: scan_and_delete(cursor) class RedisEntityStore(BaseEntityStore): """Redis-backed Entity store. Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back. """ redis_client: Any session_id: str = "default" key_prefix: str = "memory_store" ttl: Optional[int] = 60 * 60 * 24 recall_ttl: Optional[int] = 60 * 60 * 24 * 3 def __init__( self, session_id: str = "default", url: str = "redis://localhost:6379/0", key_prefix: str = "memory_store", ttl: Optional[int] = 60 * 60 * 24, recall_ttl: Optional[int] = 60 * 60 * 24 * 3, *args: Any, **kwargs: Any, ): try: import redis except ImportError: raise ImportError( "Could not import redis python package. " "Please install it with `pip install redis`." ) super().__init__(*args, **kwargs) try: self.redis_client = get_client(redis_url=url, decode_responses=True) except redis.exceptions.ConnectionError as error: logger.error(error) self.session_id = session_id self.key_prefix = key_prefix self.ttl = ttl self.recall_ttl = recall_ttl or ttl @property def full_key_prefix(self) -> str: return f"{self.key_prefix}:{self.session_id}" def get(self, key: str, default: Optional[str] = None) -> Optional[str]: res = ( self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl) or default or "" ) logger.debug(f"REDIS MEM get '{self.full_key_prefix}:{key}': '{res}'") return res def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl) logger.debug( f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}" ) def delete(self, key: str) -> None: self.redis_client.delete(f"{self.full_key_prefix}:{key}") def exists(self, key: str) -> bool: return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1 def clear(self) -> None: # iterate a list in batches of size batch_size def batched(iterable: Iterable[Any], batch_size: int) -> Iterable[Any]: iterator = iter(iterable) while batch := list(islice(iterator, batch_size)): yield batch for keybatch in batched( self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500 ): self.redis_client.delete(*keybatch) class SQLiteEntityStore(BaseEntityStore): """SQLite-backed Entity store""" session_id: str = "default" table_name: str = "memory_store" conn: Any = None class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True def __init__( self, session_id: str = "default", db_file: str = "entities.db", table_name: str = "memory_store", *args: Any, **kwargs: Any, ): try: import sqlite3 except ImportError: raise ImportError( "Could not import sqlite3 python package. " "Please install it with `pip install sqlite3`." ) super().__init__(*args, **kwargs) self.conn = sqlite3.connect(db_file) self.session_id = session_id self.table_name = table_name self._create_table_if_not_exists() @property def full_table_name(self) -> str: return f"{self.table_name}_{self.session_id}" def _create_table_if_not_exists(self) -> None: create_table_query = f""" CREATE TABLE IF NOT EXISTS {self.full_table_name} ( key TEXT PRIMARY KEY, value TEXT ) """ with self.conn: self.conn.execute(create_table_query) def get(self, key: str, default: Optional[str] = None) -> Optional[str]: query = f""" SELECT value FROM {self.full_table_name} WHERE key = ? """ cursor = self.conn.execute(query, (key,)) result = cursor.fetchone() if result is not None: value = result[0] return value return default def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) query = f""" INSERT OR REPLACE INTO {self.full_table_name} (key, value) VALUES (?, ?) """ with self.conn: self.conn.execute(query, (key, value)) def delete(self, key: str) -> None: query = f""" DELETE FROM {self.full_table_name} WHERE key = ? """ with self.conn: self.conn.execute(query, (key,)) def exists(self, key: str) -> bool: query = f""" SELECT 1 FROM {self.full_table_name} WHERE key = ? LIMIT 1 """ cursor = self.conn.execute(query, (key,)) result = cursor.fetchone() return result is not None def clear(self) -> None: query = f""" DELETE FROM {self.full_table_name} """ with self.conn: self.conn.execute(query) class ConversationEntityMemory(BaseChatMemory): """Entity extractor & summarizer memory. Extracts named entities from the recent chat history and generates summaries. With a swappable entity store, persisting entities across conversations. Defaults to an in-memory entity store, and can be swapped out for a Redis, SQLite, or other entity store. """ human_prefix: str = "Human" ai_prefix: str = "AI" llm: BaseLanguageModel entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT # Cache of recently detected entity names, if any # It is updated when load_memory_variables is called: entity_cache: List[str] = [] # Number of recent message pairs to consider when updating entities: k: int = 3 chat_history_key: str = "history" # Store to manage entity-related data: entity_store: BaseEntityStore = Field(default_factory=InMemoryEntityStore) @property def buffer(self) -> List[BaseMessage]: """Access chat memory messages.""" return self.chat_memory.messages @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return ["entities", self.chat_history_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """ Returns chat history and all generated entities with summaries if available, and updates or clears the recent entity cache. New entity name can be found when calling this method, before the entity summaries are generated, so the entity cache values may be empty if no entity descriptions are generated yet. """ # Create an LLMChain for predicting entity names from the recent chat history: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key # Extract an arbitrary window of the last message pairs from # the chat history, where the hyperparameter k is the # number of message pairs: buffer_string = get_buffer_string( self.buffer[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) # Generates a comma-separated list of named entities, # e.g. "Jane, White House, UFO" # or "NONE" if no named entities are extracted: output = chain.predict( history=buffer_string, input=inputs[prompt_input_key], ) # If no named entities are extracted, assigns an empty list. if output.strip() == "NONE": entities = [] else: # Make a list of the extracted entities: entities = [w.strip() for w in output.split(",")] # Make a dictionary of entities with summary if exists: entity_summaries = {} for entity in entities: entity_summaries[entity] = self.entity_store.get(entity, "") # Replaces the entity name cache with the most recently discussed entities, # or if no entities were extracted, clears the cache: self.entity_cache = entities # Should we return as message objects or as a string? if self.return_messages: # Get last `k` pair of chat messages: buffer: Any = self.buffer[-self.k * 2 :] else: # Reuse the string we made earlier: buffer = buffer_string return { self.chat_history_key: buffer, "entities": entity_summaries, } def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """ Save context from this conversation history to the entity store. Generates a summary for each entity in the entity cache by prompting the model, and saves these summaries to the entity store. """ super().save_context(inputs, outputs) if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key # Extract an arbitrary window of the last message pairs from # the chat history, where the hyperparameter k is the # number of message pairs: buffer_string = get_buffer_string( self.buffer[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) input_data = inputs[prompt_input_key] # Create an LLMChain for predicting entity summarization from the context chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt) # Generate new summaries for entities and save them in the entity store for entity in self.entity_cache: # Get existing summary if it exists existing_summary = self.entity_store.get(entity, "") output = chain.predict( summary=existing_summary, entity=entity, history=buffer_string, input=input_data, ) # Save the updated summary to the entity store self.entity_store.set(entity, output.strip()) def clear(self) -> None: """Clear memory contents.""" self.chat_memory.clear() self.entity_cache.clear() self.entity_store.clear()

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import logging from abc import ABC, abstractmethod from itertools import islice from typing import Any, Dict, Iterable, List, Optional from langchain_community.utilities.redis import get_client from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import ( ENTITY_EXTRACTION_PROMPT, ENTITY_SUMMARIZATION_PROMPT, ) from langchain.memory.utils import get_prompt_input_key logger = logging.getLogger(__name__) class BaseEntityStore(BaseModel, ABC): """Abstract base class for Entity store.""" @abstractmethod def get(self, key: str, default: Optional[str] = None) -> Optional[str]: """Get entity value from store.""" pass @abstractmethod def set(self, key: str, value: Optional[str]) -> None: """Set entity value in store.""" pass @abstractmethod def delete(self, key: str) -> None: """Delete entity value from store.""" pass @abstractmethod def exists(self, key: str) -> bool: """Check if entity exists in store.""" pass @abstractmethod def clear(self) -> None: """Delete all entities from store.""" pass class InMemoryEntityStore(BaseEntityStore): """In-memory Entity store.""" store: Dict[str, Optional[str]] = {} def get(self, key: str, default: Optional[str] = None) -> Optional[str]: return self.store.get(key, default) def set(self, key: str, value: Optional[str]) -> None: self.store[key] = value def delete(self, key: str) -> None: del self.store[key] def exists(self, key: str) -> bool: return key in self.store def clear(self) -> None: return self.store.clear() class UpstashRedisEntityStore(BaseEntityStore): """Upstash Redis backed Entity store. Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back. """ def __init__( self, session_id: str = "default", url: str = "", token: str = "", key_prefix: str = "memory_store", ttl: Optional[int] = 60 * 60 * 24, recall_ttl: Optional[int] = 60 * 60 * 24 * 3, *args: Any, **kwargs: Any, ): try: from upstash_redis import Redis except ImportError: raise ImportError( "Could not import upstash_redis python package. " "Please install it with `pip install upstash_redis`." ) super().__init__(*args, **kwargs) try: self.redis_client = Redis(url=url, token=token) except Exception: logger.error("Upstash Redis instance could not be initiated.") self.session_id = session_id self.key_prefix = key_prefix self.ttl = ttl self.recall_ttl = recall_ttl or ttl @property def full_key_prefix(self) -> str: return f"{self.key_prefix}:{self.session_id}" def get(self, key: str, default: Optional[str] = None) -> Optional[str]: res = ( self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl) or default or "" ) logger.debug(f"Upstash Redis MEM get '{self.full_key_prefix}:{key}': '{res}'") return res def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl) logger.debug( f"Redis MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}" ) def delete(self, key: str) -> None: self.redis_client.delete(f"{self.full_key_prefix}:{key}") def exists(self, key: str) -> bool: return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1 def clear(self) -> None: def scan_and_delete(cursor: int) -> int: cursor, keys_to_delete = self.redis_client.scan( cursor, f"{self.full_key_prefix}:*" ) self.redis_client.delete(*keys_to_delete) return cursor cursor = scan_and_delete(0) while cursor != 0: scan_and_delete(cursor) class RedisEntityStore(BaseEntityStore): """Redis-backed Entity store. Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back. """ redis_client: Any session_id: str = "default" key_prefix: str = "memory_store" ttl: Optional[int] = 60 * 60 * 24 recall_ttl: Optional[int] = 60 * 60 * 24 * 3 def __init__( self, session_id: str = "default", url: str = "redis://localhost:6379/0", key_prefix: str = "memory_store", ttl: Optional[int] = 60 * 60 * 24, recall_ttl: Optional[int] = 60 * 60 * 24 * 3, *args: Any, **kwargs: Any, ): try: import redis except ImportError: raise ImportError( "Could not import redis python package. " "Please install it with `pip install redis`." ) super().__init__(*args, **kwargs) try: self.redis_client = get_client(redis_url=url, decode_responses=True) except redis.exceptions.ConnectionError as error: logger.error(error) self.session_id = session_id self.key_prefix = key_prefix self.ttl = ttl self.recall_ttl = recall_ttl or ttl @property def full_key_prefix(self) -> str: return f"{self.key_prefix}:{self.session_id}" def get(self, key: str, default: Optional[str] = None) -> Optional[str]: res = ( self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl) or default or "" ) logger.debug(f"REDIS MEM get '{self.full_key_prefix}:{key}': '{res}'") return res def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl) logger.debug( f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}" ) def delete(self, key: str) -> None: self.redis_client.delete(f"{self.full_key_prefix}:{key}") def exists(self, key: str) -> bool: return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1 def clear(self) -> None: # iterate a list in batches of size batch_size def batched(iterable: Iterable[Any], batch_size: int) -> Iterable[Any]: iterator = iter(iterable) while batch := list(islice(iterator, batch_size)): yield batch for keybatch in batched( self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500 ): self.redis_client.delete(*keybatch) class SQLiteEntityStore(BaseEntityStore): """SQLite-backed Entity store""" session_id: str = "default" table_name: str = "memory_store" conn: Any = None class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True def __init__( self, session_id: str = "default", db_file: str = "entities.db", table_name: str = "memory_store", *args: Any, **kwargs: Any, ): try: import sqlite3 except ImportError: raise ImportError( "Could not import sqlite3 python package. " "Please install it with `pip install sqlite3`." ) super().__init__(*args, **kwargs) self.conn = sqlite3.connect(db_file) self.session_id = session_id self.table_name = table_name self._create_table_if_not_exists() @property def full_table_name(self) -> str: return f"{self.table_name}_{self.session_id}" def _create_table_if_not_exists(self) -> None: create_table_query = f""" CREATE TABLE IF NOT EXISTS {self.full_table_name} ( key TEXT PRIMARY KEY, value TEXT ) """ with self.conn: self.conn.execute(create_table_query) def get(self, key: str, default: Optional[str] = None) -> Optional[str]: query = f""" SELECT value FROM {self.full_table_name} WHERE key = ? """ cursor = self.conn.execute(query, (key,)) result = cursor.fetchone() if result is not None: value = result[0] return value return default def set(self, key: str, value: Optional[str]) -> None: if not value: return self.delete(key) query = f""" INSERT OR REPLACE INTO {self.full_table_name} (key, value) VALUES (?, ?) """ with self.conn: self.conn.execute(query, (key, value)) def delete(self, key: str) -> None: query = f""" DELETE FROM {self.full_table_name} WHERE key = ? """ with self.conn: self.conn.execute(query, (key,)) def exists(self, key: str) -> bool: query = f""" SELECT 1 FROM {self.full_table_name} WHERE key = ? LIMIT 1 """ cursor = self.conn.execute(query, (key,)) result = cursor.fetchone() return result is not None def clear(self) -> None: query = f""" DELETE FROM {self.full_table_name} """ with self.conn: self.conn.execute(query) class ConversationEntityMemory(BaseChatMemory): """Entity extractor & summarizer memory. Extracts named entities from the recent chat history and generates summaries. With a swappable entity store, persisting entities across conversations. Defaults to an in-memory entity store, and can be swapped out for a Redis, SQLite, or other entity store. """ human_prefix: str = "Human" ai_prefix: str = "AI" llm: BaseLanguageModel entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT # Cache of recently detected entity names, if any # It is updated when load_memory_variables is called: entity_cache: List[str] = [] # Number of recent message pairs to consider when updating entities: k: int = 3 chat_history_key: str = "history" # Store to manage entity-related data: entity_store: BaseEntityStore = Field(default_factory=InMemoryEntityStore) @property def buffer(self) -> List[BaseMessage]: """Access chat memory messages.""" return self.chat_memory.messages @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return ["entities", self.chat_history_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """ Returns chat history and all generated entities with summaries if available, and updates or clears the recent entity cache. New entity name can be found when calling this method, before the entity summaries are generated, so the entity cache values may be empty if no entity descriptions are generated yet. """ # Create an LLMChain for predicting entity names from the recent chat history: chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt) if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key # Extract an arbitrary window of the last message pairs from # the chat history, where the hyperparameter k is the # number of message pairs: buffer_string = get_buffer_string( self.buffer[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) # Generates a comma-separated list of named entities, # e.g. "Jane, White House, UFO" # or "NONE" if no named entities are extracted: output = chain.predict( history=buffer_string, input=inputs[prompt_input_key], ) # If no named entities are extracted, assigns an empty list. if output.strip() == "NONE": entities = [] else: # Make a list of the extracted entities: entities = [w.strip() for w in output.split(",")] # Make a dictionary of entities with summary if exists: entity_summaries = {} for entity in entities: entity_summaries[entity] = self.entity_store.get(entity, "") # Replaces the entity name cache with the most recently discussed entities, # or if no entities were extracted, clears the cache: self.entity_cache = entities # Should we return as message objects or as a string? if self.return_messages: # Get last `k` pair of chat messages: buffer: Any = self.buffer[-self.k * 2 :] else: # Reuse the string we made earlier: buffer = buffer_string return { self.chat_history_key: buffer, "entities": entity_summaries, } def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """ Save context from this conversation history to the entity store. Generates a summary for each entity in the entity cache by prompting the model, and saves these summaries to the entity store. """ super().save_context(inputs, outputs) if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key # Extract an arbitrary window of the last message pairs from # the chat history, where the hyperparameter k is the # number of message pairs: buffer_string = get_buffer_string( self.buffer[-self.k * 2 :], human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) input_data = inputs[prompt_input_key] # Create an LLMChain for predicting entity summarization from the context chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt) # Generate new summaries for entities and save them in the entity store for entity in self.entity_cache: # Get existing summary if it exists existing_summary = self.entity_store.get(entity, "") output = chain.predict( summary=existing_summary, entity=entity, history=buffer_string, input=input_data, ) # Save the updated summary to the entity store self.entity_store.set(entity, output.strip()) def clear(self) -> None: """Clear memory contents.""" self.chat_memory.clear() self.entity_cache.clear() self.entity_store.clear()

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from typing import Any, Dict, List, Optional from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.pydantic_v1 import root_validator from langchain.memory.chat_memory import BaseChatMemory, BaseMemory from langchain.memory.utils import get_prompt_input_key class ConversationBufferMemory(BaseChatMemory): """Buffer for storing conversation memory.""" human_prefix: str = "Human" ai_prefix: str = "AI" memory_key: str = "history" #: :meta private: @property def buffer(self) -> Any: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str async def abuffer(self) -> Any: """String buffer of memory.""" return ( await self.abuffer_as_messages() if self.return_messages else await self.abuffer_as_str() ) def _buffer_as_str(self, messages: List[BaseMessage]) -> str: return get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) @property def buffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" return self._buffer_as_str(self.chat_memory.messages) async def abuffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" messages = await self.chat_memory.aget_messages() return self._buffer_as_str(messages) @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages async def abuffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return await self.chat_memory.aget_messages() @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer} async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return key-value pairs given the text input to the chain.""" buffer = await self.abuffer() return {self.memory_key: buffer} class ConversationStringBufferMemory(BaseMemory): """Buffer for storing conversation memory.""" human_prefix: str = "Human" ai_prefix: str = "AI" """Prefix to use for AI generated responses.""" buffer: str = "" output_key: Optional[str] = None input_key: Optional[str] = None memory_key: str = "history" #: :meta private: @root_validator() def validate_chains(cls, values: Dict) -> Dict: """Validate that return messages is not True.""" if values.get("return_messages", False): raise ValueError( "return_messages must be False for ConversationStringBufferMemory" ) return values @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return {self.memory_key: self.buffer} async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return self.load_memory_variables(inputs) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") output_key = list(outputs.keys())[0] else: output_key = self.output_key human = f"{self.human_prefix}: " + inputs[prompt_input_key] ai = f"{self.ai_prefix}: " + outputs[output_key] self.buffer += "\n" + "\n".join([human, ai]) async def asave_context( self, inputs: Dict[str, Any], outputs: Dict[str, str] ) -> None: """Save context from this conversation to buffer.""" return self.save_context(inputs, outputs) def clear(self) -> None: """Clear memory contents.""" self.buffer = "" async def aclear(self) -> None: self.clear()

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from typing import Any, Dict, List, Optional from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.pydantic_v1 import root_validator from langchain.memory.chat_memory import BaseChatMemory, BaseMemory from langchain.memory.utils import get_prompt_input_key class ConversationBufferMemory(BaseChatMemory): """Buffer for storing conversation memory.""" human_prefix: str = "Human" ai_prefix: str = "AI" memory_key: str = "history" #: :meta private: @property def buffer(self) -> Any: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str async def abuffer(self) -> Any: """String buffer of memory.""" return ( await self.abuffer_as_messages() if self.return_messages else await self.abuffer_as_str() ) def _buffer_as_str(self, messages: List[BaseMessage]) -> str: return get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) @property def buffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" return self._buffer_as_str(self.chat_memory.messages) async def abuffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" messages = await self.chat_memory.aget_messages() return self._buffer_as_str(messages) @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages async def abuffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return await self.chat_memory.aget_messages() @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer} async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return key-value pairs given the text input to the chain.""" buffer = await self.abuffer() return {self.memory_key: buffer} class ConversationStringBufferMemory(BaseMemory): """Buffer for storing conversation memory.""" human_prefix: str = "Human" ai_prefix: str = "AI" """Prefix to use for AI generated responses.""" buffer: str = "" output_key: Optional[str] = None input_key: Optional[str] = None memory_key: str = "history" #: :meta private: @root_validator() def validate_chains(cls, values: Dict) -> Dict: """Validate that return messages is not True.""" if values.get("return_messages", False): raise ValueError( "return_messages must be False for ConversationStringBufferMemory" ) return values @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return {self.memory_key: self.buffer} async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return self.load_memory_variables(inputs) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") output_key = list(outputs.keys())[0] else: output_key = self.output_key human = f"{self.human_prefix}: " + inputs[prompt_input_key] ai = f"{self.ai_prefix}: " + outputs[output_key] self.buffer += "\n" + "\n".join([human, ai]) async def asave_context( self, inputs: Dict[str, Any], outputs: Dict[str, str] ) -> None: """Save context from this conversation to buffer.""" return self.save_context(inputs, outputs) def clear(self) -> None: """Clear memory contents.""" self.buffer = "" async def aclear(self) -> None: self.clear()

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from typing import Any, Dict, List, Optional from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.pydantic_v1 import root_validator from langchain.memory.chat_memory import BaseChatMemory, BaseMemory from langchain.memory.utils import get_prompt_input_key class ConversationBufferMemory(BaseChatMemory): """Buffer for storing conversation memory.""" human_prefix: str = "Human" ai_prefix: str = "AI" memory_key: str = "history" #: :meta private: @property def buffer(self) -> Any: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str async def abuffer(self) -> Any: """String buffer of memory.""" return ( await self.abuffer_as_messages() if self.return_messages else await self.abuffer_as_str() ) def _buffer_as_str(self, messages: List[BaseMessage]) -> str: return get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) @property def buffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" return self._buffer_as_str(self.chat_memory.messages) async def abuffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" messages = await self.chat_memory.aget_messages() return self._buffer_as_str(messages) @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages async def abuffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return await self.chat_memory.aget_messages() @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer} async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return key-value pairs given the text input to the chain.""" buffer = await self.abuffer() return {self.memory_key: buffer} class ConversationStringBufferMemory(BaseMemory): """Buffer for storing conversation memory.""" human_prefix: str = "Human" ai_prefix: str = "AI" """Prefix to use for AI generated responses.""" buffer: str = "" output_key: Optional[str] = None input_key: Optional[str] = None memory_key: str = "history" #: :meta private: @root_validator() def validate_chains(cls, values: Dict) -> Dict: """Validate that return messages is not True.""" if values.get("return_messages", False): raise ValueError( "return_messages must be False for ConversationStringBufferMemory" ) return values @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return {self.memory_key: self.buffer} async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return self.load_memory_variables(inputs) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") output_key = list(outputs.keys())[0] else: output_key = self.output_key human = f"{self.human_prefix}: " + inputs[prompt_input_key] ai = f"{self.ai_prefix}: " + outputs[output_key] self.buffer += "\n" + "\n".join([human, ai]) async def asave_context( self, inputs: Dict[str, Any], outputs: Dict[str, str] ) -> None: """Save context from this conversation to buffer.""" return self.save_context(inputs, outputs) def clear(self) -> None: """Clear memory contents.""" self.buffer = "" async def aclear(self) -> None: self.clear()

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from typing import Any, Dict, List, Optional from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.pydantic_v1 import root_validator from langchain.memory.chat_memory import BaseChatMemory, BaseMemory from langchain.memory.utils import get_prompt_input_key class ConversationBufferMemory(BaseChatMemory): """Buffer for storing conversation memory.""" human_prefix: str = "Human" ai_prefix: str = "AI" memory_key: str = "history" #: :meta private: @property def buffer(self) -> Any: """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str async def abuffer(self) -> Any: """String buffer of memory.""" return ( await self.abuffer_as_messages() if self.return_messages else await self.abuffer_as_str() ) def _buffer_as_str(self, messages: List[BaseMessage]) -> str: return get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) @property def buffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" return self._buffer_as_str(self.chat_memory.messages) async def abuffer_as_str(self) -> str: """Exposes the buffer as a string in case return_messages is True.""" messages = await self.chat_memory.aget_messages() return self._buffer_as_str(messages) @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages async def abuffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return await self.chat_memory.aget_messages() @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" return {self.memory_key: self.buffer} async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return key-value pairs given the text input to the chain.""" buffer = await self.abuffer() return {self.memory_key: buffer} class ConversationStringBufferMemory(BaseMemory): """Buffer for storing conversation memory.""" human_prefix: str = "Human" ai_prefix: str = "AI" """Prefix to use for AI generated responses.""" buffer: str = "" output_key: Optional[str] = None input_key: Optional[str] = None memory_key: str = "history" #: :meta private: @root_validator() def validate_chains(cls, values: Dict) -> Dict: """Validate that return messages is not True.""" if values.get("return_messages", False): raise ValueError( "return_messages must be False for ConversationStringBufferMemory" ) return values @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return {self.memory_key: self.buffer} async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: """Return history buffer.""" return self.load_memory_variables(inputs) def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" if self.input_key is None: prompt_input_key = get_prompt_input_key(inputs, self.memory_variables) else: prompt_input_key = self.input_key if self.output_key is None: if len(outputs) != 1: raise ValueError(f"One output key expected, got {outputs.keys()}") output_key = list(outputs.keys())[0] else: output_key = self.output_key human = f"{self.human_prefix}: " + inputs[prompt_input_key] ai = f"{self.ai_prefix}: " + outputs[output_key] self.buffer += "\n" + "\n".join([human, ai]) async def asave_context( self, inputs: Dict[str, Any], outputs: Dict[str, str] ) -> None: """Save context from this conversation to buffer.""" return self.save_context(inputs, outputs) def clear(self) -> None: """Clear memory contents.""" self.buffer = "" async def aclear(self) -> None: self.clear()

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"""**Tools** are classes that an Agent uses to interact with the world. Each tool has a **description**. Agent uses the description to choose the right tool for the job. **Class hierarchy:** .. code-block:: ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool <name> # Examples: BraveSearch, HumanInputRun **Main helpers:** .. code-block:: CallbackManagerForToolRun, AsyncCallbackManagerForToolRun """ import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain_core.tools import BaseTool, StructuredTool, Tool, tool from langchain.utils.interactive_env import is_interactive_env # Used for internal purposes _DEPRECATED_TOOLS = {"PythonAstREPLTool", "PythonREPLTool"} def _import_python_tool_PythonAstREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def _import_python_tool_PythonREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def __getattr__(name: str) -> Any: if name == "PythonAstREPLTool": return _import_python_tool_PythonAstREPLTool() elif name == "PythonREPLTool": return _import_python_tool_PythonREPLTool() else: from langchain_community import tools # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing tools from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.tools import {name}`.\n\n" "To install langchain-community run " "`pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(tools, name) __all__ = [ "AINAppOps", "AINOwnerOps", "AINRuleOps", "AINTransfer", "AINValueOps", "AIPluginTool", "APIOperation", "ArxivQueryRun", "AzureCogsFormRecognizerTool", "AzureCogsImageAnalysisTool", "AzureCogsSpeech2TextTool", "AzureCogsText2SpeechTool", "AzureCogsTextAnalyticsHealthTool", "BaseGraphQLTool", "BaseRequestsTool", "BaseSQLDatabaseTool", "BaseSparkSQLTool", "BaseTool", "BearlyInterpreterTool", "BingSearchResults", "BingSearchRun", "BraveSearch", "ClickTool", "CopyFileTool", "CurrentWebPageTool", "DeleteFileTool", "DuckDuckGoSearchResults", "DuckDuckGoSearchRun", "E2BDataAnalysisTool", "EdenAiExplicitImageTool", "EdenAiObjectDetectionTool", "EdenAiParsingIDTool", "EdenAiParsingInvoiceTool", "EdenAiSpeechToTextTool", "EdenAiTextModerationTool", "EdenAiTextToSpeechTool", "EdenaiTool", "ElevenLabsText2SpeechTool", "ExtractHyperlinksTool", "ExtractTextTool", "FileSearchTool", "GetElementsTool", "GmailCreateDraft", "GmailGetMessage", "GmailGetThread", "GmailSearch", "GmailSendMessage", "GoogleCloudTextToSpeechTool", "GooglePlacesTool", "GoogleSearchResults", "GoogleSearchRun", "GoogleSerperResults", "GoogleSerperRun", "SearchAPIResults", "SearchAPIRun", "HumanInputRun", "IFTTTWebhook", "InfoPowerBITool", "InfoSQLDatabaseTool", "InfoSparkSQLTool", "JiraAction", "JsonGetValueTool", "JsonListKeysTool", "ListDirectoryTool", "ListPowerBITool", "ListSQLDatabaseTool", "ListSparkSQLTool", "MerriamWebsterQueryRun", "MetaphorSearchResults", "MoveFileTool", "NasaAction", "NavigateBackTool", "NavigateTool", "O365CreateDraftMessage", "O365SearchEmails", "O365SearchEvents", "O365SendEvent", "O365SendMessage", "OpenAPISpec", "OpenWeatherMapQueryRun", "PubmedQueryRun", "RedditSearchRun", "QueryCheckerTool", "QueryPowerBITool", "QuerySQLCheckerTool", "QuerySQLDataBaseTool", "QuerySparkSQLTool", "ReadFileTool", "RequestsDeleteTool", "RequestsGetTool", "RequestsPatchTool", "RequestsPostTool", "RequestsPutTool", "SteamWebAPIQueryRun", "SceneXplainTool", "SearxSearchResults", "SearxSearchRun", "ShellTool", "SlackGetChannel", "SlackGetMessage", "SlackScheduleMessage", "SlackSendMessage", "SleepTool", "StdInInquireTool", "StackExchangeTool", "SteamshipImageGenerationTool", "StructuredTool", "Tool", "VectorStoreQATool", "VectorStoreQAWithSourcesTool", "WikipediaQueryRun", "WolframAlphaQueryRun", "WriteFileTool", "YahooFinanceNewsTool", "YouTubeSearchTool", "ZapierNLAListActions", "ZapierNLARunAction", "format_tool_to_openai_function", "tool", ]

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"""**Tools** are classes that an Agent uses to interact with the world. Each tool has a **description**. Agent uses the description to choose the right tool for the job. **Class hierarchy:** .. code-block:: ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool <name> # Examples: BraveSearch, HumanInputRun **Main helpers:** .. code-block:: CallbackManagerForToolRun, AsyncCallbackManagerForToolRun """ import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain_core.tools import BaseTool, StructuredTool, Tool, tool from langchain.utils.interactive_env import is_interactive_env # Used for internal purposes _DEPRECATED_TOOLS = {"PythonAstREPLTool", "PythonREPLTool"} def _import_python_tool_PythonAstREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def _import_python_tool_PythonREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def __getattr__(name: str) -> Any: if name == "PythonAstREPLTool": return _import_python_tool_PythonAstREPLTool() elif name == "PythonREPLTool": return _import_python_tool_PythonREPLTool() else: from langchain_community import tools # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing tools from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.tools import {name}`.\n\n" "To install langchain-community run " "`pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(tools, name) __all__ = [ "AINAppOps", "AINOwnerOps", "AINRuleOps", "AINTransfer", "AINValueOps", "AIPluginTool", "APIOperation", "ArxivQueryRun", "AzureCogsFormRecognizerTool", "AzureCogsImageAnalysisTool", "AzureCogsSpeech2TextTool", "AzureCogsText2SpeechTool", "AzureCogsTextAnalyticsHealthTool", "BaseGraphQLTool", "BaseRequestsTool", "BaseSQLDatabaseTool", "BaseSparkSQLTool", "BaseTool", "BearlyInterpreterTool", "BingSearchResults", "BingSearchRun", "BraveSearch", "ClickTool", "CopyFileTool", "CurrentWebPageTool", "DeleteFileTool", "DuckDuckGoSearchResults", "DuckDuckGoSearchRun", "E2BDataAnalysisTool", "EdenAiExplicitImageTool", "EdenAiObjectDetectionTool", "EdenAiParsingIDTool", "EdenAiParsingInvoiceTool", "EdenAiSpeechToTextTool", "EdenAiTextModerationTool", "EdenAiTextToSpeechTool", "EdenaiTool", "ElevenLabsText2SpeechTool", "ExtractHyperlinksTool", "ExtractTextTool", "FileSearchTool", "GetElementsTool", "GmailCreateDraft", "GmailGetMessage", "GmailGetThread", "GmailSearch", "GmailSendMessage", "GoogleCloudTextToSpeechTool", "GooglePlacesTool", "GoogleSearchResults", "GoogleSearchRun", "GoogleSerperResults", "GoogleSerperRun", "SearchAPIResults", "SearchAPIRun", "HumanInputRun", "IFTTTWebhook", "InfoPowerBITool", "InfoSQLDatabaseTool", "InfoSparkSQLTool", "JiraAction", "JsonGetValueTool", "JsonListKeysTool", "ListDirectoryTool", "ListPowerBITool", "ListSQLDatabaseTool", "ListSparkSQLTool", "MerriamWebsterQueryRun", "MetaphorSearchResults", "MoveFileTool", "NasaAction", "NavigateBackTool", "NavigateTool", "O365CreateDraftMessage", "O365SearchEmails", "O365SearchEvents", "O365SendEvent", "O365SendMessage", "OpenAPISpec", "OpenWeatherMapQueryRun", "PubmedQueryRun", "RedditSearchRun", "QueryCheckerTool", "QueryPowerBITool", "QuerySQLCheckerTool", "QuerySQLDataBaseTool", "QuerySparkSQLTool", "ReadFileTool", "RequestsDeleteTool", "RequestsGetTool", "RequestsPatchTool", "RequestsPostTool", "RequestsPutTool", "SteamWebAPIQueryRun", "SceneXplainTool", "SearxSearchResults", "SearxSearchRun", "ShellTool", "SlackGetChannel", "SlackGetMessage", "SlackScheduleMessage", "SlackSendMessage", "SleepTool", "StdInInquireTool", "StackExchangeTool", "SteamshipImageGenerationTool", "StructuredTool", "Tool", "VectorStoreQATool", "VectorStoreQAWithSourcesTool", "WikipediaQueryRun", "WolframAlphaQueryRun", "WriteFileTool", "YahooFinanceNewsTool", "YouTubeSearchTool", "ZapierNLAListActions", "ZapierNLARunAction", "format_tool_to_openai_function", "tool", ]

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"""**Tools** are classes that an Agent uses to interact with the world. Each tool has a **description**. Agent uses the description to choose the right tool for the job. **Class hierarchy:** .. code-block:: ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool <name> # Examples: BraveSearch, HumanInputRun **Main helpers:** .. code-block:: CallbackManagerForToolRun, AsyncCallbackManagerForToolRun """ import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain_core.tools import BaseTool, StructuredTool, Tool, tool from langchain.utils.interactive_env import is_interactive_env # Used for internal purposes _DEPRECATED_TOOLS = {"PythonAstREPLTool", "PythonREPLTool"} def _import_python_tool_PythonAstREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def _import_python_tool_PythonREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def __getattr__(name: str) -> Any: if name == "PythonAstREPLTool": return _import_python_tool_PythonAstREPLTool() elif name == "PythonREPLTool": return _import_python_tool_PythonREPLTool() else: from langchain_community import tools # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing tools from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.tools import {name}`.\n\n" "To install langchain-community run " "`pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(tools, name) __all__ = [ "AINAppOps", "AINOwnerOps", "AINRuleOps", "AINTransfer", "AINValueOps", "AIPluginTool", "APIOperation", "ArxivQueryRun", "AzureCogsFormRecognizerTool", "AzureCogsImageAnalysisTool", "AzureCogsSpeech2TextTool", "AzureCogsText2SpeechTool", "AzureCogsTextAnalyticsHealthTool", "BaseGraphQLTool", "BaseRequestsTool", "BaseSQLDatabaseTool", "BaseSparkSQLTool", "BaseTool", "BearlyInterpreterTool", "BingSearchResults", "BingSearchRun", "BraveSearch", "ClickTool", "CopyFileTool", "CurrentWebPageTool", "DeleteFileTool", "DuckDuckGoSearchResults", "DuckDuckGoSearchRun", "E2BDataAnalysisTool", "EdenAiExplicitImageTool", "EdenAiObjectDetectionTool", "EdenAiParsingIDTool", "EdenAiParsingInvoiceTool", "EdenAiSpeechToTextTool", "EdenAiTextModerationTool", "EdenAiTextToSpeechTool", "EdenaiTool", "ElevenLabsText2SpeechTool", "ExtractHyperlinksTool", "ExtractTextTool", "FileSearchTool", "GetElementsTool", "GmailCreateDraft", "GmailGetMessage", "GmailGetThread", "GmailSearch", "GmailSendMessage", "GoogleCloudTextToSpeechTool", "GooglePlacesTool", "GoogleSearchResults", "GoogleSearchRun", "GoogleSerperResults", "GoogleSerperRun", "SearchAPIResults", "SearchAPIRun", "HumanInputRun", "IFTTTWebhook", "InfoPowerBITool", "InfoSQLDatabaseTool", "InfoSparkSQLTool", "JiraAction", "JsonGetValueTool", "JsonListKeysTool", "ListDirectoryTool", "ListPowerBITool", "ListSQLDatabaseTool", "ListSparkSQLTool", "MerriamWebsterQueryRun", "MetaphorSearchResults", "MoveFileTool", "NasaAction", "NavigateBackTool", "NavigateTool", "O365CreateDraftMessage", "O365SearchEmails", "O365SearchEvents", "O365SendEvent", "O365SendMessage", "OpenAPISpec", "OpenWeatherMapQueryRun", "PubmedQueryRun", "RedditSearchRun", "QueryCheckerTool", "QueryPowerBITool", "QuerySQLCheckerTool", "QuerySQLDataBaseTool", "QuerySparkSQLTool", "ReadFileTool", "RequestsDeleteTool", "RequestsGetTool", "RequestsPatchTool", "RequestsPostTool", "RequestsPutTool", "SteamWebAPIQueryRun", "SceneXplainTool", "SearxSearchResults", "SearxSearchRun", "ShellTool", "SlackGetChannel", "SlackGetMessage", "SlackScheduleMessage", "SlackSendMessage", "SleepTool", "StdInInquireTool", "StackExchangeTool", "SteamshipImageGenerationTool", "StructuredTool", "Tool", "VectorStoreQATool", "VectorStoreQAWithSourcesTool", "WikipediaQueryRun", "WolframAlphaQueryRun", "WriteFileTool", "YahooFinanceNewsTool", "YouTubeSearchTool", "ZapierNLAListActions", "ZapierNLARunAction", "format_tool_to_openai_function", "tool", ]

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"""**Tools** are classes that an Agent uses to interact with the world. Each tool has a **description**. Agent uses the description to choose the right tool for the job. **Class hierarchy:** .. code-block:: ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool <name> # Examples: BraveSearch, HumanInputRun **Main helpers:** .. code-block:: CallbackManagerForToolRun, AsyncCallbackManagerForToolRun """ import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain_core.tools import BaseTool, StructuredTool, Tool, tool from langchain.utils.interactive_env import is_interactive_env # Used for internal purposes _DEPRECATED_TOOLS = {"PythonAstREPLTool", "PythonREPLTool"} def _import_python_tool_PythonAstREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def _import_python_tool_PythonREPLTool() -> Any: raise ImportError( "This tool has been moved to langchain experiment. " "This tool has access to a python REPL. " "For best practices make sure to sandbox this tool. " "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " "To keep using this code as is, install langchain experimental and " "update relevant imports replacing 'langchain' with 'langchain_experimental'" ) def __getattr__(name: str) -> Any: if name == "PythonAstREPLTool": return _import_python_tool_PythonAstREPLTool() elif name == "PythonREPLTool": return _import_python_tool_PythonREPLTool() else: from langchain_community import tools # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing tools from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.tools import {name}`.\n\n" "To install langchain-community run " "`pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(tools, name) __all__ = [ "AINAppOps", "AINOwnerOps", "AINRuleOps", "AINTransfer", "AINValueOps", "AIPluginTool", "APIOperation", "ArxivQueryRun", "AzureCogsFormRecognizerTool", "AzureCogsImageAnalysisTool", "AzureCogsSpeech2TextTool", "AzureCogsText2SpeechTool", "AzureCogsTextAnalyticsHealthTool", "BaseGraphQLTool", "BaseRequestsTool", "BaseSQLDatabaseTool", "BaseSparkSQLTool", "BaseTool", "BearlyInterpreterTool", "BingSearchResults", "BingSearchRun", "BraveSearch", "ClickTool", "CopyFileTool", "CurrentWebPageTool", "DeleteFileTool", "DuckDuckGoSearchResults", "DuckDuckGoSearchRun", "E2BDataAnalysisTool", "EdenAiExplicitImageTool", "EdenAiObjectDetectionTool", "EdenAiParsingIDTool", "EdenAiParsingInvoiceTool", "EdenAiSpeechToTextTool", "EdenAiTextModerationTool", "EdenAiTextToSpeechTool", "EdenaiTool", "ElevenLabsText2SpeechTool", "ExtractHyperlinksTool", "ExtractTextTool", "FileSearchTool", "GetElementsTool", "GmailCreateDraft", "GmailGetMessage", "GmailGetThread", "GmailSearch", "GmailSendMessage", "GoogleCloudTextToSpeechTool", "GooglePlacesTool", "GoogleSearchResults", "GoogleSearchRun", "GoogleSerperResults", "GoogleSerperRun", "SearchAPIResults", "SearchAPIRun", "HumanInputRun", "IFTTTWebhook", "InfoPowerBITool", "InfoSQLDatabaseTool", "InfoSparkSQLTool", "JiraAction", "JsonGetValueTool", "JsonListKeysTool", "ListDirectoryTool", "ListPowerBITool", "ListSQLDatabaseTool", "ListSparkSQLTool", "MerriamWebsterQueryRun", "MetaphorSearchResults", "MoveFileTool", "NasaAction", "NavigateBackTool", "NavigateTool", "O365CreateDraftMessage", "O365SearchEmails", "O365SearchEvents", "O365SendEvent", "O365SendMessage", "OpenAPISpec", "OpenWeatherMapQueryRun", "PubmedQueryRun", "RedditSearchRun", "QueryCheckerTool", "QueryPowerBITool", "QuerySQLCheckerTool", "QuerySQLDataBaseTool", "QuerySparkSQLTool", "ReadFileTool", "RequestsDeleteTool", "RequestsGetTool", "RequestsPatchTool", "RequestsPostTool", "RequestsPutTool", "SteamWebAPIQueryRun", "SceneXplainTool", "SearxSearchResults", "SearxSearchRun", "ShellTool", "SlackGetChannel", "SlackGetMessage", "SlackScheduleMessage", "SlackSendMessage", "SleepTool", "StdInInquireTool", "StackExchangeTool", "SteamshipImageGenerationTool", "StructuredTool", "Tool", "VectorStoreQATool", "VectorStoreQAWithSourcesTool", "WikipediaQueryRun", "WolframAlphaQueryRun", "WriteFileTool", "YahooFinanceNewsTool", "YouTubeSearchTool", "ZapierNLAListActions", "ZapierNLARunAction", "format_tool_to_openai_function", "tool", ]

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from functools import partial from typing import Optional from langchain_core.callbacks.manager import ( Callbacks, ) from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain.tools import Tool class RetrieverInput(BaseModel): """Input to the retriever.""" query: str = Field(description="query to look up in retriever") def _get_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = retriever.get_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) async def _aget_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = await retriever.aget_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) def create_retriever_tool( retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional[BasePromptTemplate] = None, document_separator: str = "\n\n", ) -> Tool: """Create a tool to do retrieval of documents. Args: retriever: The retriever to use for the retrieval name: The name for the tool. This will be passed to the language model, so should be unique and somewhat descriptive. description: The description for the tool. This will be passed to the language model, so should be descriptive. Returns: Tool class to pass to an agent """ document_prompt = document_prompt or PromptTemplate.from_template("{page_content}") func = partial( _get_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) afunc = partial( _aget_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) return Tool( name=name, description=description, func=func, coroutine=afunc, args_schema=RetrieverInput, )

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from functools import partial from typing import Optional from langchain_core.callbacks.manager import ( Callbacks, ) from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain.tools import Tool class RetrieverInput(BaseModel): """Input to the retriever.""" query: str = Field(description="query to look up in retriever") def _get_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = retriever.get_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) async def _aget_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = await retriever.aget_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) def create_retriever_tool( retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional[BasePromptTemplate] = None, document_separator: str = "\n\n", ) -> Tool: """Create a tool to do retrieval of documents. Args: retriever: The retriever to use for the retrieval name: The name for the tool. This will be passed to the language model, so should be unique and somewhat descriptive. description: The description for the tool. This will be passed to the language model, so should be descriptive. Returns: Tool class to pass to an agent """ document_prompt = document_prompt or PromptTemplate.from_template("{page_content}") func = partial( _get_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) afunc = partial( _aget_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) return Tool( name=name, description=description, func=func, coroutine=afunc, args_schema=RetrieverInput, )

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from functools import partial from typing import Optional from langchain_core.callbacks.manager import ( Callbacks, ) from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain.tools import Tool class RetrieverInput(BaseModel): """Input to the retriever.""" query: str = Field(description="query to look up in retriever") def _get_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = retriever.get_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) async def _aget_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = await retriever.aget_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) def create_retriever_tool( retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional[BasePromptTemplate] = None, document_separator: str = "\n\n", ) -> Tool: """Create a tool to do retrieval of documents. Args: retriever: The retriever to use for the retrieval name: The name for the tool. This will be passed to the language model, so should be unique and somewhat descriptive. description: The description for the tool. This will be passed to the language model, so should be descriptive. Returns: Tool class to pass to an agent """ document_prompt = document_prompt or PromptTemplate.from_template("{page_content}") func = partial( _get_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) afunc = partial( _aget_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) return Tool( name=name, description=description, func=func, coroutine=afunc, args_schema=RetrieverInput, )

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from functools import partial from typing import Optional from langchain_core.callbacks.manager import ( Callbacks, ) from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain.tools import Tool class RetrieverInput(BaseModel): """Input to the retriever.""" query: str = Field(description="query to look up in retriever") def _get_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = retriever.get_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) async def _aget_relevant_documents( query: str, retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, callbacks: Callbacks = None, ) -> str: docs = await retriever.aget_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) def create_retriever_tool( retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional[BasePromptTemplate] = None, document_separator: str = "\n\n", ) -> Tool: """Create a tool to do retrieval of documents. Args: retriever: The retriever to use for the retrieval name: The name for the tool. This will be passed to the language model, so should be unique and somewhat descriptive. description: The description for the tool. This will be passed to the language model, so should be descriptive. Returns: Tool class to pass to an agent """ document_prompt = document_prompt or PromptTemplate.from_template("{page_content}") func = partial( _get_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) afunc = partial( _aget_relevant_documents, retriever=retriever, document_prompt=document_prompt, document_separator=document_separator, ) return Tool( name=name, description=description, func=func, coroutine=afunc, args_schema=RetrieverInput, )

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from typing import Any, List, Sequence, Tuple, Union from langchain_core._api import deprecated from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import Callbacks from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.chat import AIMessagePromptTemplate, ChatPromptTemplate from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.tools import BaseTool from langchain.agents.agent import BaseSingleActionAgent from langchain.agents.format_scratchpad import format_xml from langchain.agents.output_parsers import XMLAgentOutputParser from langchain.agents.xml.prompt import agent_instructions from langchain.chains.llm import LLMChain from langchain.tools.render import ToolsRenderer, render_text_description @deprecated("0.1.0", alternative="create_xml_agent", removal="0.2.0") class XMLAgent(BaseSingleActionAgent): """Agent that uses XML tags. Args: tools: list of tools the agent can choose from llm_chain: The LLMChain to call to predict the next action Examples: .. code-block:: python from langchain.agents import XMLAgent from langchain tools = ... model = """ tools: List[BaseTool] """List of tools this agent has access to.""" llm_chain: LLMChain """Chain to use to predict action.""" @property def input_keys(self) -> List[str]: return ["input"] @staticmethod def get_default_prompt() -> ChatPromptTemplate: base_prompt = ChatPromptTemplate.from_template(agent_instructions) return base_prompt + AIMessagePromptTemplate.from_template( "{intermediate_steps}" ) @staticmethod def get_default_output_parser() -> XMLAgentOutputParser: return XMLAgentOutputParser() def plan( self, intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Callbacks = None, **kwargs: Any, ) -> Union[AgentAction, AgentFinish]: log = "" for action, observation in intermediate_steps: log += ( f"<tool>{action.tool}</tool><tool_input>{action.tool_input}" f"</tool_input><observation>{observation}</observation>" ) tools = "" for tool in self.tools: tools += f"{tool.name}: {tool.description}\n" inputs = { "intermediate_steps": log, "tools": tools, "question": kwargs["input"], "stop": ["</tool_input>", "</final_answer>"], } response = self.llm_chain(inputs, callbacks=callbacks) return response[self.llm_chain.output_key] async def aplan( self, intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Callbacks = None, **kwargs: Any, ) -> Union[AgentAction, AgentFinish]: log = "" for action, observation in intermediate_steps: log += ( f"<tool>{action.tool}</tool><tool_input>{action.tool_input}" f"</tool_input><observation>{observation}</observation>" ) tools = "" for tool in self.tools: tools += f"{tool.name}: {tool.description}\n" inputs = { "intermediate_steps": log, "tools": tools, "question": kwargs["input"], "stop": ["</tool_input>", "</final_answer>"], } response = await self.llm_chain.acall(inputs, callbacks=callbacks) return response[self.llm_chain.output_key] def create_xml_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate, tools_renderer: ToolsRenderer = render_text_description, ) -> Runnable: """Create an agent that uses XML to format its logic. Args: llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use, must have input keys `tools`: contains descriptions for each tool. `agent_scratchpad`: contains previous agent actions and tool outputs. tools_renderer: This controls how the tools are converted into a string and then passed into the LLM. Default is `render_text_description`. Returns: A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does. It returns as output either an AgentAction or AgentFinish. Example: .. code-block:: python from langchain import hub from langchain_community.chat_models import ChatAnthropic from langchain.agents import AgentExecutor, create_xml_agent prompt = hub.pull("hwchase17/xml-agent-convo") model = ChatAnthropic() tools = ... agent = create_xml_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"input": "hi"}) # Use with chat history from langchain_core.messages import AIMessage, HumanMessage agent_executor.invoke( { "input": "what's my name?", # Notice that chat_history is a string # since this prompt is aimed at LLMs, not chat models "chat_history": "Human: My name is Bob\\nAI: Hello Bob!", } ) Prompt: The prompt must have input keys: * `tools`: contains descriptions for each tool. * `agent_scratchpad`: contains previous agent actions and tool outputs as an XML string. Here's an example: .. code-block:: python from langchain_core.prompts import PromptTemplate template = '''You are a helpful assistant. Help the user answer any questions. You have access to the following tools: {tools} In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation> For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond: <tool>search</tool><tool_input>weather in SF</tool_input> <observation>64 degrees</observation> When you are done, respond with a final answer between <final_answer></final_answer>. For example: <final_answer>The weather in SF is 64 degrees</final_answer> Begin! Previous Conversation: {chat_history} Question: {input} {agent_scratchpad}''' prompt = PromptTemplate.from_template(template) """ # noqa: E501 missing_vars = {"tools", "agent_scratchpad"}.difference(prompt.input_variables) if missing_vars: raise ValueError(f"Prompt missing required variables: {missing_vars}") prompt = prompt.partial( tools=tools_renderer(list(tools)), ) llm_with_stop = llm.bind(stop=["</tool_input>"]) agent = ( RunnablePassthrough.assign( agent_scratchpad=lambda x: format_xml(x["intermediate_steps"]), ) | prompt | llm_with_stop | XMLAgentOutputParser() ) return agent

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from typing import Any, List, Sequence, Tuple, Union from langchain_core._api import deprecated from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import Callbacks from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.chat import AIMessagePromptTemplate, ChatPromptTemplate from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.tools import BaseTool from langchain.agents.agent import BaseSingleActionAgent from langchain.agents.format_scratchpad import format_xml from langchain.agents.output_parsers import XMLAgentOutputParser from langchain.agents.xml.prompt import agent_instructions from langchain.chains.llm import LLMChain from langchain.tools.render import ToolsRenderer, render_text_description @deprecated("0.1.0", alternative="create_xml_agent", removal="0.2.0") class XMLAgent(BaseSingleActionAgent): """Agent that uses XML tags. Args: tools: list of tools the agent can choose from llm_chain: The LLMChain to call to predict the next action Examples: .. code-block:: python from langchain.agents import XMLAgent from langchain tools = ... model = """ tools: List[BaseTool] """List of tools this agent has access to.""" llm_chain: LLMChain """Chain to use to predict action.""" @property def input_keys(self) -> List[str]: return ["input"] @staticmethod def get_default_prompt() -> ChatPromptTemplate: base_prompt = ChatPromptTemplate.from_template(agent_instructions) return base_prompt + AIMessagePromptTemplate.from_template( "{intermediate_steps}" ) @staticmethod def get_default_output_parser() -> XMLAgentOutputParser: return XMLAgentOutputParser() def plan( self, intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Callbacks = None, **kwargs: Any, ) -> Union[AgentAction, AgentFinish]: log = "" for action, observation in intermediate_steps: log += ( f"<tool>{action.tool}</tool><tool_input>{action.tool_input}" f"</tool_input><observation>{observation}</observation>" ) tools = "" for tool in self.tools: tools += f"{tool.name}: {tool.description}\n" inputs = { "intermediate_steps": log, "tools": tools, "question": kwargs["input"], "stop": ["</tool_input>", "</final_answer>"], } response = self.llm_chain(inputs, callbacks=callbacks) return response[self.llm_chain.output_key] async def aplan( self, intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Callbacks = None, **kwargs: Any, ) -> Union[AgentAction, AgentFinish]: log = "" for action, observation in intermediate_steps: log += ( f"<tool>{action.tool}</tool><tool_input>{action.tool_input}" f"</tool_input><observation>{observation}</observation>" ) tools = "" for tool in self.tools: tools += f"{tool.name}: {tool.description}\n" inputs = { "intermediate_steps": log, "tools": tools, "question": kwargs["input"], "stop": ["</tool_input>", "</final_answer>"], } response = await self.llm_chain.acall(inputs, callbacks=callbacks) return response[self.llm_chain.output_key] def create_xml_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate, tools_renderer: ToolsRenderer = render_text_description, ) -> Runnable: """Create an agent that uses XML to format its logic. Args: llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use, must have input keys `tools`: contains descriptions for each tool. `agent_scratchpad`: contains previous agent actions and tool outputs. tools_renderer: This controls how the tools are converted into a string and then passed into the LLM. Default is `render_text_description`. Returns: A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does. It returns as output either an AgentAction or AgentFinish. Example: .. code-block:: python from langchain import hub from langchain_community.chat_models import ChatAnthropic from langchain.agents import AgentExecutor, create_xml_agent prompt = hub.pull("hwchase17/xml-agent-convo") model = ChatAnthropic() tools = ... agent = create_xml_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"input": "hi"}) # Use with chat history from langchain_core.messages import AIMessage, HumanMessage agent_executor.invoke( { "input": "what's my name?", # Notice that chat_history is a string # since this prompt is aimed at LLMs, not chat models "chat_history": "Human: My name is Bob\\nAI: Hello Bob!", } ) Prompt: The prompt must have input keys: * `tools`: contains descriptions for each tool. * `agent_scratchpad`: contains previous agent actions and tool outputs as an XML string. Here's an example: .. code-block:: python from langchain_core.prompts import PromptTemplate template = '''You are a helpful assistant. Help the user answer any questions. You have access to the following tools: {tools} In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation> For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond: <tool>search</tool><tool_input>weather in SF</tool_input> <observation>64 degrees</observation> When you are done, respond with a final answer between <final_answer></final_answer>. For example: <final_answer>The weather in SF is 64 degrees</final_answer> Begin! Previous Conversation: {chat_history} Question: {input} {agent_scratchpad}''' prompt = PromptTemplate.from_template(template) """ # noqa: E501 missing_vars = {"tools", "agent_scratchpad"}.difference(prompt.input_variables) if missing_vars: raise ValueError(f"Prompt missing required variables: {missing_vars}") prompt = prompt.partial( tools=tools_renderer(list(tools)), ) llm_with_stop = llm.bind(stop=["</tool_input>"]) agent = ( RunnablePassthrough.assign( agent_scratchpad=lambda x: format_xml(x["intermediate_steps"]), ) | prompt | llm_with_stop | XMLAgentOutputParser() ) return agent

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"""**Graphs** provide a natural language interface to graph databases.""" import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain.utils.interactive_env import is_interactive_env def __getattr__(name: str) -> Any: from langchain_community import graphs # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing graphs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.graphs import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(graphs, name) __all__ = [ "MemgraphGraph", "NetworkxEntityGraph", "Neo4jGraph", "NebulaGraph", "NeptuneGraph", "KuzuGraph", "HugeGraph", "RdfGraph", "ArangoGraph", "FalkorDBGraph", ]

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"""**Graphs** provide a natural language interface to graph databases.""" import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain.utils.interactive_env import is_interactive_env def __getattr__(name: str) -> Any: from langchain_community import graphs # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing graphs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.graphs import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(graphs, name) __all__ = [ "MemgraphGraph", "NetworkxEntityGraph", "Neo4jGraph", "NebulaGraph", "NeptuneGraph", "KuzuGraph", "HugeGraph", "RdfGraph", "ArangoGraph", "FalkorDBGraph", ]

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"""**Graphs** provide a natural language interface to graph databases.""" import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain.utils.interactive_env import is_interactive_env def __getattr__(name: str) -> Any: from langchain_community import graphs # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing graphs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.graphs import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(graphs, name) __all__ = [ "MemgraphGraph", "NetworkxEntityGraph", "Neo4jGraph", "NebulaGraph", "NeptuneGraph", "KuzuGraph", "HugeGraph", "RdfGraph", "ArangoGraph", "FalkorDBGraph", ]

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"""**Graphs** provide a natural language interface to graph databases.""" import warnings from typing import Any from langchain_core._api import LangChainDeprecationWarning from langchain.utils.interactive_env import is_interactive_env def __getattr__(name: str) -> Any: from langchain_community import graphs # If not in interactive env, raise warning. if not is_interactive_env(): warnings.warn( "Importing graphs from langchain is deprecated. Importing from " "langchain will no longer be supported as of langchain==0.2.0. " "Please import from langchain-community instead:\n\n" f"`from langchain_community.graphs import {name}`.\n\n" "To install langchain-community run `pip install -U langchain-community`.", category=LangChainDeprecationWarning, ) return getattr(graphs, name) __all__ = [ "MemgraphGraph", "NetworkxEntityGraph", "Neo4jGraph", "NebulaGraph", "NeptuneGraph", "KuzuGraph", "HugeGraph", "RdfGraph", "ArangoGraph", "FalkorDBGraph", ]

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"""Chain that makes API calls and summarizes the responses to answer a question.""" from __future__ import annotations from typing import Any, Dict, List, Optional, Sequence, Tuple from urllib.parse import urlparse from langchain_community.utilities.requests import TextRequestsWrapper from langchain_core.callbacks import ( AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field, root_validator from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT from langchain.chains.base import Chain from langchain.chains.llm import LLMChain def _extract_scheme_and_domain(url: str) -> Tuple[str, str]: """Extract the scheme + domain from a given URL. Args: url (str): The input URL. Returns: return a 2-tuple of scheme and domain """ parsed_uri = urlparse(url) return parsed_uri.scheme, parsed_uri.netloc def _check_in_allowed_domain(url: str, limit_to_domains: Sequence[str]) -> bool: """Check if a URL is in the allowed domains. Args: url (str): The input URL. limit_to_domains (Sequence[str]): The allowed domains. Returns: bool: True if the URL is in the allowed domains, False otherwise. """ scheme, domain = _extract_scheme_and_domain(url) for allowed_domain in limit_to_domains: allowed_scheme, allowed_domain = _extract_scheme_and_domain(allowed_domain) if scheme == allowed_scheme and domain == allowed_domain: return True return False class APIChain(Chain): """Chain that makes API calls and summarizes the responses to answer a question. *Security Note*: This API chain uses the requests toolkit to make GET, POST, PATCH, PUT, and DELETE requests to an API. Exercise care in who is allowed to use this chain. If exposing to end users, consider that users will be able to make arbitrary requests on behalf of the server hosting the code. For example, users could ask the server to make a request to a private API that is only accessible from the server. Control access to who can submit issue requests using this toolkit and what network access it has. See https://python.langchain.com/docs/security for more information. """ api_request_chain: LLMChain api_answer_chain: LLMChain requests_wrapper: TextRequestsWrapper = Field(exclude=True) api_docs: str question_key: str = "question" #: :meta private: output_key: str = "output" #: :meta private: limit_to_domains: Optional[Sequence[str]] """Use to limit the domains that can be accessed by the API chain. * For example, to limit to just the domain `https://www.example.com`, set `limit_to_domains=["https://www.example.com"]`. * The default value is an empty tuple, which means that no domains are allowed by default. By design this will raise an error on instantiation. * Use a None if you want to allow all domains by default -- this is not recommended for security reasons, as it would allow malicious users to make requests to arbitrary URLS including internal APIs accessible from the server. """ @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.question_key] @property def output_keys(self) -> List[str]: """Expect output key. :meta private: """ return [self.output_key] @root_validator(pre=True) def validate_api_request_prompt(cls, values: Dict) -> Dict: """Check that api request prompt expects the right variables.""" input_vars = values["api_request_chain"].prompt.input_variables expected_vars = {"question", "api_docs"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values @root_validator(pre=True) def validate_limit_to_domains(cls, values: Dict) -> Dict: """Check that allowed domains are valid.""" if "limit_to_domains" not in values: raise ValueError( "You must specify a list of domains to limit access using " "`limit_to_domains`" ) if not values["limit_to_domains"] and values["limit_to_domains"] is not None: raise ValueError( "Please provide a list of domains to limit access using " "`limit_to_domains`." ) return values @root_validator(pre=True) def validate_api_answer_prompt(cls, values: Dict) -> Dict: """Check that api answer prompt expects the right variables.""" input_vars = values["api_answer_chain"].prompt.input_variables expected_vars = {"question", "api_docs", "api_url", "api_response"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = self.api_request_chain.predict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) _run_manager.on_text(api_url, color="green", end="\n", verbose=self.verbose) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = self.requests_wrapper.get(api_url) _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = self.api_answer_chain.predict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = await self.api_request_chain.apredict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) await _run_manager.on_text( api_url, color="green", end="\n", verbose=self.verbose ) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = await self.requests_wrapper.aget(api_url) await _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = await self.api_answer_chain.apredict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} @classmethod def from_llm_and_api_docs( cls, llm: BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: BasePromptTemplate = API_URL_PROMPT, api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT, limit_to_domains: Optional[Sequence[str]] = tuple(), **kwargs: Any, ) -> APIChain: """Load chain from just an LLM and the api docs.""" get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt) requests_wrapper = TextRequestsWrapper(headers=headers) get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt) return cls( api_request_chain=get_request_chain, api_answer_chain=get_answer_chain, requests_wrapper=requests_wrapper, api_docs=api_docs, limit_to_domains=limit_to_domains, **kwargs, ) @property def _chain_type(self) -> str: return "api_chain"

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"""Chain that makes API calls and summarizes the responses to answer a question.""" from __future__ import annotations from typing import Any, Dict, List, Optional, Sequence, Tuple from urllib.parse import urlparse from langchain_community.utilities.requests import TextRequestsWrapper from langchain_core.callbacks import ( AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field, root_validator from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT from langchain.chains.base import Chain from langchain.chains.llm import LLMChain def _extract_scheme_and_domain(url: str) -> Tuple[str, str]: """Extract the scheme + domain from a given URL. Args: url (str): The input URL. Returns: return a 2-tuple of scheme and domain """ parsed_uri = urlparse(url) return parsed_uri.scheme, parsed_uri.netloc def _check_in_allowed_domain(url: str, limit_to_domains: Sequence[str]) -> bool: """Check if a URL is in the allowed domains. Args: url (str): The input URL. limit_to_domains (Sequence[str]): The allowed domains. Returns: bool: True if the URL is in the allowed domains, False otherwise. """ scheme, domain = _extract_scheme_and_domain(url) for allowed_domain in limit_to_domains: allowed_scheme, allowed_domain = _extract_scheme_and_domain(allowed_domain) if scheme == allowed_scheme and domain == allowed_domain: return True return False class APIChain(Chain): """Chain that makes API calls and summarizes the responses to answer a question. *Security Note*: This API chain uses the requests toolkit to make GET, POST, PATCH, PUT, and DELETE requests to an API. Exercise care in who is allowed to use this chain. If exposing to end users, consider that users will be able to make arbitrary requests on behalf of the server hosting the code. For example, users could ask the server to make a request to a private API that is only accessible from the server. Control access to who can submit issue requests using this toolkit and what network access it has. See https://python.langchain.com/docs/security for more information. """ api_request_chain: LLMChain api_answer_chain: LLMChain requests_wrapper: TextRequestsWrapper = Field(exclude=True) api_docs: str question_key: str = "question" #: :meta private: output_key: str = "output" #: :meta private: limit_to_domains: Optional[Sequence[str]] """Use to limit the domains that can be accessed by the API chain. * For example, to limit to just the domain `https://www.example.com`, set `limit_to_domains=["https://www.example.com"]`. * The default value is an empty tuple, which means that no domains are allowed by default. By design this will raise an error on instantiation. * Use a None if you want to allow all domains by default -- this is not recommended for security reasons, as it would allow malicious users to make requests to arbitrary URLS including internal APIs accessible from the server. """ @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.question_key] @property def output_keys(self) -> List[str]: """Expect output key. :meta private: """ return [self.output_key] @root_validator(pre=True) def validate_api_request_prompt(cls, values: Dict) -> Dict: """Check that api request prompt expects the right variables.""" input_vars = values["api_request_chain"].prompt.input_variables expected_vars = {"question", "api_docs"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values @root_validator(pre=True) def validate_limit_to_domains(cls, values: Dict) -> Dict: """Check that allowed domains are valid.""" if "limit_to_domains" not in values: raise ValueError( "You must specify a list of domains to limit access using " "`limit_to_domains`" ) if not values["limit_to_domains"] and values["limit_to_domains"] is not None: raise ValueError( "Please provide a list of domains to limit access using " "`limit_to_domains`." ) return values @root_validator(pre=True) def validate_api_answer_prompt(cls, values: Dict) -> Dict: """Check that api answer prompt expects the right variables.""" input_vars = values["api_answer_chain"].prompt.input_variables expected_vars = {"question", "api_docs", "api_url", "api_response"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = self.api_request_chain.predict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) _run_manager.on_text(api_url, color="green", end="\n", verbose=self.verbose) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = self.requests_wrapper.get(api_url) _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = self.api_answer_chain.predict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = await self.api_request_chain.apredict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) await _run_manager.on_text( api_url, color="green", end="\n", verbose=self.verbose ) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = await self.requests_wrapper.aget(api_url) await _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = await self.api_answer_chain.apredict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} @classmethod def from_llm_and_api_docs( cls, llm: BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: BasePromptTemplate = API_URL_PROMPT, api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT, limit_to_domains: Optional[Sequence[str]] = tuple(), **kwargs: Any, ) -> APIChain: """Load chain from just an LLM and the api docs.""" get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt) requests_wrapper = TextRequestsWrapper(headers=headers) get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt) return cls( api_request_chain=get_request_chain, api_answer_chain=get_answer_chain, requests_wrapper=requests_wrapper, api_docs=api_docs, limit_to_domains=limit_to_domains, **kwargs, ) @property def _chain_type(self) -> str: return "api_chain"

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"""Chain that makes API calls and summarizes the responses to answer a question.""" from __future__ import annotations from typing import Any, Dict, List, Optional, Sequence, Tuple from urllib.parse import urlparse from langchain_community.utilities.requests import TextRequestsWrapper from langchain_core.callbacks import ( AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field, root_validator from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT from langchain.chains.base import Chain from langchain.chains.llm import LLMChain def _extract_scheme_and_domain(url: str) -> Tuple[str, str]: """Extract the scheme + domain from a given URL. Args: url (str): The input URL. Returns: return a 2-tuple of scheme and domain """ parsed_uri = urlparse(url) return parsed_uri.scheme, parsed_uri.netloc def _check_in_allowed_domain(url: str, limit_to_domains: Sequence[str]) -> bool: """Check if a URL is in the allowed domains. Args: url (str): The input URL. limit_to_domains (Sequence[str]): The allowed domains. Returns: bool: True if the URL is in the allowed domains, False otherwise. """ scheme, domain = _extract_scheme_and_domain(url) for allowed_domain in limit_to_domains: allowed_scheme, allowed_domain = _extract_scheme_and_domain(allowed_domain) if scheme == allowed_scheme and domain == allowed_domain: return True return False class APIChain(Chain): """Chain that makes API calls and summarizes the responses to answer a question. *Security Note*: This API chain uses the requests toolkit to make GET, POST, PATCH, PUT, and DELETE requests to an API. Exercise care in who is allowed to use this chain. If exposing to end users, consider that users will be able to make arbitrary requests on behalf of the server hosting the code. For example, users could ask the server to make a request to a private API that is only accessible from the server. Control access to who can submit issue requests using this toolkit and what network access it has. See https://python.langchain.com/docs/security for more information. """ api_request_chain: LLMChain api_answer_chain: LLMChain requests_wrapper: TextRequestsWrapper = Field(exclude=True) api_docs: str question_key: str = "question" #: :meta private: output_key: str = "output" #: :meta private: limit_to_domains: Optional[Sequence[str]] """Use to limit the domains that can be accessed by the API chain. * For example, to limit to just the domain `https://www.example.com`, set `limit_to_domains=["https://www.example.com"]`. * The default value is an empty tuple, which means that no domains are allowed by default. By design this will raise an error on instantiation. * Use a None if you want to allow all domains by default -- this is not recommended for security reasons, as it would allow malicious users to make requests to arbitrary URLS including internal APIs accessible from the server. """ @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.question_key] @property def output_keys(self) -> List[str]: """Expect output key. :meta private: """ return [self.output_key] @root_validator(pre=True) def validate_api_request_prompt(cls, values: Dict) -> Dict: """Check that api request prompt expects the right variables.""" input_vars = values["api_request_chain"].prompt.input_variables expected_vars = {"question", "api_docs"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values @root_validator(pre=True) def validate_limit_to_domains(cls, values: Dict) -> Dict: """Check that allowed domains are valid.""" if "limit_to_domains" not in values: raise ValueError( "You must specify a list of domains to limit access using " "`limit_to_domains`" ) if not values["limit_to_domains"] and values["limit_to_domains"] is not None: raise ValueError( "Please provide a list of domains to limit access using " "`limit_to_domains`." ) return values @root_validator(pre=True) def validate_api_answer_prompt(cls, values: Dict) -> Dict: """Check that api answer prompt expects the right variables.""" input_vars = values["api_answer_chain"].prompt.input_variables expected_vars = {"question", "api_docs", "api_url", "api_response"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = self.api_request_chain.predict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) _run_manager.on_text(api_url, color="green", end="\n", verbose=self.verbose) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = self.requests_wrapper.get(api_url) _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = self.api_answer_chain.predict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = await self.api_request_chain.apredict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) await _run_manager.on_text( api_url, color="green", end="\n", verbose=self.verbose ) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = await self.requests_wrapper.aget(api_url) await _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = await self.api_answer_chain.apredict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} @classmethod def from_llm_and_api_docs( cls, llm: BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: BasePromptTemplate = API_URL_PROMPT, api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT, limit_to_domains: Optional[Sequence[str]] = tuple(), **kwargs: Any, ) -> APIChain: """Load chain from just an LLM and the api docs.""" get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt) requests_wrapper = TextRequestsWrapper(headers=headers) get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt) return cls( api_request_chain=get_request_chain, api_answer_chain=get_answer_chain, requests_wrapper=requests_wrapper, api_docs=api_docs, limit_to_domains=limit_to_domains, **kwargs, ) @property def _chain_type(self) -> str: return "api_chain"

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"""Chain that makes API calls and summarizes the responses to answer a question.""" from __future__ import annotations from typing import Any, Dict, List, Optional, Sequence, Tuple from urllib.parse import urlparse from langchain_community.utilities.requests import TextRequestsWrapper from langchain_core.callbacks import ( AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field, root_validator from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT from langchain.chains.base import Chain from langchain.chains.llm import LLMChain def _extract_scheme_and_domain(url: str) -> Tuple[str, str]: """Extract the scheme + domain from a given URL. Args: url (str): The input URL. Returns: return a 2-tuple of scheme and domain """ parsed_uri = urlparse(url) return parsed_uri.scheme, parsed_uri.netloc def _check_in_allowed_domain(url: str, limit_to_domains: Sequence[str]) -> bool: """Check if a URL is in the allowed domains. Args: url (str): The input URL. limit_to_domains (Sequence[str]): The allowed domains. Returns: bool: True if the URL is in the allowed domains, False otherwise. """ scheme, domain = _extract_scheme_and_domain(url) for allowed_domain in limit_to_domains: allowed_scheme, allowed_domain = _extract_scheme_and_domain(allowed_domain) if scheme == allowed_scheme and domain == allowed_domain: return True return False class APIChain(Chain): """Chain that makes API calls and summarizes the responses to answer a question. *Security Note*: This API chain uses the requests toolkit to make GET, POST, PATCH, PUT, and DELETE requests to an API. Exercise care in who is allowed to use this chain. If exposing to end users, consider that users will be able to make arbitrary requests on behalf of the server hosting the code. For example, users could ask the server to make a request to a private API that is only accessible from the server. Control access to who can submit issue requests using this toolkit and what network access it has. See https://python.langchain.com/docs/security for more information. """ api_request_chain: LLMChain api_answer_chain: LLMChain requests_wrapper: TextRequestsWrapper = Field(exclude=True) api_docs: str question_key: str = "question" #: :meta private: output_key: str = "output" #: :meta private: limit_to_domains: Optional[Sequence[str]] """Use to limit the domains that can be accessed by the API chain. * For example, to limit to just the domain `https://www.example.com`, set `limit_to_domains=["https://www.example.com"]`. * The default value is an empty tuple, which means that no domains are allowed by default. By design this will raise an error on instantiation. * Use a None if you want to allow all domains by default -- this is not recommended for security reasons, as it would allow malicious users to make requests to arbitrary URLS including internal APIs accessible from the server. """ @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.question_key] @property def output_keys(self) -> List[str]: """Expect output key. :meta private: """ return [self.output_key] @root_validator(pre=True) def validate_api_request_prompt(cls, values: Dict) -> Dict: """Check that api request prompt expects the right variables.""" input_vars = values["api_request_chain"].prompt.input_variables expected_vars = {"question", "api_docs"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values @root_validator(pre=True) def validate_limit_to_domains(cls, values: Dict) -> Dict: """Check that allowed domains are valid.""" if "limit_to_domains" not in values: raise ValueError( "You must specify a list of domains to limit access using " "`limit_to_domains`" ) if not values["limit_to_domains"] and values["limit_to_domains"] is not None: raise ValueError( "Please provide a list of domains to limit access using " "`limit_to_domains`." ) return values @root_validator(pre=True) def validate_api_answer_prompt(cls, values: Dict) -> Dict: """Check that api answer prompt expects the right variables.""" input_vars = values["api_answer_chain"].prompt.input_variables expected_vars = {"question", "api_docs", "api_url", "api_response"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) return values def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = self.api_request_chain.predict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) _run_manager.on_text(api_url, color="green", end="\n", verbose=self.verbose) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = self.requests_wrapper.get(api_url) _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = self.api_answer_chain.predict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = await self.api_request_chain.apredict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) await _run_manager.on_text( api_url, color="green", end="\n", verbose=self.verbose ) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains ): raise ValueError( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) api_response = await self.requests_wrapper.aget(api_url) await _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose ) answer = await self.api_answer_chain.apredict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} @classmethod def from_llm_and_api_docs( cls, llm: BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: BasePromptTemplate = API_URL_PROMPT, api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT, limit_to_domains: Optional[Sequence[str]] = tuple(), **kwargs: Any, ) -> APIChain: """Load chain from just an LLM and the api docs.""" get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt) requests_wrapper = TextRequestsWrapper(headers=headers) get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt) return cls( api_request_chain=get_request_chain, api_answer_chain=get_answer_chain, requests_wrapper=requests_wrapper, api_docs=api_docs, limit_to_domains=limit_to_domains, **kwargs, ) @property def _chain_type(self) -> str: return "api_chain"

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"""Hypothetical Document Embeddings. https://arxiv.org/abs/2212.10496 """ from __future__ import annotations from typing import Any, Dict, List, Optional import numpy as np from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Extra from langchain.chains.base import Chain from langchain.chains.hyde.prompts import PROMPT_MAP from langchain.chains.llm import LLMChain class HypotheticalDocumentEmbedder(Chain, Embeddings): """Generate hypothetical document for query, and then embed that. Based on https://arxiv.org/abs/2212.10496 """ base_embeddings: Embeddings llm_chain: LLMChain class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Input keys for Hyde's LLM chain.""" return self.llm_chain.input_keys @property def output_keys(self) -> List[str]: """Output keys for Hyde's LLM chain.""" return self.llm_chain.output_keys def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call the base embeddings.""" return self.base_embeddings.embed_documents(texts) def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]: """Combine embeddings into final embeddings.""" return list(np.array(embeddings).mean(axis=0)) def embed_query(self, text: str) -> List[float]: """Generate a hypothetical document and embedded it.""" var_name = self.llm_chain.input_keys[0] result = self.llm_chain.generate([{var_name: text}]) documents = [generation.text for generation in result.generations[0]] embeddings = self.embed_documents(documents) return self.combine_embeddings(embeddings) def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: """Call the internal llm chain.""" _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() return self.llm_chain(inputs, callbacks=_run_manager.get_child()) @classmethod def from_llm( cls, llm: BaseLanguageModel, base_embeddings: Embeddings, prompt_key: Optional[str] = None, custom_prompt: Optional[BasePromptTemplate] = None, **kwargs: Any, ) -> HypotheticalDocumentEmbedder: """Load and use LLMChain with either a specific prompt key or custom prompt.""" if custom_prompt is not None: prompt = custom_prompt elif prompt_key is not None and prompt_key in PROMPT_MAP: prompt = PROMPT_MAP[prompt_key] else: raise ValueError( f"Must specify prompt_key if custom_prompt not provided. Should be one " f"of {list(PROMPT_MAP.keys())}." ) llm_chain = LLMChain(llm=llm, prompt=prompt) return cls(base_embeddings=base_embeddings, llm_chain=llm_chain, **kwargs) @property def _chain_type(self) -> str: return "hyde_chain"

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"""Hypothetical Document Embeddings. https://arxiv.org/abs/2212.10496 """ from __future__ import annotations from typing import Any, Dict, List, Optional import numpy as np from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Extra from langchain.chains.base import Chain from langchain.chains.hyde.prompts import PROMPT_MAP from langchain.chains.llm import LLMChain class HypotheticalDocumentEmbedder(Chain, Embeddings): """Generate hypothetical document for query, and then embed that. Based on https://arxiv.org/abs/2212.10496 """ base_embeddings: Embeddings llm_chain: LLMChain class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Input keys for Hyde's LLM chain.""" return self.llm_chain.input_keys @property def output_keys(self) -> List[str]: """Output keys for Hyde's LLM chain.""" return self.llm_chain.output_keys def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call the base embeddings.""" return self.base_embeddings.embed_documents(texts) def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]: """Combine embeddings into final embeddings.""" return list(np.array(embeddings).mean(axis=0)) def embed_query(self, text: str) -> List[float]: """Generate a hypothetical document and embedded it.""" var_name = self.llm_chain.input_keys[0] result = self.llm_chain.generate([{var_name: text}]) documents = [generation.text for generation in result.generations[0]] embeddings = self.embed_documents(documents) return self.combine_embeddings(embeddings) def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: """Call the internal llm chain.""" _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() return self.llm_chain(inputs, callbacks=_run_manager.get_child()) @classmethod def from_llm( cls, llm: BaseLanguageModel, base_embeddings: Embeddings, prompt_key: Optional[str] = None, custom_prompt: Optional[BasePromptTemplate] = None, **kwargs: Any, ) -> HypotheticalDocumentEmbedder: """Load and use LLMChain with either a specific prompt key or custom prompt.""" if custom_prompt is not None: prompt = custom_prompt elif prompt_key is not None and prompt_key in PROMPT_MAP: prompt = PROMPT_MAP[prompt_key] else: raise ValueError( f"Must specify prompt_key if custom_prompt not provided. Should be one " f"of {list(PROMPT_MAP.keys())}." ) llm_chain = LLMChain(llm=llm, prompt=prompt) return cls(base_embeddings=base_embeddings, llm_chain=llm_chain, **kwargs) @property def _chain_type(self) -> str: return "hyde_chain"

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"""Hypothetical Document Embeddings. https://arxiv.org/abs/2212.10496 """ from __future__ import annotations from typing import Any, Dict, List, Optional import numpy as np from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Extra from langchain.chains.base import Chain from langchain.chains.hyde.prompts import PROMPT_MAP from langchain.chains.llm import LLMChain class HypotheticalDocumentEmbedder(Chain, Embeddings): """Generate hypothetical document for query, and then embed that. Based on https://arxiv.org/abs/2212.10496 """ base_embeddings: Embeddings llm_chain: LLMChain class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Input keys for Hyde's LLM chain.""" return self.llm_chain.input_keys @property def output_keys(self) -> List[str]: """Output keys for Hyde's LLM chain.""" return self.llm_chain.output_keys def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call the base embeddings.""" return self.base_embeddings.embed_documents(texts) def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]: """Combine embeddings into final embeddings.""" return list(np.array(embeddings).mean(axis=0)) def embed_query(self, text: str) -> List[float]: """Generate a hypothetical document and embedded it.""" var_name = self.llm_chain.input_keys[0] result = self.llm_chain.generate([{var_name: text}]) documents = [generation.text for generation in result.generations[0]] embeddings = self.embed_documents(documents) return self.combine_embeddings(embeddings) def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: """Call the internal llm chain.""" _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() return self.llm_chain(inputs, callbacks=_run_manager.get_child()) @classmethod def from_llm( cls, llm: BaseLanguageModel, base_embeddings: Embeddings, prompt_key: Optional[str] = None, custom_prompt: Optional[BasePromptTemplate] = None, **kwargs: Any, ) -> HypotheticalDocumentEmbedder: """Load and use LLMChain with either a specific prompt key or custom prompt.""" if custom_prompt is not None: prompt = custom_prompt elif prompt_key is not None and prompt_key in PROMPT_MAP: prompt = PROMPT_MAP[prompt_key] else: raise ValueError( f"Must specify prompt_key if custom_prompt not provided. Should be one " f"of {list(PROMPT_MAP.keys())}." ) llm_chain = LLMChain(llm=llm, prompt=prompt) return cls(base_embeddings=base_embeddings, llm_chain=llm_chain, **kwargs) @property def _chain_type(self) -> str: return "hyde_chain"

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"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf.""" from __future__ import annotations from typing import Any, Callable, List, NamedTuple, Optional, Sequence from langchain_core._api import deprecated from langchain_core.callbacks import BaseCallbackManager from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import PromptTemplate from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser from langchain.agents.agent_types import AgentType from langchain.agents.mrkl.output_parser import MRKLOutputParser from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX from langchain.agents.tools import Tool from langchain.agents.utils import validate_tools_single_input from langchain.chains import LLMChain from langchain.tools.render import render_text_description class ChainConfig(NamedTuple): """Configuration for chain to use in MRKL system. Args: action_name: Name of the action. action: Action function to call. action_description: Description of the action. """ action_name: str action: Callable action_description: str @deprecated("0.1.0", alternative="create_react_agent", removal="0.2.0") class ZeroShotAgent(Agent): """Agent for the MRKL chain.""" output_parser: AgentOutputParser = Field(default_factory=MRKLOutputParser) @classmethod def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser: return MRKLOutputParser() @property def _agent_type(self) -> str: """Return Identifier of agent type.""" return AgentType.ZERO_SHOT_REACT_DESCRIPTION @property def observation_prefix(self) -> str: """Prefix to append the observation with.""" return "Observation: " @property def llm_prefix(self) -> str: """Prefix to append the llm call with.""" return "Thought:" @classmethod def create_prompt( cls, tools: Sequence[BaseTool], prefix: str = PREFIX, suffix: str = SUFFIX, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, ) -> PromptTemplate: """Create prompt in the style of the zero shot agent. Args: tools: List of tools the agent will have access to, used to format the prompt. prefix: String to put before the list of tools. suffix: String to put after the list of tools. input_variables: List of input variables the final prompt will expect. Returns: A PromptTemplate with the template assembled from the pieces here. """ tool_strings = render_text_description(list(tools)) tool_names = ", ".join([tool.name for tool in tools]) format_instructions = format_instructions.format(tool_names=tool_names) template = "\n\n".join([prefix, tool_strings, format_instructions, suffix]) if input_variables: return PromptTemplate(template=template, input_variables=input_variables) return PromptTemplate.from_template(template) @classmethod def from_llm_and_tools( cls, llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, prefix: str = PREFIX, suffix: str = SUFFIX, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, **kwargs: Any, ) -> Agent: """Construct an agent from an LLM and tools.""" cls._validate_tools(tools) prompt = cls.create_prompt( tools, prefix=prefix, suffix=suffix, format_instructions=format_instructions, input_variables=input_variables, ) llm_chain = LLMChain( llm=llm, prompt=prompt, callback_manager=callback_manager, ) tool_names = [tool.name for tool in tools] _output_parser = output_parser or cls._get_default_output_parser() return cls( llm_chain=llm_chain, allowed_tools=tool_names, output_parser=_output_parser, **kwargs, ) @classmethod def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: validate_tools_single_input(cls.__name__, tools) if len(tools) == 0: raise ValueError( f"Got no tools for {cls.__name__}. At least one tool must be provided." ) for tool in tools: if tool.description is None: raise ValueError( f"Got a tool {tool.name} without a description. For this agent, " f"a description must always be provided." ) super()._validate_tools(tools) @deprecated("0.1.0", removal="0.2.0") class MRKLChain(AgentExecutor): """[Deprecated] Chain that implements the MRKL system.""" @classmethod def from_chains( cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any ) -> AgentExecutor: """User friendly way to initialize the MRKL chain. This is intended to be an easy way to get up and running with the MRKL chain. Args: llm: The LLM to use as the agent LLM. chains: The chains the MRKL system has access to. **kwargs: parameters to be passed to initialization. Returns: An initialized MRKL chain. """ tools = [ Tool( name=c.action_name, func=c.action, description=c.action_description, ) for c in chains ] agent = ZeroShotAgent.from_llm_and_tools(llm, tools) return cls(agent=agent, tools=tools, **kwargs)

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"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf.""" from __future__ import annotations from typing import Any, Callable, List, NamedTuple, Optional, Sequence from langchain_core._api import deprecated from langchain_core.callbacks import BaseCallbackManager from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import PromptTemplate from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser from langchain.agents.agent_types import AgentType from langchain.agents.mrkl.output_parser import MRKLOutputParser from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX from langchain.agents.tools import Tool from langchain.agents.utils import validate_tools_single_input from langchain.chains import LLMChain from langchain.tools.render import render_text_description class ChainConfig(NamedTuple): """Configuration for chain to use in MRKL system. Args: action_name: Name of the action. action: Action function to call. action_description: Description of the action. """ action_name: str action: Callable action_description: str @deprecated("0.1.0", alternative="create_react_agent", removal="0.2.0") class ZeroShotAgent(Agent): """Agent for the MRKL chain.""" output_parser: AgentOutputParser = Field(default_factory=MRKLOutputParser) @classmethod def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser: return MRKLOutputParser() @property def _agent_type(self) -> str: """Return Identifier of agent type.""" return AgentType.ZERO_SHOT_REACT_DESCRIPTION @property def observation_prefix(self) -> str: """Prefix to append the observation with.""" return "Observation: " @property def llm_prefix(self) -> str: """Prefix to append the llm call with.""" return "Thought:" @classmethod def create_prompt( cls, tools: Sequence[BaseTool], prefix: str = PREFIX, suffix: str = SUFFIX, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, ) -> PromptTemplate: """Create prompt in the style of the zero shot agent. Args: tools: List of tools the agent will have access to, used to format the prompt. prefix: String to put before the list of tools. suffix: String to put after the list of tools. input_variables: List of input variables the final prompt will expect. Returns: A PromptTemplate with the template assembled from the pieces here. """ tool_strings = render_text_description(list(tools)) tool_names = ", ".join([tool.name for tool in tools]) format_instructions = format_instructions.format(tool_names=tool_names) template = "\n\n".join([prefix, tool_strings, format_instructions, suffix]) if input_variables: return PromptTemplate(template=template, input_variables=input_variables) return PromptTemplate.from_template(template) @classmethod def from_llm_and_tools( cls, llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, prefix: str = PREFIX, suffix: str = SUFFIX, format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, **kwargs: Any, ) -> Agent: """Construct an agent from an LLM and tools.""" cls._validate_tools(tools) prompt = cls.create_prompt( tools, prefix=prefix, suffix=suffix, format_instructions=format_instructions, input_variables=input_variables, ) llm_chain = LLMChain( llm=llm, prompt=prompt, callback_manager=callback_manager, ) tool_names = [tool.name for tool in tools] _output_parser = output_parser or cls._get_default_output_parser() return cls( llm_chain=llm_chain, allowed_tools=tool_names, output_parser=_output_parser, **kwargs, ) @classmethod def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: validate_tools_single_input(cls.__name__, tools) if len(tools) == 0: raise ValueError( f"Got no tools for {cls.__name__}. At least one tool must be provided." ) for tool in tools: if tool.description is None: raise ValueError( f"Got a tool {tool.name} without a description. For this agent, " f"a description must always be provided." ) super()._validate_tools(tools) @deprecated("0.1.0", removal="0.2.0") class MRKLChain(AgentExecutor): """[Deprecated] Chain that implements the MRKL system.""" @classmethod def from_chains( cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any ) -> AgentExecutor: """User friendly way to initialize the MRKL chain. This is intended to be an easy way to get up and running with the MRKL chain. Args: llm: The LLM to use as the agent LLM. chains: The chains the MRKL system has access to. **kwargs: parameters to be passed to initialization. Returns: An initialized MRKL chain. """ tools = [ Tool( name=c.action_name, func=c.action, description=c.action_description, ) for c in chains ] agent = ZeroShotAgent.from_llm_and_tools(llm, tools) return cls(agent=agent, tools=tools, **kwargs)

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import streamlit as st import datetime import os import psycopg2 from dotenv import load_dotenv from langchain.prompts import PromptTemplate from langchain.docstore.document import Document def log(message): current_time = datetime.datetime.now() milliseconds = current_time.microsecond // 1000 timestamp = current_time.strftime( "[%Y-%m-%d %H:%M:%S.{:03d}] ".format(milliseconds) ) st.text(timestamp + message) def check_input(question: str): if question == "": raise Exception("Please enter a question.") else: pass _postgres_prompt = """\ You are a PostgreSQL expert. Given an input question, create a syntactically correct PostgreSQL query to run and return it as the answer to the input question. Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per PostgreSQL. Never query for all columns from a table. You must query only the columns that are needed to answer the question. Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table. Create meaningful aliases for the columns. For example, if the column name is products_sold.count, you should it as total_sold_products. Note that the columns with (member_type: measure) are numeric columns and the ones with (member_type: dimension) are string columns. You should include at least one column with (member_type: measure) in your query. There are two types of queries supported against cube tables: aggregated and non-aggregated. Aggregated are those with GROUP BY statement, and non-aggregated are those without. Cube queries issued to your database will always be aggregated, and it doesn't matter if you provide GROUP BY in a query or not. Whenever you use a non-aggregated query you need to provide only column names in SQL: SELECT status, count FROM orders The same aggregated query should always aggregate measure columns using a corresponding aggregating function or special MEASURE() function: SELECT status, SUM(count) FROM orders GROUP BY 1 SELECT status, MEASURE(count) FROM orders GROUP BY 1 If you can't construct the query answer `{no_answer_text}` Only use the following table: {table_info} Only look among the following columns and pick the relevant ones: {columns_info} Question: {input_question} """ PROMPT_POSTFIX = """\ Return the answer as a JSON object with the following format: { "query": "", "filters": [{"column": \"\", "operator": \"\", "value": "\"\"}] } """ CUBE_SQL_API_PROMPT = PromptTemplate( input_variables=[ "input_question", "table_info", "columns_info", "top_k", "no_answer_text", ], template=_postgres_prompt, ) _NO_ANSWER_TEXT = "I can't answer this question." def call_sql_api(sql_query: str): load_dotenv() CONN_STR = os.environ["DATABASE_URL"] # Initializing Cube SQL API connection) connection = psycopg2.connect(CONN_STR) cursor = connection.cursor() cursor.execute(sql_query) columns = [desc[0] for desc in cursor.description] rows = cursor.fetchall() cursor.close() connection.close() return columns, rows def create_docs_from_values(columns_values, table_name, column_name): value_docs = [] for column_value in columns_values: print(column_value) metadata = dict( table_name=table_name, column_name=column_name, ) page_content = column_value value_docs.append(Document(page_content=page_content, metadata=metadata)) return value_docs

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import os import pandas as pd from langchain.chains import LLMChain from langchain.llms import OpenAI from langchain.prompts import PromptTemplate import mlflow assert ( "OPENAI_API_KEY" in os.environ ), "Please set the OPENAI_API_KEY environment variable to run this example." def build_and_evalute_model_with_prompt(prompt_template): mlflow.start_run() mlflow.log_param("prompt_template", prompt_template) # Create a news summarization model using prompt engineering with LangChain. Log the model # to MLflow Tracking llm = OpenAI(temperature=0.9) prompt = PromptTemplate(input_variables=["article"], template=prompt_template) chain = LLMChain(llm=llm, prompt=prompt) logged_model = mlflow.langchain.log_model(chain, artifact_path="model") # Evaluate the model on a small sample dataset sample_data = pd.read_csv("summarization_example_data.csv") mlflow.evaluate( model=logged_model.model_uri, model_type="text-summarization", data=sample_data, targets="highlights", ) mlflow.end_run() prompt_template_1 = ( "Write a summary of the following article that is between triple backticks: ```{article}```" ) print(f"Bulding and evaluating model with prompt: '{prompt_template_1}'") build_and_evalute_model_with_prompt(prompt_template_1) prompt_template_2 = ( "Write a summary of the following article that is between triple backticks. Be concise. Make" " sure the summary includes important nouns and dates and keywords in the original text." " Just return the summary. Do not include any text other than the summary: ```{article}```" ) print(f"Building and evaluating model with prompt: '{prompt_template_2}'") build_and_evalute_model_with_prompt(prompt_template_2) # Load the evaluation results results: pd.DataFrame = mlflow.load_table( "eval_results_table.json", extra_columns=["run_id", "params.prompt_template"] ) results_grouped_by_article = results.sort_values(by="id") print("Evaluation results:") print(results_grouped_by_article[["run_id", "params.prompt_template", "article", "outputs"]]) # Score the best model on a new article new_article = """ Adnan Januzaj swapped the lush turf of Old Trafford for the green baize at Sheffield when he turned up at the snooker World Championships on Wednesday. The Manchester United winger, who has endured a frustrating season under Louis van Gaal, had turned out for the Under 21 side at Fulham on Tuesday night amid reports he could be farmed out on loan next season. But Januzaj may want to consider trying his hand at another sport after displaying his silky skillls on a mini pool table. Adnan Januzaj (left) cheered on\xa0Shaun Murphy (right) at the World Championship in Sheffield. Januzaj shows off his potting skills on a mini pool table at the Crucible on Wednesday. The 20-year-old Belgium international was at the Crucible to cheer on his friend Shaun Murphy in his quarter-final against Anthony McGill. The 2005 winner moved a step closer to an elusive second title in Sheffield with a 13-8 victory, sealed with a 67 break. Three centuries in the match, and the way he accelerated away from 6-6, showed Murphy is a man to fear, and next for him will be Neil Robertson or Barry Hawkins. Januzaj turned out for Under 21s in the 4-1 victory at Fulham on Tuesday night. """ print( f"Scoring the model with prompt '{prompt_template_2}' on the article '{new_article[:70] + '...'}'" ) best_model = mlflow.pyfunc.load_model(f"runs:/{mlflow.last_active_run().info.run_id}/model") summary = best_model.predict({"article": new_article}) print(f"Summary: {summary}")

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import os import voyager.utils as U from langchain.chat_models import ChatOpenAI from langchain.embeddings.openai import OpenAIEmbeddings from langchain.schema import HumanMessage, SystemMessage from langchain.vectorstores import Chroma from voyager.prompts import load_prompt from voyager.control_primitives import load_control_primitives class SkillManager: def __init__( self, model_name="gpt-3.5-turbo", temperature=0, retrieval_top_k=5, request_timout=120, ckpt_dir="ckpt", resume=False, ): self.llm = ChatOpenAI( model_name=model_name, temperature=temperature, request_timeout=request_timout, ) U.f_mkdir(f"{ckpt_dir}/skill/code") U.f_mkdir(f"{ckpt_dir}/skill/description") U.f_mkdir(f"{ckpt_dir}/skill/vectordb") # programs for env execution self.control_primitives = load_control_primitives() if resume: print(f"\033[33mLoading Skill Manager from {ckpt_dir}/skill\033[0m") self.skills = U.load_json(f"{ckpt_dir}/skill/skills.json") else: self.skills = {} self.retrieval_top_k = retrieval_top_k self.ckpt_dir = ckpt_dir self.vectordb = Chroma( collection_name="skill_vectordb", embedding_function=OpenAIEmbeddings(), persist_directory=f"{ckpt_dir}/skill/vectordb", ) assert self.vectordb._collection.count() == len(self.skills), ( f"Skill Manager's vectordb is not synced with skills.json.\n" f"There are {self.vectordb._collection.count()} skills in vectordb but {len(self.skills)} skills in skills.json.\n" f"Did you set resume=False when initializing the manager?\n" f"You may need to manually delete the vectordb directory for running from scratch." ) @property def programs(self): programs = "" for skill_name, entry in self.skills.items(): programs += f"{entry['code']}\n\n" for primitives in self.control_primitives: programs += f"{primitives}\n\n" return programs def add_new_skill(self, info): if info["task"].startswith("Deposit useless items into the chest at"): # No need to reuse the deposit skill return program_name = info["program_name"] program_code = info["program_code"] skill_description = self.generate_skill_description(program_name, program_code) print( f"\033[33mSkill Manager generated description for {program_name}:\n{skill_description}\033[0m" ) if program_name in self.skills: print(f"\033[33mSkill {program_name} already exists. Rewriting!\033[0m") self.vectordb._collection.delete(ids=[program_name]) i = 2 while f"{program_name}V{i}.js" in os.listdir(f"{self.ckpt_dir}/skill/code"): i += 1 dumped_program_name = f"{program_name}V{i}" else: dumped_program_name = program_name self.vectordb.add_texts( texts=[skill_description], ids=[program_name], metadatas=[{"name": program_name}], ) self.skills[program_name] = { "code": program_code, "description": skill_description, } assert self.vectordb._collection.count() == len( self.skills ), "vectordb is not synced with skills.json" U.dump_text( program_code, f"{self.ckpt_dir}/skill/code/{dumped_program_name}.js" ) U.dump_text( skill_description, f"{self.ckpt_dir}/skill/description/{dumped_program_name}.txt", ) U.dump_json(self.skills, f"{self.ckpt_dir}/skill/skills.json") self.vectordb.persist() def generate_skill_description(self, program_name, program_code): messages = [ SystemMessage(content=load_prompt("skill")), HumanMessage( content=program_code + "\n\n" + f"The main function is `{program_name}`." ), ] skill_description = f" // { self.llm(messages).content}" return f"async function {program_name}(bot) {{\n{skill_description}\n}}" def retrieve_skills(self, query): k = min(self.vectordb._collection.count(), self.retrieval_top_k) if k == 0: return [] print(f"\033[33mSkill Manager retrieving for {k} skills\033[0m") docs_and_scores = self.vectordb.similarity_search_with_score(query, k=k) print( f"\033[33mSkill Manager retrieved skills: " f"{', '.join([doc.metadata['name'] for doc, _ in docs_and_scores])}\033[0m" ) skills = [] for doc, _ in docs_and_scores: skills.append(self.skills[doc.metadata["name"]]["code"]) return skills

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from langflow import CustomComponent from langchain.agents import AgentExecutor, create_json_agent from langflow.field_typing import ( BaseLanguageModel, ) from langchain_community.agent_toolkits.json.toolkit import JsonToolkit class JsonAgentComponent(CustomComponent): display_name = "JsonAgent" description = "Construct a json agent from an LLM and tools." def build_config(self): return { "llm": {"display_name": "LLM"}, "toolkit": {"display_name": "Toolkit"}, } def build( self, llm: BaseLanguageModel, toolkit: JsonToolkit, ) -> AgentExecutor: return create_json_agent(llm=llm, toolkit=toolkit)

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import os from fedml.serving import FedMLPredictor from fedml.serving import FedMLInferenceRunner from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline import torch from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextGenerationPipeline, ) class Chatbot(FedMLPredictor): # Inherit FedMLClientPredictor def __init__(self): super().__init__() PROMPT_FOR_GENERATION_FORMAT = f""""Below is an instruction that describes a task. Write a response that appropriately completes the request." ### Instruction: {{instruction}} ### Response: """ prompt = PromptTemplate( input_variables=["instruction"], template=PROMPT_FOR_GENERATION_FORMAT ) config = AutoConfig.from_pretrained("EleutherAI/pythia-70m") model = AutoModelForCausalLM.from_pretrained( "EleutherAI/pythia-70m", torch_dtype=torch.float32, # float 16 not supported on CPU trust_remote_code=True, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-70m", device_map="auto") hf_pipeline = HuggingFacePipeline( pipeline=TextGenerationPipeline( model=model, tokenizer=tokenizer, return_full_text=True, task="text-generation", do_sample=True, max_new_tokens=256, top_p=0.92, top_k=0 ) ) self.chatbot = LLMChain(llm=hf_pipeline, prompt=prompt, verbose=True) def predict(self, request:dict): input_dict = request question: str = input_dict.get("text", "").strip() if len(question) == 0: response_text = "<received empty input; no response generated.>" else: response_text = self.chatbot.predict(instruction=question) return {"generated_text": str(response_text)} if __name__ == "__main__": print("Program starts...") # Parse arguments import argparse parser = argparse.ArgumentParser() parser.add_argument("--batch_size", type=int, default=50051, help="port number") args = parser.parse_args() print(f"args.batch_size: {args.batch_size}") # Parse environment variables local_rank = int(os.environ.get("LOCAL_RANK", 100)) print(f"local rank: {local_rank}") chatbot = Chatbot() fedml_inference_runner = FedMLInferenceRunner(chatbot) fedml_inference_runner.run()

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from langchain.utilities import BashProcess from langchain.agents import load_tools def get_built_in_tools(tools: list[str]): bash = BashProcess() load_tools(["human"]) return [bash]

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# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # This would makes sure Python is aware there is more than one sub-package within bigdl, # physically located elsewhere. # Otherwise there would be module not found error in non-pip's setting as Python would # only search the first bigdl package and end up finding only one sub-package. # This file is adapted from # https://github.com/hwchase17/langchain/blob/master/langchain/llms/huggingface_pipeline.py # The MIT License # Copyright (c) Harrison Chase # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import importlib.util import logging from typing import Any, List, Mapping, Optional from pydantic import Extra from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens DEFAULT_MODEL_ID = "gpt2" DEFAULT_TASK = "text-generation" VALID_TASKS = ("text2text-generation", "text-generation", "summarization") class TransformersPipelineLLM(LLM): """Wrapper around the BigDL-LLM Transformer-INT4 model in Transformer.pipeline() Example: .. code-block:: python from bigdl.llm.langchain.llms import TransformersPipelineLLM llm = TransformersPipelineLLM.from_model_id(model_id="decapoda-research/llama-7b-hf") """ pipeline: Any #: :meta private: model_id: str = DEFAULT_MODEL_ID """Model name or model path to use.""" model_kwargs: Optional[dict] = None """Key word arguments passed to the model.""" pipeline_kwargs: Optional[dict] = None """Key word arguments passed to the pipeline.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @classmethod def from_model_id( cls, model_id: str, task: str, model_kwargs: Optional[dict] = None, pipeline_kwargs: Optional[dict] = None, **kwargs: Any, ) -> LLM: """Construct the pipeline object from model_id and task.""" try: from bigdl.llm.transformers import ( AutoModel, AutoModelForCausalLM, # AutoModelForSeq2SeqLM, ) from transformers import AutoTokenizer, LlamaTokenizer from transformers import pipeline as hf_pipeline except ImportError: raise ValueError( "Could not import transformers python package. " "Please install it with `pip install transformers`." ) _model_kwargs = model_kwargs or {} # TODO: may refactore this code in the future try: tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs) except: tokenizer = LlamaTokenizer.from_pretrained(model_id, **_model_kwargs) try: if task == "text-generation": model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, **_model_kwargs) elif task in ("text2text-generation", "summarization"): # TODO: support this when related PR merged model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_4bit=True, **_model_kwargs) else: raise ValueError( f"Got invalid task {task}, " f"currently only {VALID_TASKS} are supported" ) except ImportError as e: raise ValueError( f"Could not load the {task} model due to missing dependencies." ) from e if "trust_remote_code" in _model_kwargs: _model_kwargs = { k: v for k, v in _model_kwargs.items() if k != "trust_remote_code" } _pipeline_kwargs = pipeline_kwargs or {} pipeline = hf_pipeline( task=task, model=model, tokenizer=tokenizer, device='cpu', # only cpu now model_kwargs=_model_kwargs, **_pipeline_kwargs, ) if pipeline.task not in VALID_TASKS: raise ValueError( f"Got invalid task {pipeline.task}, " f"currently only {VALID_TASKS} are supported" ) return cls( pipeline=pipeline, model_id=model_id, model_kwargs=_model_kwargs, pipeline_kwargs=_pipeline_kwargs, **kwargs, ) @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { "model_id": self.model_id, "model_kwargs": self.model_kwargs, "pipeline_kwargs": self.pipeline_kwargs, } @property def _llm_type(self) -> str: return "BigDL-llm" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: response = self.pipeline(prompt) if self.pipeline.task == "text-generation": # Text generation return includes the starter text. text = response[0]["generated_text"][len(prompt) :] elif self.pipeline.task == "text2text-generation": text = response[0]["generated_text"] elif self.pipeline.task == "summarization": text = response[0]["summary_text"] else: raise ValueError( f"Got invalid task {self.pipeline.task}, " f"currently only {VALID_TASKS} are supported" ) if stop is not None: # This is a bit hacky, but I can't figure out a better way to enforce # stop tokens when making calls to huggingface_hub. text = enforce_stop_tokens(text, stop) return text

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""" This module provides an implementation for generating question data from documents. Supported types of document sources include: - plain text - unstructured files: Text, PDF, PowerPoint, HTML, Images, Excel spreadsheets, Word documents, Markdown, etc. - documents from Google Drive (provide file id). Currently support only one document a time. """ import ast import asyncio import csv import os import pickle import re from typing import Any, Dict, Iterator, List from langchain.document_loaders import GoogleDriveLoader, UnstructuredFileLoader from langchain.schema import Document from langchain.text_splitter import RecursiveCharacterTextSplitter from tqdm import tqdm from yival.common import utils from yival.common.model_utils import llm_completion from yival.data_generators.base_data_generator import BaseDataGenerator from yival.schemas.common_structures import InputData from yival.schemas.data_generator_configs import DocumentDataGeneratorConfig from yival.schemas.model_configs import Request PROMPT_TEMPLATE = """ Context information is below. --------------------- {CONTEXT} --------------------- Please do not introduce priori knowledge, only consider the content of the previous context information, generate 5 questions based on the following query. Answer ONLY a python list containing all the questions generated. Keep your output crisp, with only a '[]' bracketed list. {QUERY} """ class DocumentDataGenerator(BaseDataGenerator): config: DocumentDataGeneratorConfig default_config: DocumentDataGeneratorConfig = DocumentDataGeneratorConfig( prompt=PROMPT_TEMPLATE, document="", source="text", num_questions_per_chunk=5, text_question_template=None, document_chunk_size=512, number_of_examples=1, question_gen_query=f"You are a Teacher/Professor. Your task is to setup \ 5 questions for an upcoming quiz/examination. The questions \ should be diverse in nature across the document. Restrict \ the questions to the context information provided." ) def __init__(self, config: DocumentDataGeneratorConfig): super().__init__(config) self.config = config def load_document(self, source: str, document: str): if source == 'text': doc = Document(page_content=document) return doc elif source == 'file': file_loader = UnstructuredFileLoader(document) docs = file_loader.load() doc = docs[0] return doc elif source == 'drive': drive_loader = GoogleDriveLoader(file_ids=[document]) docs = drive_loader.load() doc = docs[0] return doc else: return None def get_doc_context(self, doc: Document, chunk_size: int) -> List[str]: # Split Document into chunks splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size) splits = splitter.split_documents([doc]) # Generate contexts from splits contexts = [str(split.page_content) for split in splits] return contexts def prepare_messages(self) -> List[Dict[str, Any]]: """Prepare the messages for GPT API based on configurations.""" if not self.config.prompt: self.config.prompt = PROMPT_TEMPLATE document = self.load_document(self.config.source, self.config.document) if document: contexts = self.get_doc_context(document, self.config.chunk_size) else: raise TypeError contents = [] for context in contexts: content = "Context information is below.\n---------------------\n\n" + context + "\n" content = content + "---------------------\nPlease do not introduce priori knowledge,\n" content = content + "only consider the content of the previous context information,\n generate " content = content + str( self.config.num_questions_per_chunk ) + " questions based on the following query." content = content + "Answer ONLY a python list containing all the questions generated.\n" content = content + "Context information is below.\n---------------------\n\n" content = content + "Keep your output crisp, with only a '[]' bracketed list.\n" content = content + self.config.question_gen_query + "\n" if self.config.text_question_template: content = content + "Please generate the questions according to the following template:\n" + self.config.text_question_template + "\n" contents.append(content) return [{"role": "user", "content": content} for content in contents] def process_output( self, output_content: str, all_data: List[InputData], chunk: List[InputData] ): """Process the output from GPT API and update data lists.""" output_ls = eval(output_content) input_data_instance = InputData( example_id=super().generate_example_id(output_content), content={"data": output_ls} ) all_data.append(input_data_instance) chunk.append(input_data_instance) def generate_examples(self) -> Iterator[List[InputData]]: all_data: List[InputData] = [] # Loading data from existing path if exists if self.config.output_path and os.path.exists(self.config.output_path): with open(self.config.output_path, 'rb') as file: all_data = pickle.load(file) for i in range(0, len(all_data), self.config.chunk_size): yield all_data[i:i + self.config.chunk_size] return chunk: List[InputData] = [] while len(all_data) < self.config.number_of_examples: messages = self.prepare_messages() message_batches = [ messages ] * (self.config.number_of_examples - len(all_data)) with tqdm( total=self.config.number_of_examples, desc="Generating Examples", unit="example" ) as pbar: responses = asyncio.run( utils.parallel_completions( message_batches, self.config.model_name, self.config.max_token, pbar=pbar ) ) for r in responses: self.process_output( r["choices"][0]["message"]["content"], all_data, chunk ) if chunk and len(chunk) >= self.config.chunk_size: yield chunk chunk = [] if self.config.output_path: with open(self.config.output_path, 'wb') as file: pickle.dump(all_data, file) print( f"Data succesfully generated and saved to {self.config.output_path}" ) if self.config.output_csv_path: with open(self.config.output_csv_path, 'w', newline='') as csvfile: rows = [ BaseDataGenerator.input_data_to_csv_row(data) for data in all_data ] header = rows[0].keys() writer = csv.DictWriter(csvfile, fieldnames=header) writer.writeheader() for row in rows: writer.writerow(row) print( f"Data succesfully generated and saved to {self.config.output_csv_path}" ) if chunk: yield chunk BaseDataGenerator.register_data_generator( "document_data_generator", DocumentDataGenerator, DocumentDataGeneratorConfig ) def main(): import time start_time = time.time() generator = DocumentDataGenerator(DocumentDataGenerator.default_config) res = generator.generate_examples() for d in res: print(d) end_time = time.time() elapsed_time = end_time - start_time print(f"Execution time: {elapsed_time:.2f} seconds") if __name__ == "__main__": main()

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from typing import AsyncGenerator, Optional, Tuple from langchain import ConversationChain import logging from typing import Optional, Tuple from pydantic.v1 import SecretStr from vocode.streaming.agent.base_agent import RespondAgent from vocode.streaming.agent.utils import get_sentence_from_buffer from langchain import ConversationChain from langchain.schema import ChatMessage, AIMessage, HumanMessage from langchain_community.chat_models import ChatAnthropic import logging from vocode import getenv from vocode.streaming.models.agent import ChatAnthropicAgentConfig from langchain.prompts import ( ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate, ) from vocode import getenv from vocode.streaming.models.agent import ChatAnthropicAgentConfig from langchain.memory import ConversationBufferMemory SENTENCE_ENDINGS = [".", "!", "?"] class ChatAnthropicAgent(RespondAgent[ChatAnthropicAgentConfig]): def __init__( self, agent_config: ChatAnthropicAgentConfig, logger: Optional[logging.Logger] = None, anthropic_api_key: Optional[SecretStr] = None, ): super().__init__(agent_config=agent_config, logger=logger) import anthropic # Convert anthropic_api_key to SecretStr if it's not None and not already a SecretStr if anthropic_api_key is not None and not isinstance( anthropic_api_key, SecretStr ): anthropic_api_key = SecretStr(anthropic_api_key) else: # Retrieve anthropic_api_key from environment variable and convert to SecretStr env_key = getenv("ANTHROPIC_API_KEY") if env_key: anthropic_api_key = SecretStr(env_key) if not anthropic_api_key: raise ValueError( "ANTHROPIC_API_KEY must be set in environment or passed in as a SecretStr" ) self.prompt = ChatPromptTemplate.from_messages( [ MessagesPlaceholder(variable_name="history"), HumanMessagePromptTemplate.from_template("{input}"), ] ) self.llm = ChatAnthropic( model_name=agent_config.model_name, anthropic_api_key=anthropic_api_key, ) # streaming not well supported by langchain, so we will connect directly self.anthropic_client = ( anthropic.AsyncAnthropic(api_key=str(anthropic_api_key)) if agent_config.generate_responses else None ) self.memory = ConversationBufferMemory(return_messages=True) self.memory.chat_memory.messages.append( HumanMessage(content=self.agent_config.prompt_preamble) ) if agent_config.initial_message: self.memory.chat_memory.messages.append( AIMessage(content=agent_config.initial_message.text) ) self.conversation = ConversationChain( memory=self.memory, prompt=self.prompt, llm=self.llm ) async def respond( self, human_input, conversation_id: str, is_interrupt: bool = False, ) -> Tuple[str, bool]: text = await self.conversation.apredict(input=human_input) self.logger.debug(f"LLM response: {text}") return text, False async def generate_response( self, human_input, conversation_id: str, is_interrupt: bool = False, ) -> AsyncGenerator[Tuple[str, bool], None]: self.memory.chat_memory.messages.append(HumanMessage(content=human_input)) bot_memory_message = AIMessage(content="") self.memory.chat_memory.messages.append(bot_memory_message) prompt = self.llm._convert_messages_to_prompt(self.memory.chat_memory.messages) if self.anthropic_client: streamed_response = await self.anthropic_client.completions.create( prompt=prompt, max_tokens_to_sample=self.agent_config.max_tokens_to_sample, model=self.agent_config.model_name, stream=True, ) buffer = "" async for completion in streamed_response: buffer += completion.completion sentence, remainder = get_sentence_from_buffer(buffer) if sentence: bot_memory_message.content = bot_memory_message.content + sentence buffer = remainder yield sentence, True continue def update_last_bot_message_on_cut_off(self, message: str): for memory_message in self.memory.chat_memory.messages[::-1]: if ( isinstance(memory_message, ChatMessage) and memory_message.role == "assistant" ) or isinstance(memory_message, AIMessage): memory_message.content = message return

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from typing import Any, Dict from langchain.base_language import BaseLanguageModel from langchain.prompts import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, ) from langchain.chains import ConversationChain from real_agents.adapters.executors.base import BaseExecutor from real_agents.adapters.memory import ConversationBufferMemory class ChatExecutor(BaseExecutor): """Chat Executor.""" _DEFAULT_TEMPLATE = "The following is a friendly conversation between a human and an AI. \ The AI is talkative and provides lots of specific details from its context. \ If the AI does not know the answer to a question, it truthfully says it does not know." output_key: str = "result" def __init__(self) -> None: """Initialize the executor""" self.memory = ConversationBufferMemory(return_messages=True) def run( self, user_intent: str, llm: BaseLanguageModel, verbose: bool = True, ) -> Dict[str, Any]: """Run the executor. Args: user_intent: User intent to execute. grounding_source: Grounding source to execute the program on. llm: Language model to use. verbose: Whether to print the logging. Returns: Result of string. """ prompt = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template(self._DEFAULT_TEMPLATE), MessagesPlaceholder(variable_name="history"), HumanMessagePromptTemplate.from_template("{input}"), ] ) method = ConversationChain( llm=llm, prompt=prompt, verbose=verbose, memory=self.memory, ) result = method.predict(input=user_intent) output = {self.output_key: result} return output

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import os from dotenv import load_dotenv, find_dotenv from langchain import HuggingFaceHub from langchain import PromptTemplate, LLMChain, OpenAI from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chains.summarize import load_summarize_chain from langchain.document_loaders import YoutubeLoader import textwrap # -------------------------------------------------------------- # Load the HuggingFaceHub API token from the .env file # -------------------------------------------------------------- load_dotenv(find_dotenv()) HUGGINGFACEHUB_API_TOKEN = os.environ["HUGGINGFACEHUB_API_TOKEN"] # -------------------------------------------------------------- # Load the LLM model from the HuggingFaceHub # -------------------------------------------------------------- repo_id = "tiiuae/falcon-7b-instruct" # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options falcon_llm = HuggingFaceHub( repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_new_tokens": 500} ) # -------------------------------------------------------------- # Create a PromptTemplate and LLMChain # -------------------------------------------------------------- template = """Question: {question} Answer: Let's think step by step.""" prompt = PromptTemplate(template=template, input_variables=["question"]) llm_chain = LLMChain(prompt=prompt, llm=falcon_llm) # -------------------------------------------------------------- # Run the LLMChain # -------------------------------------------------------------- question = "How do I make a sandwich?" response = llm_chain.run(question) wrapped_text = textwrap.fill( response, width=100, break_long_words=False, replace_whitespace=False ) print(wrapped_text) # -------------------------------------------------------------- # Load a video transcript from YouTube # -------------------------------------------------------------- video_url = "https://www.youtube.com/watch?v=riXpu1tHzl0" loader = YoutubeLoader.from_youtube_url(video_url) transcript = loader.load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000) docs = text_splitter.split_documents(transcript) # -------------------------------------------------------------- # Summarization with LangChain # -------------------------------------------------------------- # Add map_prompt and combine_prompt to the chain for custom summarization chain = load_summarize_chain(falcon_llm, chain_type="map_reduce", verbose=True) print(chain.llm_chain.prompt.template) print(chain.combine_document_chain.llm_chain.prompt.template) # -------------------------------------------------------------- # Test the Falcon model with text summarization # -------------------------------------------------------------- output_summary = chain.run(docs) wrapped_text = textwrap.fill( output_summary, width=100, break_long_words=False, replace_whitespace=False ) print(wrapped_text) # -------------------------------------------------------------- # Load an OpenAI model for comparison # -------------------------------------------------------------- openai_llm = OpenAI( model_name="text-davinci-003", temperature=0.1, max_tokens=500 ) # max token length is 4097 chain = load_summarize_chain(openai_llm, chain_type="map_reduce", verbose=True) output_summary = chain.run(docs) wrapped_text = textwrap.fill( output_summary, width=100, break_long_words=False, replace_whitespace=False ) print(wrapped_text)

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from dotenv import load_dotenv from langchain import OpenAI from langchain.document_loaders.csv_loader import CSVLoader load_dotenv() filepath = "academy/academy.csv" loader = CSVLoader(filepath) data = loader.load() print(data) llm = OpenAI(temperature=0) from langchain.agents import create_csv_agent agent = create_csv_agent(llm, filepath, verbose=True) agent.run("What percentage of the respondents are students versus professionals?") agent.run("List the top 3 devices that the respondents use to submit their responses") agent.run("Consider iOS and Android as mobile devices. What is the percentage of respondents that discovered us through social media submitting this from a mobile device?")

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from waifu.llm.Brain import Brain from waifu.llm.VectorDB import VectorDB from waifu.llm.SentenceTransformer import STEmbedding from langchain.chat_models import ChatOpenAI from langchain.embeddings import OpenAIEmbeddings from typing import Any, List, Mapping, Optional from langchain.schema import BaseMessage import openai class GPT(Brain): def __init__(self, api_key: str, name: str, stream: bool=False, callback=None, model: str='gpt-3.5-turbo', proxy: str=''): self.llm = ChatOpenAI(openai_api_key=api_key, model_name=model, streaming=stream, callbacks=[callback], temperature=0.85) self.llm_nonstream = ChatOpenAI(openai_api_key=api_key, model_name=model) self.embedding = OpenAIEmbeddings(openai_api_key=api_key) # self.embedding = STEmbedding() self.vectordb = VectorDB(self.embedding, f'./memory/{name}.csv') if proxy != '': openai.proxy = proxy def think(self, messages: List[BaseMessage]): return self.llm(messages).content def think_nonstream(self, messages: List[BaseMessage]): return self.llm_nonstream(messages).content def store_memory(self, text: str | list): '''保存记忆 embedding''' self.vectordb.store(text) def extract_memory(self, text: str, top_n: int = 10): '''提取 top_n 条相关记忆''' return self.vectordb.query(text, top_n)

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import re from typing import Union from langchain.schema import AgentAction, AgentFinish, OutputParserException from src.agents.agent import AgentOutputParser class ReActOutputParser(AgentOutputParser): """Output parser for the ReAct agent.""" def parse(self, text: str) -> Union[AgentAction, AgentFinish]: action_prefix = "Action: " if not text.strip().split("\n")[-1].startswith(action_prefix): raise OutputParserException(f"Could not parse LLM Output: {text}") action_block = text.strip().split("\n")[-1] action_str = action_block[len(action_prefix) :] # Parse out the action and the directive. re_matches = re.search(r"(.*?)\[(.*?)\]", action_str) if re_matches is None: raise OutputParserException( f"Could not parse action directive: {action_str}" ) action, action_input = re_matches.group(1), re_matches.group(2) if action == "Finish": return AgentFinish({"output": action_input}, text) else: return AgentAction(action, action_input, text) @property def _type(self) -> str: return "react"

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