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from datetime import datetime, timedelta import faiss from langchain.docstore import InMemoryDocstore from langchain.retrievers import TimeWeightedVectorStoreRetriever from langchain_community.vectorstores import FAISS from langchain_core.documents import Document from langchain_openai import OpenAIEmbeddings embeddings_model = OpenAIEmbeddings() embedding_size = 1536 index = faiss.IndexFlatL2(embedding_size) vectorstore = FAISS(embeddings_model, index,
InMemoryDocstore({})
langchain.docstore.InMemoryDocstore
from langchain_community.llms import Ollama llm = Ollama(model="llama2") llm("The first man on the moon was ...") from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler llm = Ollama( model="llama2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]) ) llm("The first man on the moon was ...") from langchain_community.llms import Ollama llm = Ollama(model="llama2:13b") llm("The first man on the moon was ... think step by step") get_ipython().run_line_magic('env', 'CMAKE_ARGS="-DLLAMA_METAL=on"') get_ipython().run_line_magic('env', 'FORCE_CMAKE=1') get_ipython().run_line_magic('pip', 'install --upgrade --quiet llama-cpp-python --no-cache-dirclear') from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain_community.llms import LlamaCpp llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin", n_gpu_layers=1, n_batch=512, n_ctx=2048, f16_kv=True, callback_manager=CallbackManager([
StreamingStdOutCallbackHandler()
langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler
import os from langchain.chains import ConversationalRetrievalChain from langchain_community.vectorstores import Vectara from langchain_openai import OpenAI from langchain_community.document_loaders import TextLoader loader = TextLoader("state_of_the_union.txt") documents = loader.load() vectara = Vectara.from_documents(documents, embedding=None) from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) openai_api_key = os.environ["OPENAI_API_KEY"] llm = OpenAI(openai_api_key=openai_api_key, temperature=0) retriever = vectara.as_retriever() d = retriever.get_relevant_documents( "What did the president say about Ketanji Brown Jackson", k=2 ) print(d) bot = ConversationalRetrievalChain.from_llm( llm, retriever, memory=memory, verbose=False ) query = "What did the president say about Ketanji Brown Jackson" result = bot.invoke({"question": query}) result["answer"] query = "Did he mention who she suceeded" result = bot.invoke({"question": query}) result["answer"] bot = ConversationalRetrievalChain.from_llm( OpenAI(temperature=0), vectara.as_retriever() ) chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = bot.invoke({"question": query, "chat_history": chat_history}) result["answer"] chat_history = [(query, result["answer"])] query = "Did he mention who she suceeded" result = bot.invoke({"question": query, "chat_history": chat_history}) result["answer"] bot = ConversationalRetrievalChain.from_llm( llm, vectara.as_retriever(), return_source_documents=True ) chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = bot.invoke({"question": query, "chat_history": chat_history}) result["source_documents"][0] from langchain.chains import LLMChain from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT from langchain.chains.question_answering import load_qa_chain question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) doc_chain = load_qa_chain(llm, chain_type="map_reduce") chain = ConversationalRetrievalChain( retriever=vectara.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain, ) chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = chain({"question": query, "chat_history": chat_history}) result["answer"] from langchain.chains.qa_with_sources import load_qa_with_sources_chain question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce") chain = ConversationalRetrievalChain( retriever=vectara.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain, ) chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = chain({"question": query, "chat_history": chat_history}) result["answer"] from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chains.conversational_retrieval.prompts import ( CONDENSE_QUESTION_PROMPT, QA_PROMPT, ) from langchain.chains.llm import LLMChain from langchain.chains.question_answering import load_qa_chain llm = OpenAI(temperature=0, openai_api_key=openai_api_key) streaming_llm = OpenAI( streaming=True, callbacks=[
StreamingStdOutCallbackHandler()
langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler
get_ipython().system("python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken") import getpass import os from langchain.chains import RetrievalQA from langchain_community.vectorstores import DeepLake from langchain_openai import OpenAI, OpenAIEmbeddings from langchain_text_splitters import ( CharacterTextSplitter, RecursiveCharacterTextSplitter, ) os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") activeloop_token = getpass.getpass("Activeloop Token:") os.environ["ACTIVELOOP_TOKEN"] = activeloop_token os.environ["ACTIVELOOP_ORG"] = getpass.getpass("Activeloop Org:") org_id = os.environ["ACTIVELOOP_ORG"] embeddings = OpenAIEmbeddings() dataset_path = "hub://" + org_id + "/data" with open("messages.txt") as f: state_of_the_union = f.read() text_splitter =
CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
langchain_text_splitters.CharacterTextSplitter
from langchain.prompts import ( ChatPromptTemplate, FewShotChatMessagePromptTemplate, ) examples = [ {"input": "2+2", "output": "4"}, {"input": "2+3", "output": "5"}, ] example_prompt = ChatPromptTemplate.from_messages( [ ("human", "{input}"), ("ai", "{output}"), ] ) few_shot_prompt = FewShotChatMessagePromptTemplate( example_prompt=example_prompt, examples=examples, ) print(few_shot_prompt.format()) final_prompt = ChatPromptTemplate.from_messages( [ ("system", "You are a wondrous wizard of math."), few_shot_prompt, ("human", "{input}"), ] ) from langchain_community.chat_models import ChatAnthropic chain = final_prompt | ChatAnthropic(temperature=0.0) chain.invoke({"input": "What's the square of a triangle?"}) from langchain.prompts import SemanticSimilarityExampleSelector from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings examples = [ {"input": "2+2", "output": "4"}, {"input": "2+3", "output": "5"}, {"input": "2+4", "output": "6"}, {"input": "What did the cow say to the moon?", "output": "nothing at all"}, { "input": "Write me a poem about the moon", "output": "One for the moon, and one for me, who are we to talk about the moon?", }, ] to_vectorize = [" ".join(example.values()) for example in examples] embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_texts(to_vectorize, embeddings, metadatas=examples) example_selector = SemanticSimilarityExampleSelector( vectorstore=vectorstore, k=2, ) example_selector.select_examples({"input": "horse"}) from langchain.prompts import ( ChatPromptTemplate, FewShotChatMessagePromptTemplate, ) few_shot_prompt = FewShotChatMessagePromptTemplate( input_variables=["input"], example_selector=example_selector, example_prompt=ChatPromptTemplate.from_messages( [("human", "{input}"), ("ai", "{output}")] ), ) print(few_shot_prompt.format(input="What's 3+3?")) final_prompt = ChatPromptTemplate.from_messages( [ ("system", "You are a wondrous wizard of math."), few_shot_prompt, ("human", "{input}"), ] ) print(few_shot_prompt.format(input="What's 3+3?")) from langchain_community.chat_models import ChatAnthropic chain = final_prompt |
ChatAnthropic(temperature=0.0)
langchain_community.chat_models.ChatAnthropic
from langchain import hub from langchain.agents import AgentExecutor, create_react_agent from langchain_community.tools import WikipediaQueryRun from langchain_community.utilities import WikipediaAPIWrapper from langchain_openai import ChatOpenAI api_wrapper =
WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)
langchain_community.utilities.WikipediaAPIWrapper
get_ipython().run_line_magic('pip', 'install -U --quiet langchain langchain_community openai chromadb langchain-experimental') get_ipython().run_line_magic('pip', 'install --quiet "unstructured[all-docs]" pypdf pillow pydantic lxml pillow matplotlib chromadb tiktoken') import logging import zipfile import requests logging.basicConfig(level=logging.INFO) data_url = "https://storage.googleapis.com/benchmarks-artifacts/langchain-docs-benchmarking/cj.zip" result = requests.get(data_url) filename = "cj.zip" with open(filename, "wb") as file: file.write(result.content) with zipfile.ZipFile(filename, "r") as zip_ref: zip_ref.extractall() from langchain_community.document_loaders import PyPDFLoader loader = PyPDFLoader("./cj/cj.pdf") docs = loader.load() tables = [] texts = [d.page_content for d in docs] len(texts) from langchain.prompts import PromptTemplate from langchain_community.chat_models import ChatVertexAI from langchain_community.llms import VertexAI from langchain_core.messages import AIMessage from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnableLambda def generate_text_summaries(texts, tables, summarize_texts=False): """ Summarize text elements texts: List of str tables: List of str summarize_texts: Bool to summarize texts """ prompt_text = """You are an assistant tasked with summarizing tables and text for retrieval. \ These summaries will be embedded and used to retrieve the raw text or table elements. \ Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} """ prompt = PromptTemplate.from_template(prompt_text) empty_response = RunnableLambda( lambda x:
AIMessage(content="Error processing document")
langchain_core.messages.AIMessage
import getpass import os os.environ["OPENAI_API_KEY"] = getpass.getpass() from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import FAISS from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import CharacterTextSplitter loader = TextLoader("../../modules/state_of_the_union.txt") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() db = FAISS.from_documents(docs, embeddings) query = "What did the president say about Ketanji Brown Jackson" docs = db.similarity_search(query) print(docs[0].page_content) retriever = db.as_retriever() docs = retriever.invoke(query) print(docs[0].page_content) docs_and_scores = db.similarity_search_with_score(query) docs_and_scores[0] embedding_vector = embeddings.embed_query(query) docs_and_scores = db.similarity_search_by_vector(embedding_vector) db.save_local("faiss_index") new_db =
FAISS.load_local("faiss_index", embeddings)
langchain_community.vectorstores.FAISS.load_local
import random from docarray import BaseDoc from docarray.typing import NdArray from langchain.retrievers import DocArrayRetriever from langchain_community.embeddings import FakeEmbeddings embeddings = FakeEmbeddings(size=32) class MyDoc(BaseDoc): title: str title_embedding: NdArray[32] year: int color: str from docarray.index import InMemoryExactNNIndex db = InMemoryExactNNIndex[MyDoc]() db.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ] ) filter_query = {"year": {"$lte": 90}} retriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query, ) doc = retriever.get_relevant_documents("some query") print(doc) from docarray.index import HnswDocumentIndex db = HnswDocumentIndex[MyDoc](work_dir="hnsw_index") db.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ] ) filter_query = {"year": {"$lte": 90}} retriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query, ) doc = retriever.get_relevant_documents("some query") print(doc) from pydantic import Field class WeaviateDoc(BaseDoc): title: str title_embedding: NdArray[32] = Field(is_embedding=True) year: int color: str from docarray.index import WeaviateDocumentIndex dbconfig = WeaviateDocumentIndex.DBConfig(host="http://localhost:8080") db = WeaviateDocumentIndex[WeaviateDoc](db_config=dbconfig) db.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ] ) filter_query = {"path": ["year"], "operator": "LessThanEqual", "valueInt": "90"} retriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query, ) doc = retriever.get_relevant_documents("some query") print(doc) from docarray.index import ElasticDocIndex db = ElasticDocIndex[MyDoc]( hosts="http://localhost:9200", index_name="docarray_retriever" ) db.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ] ) filter_query = {"range": {"year": {"lte": 90}}} retriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query, ) doc = retriever.get_relevant_documents("some query") print(doc) from docarray.index import QdrantDocumentIndex from qdrant_client.http import models as rest qdrant_config = QdrantDocumentIndex.DBConfig(path=":memory:") db = QdrantDocumentIndex[MyDoc](qdrant_config) db.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ] ) filter_query = rest.Filter( must=[ rest.FieldCondition( key="year", range=rest.Range( gte=10, lt=90, ), ) ] ) retriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query, ) doc = retriever.get_relevant_documents("some query") print(doc) movies = [ { "title": "Inception", "description": "A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.", "director": "Christopher Nolan", "rating": 8.8, }, { "title": "The Dark Knight", "description": "When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.", "director": "Christopher Nolan", "rating": 9.0, }, { "title": "Interstellar", "description": "Interstellar explores the boundaries of human exploration as a group of astronauts venture through a wormhole in space. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice.", "director": "Christopher Nolan", "rating": 8.6, }, { "title": "Pulp Fiction", "description": "The lives of two mob hitmen, a boxer, a gangster's wife, and a pair of diner bandits intertwine in four tales of violence and redemption.", "director": "Quentin Tarantino", "rating": 8.9, }, { "title": "Reservoir Dogs", "description": "When a simple jewelry heist goes horribly wrong, the surviving criminals begin to suspect that one of them is a police informant.", "director": "Quentin Tarantino", "rating": 8.3, }, { "title": "The Godfather", "description": "An aging patriarch of an organized crime dynasty transfers control of his empire to his reluctant son.", "director": "Francis Ford Coppola", "rating": 9.2, }, ] import getpass import os os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") from docarray import BaseDoc, DocList from docarray.typing import NdArray from langchain_openai import OpenAIEmbeddings class MyDoc(BaseDoc): title: str description: str description_embedding: NdArray[1536] rating: float director: str embeddings =
OpenAIEmbeddings()
langchain_openai.OpenAIEmbeddings
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai') from langchain.model_laboratory import ModelLaboratory from langchain.prompts import PromptTemplate from langchain_community.llms import Cohere, HuggingFaceHub from langchain_openai import OpenAI import getpass import os os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:") os.environ["OPENAI_API_KEY"] = getpass.getpass("Open API Key:") os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass("Hugging Face API Key:") llms = [ OpenAI(temperature=0), Cohere(temperature=0), HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 1}), ] model_lab = ModelLaboratory.from_llms(llms) model_lab.compare("What color is a flamingo?") prompt = PromptTemplate( template="What is the capital of {state}?", input_variables=["state"] ) model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt) model_lab_with_prompt.compare("New York") from langchain.chains import SelfAskWithSearchChain from langchain_community.utilities import SerpAPIWrapper open_ai_llm = OpenAI(temperature=0) search =
SerpAPIWrapper()
langchain_community.utilities.SerpAPIWrapper
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-text-splitters tiktoken') with open("../../state_of_the_union.txt") as f: state_of_the_union = f.read() from langchain_text_splitters import CharacterTextSplitter text_splitter = CharacterTextSplitter.from_tiktoken_encoder( chunk_size=100, chunk_overlap=0 ) texts = text_splitter.split_text(state_of_the_union) print(texts[0]) from langchain_text_splitters import TokenTextSplitter text_splitter = TokenTextSplitter(chunk_size=10, chunk_overlap=0) texts = text_splitter.split_text(state_of_the_union) print(texts[0]) get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy') with open("../../state_of_the_union.txt") as f: state_of_the_union = f.read() from langchain_text_splitters import SpacyTextSplitter text_splitter = SpacyTextSplitter(chunk_size=1000) texts = text_splitter.split_text(state_of_the_union) print(texts[0]) from langchain_text_splitters import SentenceTransformersTokenTextSplitter splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0) text = "Lorem " count_start_and_stop_tokens = 2 text_token_count = splitter.count_tokens(text=text) - count_start_and_stop_tokens print(text_token_count) token_multiplier = splitter.maximum_tokens_per_chunk // text_token_count + 1 text_to_split = text * token_multiplier print(f"tokens in text to split: {splitter.count_tokens(text=text_to_split)}") text_chunks = splitter.split_text(text=text_to_split) print(text_chunks[1]) with open("../../state_of_the_union.txt") as f: state_of_the_union = f.read() from langchain_text_splitters import NLTKTextSplitter text_splitter = NLTKTextSplitter(chunk_size=1000) texts = text_splitter.split_text(state_of_the_union) print(texts[0]) with open("./your_korean_doc.txt") as f: korean_document = f.read() from langchain_text_splitters import KonlpyTextSplitter text_splitter =
KonlpyTextSplitter()
langchain_text_splitters.KonlpyTextSplitter
get_ipython().system(' pip install langchain replicate') from langchain_community.chat_models import ChatOllama llama2_chat = ChatOllama(model="llama2:13b-chat") llama2_code = ChatOllama(model="codellama:7b-instruct") from langchain_community.llms import Replicate replicate_id = "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d" llama2_chat_replicate = Replicate( model=replicate_id, input={"temperature": 0.01, "max_length": 500, "top_p": 1} ) llm = llama2_chat from langchain_community.utilities import SQLDatabase db = SQLDatabase.from_uri("sqlite:///nba_roster.db", sample_rows_in_table_info=0) def get_schema(_): return db.get_table_info() def run_query(query): return db.run(query) from langchain_core.prompts import ChatPromptTemplate template = """Based on the table schema below, write a SQL query that would answer the user's question: {schema} Question: {question} SQL Query:""" prompt = ChatPromptTemplate.from_messages( [ ("system", "Given an input question, convert it to a SQL query. No pre-amble."), ("human", template), ] ) from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough sql_response = (
RunnablePassthrough.assign(schema=get_schema)
langchain_core.runnables.RunnablePassthrough.assign
get_ipython().run_line_magic('pip', 'install --upgrade --quiet multion langchain -q') from langchain_community.agent_toolkits import MultionToolkit toolkit =
MultionToolkit()
langchain_community.agent_toolkits.MultionToolkit
get_ipython().system('pip install -U openai langchain langchain-experimental') from langchain_core.messages import HumanMessage, SystemMessage from langchain_openai import ChatOpenAI chat = ChatOpenAI(model="gpt-4-vision-preview", max_tokens=256) chat.invoke( [ HumanMessage( content=[ {"type": "text", "text": "What is this image showing"}, { "type": "image_url", "image_url": { "url": "https://raw.githubusercontent.com/langchain-ai/langchain/master/docs/static/img/langchain_stack.png", "detail": "auto", }, }, ] ) ] ) from langchain.agents.openai_assistant import OpenAIAssistantRunnable interpreter_assistant = OpenAIAssistantRunnable.create_assistant( name="langchain assistant", instructions="You are a personal math tutor. Write and run code to answer math questions.", tools=[{"type": "code_interpreter"}], model="gpt-4-1106-preview", ) output = interpreter_assistant.invoke({"content": "What's 10 - 4 raised to the 2.7"}) output get_ipython().system('pip install e2b duckduckgo-search') from langchain.tools import DuckDuckGoSearchRun, E2BDataAnalysisTool tools = [E2BDataAnalysisTool(api_key="..."), DuckDuckGoSearchRun()] agent = OpenAIAssistantRunnable.create_assistant( name="langchain assistant e2b tool", instructions="You are a personal math tutor. Write and run code to answer math questions. You can also search the internet.", tools=tools, model="gpt-4-1106-preview", as_agent=True, ) from langchain.agents import AgentExecutor agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"content": "What's the weather in SF today divided by 2.7"}) agent = OpenAIAssistantRunnable.create_assistant( name="langchain assistant e2b tool", instructions="You are a personal math tutor. Write and run code to answer math questions.", tools=tools, model="gpt-4-1106-preview", as_agent=True, ) from langchain_core.agents import AgentFinish def execute_agent(agent, tools, input): tool_map = {tool.name: tool for tool in tools} response = agent.invoke(input) while not isinstance(response, AgentFinish): tool_outputs = [] for action in response: tool_output = tool_map[action.tool].invoke(action.tool_input) print(action.tool, action.tool_input, tool_output, end="\n\n") tool_outputs.append( {"output": tool_output, "tool_call_id": action.tool_call_id} ) response = agent.invoke( { "tool_outputs": tool_outputs, "run_id": action.run_id, "thread_id": action.thread_id, } ) return response response = execute_agent(agent, tools, {"content": "What's 10 - 4 raised to the 2.7"}) print(response.return_values["output"]) next_response = execute_agent( agent, tools, {"content": "now add 17.241", "thread_id": response.thread_id} ) print(next_response.return_values["output"]) chat = ChatOpenAI(model="gpt-3.5-turbo-1106").bind( response_format={"type": "json_object"} ) output = chat.invoke( [ SystemMessage( content="Extract the 'name' and 'origin' of any companies mentioned in the following statement. Return a JSON list." ), HumanMessage( content="Google was founded in the USA, while Deepmind was founded in the UK" ), ] ) print(output.content) import json json.loads(output.content) chat = ChatOpenAI(model="gpt-3.5-turbo-1106") output = chat.generate( [ [ SystemMessage( content="Extract the 'name' and 'origin' of any companies mentioned in the following statement. Return a JSON list." ), HumanMessage( content="Google was founded in the USA, while Deepmind was founded in the UK" ), ] ] ) print(output.llm_output) from typing import Literal from langchain.output_parsers.openai_tools import PydanticToolsParser from langchain.utils.openai_functions import convert_pydantic_to_openai_tool from langchain_core.prompts import ChatPromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field class GetCurrentWeather(BaseModel): """Get the current weather in a location.""" location: str = Field(description="The city and state, e.g. San Francisco, CA") unit: Literal["celsius", "fahrenheit"] = Field( default="fahrenheit", description="The temperature unit, default to fahrenheit" ) prompt = ChatPromptTemplate.from_messages( [("system", "You are a helpful assistant"), ("user", "{input}")] ) model = ChatOpenAI(model="gpt-3.5-turbo-1106").bind( tools=[
convert_pydantic_to_openai_tool(GetCurrentWeather)
langchain.utils.openai_functions.convert_pydantic_to_openai_tool
from langchain.prompts.pipeline import PipelinePromptTemplate from langchain.prompts.prompt import PromptTemplate full_template = """{introduction} {example} {start}""" full_prompt = PromptTemplate.from_template(full_template) introduction_template = """You are impersonating {person}.""" introduction_prompt = PromptTemplate.from_template(introduction_template) example_template = """Here's an example of an interaction: Q: {example_q} A: {example_a}""" example_prompt = PromptTemplate.from_template(example_template) start_template = """Now, do this for real! Q: {input} A:""" start_prompt =
PromptTemplate.from_template(start_template)
langchain.prompts.prompt.PromptTemplate.from_template
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai duckduckgo-search') from langchain.tools import DuckDuckGoSearchRun from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI search =
DuckDuckGoSearchRun()
langchain.tools.DuckDuckGoSearchRun
from langchain_core.pydantic_v1 import BaseModel, Field class Joke(BaseModel): setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") from langchain_openai import ChatOpenAI model = ChatOpenAI() model_with_structure = model.with_structured_output(Joke) model_with_structure.invoke("Tell me a joke about cats") model_with_structure = model.with_structured_output(Joke, method="json_mode") model_with_structure.invoke( "Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys" ) from langchain_fireworks import ChatFireworks model = ChatFireworks(model="accounts/fireworks/models/firefunction-v1") model_with_structure = model.with_structured_output(Joke) model_with_structure.invoke("Tell me a joke about cats") model_with_structure = model.with_structured_output(Joke, method="json_mode") model_with_structure.invoke( "Tell me a joke about dogs, respond in JSON with `setup` and `punchline` keys" ) from langchain_mistralai import ChatMistralAI model =
ChatMistralAI(model="mistral-large-latest")
langchain_mistralai.ChatMistralAI
from langchain.prompts import PromptTemplate prompt = ( PromptTemplate.from_template("Tell me a joke about {topic}") + ", make it funny" + "\n\nand in {language}" ) prompt prompt.format(topic="sports", language="spanish") from langchain.chains import LLMChain from langchain_openai import ChatOpenAI model = ChatOpenAI() chain = LLMChain(llm=model, prompt=prompt) chain.run(topic="sports", language="spanish") from langchain_core.messages import AIMessage, HumanMessage, SystemMessage prompt = SystemMessage(content="You are a nice pirate") new_prompt = ( prompt + HumanMessage(content="hi") +
AIMessage(content="what?")
langchain_core.messages.AIMessage
from langchain.chains import create_citation_fuzzy_match_chain from langchain_openai import ChatOpenAI question = "What did the author do during college?" context = """ My name is Jason Liu, and I grew up in Toronto Canada but I was born in China. I went to an arts highschool but in university I studied Computational Mathematics and physics. As part of coop I worked at many companies including Stitchfix, Facebook. I also started the Data Science club at the University of Waterloo and I was the president of the club for 2 years. """ llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") chain =
create_citation_fuzzy_match_chain(llm)
langchain.chains.create_citation_fuzzy_match_chain
get_ipython().system('pip install gymnasium') import tenacity from langchain.output_parsers import RegexParser from langchain.schema import ( HumanMessage, SystemMessage, ) class GymnasiumAgent: @classmethod def get_docs(cls, env): return env.unwrapped.__doc__ def __init__(self, model, env): self.model = model self.env = env self.docs = self.get_docs(env) self.instructions = """ Your goal is to maximize your return, i.e. the sum of the rewards you receive. I will give you an observation, reward, terminiation flag, truncation flag, and the return so far, formatted as: Observation: <observation> Reward: <reward> Termination: <termination> Truncation: <truncation> Return: <sum_of_rewards> You will respond with an action, formatted as: Action: <action> where you replace <action> with your actual action. Do nothing else but return the action. """ self.action_parser =
RegexParser( regex=r"Action: (.*)
langchain.output_parsers.RegexParser
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-speech') from langchain_community.document_loaders import GoogleSpeechToTextLoader project_id = "<PROJECT_ID>" file_path = "gs://cloud-samples-data/speech/audio.flac" loader =
GoogleSpeechToTextLoader(project_id=project_id, file_path=file_path)
langchain_community.document_loaders.GoogleSpeechToTextLoader
from langchain_community.llms import Ollama llm = Ollama(model="llama2") llm("The first man on the moon was ...") from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler llm = Ollama( model="llama2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]) ) llm("The first man on the moon was ...") from langchain_community.llms import Ollama llm = Ollama(model="llama2:13b") llm("The first man on the moon was ... think step by step") get_ipython().run_line_magic('env', 'CMAKE_ARGS="-DLLAMA_METAL=on"') get_ipython().run_line_magic('env', 'FORCE_CMAKE=1') get_ipython().run_line_magic('pip', 'install --upgrade --quiet llama-cpp-python --no-cache-dirclear') from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain_community.llms import LlamaCpp llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin", n_gpu_layers=1, n_batch=512, n_ctx=2048, f16_kv=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, ) llm("The first man on the moon was ... Let's think step by step") get_ipython().run_line_magic('pip', 'install gpt4all') from langchain_community.llms import GPT4All llm = GPT4All( model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin" ) llm("The first man on the moon was ... Let's think step by step") from langchain_community.llms.llamafile import Llamafile llm = Llamafile() llm.invoke("The first man on the moon was ... Let's think step by step.") llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin", n_gpu_layers=1, n_batch=512, n_ctx=2048, f16_kv=True, callback_manager=CallbackManager([
StreamingStdOutCallbackHandler()
langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler
meals = [ "Beef Enchiladas with Feta cheese. Mexican-Greek fusion", "Chicken Flatbreads with red sauce. Italian-Mexican fusion", "Veggie sweet potato quesadillas with vegan cheese", "One-Pan Tortelonni bake with peppers and onions", ] from langchain_openai import OpenAI llm = OpenAI(model="gpt-3.5-turbo-instruct") from langchain.prompts import PromptTemplate PROMPT_TEMPLATE = """Here is the description of a meal: "{meal}". Embed the meal into the given text: "{text_to_personalize}". Prepend a personalized message including the user's name "{user}" and their preference "{preference}". Make it sound good. """ PROMPT = PromptTemplate( input_variables=["meal", "text_to_personalize", "user", "preference"], template=PROMPT_TEMPLATE, ) import langchain_experimental.rl_chain as rl_chain chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT) response = chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Tom"), preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]), text_to_personalize="This is the weeks specialty dish, our master chefs \ believe you will love it!", ) print(response["response"]) for _ in range(5): try: response = chain.run( meal=rl_chain.ToSelectFrom(meals), user=
rl_chain.BasedOn("Tom")
langchain_experimental.rl_chain.BasedOn
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent from langchain.chains import LLMChain from langchain.memory import ConversationBufferMemory from langchain_community.utilities import GoogleSearchAPIWrapper from langchain_openai import OpenAI search = GoogleSearchAPIWrapper() tools = [ Tool( name="Search", func=search.run, description="useful for when you need to answer questions about current events", ) ] prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:""" suffix = """Begin!" {chat_history} Question: {input} {agent_scratchpad}""" prompt = ZeroShotAgent.create_prompt( tools, prefix=prefix, suffix=suffix, input_variables=["input", "chat_history", "agent_scratchpad"], ) memory = ConversationBufferMemory(memory_key="chat_history") llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt) agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True) agent_chain = AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, verbose=True, memory=memory ) agent_chain.run(input="How many people live in canada?") agent_chain.run(input="what is their national anthem called?") prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:""" suffix = """Begin!" Question: {input} {agent_scratchpad}""" prompt = ZeroShotAgent.create_prompt( tools, prefix=prefix, suffix=suffix, input_variables=["input", "agent_scratchpad"] ) llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt) agent =
ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
langchain.agents.ZeroShotAgent
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_openai.chat_models import ChatOpenAI model = ChatOpenAI() prompt = ChatPromptTemplate.from_messages( [ ( "system", "You're an assistant who's good at {ability}. Respond in 20 words or fewer", ), MessagesPlaceholder(variable_name="history"), ("human", "{input}"), ] ) runnable = prompt | model from langchain_community.chat_message_histories import ChatMessageHistory from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.runnables.history import RunnableWithMessageHistory store = {} def get_session_history(session_id: str) -> BaseChatMessageHistory: if session_id not in store: store[session_id] = ChatMessageHistory() return store[session_id] with_message_history = RunnableWithMessageHistory( runnable, get_session_history, input_messages_key="input", history_messages_key="history", ) with_message_history.invoke( {"ability": "math", "input": "What does cosine mean?"}, config={"configurable": {"session_id": "abc123"}}, ) with_message_history.invoke( {"ability": "math", "input": "What?"}, config={"configurable": {"session_id": "abc123"}}, ) with_message_history.invoke( {"ability": "math", "input": "What?"}, config={"configurable": {"session_id": "def234"}}, ) from langchain_core.runnables import ConfigurableFieldSpec store = {} def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory: if (user_id, conversation_id) not in store: store[(user_id, conversation_id)] = ChatMessageHistory() return store[(user_id, conversation_id)] with_message_history = RunnableWithMessageHistory( runnable, get_session_history, input_messages_key="input", history_messages_key="history", history_factory_config=[ ConfigurableFieldSpec( id="user_id", annotation=str, name="User ID", description="Unique identifier for the user.", default="", is_shared=True, ), ConfigurableFieldSpec( id="conversation_id", annotation=str, name="Conversation ID", description="Unique identifier for the conversation.", default="", is_shared=True, ), ], ) with_message_history.invoke( {"ability": "math", "input": "Hello"}, config={"configurable": {"user_id": "123", "conversation_id": "1"}}, ) from langchain_core.messages import HumanMessage from langchain_core.runnables import RunnableParallel chain = RunnableParallel({"output_message": ChatOpenAI()}) def get_session_history(session_id: str) -> BaseChatMessageHistory: if session_id not in store: store[session_id] = ChatMessageHistory() return store[session_id] with_message_history = RunnableWithMessageHistory( chain, get_session_history, output_messages_key="output_message", ) with_message_history.invoke( [HumanMessage(content="What did Simone de Beauvoir believe about free will")], config={"configurable": {"session_id": "baz"}}, ) with_message_history.invoke( [HumanMessage(content="How did this compare to Sartre")], config={"configurable": {"session_id": "baz"}}, ) RunnableWithMessageHistory( ChatOpenAI(), get_session_history, ) from operator import itemgetter RunnableWithMessageHistory( itemgetter("input_messages") | ChatOpenAI(), get_session_history, input_messages_key="input_messages", ) get_ipython().run_line_magic('pip', 'install --upgrade --quiet redis') REDIS_URL = "redis://localhost:6379/0" from langchain_community.chat_message_histories import RedisChatMessageHistory def get_message_history(session_id: str) -> RedisChatMessageHistory: return
RedisChatMessageHistory(session_id, url=REDIS_URL)
langchain_community.chat_message_histories.RedisChatMessageHistory
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_openai import ChatOpenAI template = """Answer the users question based only on the following context: <context> {context} </context> Question: {question} """ prompt = ChatPromptTemplate.from_template(template) model = ChatOpenAI(temperature=0) search = DuckDuckGoSearchAPIWrapper() def retriever(query): return search.run(query) chain = ( {"context": retriever, "question": RunnablePassthrough()} | prompt | model | StrOutputParser() ) simple_query = "what is langchain?" chain.invoke(simple_query) distracted_query = "man that sam bankman fried trial was crazy! what is langchain?" chain.invoke(distracted_query) retriever(distracted_query) template = """Provide a better search query for \ web search engine to answer the given question, end \ the queries with ’**’. Question: \ {x} Answer:""" rewrite_prompt = ChatPromptTemplate.from_template(template) from langchain import hub rewrite_prompt = hub.pull("langchain-ai/rewrite") print(rewrite_prompt.template) def _parse(text): return text.strip("**") rewriter = rewrite_prompt | ChatOpenAI(temperature=0) | StrOutputParser() | _parse rewriter.invoke({"x": distracted_query}) rewrite_retrieve_read_chain = ( { "context": {"x":
RunnablePassthrough()
langchain_core.runnables.RunnablePassthrough
get_ipython().run_line_magic('pip', 'install --upgrade --quiet cohere') get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss') get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu') import getpass import os os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:") def pretty_print_docs(docs): print( f"\n{'-' * 100}\n".join( [f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)] ) ) from langchain_community.document_loaders import TextLoader from langchain_community.embeddings import CohereEmbeddings from langchain_community.vectorstores import FAISS from langchain_text_splitters import RecursiveCharacterTextSplitter documents = TextLoader("../../modules/state_of_the_union.txt").load() text_splitter =
RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
langchain_text_splitters.RecursiveCharacterTextSplitter
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-cloud-text-to-speech') from langchain.tools import GoogleCloudTextToSpeechTool text_to_speak = "Hello world!" tts =
GoogleCloudTextToSpeechTool()
langchain.tools.GoogleCloudTextToSpeechTool
import uuid from pathlib import Path import langchain import torch from bs4 import BeautifulSoup as Soup from langchain.retrievers.multi_vector import MultiVectorRetriever from langchain.storage import InMemoryByteStore, LocalFileStore from langchain_community.document_loaders.recursive_url_loader import ( RecursiveUrlLoader, ) from langchain_community.vectorstores import Chroma from langchain_text_splitters import RecursiveCharacterTextSplitter # noqa DOCSTORE_DIR = "." DOCSTORE_ID_KEY = "doc_id" loader = RecursiveUrlLoader( "https://ar5iv.labs.arxiv.org/html/1706.03762", max_depth=2, extractor=lambda x: Soup(x, "html.parser").text, ) data = loader.load() print(f"Loaded {len(data)} documents") text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) all_splits = text_splitter.split_documents(data) print(f"Split into {len(all_splits)} documents") from langchain_community.embeddings import QuantizedBiEncoderEmbeddings from langchain_core.embeddings import Embeddings model_name = "Intel/bge-small-en-v1.5-rag-int8-static" encode_kwargs = {"normalize_embeddings": True} # set True to compute cosine similarity model_inc = QuantizedBiEncoderEmbeddings( model_name=model_name, encode_kwargs=encode_kwargs, query_instruction="Represent this sentence for searching relevant passages: ", ) def get_multi_vector_retriever( docstore_id_key: str, collection_name: str, embedding_function: Embeddings ): """Create the composed retriever object.""" vectorstore = Chroma( collection_name=collection_name, embedding_function=embedding_function, ) store = InMemoryByteStore() return MultiVectorRetriever( vectorstore=vectorstore, byte_store=store, id_key=docstore_id_key, ) retriever = get_multi_vector_retriever(DOCSTORE_ID_KEY, "multi_vec_store", model_inc) child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400) id_key = "doc_id" doc_ids = [str(uuid.uuid4()) for _ in all_splits] sub_docs = [] for i, doc in enumerate(all_splits): _id = doc_ids[i] _sub_docs = child_text_splitter.split_documents([doc]) for _doc in _sub_docs: _doc.metadata[id_key] = _id sub_docs.extend(_sub_docs) retriever.vectorstore.add_documents(sub_docs) retriever.docstore.mset(list(zip(doc_ids, all_splits))) import torch from langchain.llms.huggingface_pipeline import HuggingFacePipeline from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_id = "Intel/neural-chat-7b-v3-3" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16 ) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=100) hf =
HuggingFacePipeline(pipeline=pipe)
langchain.llms.huggingface_pipeline.HuggingFacePipeline
from typing import List from langchain.prompts.chat import ( HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) from langchain.schema import ( AIMessage, BaseMessage, HumanMessage, SystemMessage, ) from langchain_openai import ChatOpenAI class CAMELAgent: def __init__( self, system_message: SystemMessage, model: ChatOpenAI, ) -> None: self.system_message = system_message self.model = model self.init_messages() def reset(self) -> None: self.init_messages() return self.stored_messages def init_messages(self) -> None: self.stored_messages = [self.system_message] def update_messages(self, message: BaseMessage) -> List[BaseMessage]: self.stored_messages.append(message) return self.stored_messages def step( self, input_message: HumanMessage, ) -> AIMessage: messages = self.update_messages(input_message) output_message = self.model(messages) self.update_messages(output_message) return output_message import os os.environ["OPENAI_API_KEY"] = "" assistant_role_name = "Python Programmer" user_role_name = "Stock Trader" task = "Develop a trading bot for the stock market" word_limit = 50 # word limit for task brainstorming task_specifier_sys_msg = SystemMessage(content="You can make a task more specific.") task_specifier_prompt = """Here is a task that {assistant_role_name} will help {user_role_name} to complete: {task}. Please make it more specific. Be creative and imaginative. Please reply with the specified task in {word_limit} words or less. Do not add anything else.""" task_specifier_template = HumanMessagePromptTemplate.from_template( template=task_specifier_prompt ) task_specify_agent = CAMELAgent(task_specifier_sys_msg, ChatOpenAI(temperature=1.0)) task_specifier_msg = task_specifier_template.format_messages( assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task, word_limit=word_limit, )[0] specified_task_msg = task_specify_agent.step(task_specifier_msg) print(f"Specified task: {specified_task_msg.content}") specified_task = specified_task_msg.content assistant_inception_prompt = """Never forget you are a {assistant_role_name} and I am a {user_role_name}. Never flip roles! Never instruct me! We share a common interest in collaborating to successfully complete a task. You must help me to complete the task. Here is the task: {task}. Never forget our task! I must instruct you based on your expertise and my needs to complete the task. I must give you one instruction at a time. You must write a specific solution that appropriately completes the requested instruction. You must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons. Do not add anything else other than your solution to my instruction. You are never supposed to ask me any questions you only answer questions. You are never supposed to reply with a flake solution. Explain your solutions. Your solution must be declarative sentences and simple present tense. Unless I say the task is completed, you should always start with: Solution: <YOUR_SOLUTION> <YOUR_SOLUTION> should be specific and provide preferable implementations and examples for task-solving. Always end <YOUR_SOLUTION> with: Next request.""" user_inception_prompt = """Never forget you are a {user_role_name} and I am a {assistant_role_name}. Never flip roles! You will always instruct me. We share a common interest in collaborating to successfully complete a task. I must help you to complete the task. Here is the task: {task}. Never forget our task! You must instruct me based on my expertise and your needs to complete the task ONLY in the following two ways: 1. Instruct with a necessary input: Instruction: <YOUR_INSTRUCTION> Input: <YOUR_INPUT> 2. Instruct without any input: Instruction: <YOUR_INSTRUCTION> Input: None The "Instruction" describes a task or question. The paired "Input" provides further context or information for the requested "Instruction". You must give me one instruction at a time. I must write a response that appropriately completes the requested instruction. I must decline your instruction honestly if I cannot perform the instruction due to physical, moral, legal reasons or my capability and explain the reasons. You should instruct me not ask me questions. Now you must start to instruct me using the two ways described above. Do not add anything else other than your instruction and the optional corresponding input! Keep giving me instructions and necessary inputs until you think the task is completed. When the task is completed, you must only reply with a single word <CAMEL_TASK_DONE>. Never say <CAMEL_TASK_DONE> unless my responses have solved your task.""" def get_sys_msgs(assistant_role_name: str, user_role_name: str, task: str): assistant_sys_template = SystemMessagePromptTemplate.from_template( template=assistant_inception_prompt ) assistant_sys_msg = assistant_sys_template.format_messages( assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task, )[0] user_sys_template = SystemMessagePromptTemplate.from_template( template=user_inception_prompt ) user_sys_msg = user_sys_template.format_messages( assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task, )[0] return assistant_sys_msg, user_sys_msg assistant_sys_msg, user_sys_msg = get_sys_msgs( assistant_role_name, user_role_name, specified_task ) assistant_agent = CAMELAgent(assistant_sys_msg, ChatOpenAI(temperature=0.2)) user_agent = CAMELAgent(user_sys_msg, ChatOpenAI(temperature=0.2)) assistant_agent.reset() user_agent.reset() user_msg = HumanMessage( content=( f"{user_sys_msg.content}. " "Now start to give me introductions one by one. " "Only reply with Instruction and Input." ) ) assistant_msg =
HumanMessage(content=f"{assistant_sys_msg.content}")
langchain.schema.HumanMessage
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 nltk') get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain_experimental') get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pydantic') import os import boto3 comprehend_client = boto3.client("comprehend", region_name="us-east-1") from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain comprehend_moderation = AmazonComprehendModerationChain( client=comprehend_client, verbose=True, # optional ) from langchain.prompts import PromptTemplate from langchain_community.llms.fake import FakeListLLM from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ( ModerationPiiError, ) template = """Question: {question} Answer:""" prompt = PromptTemplate.from_template(template) responses = [ "Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.", "Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.", ] llm =
FakeListLLM(responses=responses)
langchain_community.llms.fake.FakeListLLM
get_ipython().run_line_magic('pip', 'install --upgrade --quiet marqo') from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import Marqo from langchain_text_splitters import CharacterTextSplitter from langchain_community.document_loaders import TextLoader loader = TextLoader("../../modules/state_of_the_union.txt") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents) import marqo marqo_url = "http://localhost:8882" # if using marqo cloud replace with your endpoint (console.marqo.ai) marqo_api_key = "" # if using marqo cloud replace with your api key (console.marqo.ai) client = marqo.Client(url=marqo_url, api_key=marqo_api_key) index_name = "langchain-demo" docsearch = Marqo.from_documents(docs, index_name=index_name) query = "What did the president say about Ketanji Brown Jackson" result_docs = docsearch.similarity_search(query) print(result_docs[0].page_content) result_docs = docsearch.similarity_search_with_score(query) print(result_docs[0][0].page_content, result_docs[0][1], sep="\n") index_name = "langchain-multimodal-demo" try: client.delete_index(index_name) except Exception: print(f"Creating {index_name}") settings = {"treat_urls_and_pointers_as_images": True, "model": "ViT-L/14"} client.create_index(index_name, **settings) client.index(index_name).add_documents( [ { "caption": "Bus", "image": "https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image4.jpg", }, { "caption": "Plane", "image": "https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image2.jpg", }, ], ) def get_content(res): """Helper to format Marqo's documents into text to be used as page_content""" return f"{res['caption']}: {res['image']}" docsearch = Marqo(client, index_name, page_content_builder=get_content) query = "vehicles that fly" doc_results = docsearch.similarity_search(query) for doc in doc_results: print(doc.page_content) index_name = "langchain-byo-index-demo" try: client.delete_index(index_name) except Exception: print(f"Creating {index_name}") client.create_index(index_name) client.index(index_name).add_documents( [ { "Title": "Smartphone", "Description": "A smartphone is a portable computer device that combines mobile telephone " "functions and computing functions into one unit.", }, { "Title": "Telephone", "Description": "A telephone is a telecommunications device that permits two or more users to" "conduct a conversation when they are too far apart to be easily heard directly.", }, ], ) def get_content(res): """Helper to format Marqo's documents into text to be used as page_content""" if "text" in res: return res["text"] return res["Description"] docsearch = Marqo(client, index_name, page_content_builder=get_content) docsearch.add_texts(["This is a document that is about elephants"]) query = "modern communications devices" doc_results = docsearch.similarity_search(query) print(doc_results[0].page_content) query = "elephants" doc_results = docsearch.similarity_search(query, page_content_builder=get_content) print(doc_results[0].page_content) query = {"communications devices": 1.0} doc_results = docsearch.similarity_search(query) print(doc_results[0].page_content) query = {"communications devices": 1.0, "technology post 2000": -1.0} doc_results = docsearch.similarity_search(query) print(doc_results[0].page_content) import getpass import os from langchain.chains import RetrievalQAWithSourcesChain from langchain_openai import OpenAI os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") with open("../../modules/state_of_the_union.txt") as f: state_of_the_union = f.read() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_text(state_of_the_union) index_name = "langchain-qa-with-retrieval" docsearch =
Marqo.from_documents(docs, index_name=index_name)
langchain_community.vectorstores.Marqo.from_documents
from langchain.agents import AgentType, initialize_agent, load_tools from langchain_openai import ChatOpenAI, OpenAI llm = ChatOpenAI(temperature=0.0) math_llm = OpenAI(temperature=0.0) tools = load_tools( ["human", "llm-math"], llm=math_llm, ) agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, ) agent_chain.run("What's my friend Eric's surname?") def get_input() -> str: print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.") contents = [] while True: try: line = input() except EOFError: break if line == "q": break contents.append(line) return "\n".join(contents) tools = load_tools(["human", "ddg-search"], llm=math_llm, input_func=get_input) from langchain.tools import HumanInputRun tool =
HumanInputRun(input_func=get_input)
langchain.tools.HumanInputRun
get_ipython().run_line_magic('pip', 'install --upgrade --quiet ain-py') import os os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = "" import os from ain.account import Account if os.environ.get("AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY", None): account = Account(os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"]) else: account = Account.create() os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = account.private_key print( f""" address: {account.address} private_key: {account.private_key} """ ) from langchain_community.agent_toolkits.ainetwork.toolkit import AINetworkToolkit toolkit =
AINetworkToolkit()
langchain_community.agent_toolkits.ainetwork.toolkit.AINetworkToolkit
from langchain_community.document_loaders import VsdxLoader loader =
VsdxLoader(file_path="./example_data/fake.vsdx")
langchain_community.document_loaders.VsdxLoader
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pyairtable') from langchain_community.document_loaders import AirtableLoader api_key = "xxx" base_id = "xxx" table_id = "xxx" loader =
AirtableLoader(api_key, table_id, base_id)
langchain_community.document_loaders.AirtableLoader
from langchain_community.document_loaders import OBSDirectoryLoader endpoint = "your-endpoint" config = {"ak": "your-access-key", "sk": "your-secret-key"} loader = OBSDirectoryLoader("your-bucket-name", endpoint=endpoint, config=config) loader.load() loader = OBSDirectoryLoader( "your-bucket-name", endpoint=endpoint, config=config, prefix="test_prefix" ) loader.load() config = {"get_token_from_ecs": True} loader = OBSDirectoryLoader("your-bucket-name", endpoint=endpoint, config=config) loader.load() loader =
OBSDirectoryLoader("your-bucket-name", endpoint=endpoint)
langchain_community.document_loaders.OBSDirectoryLoader
from langchain.agents import Tool from langchain_community.tools.file_management.read import ReadFileTool from langchain_community.tools.file_management.write import WriteFileTool from langchain_community.utilities import SerpAPIWrapper search = SerpAPIWrapper() tools = [ Tool( name="search", func=search.run, description="useful for when you need to answer questions about current events. You should ask targeted questions", ), WriteFileTool(),
ReadFileTool()
langchain_community.tools.file_management.read.ReadFileTool
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scann') from langchain_community.document_loaders import TextLoader from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores import ScaNN from langchain_text_splitters import CharacterTextSplitter loader =
TextLoader("state_of_the_union.txt")
langchain_community.document_loaders.TextLoader
get_ipython().system('poetry run pip install dgml-utils==0.3.0 --upgrade --quiet') import os from langchain_community.document_loaders import DocugamiLoader DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY") docset_id = "26xpy3aes7xp" document_ids = ["d7jqdzcj50sj", "cgd1eacfkchw"] loader = DocugamiLoader(docset_id=docset_id, document_ids=document_ids) chunks = loader.load() len(chunks) loader.min_text_length = 64 loader.include_xml_tags = True chunks = loader.load() for chunk in chunks[:5]: print(chunk) get_ipython().system('poetry run pip install --upgrade langchain-openai tiktoken chromadb hnswlib') loader = DocugamiLoader(docset_id="zo954yqy53wp") chunks = loader.load() for chunk in chunks: stripped_metadata = chunk.metadata.copy() for key in chunk.metadata: if key not in ["name", "xpath", "id", "structure"]: del stripped_metadata[key] chunk.metadata = stripped_metadata print(len(chunks)) from langchain.chains import RetrievalQA from langchain_community.vectorstores.chroma import Chroma from langchain_openai import OpenAI, OpenAIEmbeddings embedding = OpenAIEmbeddings() vectordb = Chroma.from_documents(documents=chunks, embedding=embedding) retriever = vectordb.as_retriever() qa_chain = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True ) qa_chain("What can tenants do with signage on their properties?") chain_response = qa_chain("What is rentable area for the property owned by DHA Group?") chain_response["result"] # correct answer should be 13,500 sq ft chain_response["source_documents"] loader = DocugamiLoader(docset_id="zo954yqy53wp") loader.include_xml_tags = ( True # for additional semantics from the Docugami knowledge graph ) chunks = loader.load() print(chunks[0].metadata) get_ipython().system('poetry run pip install --upgrade lark --quiet') from langchain.chains.query_constructor.schema import AttributeInfo from langchain.retrievers.self_query.base import SelfQueryRetriever from langchain_community.vectorstores.chroma import Chroma EXCLUDE_KEYS = ["id", "xpath", "structure"] metadata_field_info = [ AttributeInfo( name=key, description=f"The {key} for this chunk", type="string", ) for key in chunks[0].metadata if key.lower() not in EXCLUDE_KEYS ] document_content_description = "Contents of this chunk" llm = OpenAI(temperature=0) vectordb = Chroma.from_documents(documents=chunks, embedding=embedding) retriever = SelfQueryRetriever.from_llm( llm, vectordb, document_content_description, metadata_field_info, verbose=True ) qa_chain = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True, verbose=True, ) qa_chain( "What is rentable area for the property owned by DHA Group?" ) # correct answer should be 13,500 sq ft from typing import Dict, List from langchain_community.document_loaders import DocugamiLoader from langchain_core.documents import Document loader =
DocugamiLoader(docset_id="zo954yqy53wp")
langchain_community.document_loaders.DocugamiLoader
get_ipython().run_line_magic('pip', 'install --upgrade --quiet metal_sdk') from metal_sdk.metal import Metal API_KEY = "" CLIENT_ID = "" INDEX_ID = "" metal = Metal(API_KEY, CLIENT_ID, INDEX_ID) metal.index({"text": "foo1"}) metal.index({"text": "foo"}) from langchain.retrievers import MetalRetriever retriever =
MetalRetriever(metal, params={"limit": 2})
langchain.retrievers.MetalRetriever
get_ipython().run_line_magic('pip', 'install --upgrade --quiet marqo') from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import Marqo from langchain_text_splitters import CharacterTextSplitter from langchain_community.document_loaders import TextLoader loader =
TextLoader("../../modules/state_of_the_union.txt")
langchain_community.document_loaders.TextLoader
get_ipython().run_line_magic('pip', 'install --upgrade --quiet elevenlabs') import os os.environ["ELEVEN_API_KEY"] = "" from langchain.tools import ElevenLabsText2SpeechTool text_to_speak = "Hello world! I am the real slim shady" tts =
ElevenLabsText2SpeechTool()
langchain.tools.ElevenLabsText2SpeechTool
get_ipython().run_line_magic('pip', 'install --upgrade --quiet arxiv') from langchain import hub from langchain.agents import AgentExecutor, create_react_agent, load_tools from langchain_openai import ChatOpenAI llm = ChatOpenAI(temperature=0.0) tools = load_tools( ["arxiv"], ) prompt = hub.pull("hwchase17/react") agent =
create_react_agent(llm, tools, prompt)
langchain.agents.create_react_agent
import runhouse as rh from langchain_community.embeddings import ( SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, SelfHostedHuggingFaceInstructEmbeddings, ) gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False) embeddings =
SelfHostedHuggingFaceEmbeddings(hardware=gpu)
langchain_community.embeddings.SelfHostedHuggingFaceEmbeddings
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results') import os from langchain_community.tools.google_scholar import GoogleScholarQueryRun from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper os.environ["SERP_API_KEY"] = "" tool = GoogleScholarQueryRun(api_wrapper=
GoogleScholarAPIWrapper()
langchain_community.utilities.google_scholar.GoogleScholarAPIWrapper
get_ipython().run_line_magic('pip', 'install --upgrade --quiet manifest-ml') from langchain_community.llms.manifest import ManifestWrapper from manifest import Manifest manifest = Manifest( client_name="huggingface", client_connection="http://127.0.0.1:5000" ) print(manifest.client_pool.get_current_client().get_model_params()) llm = ManifestWrapper( client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 256} ) from langchain.chains.mapreduce import MapReduceChain from langchain.prompts import PromptTemplate from langchain_text_splitters import CharacterTextSplitter _prompt = """Write a concise summary of the following: {text} CONCISE SUMMARY:""" prompt = PromptTemplate.from_template(_prompt) text_splitter = CharacterTextSplitter() mp_chain = MapReduceChain.from_params(llm, prompt, text_splitter) with open("../../modules/state_of_the_union.txt") as f: state_of_the_union = f.read() mp_chain.run(state_of_the_union) from langchain.model_laboratory import ModelLaboratory manifest1 = ManifestWrapper( client=Manifest( client_name="huggingface", client_connection="http://127.0.0.1:5000" ), llm_kwargs={"temperature": 0.01}, ) manifest2 = ManifestWrapper( client=Manifest( client_name="huggingface", client_connection="http://127.0.0.1:5001" ), llm_kwargs={"temperature": 0.01}, ) manifest3 = ManifestWrapper( client=Manifest( client_name="huggingface", client_connection="http://127.0.0.1:5002" ), llm_kwargs={"temperature": 0.01}, ) llms = [manifest1, manifest2, manifest3] model_lab =
ModelLaboratory(llms)
langchain.model_laboratory.ModelLaboratory
from typing import List from langchain.prompts import PromptTemplate from langchain_core.output_parsers import JsonOutputParser from langchain_core.pydantic_v1 import BaseModel, Field from langchain_openai import ChatOpenAI model = ChatOpenAI(temperature=0) class Joke(BaseModel): setup: str = Field(description="question to set up a joke") punchline: str = Field(description="answer to resolve the joke") joke_query = "Tell me a joke." parser = JsonOutputParser(pydantic_object=Joke) prompt = PromptTemplate( template="Answer the user query.\n{format_instructions}\n{query}\n", input_variables=["query"], partial_variables={"format_instructions": parser.get_format_instructions()}, ) chain = prompt | model | parser chain.invoke({"query": joke_query}) for s in chain.stream({"query": joke_query}): print(s) joke_query = "Tell me a joke." parser =
JsonOutputParser()
langchain_core.output_parsers.JsonOutputParser
get_ipython().run_line_magic('pip', 'install --upgrade --quiet cos-python-sdk-v5') from langchain_community.document_loaders import TencentCOSDirectoryLoader from qcloud_cos import CosConfig conf = CosConfig( Region="your cos region", SecretId="your cos secret_id", SecretKey="your cos secret_key", ) loader = TencentCOSDirectoryLoader(conf=conf, bucket="you_cos_bucket") loader.load() loader =
TencentCOSDirectoryLoader(conf=conf, bucket="you_cos_bucket", prefix="fake")
langchain_community.document_loaders.TencentCOSDirectoryLoader
get_ipython().system('pip install -U oci') from langchain_community.llms import OCIGenAI llm = OCIGenAI( model_id="MY_MODEL", service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com", compartment_id="MY_OCID", ) response = llm.invoke("Tell me one fact about earth", temperature=0.7) print(response) from langchain.chains import LLMChain from langchain_core.prompts import PromptTemplate llm = OCIGenAI( model_id="MY_MODEL", service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com", compartment_id="MY_OCID", auth_type="SECURITY_TOKEN", auth_profile="MY_PROFILE", # replace with your profile name model_kwargs={"temperature": 0.7, "top_p": 0.75, "max_tokens": 200}, ) prompt = PromptTemplate(input_variables=["query"], template="{query}") llm_chain = LLMChain(llm=llm, prompt=prompt) response = llm_chain.invoke("what is the capital of france?") print(response) from langchain.schema.output_parser import StrOutputParser from langchain.schema.runnable import RunnablePassthrough from langchain_community.embeddings import OCIGenAIEmbeddings from langchain_community.vectorstores import FAISS embeddings = OCIGenAIEmbeddings( model_id="MY_EMBEDDING_MODEL", service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com", compartment_id="MY_OCID", ) vectorstore = FAISS.from_texts( [ "Larry Ellison co-founded Oracle Corporation in 1977 with Bob Miner and Ed Oates.", "Oracle Corporation is an American multinational computer technology company headquartered in Austin, Texas, United States.", ], embedding=embeddings, ) retriever = vectorstore.as_retriever() template = """Answer the question based only on the following context: {context} Question: {question} """ prompt =
PromptTemplate.from_template(template)
langchain_core.prompts.PromptTemplate.from_template
from datetime import datetime, timedelta import faiss from langchain.docstore import InMemoryDocstore from langchain.retrievers import TimeWeightedVectorStoreRetriever from langchain_community.vectorstores import FAISS from langchain_core.documents import Document from langchain_openai import OpenAIEmbeddings embeddings_model = OpenAIEmbeddings() embedding_size = 1536 index = faiss.IndexFlatL2(embedding_size) vectorstore = FAISS(embeddings_model, index, InMemoryDocstore({}), {}) retriever = TimeWeightedVectorStoreRetriever( vectorstore=vectorstore, decay_rate=0.0000000000000000000000001, k=1 ) yesterday = datetime.now() - timedelta(days=1) retriever.add_documents( [Document(page_content="hello world", metadata={"last_accessed_at": yesterday})] ) retriever.add_documents([Document(page_content="hello foo")]) retriever.get_relevant_documents("hello world") embeddings_model = OpenAIEmbeddings() embedding_size = 1536 index = faiss.IndexFlatL2(embedding_size) vectorstore = FAISS(embeddings_model, index,
InMemoryDocstore({})
langchain.docstore.InMemoryDocstore
from langchain.prompts import ChatMessagePromptTemplate prompt = "May the {subject} be with you" chat_message_prompt = ChatMessagePromptTemplate.from_template( role="Jedi", template=prompt ) chat_message_prompt.format(subject="force") from langchain.prompts import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, ) human_prompt = "Summarize our conversation so far in {word_count} words." human_message_template = HumanMessagePromptTemplate.from_template(human_prompt) chat_prompt = ChatPromptTemplate.from_messages( [MessagesPlaceholder(variable_name="conversation"), human_message_template] ) from langchain_core.messages import AIMessage, HumanMessage human_message =
HumanMessage(content="What is the best way to learn programming?")
langchain_core.messages.HumanMessage
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain') get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai') get_ipython().run_line_magic('pip', 'install --upgrade --quiet psycopg2-binary') get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken') YBUSER = "[SANDBOX USER]" YBPASSWORD = "[SANDBOX PASSWORD]" YBDATABASE = "[SANDBOX_DATABASE]" YBHOST = "trialsandbox.sandbox.aws.yellowbrickcloud.com" OPENAI_API_KEY = "[OPENAI API KEY]" import os import pathlib import re import sys import urllib.parse as urlparse from getpass import getpass import psycopg2 from IPython.display import Markdown, display from langchain.chains import LLMChain, RetrievalQAWithSourcesChain from langchain.docstore.document import Document from langchain_community.vectorstores import Yellowbrick from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter yellowbrick_connection_string = ( f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YBDATABASE}" ) YB_DOC_DATABASE = "sample_data" YB_DOC_TABLE = "yellowbrick_documentation" embedding_table = "my_embeddings" os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY from langchain.prompts.chat import ( ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) system_template = """If you don't know the answer, Make up your best guess.""" messages = [ SystemMessagePromptTemplate.from_template(system_template), HumanMessagePromptTemplate.from_template("{question}"), ] prompt = ChatPromptTemplate.from_messages(messages) chain_type_kwargs = {"prompt": prompt} llm = ChatOpenAI( model_name="gpt-3.5-turbo", # Modify model_name if you have access to GPT-4 temperature=0, max_tokens=256, ) chain = LLMChain( llm=llm, prompt=prompt, verbose=False, ) def print_result_simple(query): result = chain(query) output_text = f"""### Question: {query} {result['text']} """ display(Markdown(output_text)) print_result_simple("How many databases can be in a Yellowbrick Instance?") print_result_simple("What's an easy way to add users in bulk to Yellowbrick?") try: conn = psycopg2.connect(yellowbrick_connection_string) except psycopg2.Error as e: print(f"Error connecting to the database: {e}") exit(1) cursor = conn.cursor() create_table_query = f""" CREATE TABLE if not exists {embedding_table} ( id uuid, embedding_id integer, text character varying(60000), metadata character varying(1024), embedding double precision ) DISTRIBUTE ON (id); truncate table {embedding_table}; """ try: cursor.execute(create_table_query) print(f"Table '{embedding_table}' created successfully!") except psycopg2.Error as e: print(f"Error creating table: {e}") conn.rollback() conn.commit() cursor.close() conn.close() yellowbrick_doc_connection_string = ( f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YB_DOC_DATABASE}" ) conn = psycopg2.connect(yellowbrick_doc_connection_string) cursor = conn.cursor() query = f"SELECT path, document FROM {YB_DOC_TABLE}" cursor.execute(query) yellowbrick_documents = cursor.fetchall() print(f"Extracted {len(yellowbrick_documents)} documents successfully!") cursor.close() conn.close() DOCUMENT_BASE_URL = "https://docs.yellowbrick.com/6.7.1/" # Actual URL separator = "\n## " # This separator assumes Markdown docs from the repo uses ### as logical main header most of the time chunk_size_limit = 2000 max_chunk_overlap = 200 documents = [ Document( page_content=document[1], metadata={"source": DOCUMENT_BASE_URL + document[0].replace(".md", ".html")}, ) for document in yellowbrick_documents ] text_splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size_limit, chunk_overlap=max_chunk_overlap, separators=[separator, "\nn", "\n", ",", " ", ""], ) split_docs = text_splitter.split_documents(documents) docs_text = [doc.page_content for doc in split_docs] embeddings = OpenAIEmbeddings() vector_store = Yellowbrick.from_documents( documents=split_docs, embedding=embeddings, connection_string=yellowbrick_connection_string, table=embedding_table, ) print(f"Created vector store with {len(documents)} documents") system_template = """Use the following pieces of context to answer the users question. Take note of the sources and include them in the answer in the format: "SOURCES: source1 source2", use "SOURCES" in capital letters regardless of the number of sources. If you don't know the answer, just say that "I don't know", don't try to make up an answer. ---------------- {summaries}""" messages = [
SystemMessagePromptTemplate.from_template(system_template)
langchain.prompts.chat.SystemMessagePromptTemplate.from_template
from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint from langchain_community.llms.azureml_endpoint import ( AzureMLEndpointApiType, LlamaContentFormatter, ) from langchain_core.messages import HumanMessage llm = AzureMLOnlineEndpoint( endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score", endpoint_api_type=AzureMLEndpointApiType.realtime, endpoint_api_key="my-api-key", content_formatter=LlamaContentFormatter(), model_kwargs={"temperature": 0.8, "max_new_tokens": 400}, ) response = llm.invoke("Write me a song about sparkling water:") response response = llm.invoke("Write me a song about sparkling water:", temperature=0.5) response from langchain_community.llms.azureml_endpoint import ( AzureMLEndpointApiType, LlamaContentFormatter, ) from langchain_core.messages import HumanMessage llm = AzureMLOnlineEndpoint( endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/completions", endpoint_api_type=AzureMLEndpointApiType.serverless, endpoint_api_key="my-api-key", content_formatter=LlamaContentFormatter(), model_kwargs={"temperature": 0.8, "max_new_tokens": 400}, ) response = llm.invoke("Write me a song about sparkling water:") response import json import os from typing import Dict from langchain_community.llms.azureml_endpoint import ( AzureMLOnlineEndpoint, ContentFormatterBase, ) class CustomFormatter(ContentFormatterBase): content_type = "application/json" accepts = "application/json" def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: input_str = json.dumps( { "inputs": [prompt], "parameters": model_kwargs, "options": {"use_cache": False, "wait_for_model": True}, } ) return str.encode(input_str) def format_response_payload(self, output: bytes) -> str: response_json = json.loads(output) return response_json[0]["summary_text"] content_formatter = CustomFormatter() llm = AzureMLOnlineEndpoint( endpoint_api_type="realtime", endpoint_api_key=os.getenv("BART_ENDPOINT_API_KEY"), endpoint_url=os.getenv("BART_ENDPOINT_URL"), model_kwargs={"temperature": 0.8, "max_new_tokens": 400}, content_formatter=content_formatter, ) large_text = """On January 7, 2020, Blockberry Creative announced that HaSeul would not participate in the promotion for Loona's next album because of mental health concerns. She was said to be diagnosed with "intermittent anxiety symptoms" and would be taking time to focus on her health.[39] On February 5, 2020, Loona released their second EP titled [#] (read as hash), along with the title track "So What".[40] Although HaSeul did not appear in the title track, her vocals are featured on three other songs on the album, including "365". Once peaked at number 1 on the daily Gaon Retail Album Chart,[41] the EP then debuted at number 2 on the weekly Gaon Album Chart. On March 12, 2020, Loona won their first music show trophy with "So What" on Mnet's M Countdown.[42] On October 19, 2020, Loona released their third EP titled [12:00] (read as midnight),[43] accompanied by its first single "Why Not?". HaSeul was again not involved in the album, out of her own decision to focus on the recovery of her health.[44] The EP then became their first album to enter the Billboard 200, debuting at number 112.[45] On November 18, Loona released the music video for "Star", another song on [12:00].[46] Peaking at number 40, "Star" is Loona's first entry on the Billboard Mainstream Top 40, making them the second K-pop girl group to enter the chart.[47] On June 1, 2021, Loona announced that they would be having a comeback on June 28, with their fourth EP, [&] (read as and). [48] The following day, on June 2, a teaser was posted to Loona's official social media accounts showing twelve sets of eyes, confirming the return of member HaSeul who had been on hiatus since early 2020.[49] On June 12, group members YeoJin, Kim Lip, Choerry, and Go Won released the song "Yum-Yum" as a collaboration with Cocomong.[50] On September 8, they released another collaboration song named "Yummy-Yummy".[51] On June 27, 2021, Loona announced at the end of their special clip that they are making their Japanese debut on September 15 under Universal Music Japan sublabel EMI Records.[52] On August 27, it was announced that Loona will release the double A-side single, "Hula Hoop / Star Seed" on September 15, with a physical CD release on October 20.[53] In December, Chuu filed an injunction to suspend her exclusive contract with Blockberry Creative.[54][55] """ summarized_text = llm.invoke(large_text) print(summarized_text) from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from langchain_community.llms.azureml_endpoint import DollyContentFormatter formatter_template = "Write a {word_count} word essay about {topic}." prompt = PromptTemplate( input_variables=["word_count", "topic"], template=formatter_template ) content_formatter =
DollyContentFormatter()
langchain_community.llms.azureml_endpoint.DollyContentFormatter
from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint from langchain_community.llms.azureml_endpoint import ( AzureMLEndpointApiType, LlamaContentFormatter, ) from langchain_core.messages import HumanMessage llm = AzureMLOnlineEndpoint( endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score", endpoint_api_type=AzureMLEndpointApiType.realtime, endpoint_api_key="my-api-key", content_formatter=
LlamaContentFormatter()
langchain_community.llms.azureml_endpoint.LlamaContentFormatter
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"} get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-datastore') PROJECT_ID = "my-project-id" # @param {type:"string"} get_ipython().system('gcloud config set project {PROJECT_ID}') from google.colab import auth auth.authenticate_user() get_ipython().system('gcloud services enable datastore.googleapis.com') from langchain_core.documents import Document from langchain_google_datastore import DatastoreSaver data = [Document(page_content="Hello, World!")] saver = DatastoreSaver() saver.upsert_documents(data) saver =
DatastoreSaver("Collection")
langchain_google_datastore.DatastoreSaver
from langchain.output_parsers import XMLOutputParser from langchain.prompts import PromptTemplate from langchain_community.chat_models import ChatAnthropic model = ChatAnthropic(model="claude-2", max_tokens_to_sample=512, temperature=0.1) actor_query = "Generate the shortened filmography for Tom Hanks." output = model.invoke( f"""{actor_query} Please enclose the movies in <movie></movie> tags""" ) print(output.content) parser = XMLOutputParser() prompt = PromptTemplate( template="""{query}\n{format_instructions}""", input_variables=["query"], partial_variables={"format_instructions": parser.get_format_instructions()}, ) chain = prompt | model | parser output = chain.invoke({"query": actor_query}) print(output) parser =
XMLOutputParser(tags=["movies", "actor", "film", "name", "genre"])
langchain.output_parsers.XMLOutputParser
get_ipython().run_line_magic('pip', 'install --upgrade --quiet beautifulsoup4') from langchain_community.document_loaders import ReadTheDocsLoader loader =
ReadTheDocsLoader("rtdocs", features="html.parser")
langchain_community.document_loaders.ReadTheDocsLoader
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tigrisdb openapi-schema-pydantic langchain-openai tiktoken') import getpass import os os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") os.environ["TIGRIS_PROJECT"] = getpass.getpass("Tigris Project Name:") os.environ["TIGRIS_CLIENT_ID"] = getpass.getpass("Tigris Client Id:") os.environ["TIGRIS_CLIENT_SECRET"] = getpass.getpass("Tigris Client Secret:") from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import Tigris from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import CharacterTextSplitter loader = TextLoader("../../../state_of_the_union.txt") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() vector_store =
Tigris.from_documents(docs, embeddings, index_name="my_embeddings")
langchain_community.vectorstores.Tigris.from_documents
from langchain_community.embeddings import TensorflowHubEmbeddings embeddings =
TensorflowHubEmbeddings()
langchain_community.embeddings.TensorflowHubEmbeddings
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-openai langchain-anthropic langchain-community wikipedia') import getpass import os os.environ["OPENAI_API_KEY"] = getpass.getpass() os.environ["ANTHROPIC_API_KEY"] = getpass.getpass() from langchain_community.retrievers import WikipediaRetriever from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) wiki = WikipediaRetriever(top_k_results=6, doc_content_chars_max=2000) prompt = ChatPromptTemplate.from_messages( [ ( "system", "You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question. If none of the articles answer the question, just say you don't know.\n\nHere are the Wikipedia articles:{context}", ), ("human", "{question}"), ] ) prompt.pretty_print() from operator import itemgetter from typing import List from langchain_core.documents import Document from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import ( RunnableLambda, RunnableParallel, RunnablePassthrough, ) def format_docs(docs: List[Document]) -> str: """Convert Documents to a single string.:""" formatted = [ f"Article Title: {doc.metadata['title']}\nArticle Snippet: {doc.page_content}" for doc in docs ] return "\n\n" + "\n\n".join(formatted) format = itemgetter("docs") | RunnableLambda(format_docs) answer = prompt | llm | StrOutputParser() chain = ( RunnableParallel(question=RunnablePassthrough(), docs=wiki) .assign(context=format) .assign(answer=answer) .pick(["answer", "docs"]) ) chain.invoke("How fast are cheetahs?") from langchain_core.pydantic_v1 import BaseModel, Field class cited_answer(BaseModel): """Answer the user question based only on the given sources, and cite the sources used.""" answer: str = Field( ..., description="The answer to the user question, which is based only on the given sources.", ) citations: List[int] = Field( ..., description="The integer IDs of the SPECIFIC sources which justify the answer.", ) llm_with_tool = llm.bind_tools( [cited_answer], tool_choice="cited_answer", ) example_q = """What Brian's height? Source: 1 Information: Suzy is 6'2" Source: 2 Information: Jeremiah is blonde Source: 3 Information: Brian is 3 inches shorted than Suzy""" llm_with_tool.invoke(example_q) from langchain.output_parsers.openai_tools import JsonOutputKeyToolsParser output_parser = JsonOutputKeyToolsParser(key_name="cited_answer", return_single=True) (llm_with_tool | output_parser).invoke(example_q) def format_docs_with_id(docs: List[Document]) -> str: formatted = [ f"Source ID: {i}\nArticle Title: {doc.metadata['title']}\nArticle Snippet: {doc.page_content}" for i, doc in enumerate(docs) ] return "\n\n" + "\n\n".join(formatted) format_1 = itemgetter("docs") | RunnableLambda(format_docs_with_id) answer_1 = prompt | llm_with_tool | output_parser chain_1 = ( RunnableParallel(question=RunnablePassthrough(), docs=wiki) .assign(context=format_1) .assign(cited_answer=answer_1) .pick(["cited_answer", "docs"]) ) chain_1.invoke("How fast are cheetahs?") class Citation(BaseModel): source_id: int = Field( ..., description="The integer ID of a SPECIFIC source which justifies the answer.", ) quote: str = Field( ..., description="The VERBATIM quote from the specified source that justifies the answer.", ) class quoted_answer(BaseModel): """Answer the user question based only on the given sources, and cite the sources used.""" answer: str = Field( ..., description="The answer to the user question, which is based only on the given sources.", ) citations: List[Citation] = Field( ..., description="Citations from the given sources that justify the answer." ) output_parser_2 = JsonOutputKeyToolsParser(key_name="quoted_answer", return_single=True) llm_with_tool_2 = llm.bind_tools( [quoted_answer], tool_choice="quoted_answer", ) format_2 = itemgetter("docs") | RunnableLambda(format_docs_with_id) answer_2 = prompt | llm_with_tool_2 | output_parser_2 chain_2 = ( RunnableParallel(question=RunnablePassthrough(), docs=wiki) .assign(context=format_2) .assign(quoted_answer=answer_2) .pick(["quoted_answer", "docs"]) ) chain_2.invoke("How fast are cheetahs?") from langchain_anthropic import ChatAnthropicMessages anthropic = ChatAnthropicMessages(model_name="claude-instant-1.2") system = """You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, \ answer the user question and provide citations. If none of the articles answer the question, just say you don't know. Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that \ justifies the answer and the ID of the quote article. Return a citation for every quote across all articles \ that justify the answer. Use the following format for your final output: <cited_answer> <answer></answer> <citations> <citation><source_id></source_id><quote></quote></citation> <citation><source_id></source_id><quote></quote></citation> ... </citations> </cited_answer> Here are the Wikipedia articles:{context}""" prompt_3 = ChatPromptTemplate.from_messages( [("system", system), ("human", "{question}")] ) from langchain_core.output_parsers import XMLOutputParser def format_docs_xml(docs: List[Document]) -> str: formatted = [] for i, doc in enumerate(docs): doc_str = f"""\ <source id=\"{i}\"> <title>{doc.metadata['title']}</title> <article_snippet>{doc.page_content}</article_snippet> </source>""" formatted.append(doc_str) return "\n\n<sources>" + "\n".join(formatted) + "</sources>" format_3 = itemgetter("docs") | RunnableLambda(format_docs_xml) answer_3 = prompt_3 | anthropic | XMLOutputParser() | itemgetter("cited_answer") chain_3 = ( RunnableParallel(question=RunnablePassthrough(), docs=wiki) .assign(context=format_3) .assign(cited_answer=answer_3) .pick(["cited_answer", "docs"]) ) chain_3.invoke("How fast are cheetahs?") from langchain.retrievers.document_compressors import EmbeddingsFilter from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter splitter = RecursiveCharacterTextSplitter( chunk_size=400, chunk_overlap=0, separators=["\n\n", "\n", ".", " "], keep_separator=False, ) compressor = EmbeddingsFilter(embeddings=OpenAIEmbeddings(), k=10) def split_and_filter(input) -> List[Document]: docs = input["docs"] question = input["question"] split_docs = splitter.split_documents(docs) stateful_docs = compressor.compress_documents(split_docs, question) return [stateful_doc for stateful_doc in stateful_docs] retrieve = ( RunnableParallel(question=RunnablePassthrough(), docs=wiki) | split_and_filter ) docs = retrieve.invoke("How fast are cheetahs?") for doc in docs: print(doc.page_content) print("\n\n") chain_4 = ( RunnableParallel(question=RunnablePassthrough(), docs=retrieve) .assign(context=format) .assign(answer=answer) .pick(["answer", "docs"]) ) chain_4.invoke("How fast are cheetahs?") class Citation(BaseModel): source_id: int = Field( ..., description="The integer ID of a SPECIFIC source which justifies the answer.", ) quote: str = Field( ..., description="The VERBATIM quote from the specified source that justifies the answer.", ) class annotated_answer(BaseModel): """Annotate the answer to the user question with quote citations that justify the answer.""" citations: List[Citation] = Field( ..., description="Citations from the given sources that justify the answer." ) llm_with_tools_5 = llm.bind_tools( [annotated_answer], tool_choice="annotated_answer", ) from langchain_core.prompts import MessagesPlaceholder prompt_5 = ChatPromptTemplate.from_messages( [ ( "system", "You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question. If none of the articles answer the question, just say you don't know.\n\nHere are the Wikipedia articles:{context}", ), ("human", "{question}"),
MessagesPlaceholder("chat_history", optional=True)
langchain_core.prompts.MessagesPlaceholder
import os import pprint os.environ["SERPER_API_KEY"] = "" from langchain_community.utilities import GoogleSerperAPIWrapper search = GoogleSerperAPIWrapper() search.run("Obama's first name?") os.environ["OPENAI_API_KEY"] = "" from langchain.agents import AgentType, Tool, initialize_agent from langchain_community.utilities import GoogleSerperAPIWrapper from langchain_openai import OpenAI llm = OpenAI(temperature=0) search = GoogleSerperAPIWrapper() tools = [ Tool( name="Intermediate Answer", func=search.run, description="useful for when you need to ask with search", ) ] self_ask_with_search = initialize_agent( tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True ) self_ask_with_search.run( "What is the hometown of the reigning men's U.S. Open champion?" ) search =
GoogleSerperAPIWrapper()
langchain_community.utilities.GoogleSerperAPIWrapper
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_openai import ChatOpenAI template = """Answer the users question based only on the following context: <context> {context} </context> Question: {question} """ prompt = ChatPromptTemplate.from_template(template) model = ChatOpenAI(temperature=0) search = DuckDuckGoSearchAPIWrapper() def retriever(query): return search.run(query) chain = ( {"context": retriever, "question": RunnablePassthrough()} | prompt | model | StrOutputParser() ) simple_query = "what is langchain?" chain.invoke(simple_query) distracted_query = "man that sam bankman fried trial was crazy! what is langchain?" chain.invoke(distracted_query) retriever(distracted_query) template = """Provide a better search query for \ web search engine to answer the given question, end \ the queries with ’**’. Question: \ {x} Answer:""" rewrite_prompt = ChatPromptTemplate.from_template(template) from langchain import hub rewrite_prompt = hub.pull("langchain-ai/rewrite") print(rewrite_prompt.template) def _parse(text): return text.strip("**") rewriter = rewrite_prompt | ChatOpenAI(temperature=0) | StrOutputParser() | _parse rewriter.invoke({"x": distracted_query}) rewrite_retrieve_read_chain = ( { "context": {"x": RunnablePassthrough()} | rewriter | retriever, "question": RunnablePassthrough(), } | prompt | model |
StrOutputParser()
langchain_core.output_parsers.StrOutputParser
from typing import List from langchain.output_parsers import YamlOutputParser from langchain.prompts import PromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain_openai import ChatOpenAI model =
ChatOpenAI(temperature=0)
langchain_openai.ChatOpenAI
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "cassio>=0.1.4"') import os from getpass import getpass from datasets import ( load_dataset, ) from langchain_community.document_loaders import PyPDFLoader from langchain_core.documents import Document from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter os.environ["OPENAI_API_KEY"] = getpass("OPENAI_API_KEY = ") embe = OpenAIEmbeddings() from langchain_community.vectorstores import Cassandra from cassandra.cluster import Cluster cluster = Cluster(["127.0.0.1"]) session = cluster.connect() import cassio CASSANDRA_KEYSPACE = input("CASSANDRA_KEYSPACE = ") cassio.init(session=session, keyspace=CASSANDRA_KEYSPACE) vstore = Cassandra( embedding=embe, table_name="cassandra_vector_demo", ) ASTRA_DB_ID = input("ASTRA_DB_ID = ") ASTRA_DB_APPLICATION_TOKEN = getpass("ASTRA_DB_APPLICATION_TOKEN = ") desired_keyspace = input("ASTRA_DB_KEYSPACE (optional, can be left empty) = ") if desired_keyspace: ASTRA_DB_KEYSPACE = desired_keyspace else: ASTRA_DB_KEYSPACE = None import cassio cassio.init( database_id=ASTRA_DB_ID, token=ASTRA_DB_APPLICATION_TOKEN, keyspace=ASTRA_DB_KEYSPACE, ) vstore = Cassandra( embedding=embe, table_name="cassandra_vector_demo", ) philo_dataset = load_dataset("datastax/philosopher-quotes")["train"] docs = [] for entry in philo_dataset: metadata = {"author": entry["author"]} doc = Document(page_content=entry["quote"], metadata=metadata) docs.append(doc) inserted_ids = vstore.add_documents(docs) print(f"\nInserted {len(inserted_ids)} documents.") texts = ["I think, therefore I am.", "To the things themselves!"] metadatas = [{"author": "descartes"}, {"author": "husserl"}] ids = ["desc_01", "huss_xy"] inserted_ids_2 = vstore.add_texts(texts=texts, metadatas=metadatas, ids=ids) print(f"\nInserted {len(inserted_ids_2)} documents.") results = vstore.similarity_search("Our life is what we make of it", k=3) for res in results: print(f"* {res.page_content} [{res.metadata}]") results_filtered = vstore.similarity_search( "Our life is what we make of it", k=3, filter={"author": "plato"}, ) for res in results_filtered: print(f"* {res.page_content} [{res.metadata}]") results = vstore.similarity_search_with_score("Our life is what we make of it", k=3) for res, score in results: print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]") results = vstore.max_marginal_relevance_search( "Our life is what we make of it", k=3, filter={"author": "aristotle"}, ) for res in results: print(f"* {res.page_content} [{res.metadata}]") delete_1 = vstore.delete(inserted_ids[:3]) print(f"all_succeed={delete_1}") # True, all documents deleted delete_2 = vstore.delete(inserted_ids[2:5]) print(f"some_succeeds={delete_2}") # True, though some IDs were gone already get_ipython().system('curl -L "https://github.com/awesome-astra/datasets/blob/main/demo-resources/what-is-philosophy/what-is-philosophy.pdf?raw=true" -o "what-is-philosophy.pdf"') pdf_loader =
PyPDFLoader("what-is-philosophy.pdf")
langchain_community.document_loaders.PyPDFLoader
from langchain_community.chat_message_histories import StreamlitChatMessageHistory history = StreamlitChatMessageHistory(key="chat_messages") history.add_user_message("hi!") history.add_ai_message("whats up?") history.messages from langchain_community.chat_message_histories import StreamlitChatMessageHistory msgs =
StreamlitChatMessageHistory(key="special_app_key")
langchain_community.chat_message_histories.StreamlitChatMessageHistory
get_ipython().run_line_magic('pip', 'install --upgrade --quiet gpt4all > /dev/null') from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from langchain_community.llms import GPT4All template = """Question: {question} Answer: Let's think step by step.""" prompt = PromptTemplate.from_template(template) local_path = ( "./models/ggml-gpt4all-l13b-snoozy.bin" # replace with your desired local file path ) callbacks = [
StreamingStdOutCallbackHandler()
langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler
get_ipython().run_line_magic('pip', 'install --upgrade --quiet duckduckgo-search') from langchain.tools import DuckDuckGoSearchRun search =
DuckDuckGoSearchRun()
langchain.tools.DuckDuckGoSearchRun
import os from langchain.agents import AgentType, initialize_agent from langchain_community.tools.connery import ConneryService from langchain_openai import ChatOpenAI os.environ["CONNERY_RUNNER_URL"] = "" os.environ["CONNERY_RUNNER_API_KEY"] = "" os.environ["OPENAI_API_KEY"] = "" recepient_email = "test@example.com" connery_service =
ConneryService()
langchain_community.tools.connery.ConneryService
import json from langchain.adapters.openai import convert_message_to_dict from langchain_core.messages import AIMessage with open("example_data/dataset_twitter-scraper_2023-08-23_22-13-19-740.json") as f: data = json.load(f) tweets = [d["full_text"] for d in data if "t.co" not in d["full_text"]] messages = [
AIMessage(content=t)
langchain_core.messages.AIMessage
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymysql') from langchain.chains import RetrievalQA from langchain_community.document_loaders import ( DirectoryLoader, UnstructuredMarkdownLoader, ) from langchain_community.vectorstores import StarRocks from langchain_community.vectorstores.starrocks import StarRocksSettings from langchain_openai import OpenAI, OpenAIEmbeddings from langchain_text_splitters import TokenTextSplitter update_vectordb = False loader = DirectoryLoader( "./docs", glob="**/*.md", loader_cls=UnstructuredMarkdownLoader ) documents = loader.load() text_splitter = TokenTextSplitter(chunk_size=400, chunk_overlap=50) split_docs = text_splitter.split_documents(documents) update_vectordb = True split_docs[-20] print("# docs = %d, # splits = %d" % (len(documents), len(split_docs))) def gen_starrocks(update_vectordb, embeddings, settings): if update_vectordb: docsearch =
StarRocks.from_documents(split_docs, embeddings, config=settings)
langchain_community.vectorstores.StarRocks.from_documents
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-nvidia-ai-endpoints') import getpass import os if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"): nvapi_key = getpass.getpass("Enter your NVIDIA API key: ") assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is not a valid key" os.environ["NVIDIA_API_KEY"] = nvapi_key from langchain_nvidia_ai_endpoints import ChatNVIDIA llm = ChatNVIDIA(model="mixtral_8x7b") result = llm.invoke("Write a ballad about LangChain.") print(result.content) print(llm.batch(["What's 2*3?", "What's 2*6?"])) for chunk in llm.stream("How far can a seagull fly in one day?"): print(chunk.content, end="|") async for chunk in llm.astream( "How long does it take for monarch butterflies to migrate?" ): print(chunk.content, end="|") ChatNVIDIA.get_available_models() from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_nvidia_ai_endpoints import ChatNVIDIA prompt = ChatPromptTemplate.from_messages( [("system", "You are a helpful AI assistant named Fred."), ("user", "{input}")] ) chain = prompt |
ChatNVIDIA(model="llama2_13b")
langchain_nvidia_ai_endpoints.ChatNVIDIA
get_ipython().system('pip install boto3') from langchain_experimental.recommenders import AmazonPersonalize recommender_arn = "<insert_arn>" client = AmazonPersonalize( credentials_profile_name="default", region_name="us-west-2", recommender_arn=recommender_arn, ) client.get_recommendations(user_id="1") from langchain.llms.bedrock import Bedrock from langchain_experimental.recommenders import AmazonPersonalizeChain bedrock_llm = Bedrock(model_id="anthropic.claude-v2", region_name="us-west-2") chain = AmazonPersonalizeChain.from_llm( llm=bedrock_llm, client=client, return_direct=False ) response = chain({"user_id": "1"}) print(response) from langchain.prompts.prompt import PromptTemplate RANDOM_PROMPT_QUERY = """ You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. The movies to recommend and their information is contained in the <movie> tag. All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. Put the email between <email> tags. <movie> {result} </movie> Assistant: """ RANDOM_PROMPT = PromptTemplate(input_variables=["result"], template=RANDOM_PROMPT_QUERY) chain = AmazonPersonalizeChain.from_llm( llm=bedrock_llm, client=client, return_direct=False, prompt_template=RANDOM_PROMPT ) chain.run({"user_id": "1", "item_id": "234"}) from langchain.chains import LLMChain, SequentialChain RANDOM_PROMPT_QUERY_2 = """ You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. You want the email to impress the user, so make it appealing to them. The movies to recommend and their information is contained in the <movie> tag. All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. Put the email between <email> tags. <movie> {result} </movie> Assistant: """ RANDOM_PROMPT_2 = PromptTemplate( input_variables=["result"], template=RANDOM_PROMPT_QUERY_2 ) personalize_chain_instance = AmazonPersonalizeChain.from_llm( llm=bedrock_llm, client=client, return_direct=True ) random_chain_instance =
LLMChain(llm=bedrock_llm, prompt=RANDOM_PROMPT_2)
langchain.chains.LLMChain
get_ipython().run_line_magic('pip', 'install -U --quiet langchain langchain_community openai chromadb langchain-experimental') get_ipython().run_line_magic('pip', 'install --quiet "unstructured[all-docs]" pypdf pillow pydantic lxml pillow matplotlib chromadb tiktoken') import logging import zipfile import requests logging.basicConfig(level=logging.INFO) data_url = "https://storage.googleapis.com/benchmarks-artifacts/langchain-docs-benchmarking/cj.zip" result = requests.get(data_url) filename = "cj.zip" with open(filename, "wb") as file: file.write(result.content) with zipfile.ZipFile(filename, "r") as zip_ref: zip_ref.extractall() from langchain_community.document_loaders import PyPDFLoader loader = PyPDFLoader("./cj/cj.pdf") docs = loader.load() tables = [] texts = [d.page_content for d in docs] len(texts) from langchain.prompts import PromptTemplate from langchain_community.chat_models import ChatVertexAI from langchain_community.llms import VertexAI from langchain_core.messages import AIMessage from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnableLambda def generate_text_summaries(texts, tables, summarize_texts=False): """ Summarize text elements texts: List of str tables: List of str summarize_texts: Bool to summarize texts """ prompt_text = """You are an assistant tasked with summarizing tables and text for retrieval. \ These summaries will be embedded and used to retrieve the raw text or table elements. \ Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} """ prompt = PromptTemplate.from_template(prompt_text) empty_response = RunnableLambda( lambda x: AIMessage(content="Error processing document") ) model = VertexAI( temperature=0, model_name="gemini-pro", max_output_tokens=1024 ).with_fallbacks([empty_response]) summarize_chain = {"element": lambda x: x} | prompt | model | StrOutputParser() text_summaries = [] table_summaries = [] if texts and summarize_texts: text_summaries = summarize_chain.batch(texts, {"max_concurrency": 1}) elif texts: text_summaries = texts if tables: table_summaries = summarize_chain.batch(tables, {"max_concurrency": 1}) return text_summaries, table_summaries text_summaries, table_summaries = generate_text_summaries( texts, tables, summarize_texts=True ) len(text_summaries) import base64 import os from langchain_core.messages import HumanMessage def encode_image(image_path): """Getting the base64 string""" with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode("utf-8") def image_summarize(img_base64, prompt): """Make image summary""" model =
ChatVertexAI(model_name="gemini-pro-vision", max_output_tokens=1024)
langchain_community.chat_models.ChatVertexAI
get_ipython().run_line_magic('', 'pip install --upgrade --quiet flashrank') get_ipython().run_line_magic('', 'pip install --upgrade --quiet faiss') get_ipython().run_line_magic('', 'pip install --upgrade --quiet faiss_cpu') def pretty_print_docs(docs): print( f"\n{'-' * 100}\n".join( [f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)] ) ) import getpass import os os.environ["OPENAI_API_KEY"] = getpass.getpass() from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import FAISS from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter documents = TextLoader( "../../modules/state_of_the_union.txt", ).load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100) texts = text_splitter.split_documents(documents) embedding = OpenAIEmbeddings(model="text-embedding-ada-002") retriever =
FAISS.from_documents(texts, embedding)
langchain_community.vectorstores.FAISS.from_documents
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "cassio>=0.1.4"') import os from getpass import getpass from datasets import ( load_dataset, ) from langchain_community.document_loaders import PyPDFLoader from langchain_core.documents import Document from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter os.environ["OPENAI_API_KEY"] = getpass("OPENAI_API_KEY = ") embe = OpenAIEmbeddings() from langchain_community.vectorstores import Cassandra from cassandra.cluster import Cluster cluster = Cluster(["127.0.0.1"]) session = cluster.connect() import cassio CASSANDRA_KEYSPACE = input("CASSANDRA_KEYSPACE = ") cassio.init(session=session, keyspace=CASSANDRA_KEYSPACE) vstore = Cassandra( embedding=embe, table_name="cassandra_vector_demo", ) ASTRA_DB_ID = input("ASTRA_DB_ID = ") ASTRA_DB_APPLICATION_TOKEN = getpass("ASTRA_DB_APPLICATION_TOKEN = ") desired_keyspace = input("ASTRA_DB_KEYSPACE (optional, can be left empty) = ") if desired_keyspace: ASTRA_DB_KEYSPACE = desired_keyspace else: ASTRA_DB_KEYSPACE = None import cassio cassio.init( database_id=ASTRA_DB_ID, token=ASTRA_DB_APPLICATION_TOKEN, keyspace=ASTRA_DB_KEYSPACE, ) vstore = Cassandra( embedding=embe, table_name="cassandra_vector_demo", ) philo_dataset = load_dataset("datastax/philosopher-quotes")["train"] docs = [] for entry in philo_dataset: metadata = {"author": entry["author"]} doc =
Document(page_content=entry["quote"], metadata=metadata)
langchain_core.documents.Document
import os import comet_llm os.environ["LANGCHAIN_COMET_TRACING"] = "true" comet_llm.init() os.environ["COMET_PROJECT_NAME"] = "comet-example-langchain-tracing" from langchain.agents import AgentType, initialize_agent, load_tools from langchain.llms import OpenAI llm =
OpenAI(temperature=0)
langchain.llms.OpenAI
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy') get_ipython().system('python3 -m spacy download en_core_web_sm') get_ipython().run_line_magic('pip', 'install --upgrade --quiet nomic') import time from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import AtlasDB from langchain_text_splitters import SpacyTextSplitter ATLAS_TEST_API_KEY = "7xDPkYXSYDc1_ErdTPIcoAR9RNd8YDlkS3nVNXcVoIMZ6" loader = TextLoader("../../modules/state_of_the_union.txt") documents = loader.load() text_splitter =
SpacyTextSplitter(separator="|")
langchain_text_splitters.SpacyTextSplitter
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub gpt4all chromadb') from langchain_community.document_loaders import WebBaseLoader from langchain_text_splitters import RecursiveCharacterTextSplitter loader =
WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
langchain_community.document_loaders.WebBaseLoader
get_ipython().run_line_magic('pip', 'install --upgrade --quiet redis redisvl langchain-openai tiktoken') import getpass import os os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() redis_url = "redis://localhost:6379" redis_url = "redis://:secret@redis:7379/2" redis_url = "redis://joe:secret@redis/0" redis_url = "redis+sentinel://localhost:26379" redis_url = "redis+sentinel://joe:secret@redis" redis_url = "redis+sentinel://redis:26379/zone-1/2" redis_url = "rediss://localhost:6379" redis_url = "rediss+sentinel://localhost" metadata = [ { "user": "john", "age": 18, "job": "engineer", "credit_score": "high", }, { "user": "derrick", "age": 45, "job": "doctor", "credit_score": "low", }, { "user": "nancy", "age": 94, "job": "doctor", "credit_score": "high", }, { "user": "tyler", "age": 100, "job": "engineer", "credit_score": "high", }, { "user": "joe", "age": 35, "job": "dentist", "credit_score": "medium", }, ] texts = ["foo", "foo", "foo", "bar", "bar"] from langchain_community.vectorstores.redis import Redis rds = Redis.from_texts( texts, embeddings, metadatas=metadata, redis_url="redis://localhost:6379", index_name="users", ) rds.index_name get_ipython().system('rvl index listall') get_ipython().system('rvl index info -i users') get_ipython().system('rvl stats -i users') results = rds.similarity_search("foo") print(results[0].page_content) results = rds.similarity_search("foo", k=3) meta = results[1].metadata print("Key of the document in Redis: ", meta.pop("id")) print("Metadata of the document: ", meta) results = rds.similarity_search_with_score("foo", k=5) for result in results: print(f"Content: {result[0].page_content} --- Score: {result[1]}") results = rds.similarity_search_with_score("foo", k=5, distance_threshold=0.1) for result in results: print(f"Content: {result[0].page_content} --- Score: {result[1]}") results = rds.similarity_search_with_relevance_scores("foo", k=5) for result in results: print(f"Content: {result[0].page_content} --- Similiarity: {result[1]}") results = rds.similarity_search_with_relevance_scores("foo", k=5, score_threshold=0.9) for result in results: print(f"Content: {result[0].page_content} --- Similarity: {result[1]}") new_document = ["baz"] new_metadata = [{"user": "sam", "age": 50, "job": "janitor", "credit_score": "high"}] rds.add_texts(new_document, new_metadata) results = rds.similarity_search("baz", k=3) print(results[0].metadata) results = rds.max_marginal_relevance_search("foo") results = rds.max_marginal_relevance_search("foo", lambda_mult=0.1) rds.write_schema("redis_schema.yaml") new_rds = Redis.from_existing_index( embeddings, index_name="users", redis_url="redis://localhost:6379", schema="redis_schema.yaml", ) results = new_rds.similarity_search("foo", k=3) print(results[0].metadata) new_rds.schema == rds.schema index_schema = { "tag": [{"name": "credit_score"}], "text": [{"name": "user"}, {"name": "job"}], "numeric": [{"name": "age"}], } rds, keys = Redis.from_texts_return_keys( texts, embeddings, metadatas=metadata, redis_url="redis://localhost:6379", index_name="users_modified", index_schema=index_schema, # pass in the new index schema ) from langchain_community.vectorstores.redis import RedisText is_engineer = RedisText("job") == "engineer" results = rds.similarity_search("foo", k=3, filter=is_engineer) print("Job:", results[0].metadata["job"]) print("Engineers in the dataset:", len(results)) starts_with_doc = RedisText("job") % "doc*" results = rds.similarity_search("foo", k=3, filter=starts_with_doc) for result in results: print("Job:", result.metadata["job"]) print("Jobs in dataset that start with 'doc':", len(results)) from langchain_community.vectorstores.redis import RedisNum is_over_18 = RedisNum("age") > 18 is_under_99 = RedisNum("age") < 99 age_range = is_over_18 & is_under_99 results = rds.similarity_search("foo", filter=age_range) for result in results: print("User:", result.metadata["user"], "is", result.metadata["age"]) age_range = (
RedisNum("age")
langchain_community.vectorstores.redis.RedisNum
import re from typing import Union from langchain.agents import ( AgentExecutor, AgentOutputParser, LLMSingleActionAgent, Tool, ) from langchain.chains import LLMChain from langchain.prompts import StringPromptTemplate from langchain_community.utilities import SerpAPIWrapper from langchain_core.agents import AgentAction, AgentFinish from langchain_openai import OpenAI search =
SerpAPIWrapper()
langchain_community.utilities.SerpAPIWrapper
meals = [ "Beef Enchiladas with Feta cheese. Mexican-Greek fusion", "Chicken Flatbreads with red sauce. Italian-Mexican fusion", "Veggie sweet potato quesadillas with vegan cheese", "One-Pan Tortelonni bake with peppers and onions", ] from langchain_openai import OpenAI llm = OpenAI(model="gpt-3.5-turbo-instruct") from langchain.prompts import PromptTemplate PROMPT_TEMPLATE = """Here is the description of a meal: "{meal}". Embed the meal into the given text: "{text_to_personalize}". Prepend a personalized message including the user's name "{user}" and their preference "{preference}". Make it sound good. """ PROMPT = PromptTemplate( input_variables=["meal", "text_to_personalize", "user", "preference"], template=PROMPT_TEMPLATE, ) import langchain_experimental.rl_chain as rl_chain chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT) response = chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Tom"), preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]), text_to_personalize="This is the weeks specialty dish, our master chefs \ believe you will love it!", ) print(response["response"]) for _ in range(5): try: response = chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Tom"), preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]), text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!", ) except Exception as e: print(e) print(response["response"]) print() scoring_criteria_template = ( "Given {preference} rank how good or bad this selection is {meal}" ) chain = rl_chain.PickBest.from_llm( llm=llm, prompt=PROMPT, selection_scorer=rl_chain.AutoSelectionScorer( llm=llm, scoring_criteria_template_str=scoring_criteria_template ), ) response = chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Tom"), preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]), text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!", ) print(response["response"]) selection_metadata = response["selection_metadata"] print( f"selected index: {selection_metadata.selected.index}, score: {selection_metadata.selected.score}" ) class CustomSelectionScorer(rl_chain.SelectionScorer): def score_response( self, inputs, llm_response: str, event: rl_chain.PickBestEvent ) -> float: print(event.based_on) print(event.to_select_from) selected_meal = event.to_select_from["meal"][event.selected.index] print(f"selected meal: {selected_meal}") if "Tom" in event.based_on["user"]: if "Vegetarian" in event.based_on["preference"]: if "Chicken" in selected_meal or "Beef" in selected_meal: return 0.0 else: return 1.0 else: if "Chicken" in selected_meal or "Beef" in selected_meal: return 1.0 else: return 0.0 else: raise NotImplementedError("I don't know how to score this user") chain = rl_chain.PickBest.from_llm( llm=llm, prompt=PROMPT, selection_scorer=CustomSelectionScorer(), ) response = chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Tom"), preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]), text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!", ) class CustomSelectionScorer(rl_chain.SelectionScorer): def score_preference(self, preference, selected_meal): if "Vegetarian" in preference: if "Chicken" in selected_meal or "Beef" in selected_meal: return 0.0 else: return 1.0 else: if "Chicken" in selected_meal or "Beef" in selected_meal: return 1.0 else: return 0.0 def score_response( self, inputs, llm_response: str, event: rl_chain.PickBestEvent ) -> float: selected_meal = event.to_select_from["meal"][event.selected.index] if "Tom" in event.based_on["user"]: return self.score_preference(event.based_on["preference"], selected_meal) elif "Anna" in event.based_on["user"]: return self.score_preference(event.based_on["preference"], selected_meal) else: raise NotImplementedError("I don't know how to score this user") chain = rl_chain.PickBest.from_llm( llm=llm, prompt=PROMPT, selection_scorer=CustomSelectionScorer(), metrics_step=5, metrics_window_size=5, # rolling window average ) random_chain = rl_chain.PickBest.from_llm( llm=llm, prompt=PROMPT, selection_scorer=CustomSelectionScorer(), metrics_step=5, metrics_window_size=5, # rolling window average policy=rl_chain.PickBestRandomPolicy, # set the random policy instead of default ) for _ in range(20): try: chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Tom"), preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]), text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!", ) random_chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Tom"), preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]), text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!", ) chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Anna"), preference=rl_chain.BasedOn(["Loves meat", "especially beef"]), text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!", ) random_chain.run( meal=rl_chain.ToSelectFrom(meals), user=rl_chain.BasedOn("Anna"), preference=rl_chain.BasedOn(["Loves meat", "especially beef"]), text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!", ) except Exception as e: print(e) from matplotlib import pyplot as plt chain.metrics.to_pandas()["score"].plot(label="default learning policy") random_chain.metrics.to_pandas()["score"].plot(label="random selection policy") plt.legend() print( f"The final average score for the default policy, calculated over a rolling window, is: {chain.metrics.to_pandas()['score'].iloc[-1]}" ) print( f"The final average score for the random policy, calculated over a rolling window, is: {random_chain.metrics.to_pandas()['score'].iloc[-1]}" ) from langchain.globals import set_debug from langchain.prompts.prompt import PromptTemplate set_debug(True) REWARD_PROMPT_TEMPLATE = """ Given {preference} rank how good or bad this selection is {meal} IMPORTANT: you MUST return a single number between -1 and 1, -1 being bad, 1 being good """ REWARD_PROMPT = PromptTemplate( input_variables=["preference", "meal"], template=REWARD_PROMPT_TEMPLATE, ) chain = rl_chain.PickBest.from_llm( llm=llm, prompt=PROMPT, selection_scorer=rl_chain.AutoSelectionScorer(llm=llm, prompt=REWARD_PROMPT), ) chain.run( meal=rl_chain.ToSelectFrom(meals), user=
rl_chain.BasedOn("Tom")
langchain_experimental.rl_chain.BasedOn
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub gpt4all chromadb') from langchain_community.document_loaders import WebBaseLoader from langchain_text_splitters import RecursiveCharacterTextSplitter loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") data = loader.load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) all_splits = text_splitter.split_documents(data) from langchain_community.embeddings import GPT4AllEmbeddings from langchain_community.vectorstores import Chroma vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings()) question = "What are the approaches to Task Decomposition?" docs = vectorstore.similarity_search(question) len(docs) docs[0] get_ipython().run_line_magic('pip', 'install --upgrade --quiet llama-cpp-python') get_ipython().system(' CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 /Users/rlm/miniforge3/envs/llama/bin/pip install -U llama-cpp-python --no-cache-dir') from langchain_community.llms import LlamaCpp n_gpu_layers = 1 # Metal set to 1 is enough. n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip. llm = LlamaCpp( model_path="/Users/rlm/Desktop/Code/llama.cpp/models/llama-2-13b-chat.ggufv3.q4_0.bin", n_gpu_layers=n_gpu_layers, n_batch=n_batch, n_ctx=2048, f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls verbose=True, ) llm.invoke("Simulate a rap battle between Stephen Colbert and John Oliver") from langchain_community.llms import GPT4All gpt4all = GPT4All( model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin", max_tokens=2048, ) from langchain_community.llms.llamafile import Llamafile llamafile =
Llamafile()
langchain_community.llms.llamafile.Llamafile
from langchain_openai import OpenAIEmbeddings from langchain_pinecone import PineconeVectorStore all_documents = { "doc1": "Climate change and economic impact.", "doc2": "Public health concerns due to climate change.", "doc3": "Climate change: A social perspective.", "doc4": "Technological solutions to climate change.", "doc5": "Policy changes needed to combat climate change.", "doc6": "Climate change and its impact on biodiversity.", "doc7": "Climate change: The science and models.", "doc8": "Global warming: A subset of climate change.", "doc9": "How climate change affects daily weather.", "doc10": "The history of climate change activism.", } vectorstore = PineconeVectorStore.from_texts( list(all_documents.values()), OpenAIEmbeddings(), index_name="rag-fusion" ) from langchain_core.output_parsers import StrOutputParser from langchain_openai import ChatOpenAI from langchain import hub prompt = hub.pull("langchain-ai/rag-fusion-query-generation") generate_queries = ( prompt | ChatOpenAI(temperature=0) | StrOutputParser() | (lambda x: x.split("\n")) ) original_query = "impact of climate change" vectorstore = PineconeVectorStore.from_existing_index("rag-fusion", OpenAIEmbeddings()) retriever = vectorstore.as_retriever() from langchain.load import dumps, loads def reciprocal_rank_fusion(results: list[list], k=60): fused_scores = {} for docs in results: for rank, doc in enumerate(docs): doc_str =
dumps(doc)
langchain.load.dumps
from langchain_experimental.llm_bash.base import LLMBashChain from langchain_openai import OpenAI llm = OpenAI(temperature=0) text = "Please write a bash script that prints 'Hello World' to the console." bash_chain =
LLMBashChain.from_llm(llm, verbose=True)
langchain_experimental.llm_bash.base.LLMBashChain.from_llm
from langchain.tools import BraveSearch api_key = "API KEY" tool =
BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": 3})
langchain.tools.BraveSearch.from_api_key
from typing import Optional from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from langchain_experimental.autonomous_agents import BabyAGI from langchain_openai import OpenAI, OpenAIEmbeddings get_ipython().run_line_magic('pip', 'install faiss-cpu > /dev/null') get_ipython().run_line_magic('pip', 'install google-search-results > /dev/null') from langchain.docstore import InMemoryDocstore from langchain_community.vectorstores import FAISS embeddings_model = OpenAIEmbeddings() import faiss embedding_size = 1536 index = faiss.IndexFlatL2(embedding_size) vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {}) from langchain.agents import AgentExecutor, Tool, ZeroShotAgent from langchain.chains import LLMChain from langchain_community.utilities import SerpAPIWrapper from langchain_openai import OpenAI todo_prompt = PromptTemplate.from_template( "You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}" ) todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt) search = SerpAPIWrapper() tools = [ Tool( name="Search", func=search.run, description="useful for when you need to answer questions about current events", ), Tool( name="TODO", func=todo_chain.run, description="useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!", ), ] prefix = """You are an AI who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.""" suffix = """Question: {task} {agent_scratchpad}""" prompt = ZeroShotAgent.create_prompt( tools, prefix=prefix, suffix=suffix, input_variables=["objective", "task", "context", "agent_scratchpad"], ) llm = OpenAI(temperature=0) llm_chain = LLMChain(llm=llm, prompt=prompt) tool_names = [tool.name for tool in tools] agent =
ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
langchain.agents.ZeroShotAgent
from langchain_community.utils.openai_functions import ( convert_pydantic_to_openai_function, ) from langchain_core.prompts import ChatPromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field, validator from langchain_openai import ChatOpenAI class Joke(BaseModel): """Joke to tell user.""" setup: str = Field(description="question to set up a joke") punchline: str = Field(description="answer to resolve the joke") openai_functions = [convert_pydantic_to_openai_function(Joke)] model = ChatOpenAI(temperature=0) prompt =
ChatPromptTemplate.from_messages( [("system", "You are helpful assistant"), ("user", "{input}")
langchain_core.prompts.ChatPromptTemplate.from_messages
import os from langchain.chains import ConversationalRetrievalChain from langchain_community.vectorstores import Vectara from langchain_openai import OpenAI from langchain_community.document_loaders import TextLoader loader = TextLoader("state_of_the_union.txt") documents = loader.load() vectara = Vectara.from_documents(documents, embedding=None) from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) openai_api_key = os.environ["OPENAI_API_KEY"] llm = OpenAI(openai_api_key=openai_api_key, temperature=0) retriever = vectara.as_retriever() d = retriever.get_relevant_documents( "What did the president say about Ketanji Brown Jackson", k=2 ) print(d) bot = ConversationalRetrievalChain.from_llm( llm, retriever, memory=memory, verbose=False ) query = "What did the president say about Ketanji Brown Jackson" result = bot.invoke({"question": query}) result["answer"] query = "Did he mention who she suceeded" result = bot.invoke({"question": query}) result["answer"] bot = ConversationalRetrievalChain.from_llm( OpenAI(temperature=0), vectara.as_retriever() ) chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = bot.invoke({"question": query, "chat_history": chat_history}) result["answer"] chat_history = [(query, result["answer"])] query = "Did he mention who she suceeded" result = bot.invoke({"question": query, "chat_history": chat_history}) result["answer"] bot = ConversationalRetrievalChain.from_llm( llm, vectara.as_retriever(), return_source_documents=True ) chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = bot.invoke({"question": query, "chat_history": chat_history}) result["source_documents"][0] from langchain.chains import LLMChain from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT from langchain.chains.question_answering import load_qa_chain question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) doc_chain = load_qa_chain(llm, chain_type="map_reduce") chain = ConversationalRetrievalChain( retriever=vectara.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain, ) chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = chain({"question": query, "chat_history": chat_history}) result["answer"] from langchain.chains.qa_with_sources import load_qa_with_sources_chain question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce") chain = ConversationalRetrievalChain( retriever=vectara.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain, ) chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = chain({"question": query, "chat_history": chat_history}) result["answer"] from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chains.conversational_retrieval.prompts import ( CONDENSE_QUESTION_PROMPT, QA_PROMPT, ) from langchain.chains.llm import LLMChain from langchain.chains.question_answering import load_qa_chain llm = OpenAI(temperature=0, openai_api_key=openai_api_key) streaming_llm = OpenAI( streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0, openai_api_key=openai_api_key, ) question_generator =
LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
langchain.chains.llm.LLMChain
from langchain.chains import LLMChain from langchain.memory import ConversationBufferWindowMemory from langchain.prompts import PromptTemplate from langchain_openai import OpenAI def initialize_chain(instructions, memory=None): if memory is None: memory =
ConversationBufferWindowMemory()
langchain.memory.ConversationBufferWindowMemory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_openai.chat_models import ChatOpenAI model = ChatOpenAI() prompt = ChatPromptTemplate.from_messages( [ ( "system", "You're an assistant who's good at {ability}. Respond in 20 words or fewer", ), MessagesPlaceholder(variable_name="history"), ("human", "{input}"), ] ) runnable = prompt | model from langchain_community.chat_message_histories import ChatMessageHistory from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.runnables.history import RunnableWithMessageHistory store = {} def get_session_history(session_id: str) -> BaseChatMessageHistory: if session_id not in store: store[session_id] = ChatMessageHistory() return store[session_id] with_message_history = RunnableWithMessageHistory( runnable, get_session_history, input_messages_key="input", history_messages_key="history", ) with_message_history.invoke( {"ability": "math", "input": "What does cosine mean?"}, config={"configurable": {"session_id": "abc123"}}, ) with_message_history.invoke( {"ability": "math", "input": "What?"}, config={"configurable": {"session_id": "abc123"}}, ) with_message_history.invoke( {"ability": "math", "input": "What?"}, config={"configurable": {"session_id": "def234"}}, ) from langchain_core.runnables import ConfigurableFieldSpec store = {} def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory: if (user_id, conversation_id) not in store: store[(user_id, conversation_id)] = ChatMessageHistory() return store[(user_id, conversation_id)] with_message_history = RunnableWithMessageHistory( runnable, get_session_history, input_messages_key="input", history_messages_key="history", history_factory_config=[ ConfigurableFieldSpec( id="user_id", annotation=str, name="User ID", description="Unique identifier for the user.", default="", is_shared=True, ), ConfigurableFieldSpec( id="conversation_id", annotation=str, name="Conversation ID", description="Unique identifier for the conversation.", default="", is_shared=True, ), ], ) with_message_history.invoke( {"ability": "math", "input": "Hello"}, config={"configurable": {"user_id": "123", "conversation_id": "1"}}, ) from langchain_core.messages import HumanMessage from langchain_core.runnables import RunnableParallel chain = RunnableParallel({"output_message": ChatOpenAI()}) def get_session_history(session_id: str) -> BaseChatMessageHistory: if session_id not in store: store[session_id] = ChatMessageHistory() return store[session_id] with_message_history = RunnableWithMessageHistory( chain, get_session_history, output_messages_key="output_message", ) with_message_history.invoke( [HumanMessage(content="What did Simone de Beauvoir believe about free will")], config={"configurable": {"session_id": "baz"}}, ) with_message_history.invoke( [HumanMessage(content="How did this compare to Sartre")], config={"configurable": {"session_id": "baz"}}, ) RunnableWithMessageHistory(
ChatOpenAI()
langchain_openai.chat_models.ChatOpenAI
from langchain.retrievers.multi_vector import MultiVectorRetriever from langchain.storage import InMemoryByteStore from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter loaders = [ TextLoader("../../paul_graham_essay.txt"), TextLoader("../../state_of_the_union.txt"), ] docs = [] for loader in loaders: docs.extend(loader.load()) text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000) docs = text_splitter.split_documents(docs) vectorstore = Chroma( collection_name="full_documents", embedding_function=OpenAIEmbeddings() ) store = InMemoryByteStore() id_key = "doc_id" retriever = MultiVectorRetriever( vectorstore=vectorstore, byte_store=store, id_key=id_key, ) import uuid doc_ids = [str(uuid.uuid4()) for _ in docs] child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400) sub_docs = [] for i, doc in enumerate(docs): _id = doc_ids[i] _sub_docs = child_text_splitter.split_documents([doc]) for _doc in _sub_docs: _doc.metadata[id_key] = _id sub_docs.extend(_sub_docs) retriever.vectorstore.add_documents(sub_docs) retriever.docstore.mset(list(zip(doc_ids, docs))) retriever.vectorstore.similarity_search("justice breyer")[0] len(retriever.get_relevant_documents("justice breyer")[0].page_content) from langchain.retrievers.multi_vector import SearchType retriever.search_type = SearchType.mmr len(retriever.get_relevant_documents("justice breyer")[0].page_content) import uuid from langchain_core.documents import Document from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI chain = ( {"doc": lambda x: x.page_content} | ChatPromptTemplate.from_template("Summarize the following document:\n\n{doc}") | ChatOpenAI(max_retries=0) | StrOutputParser() ) summaries = chain.batch(docs, {"max_concurrency": 5}) vectorstore = Chroma(collection_name="summaries", embedding_function=OpenAIEmbeddings()) store =
InMemoryByteStore()
langchain.storage.InMemoryByteStore
get_ipython().run_line_magic('pip', 'install --upgrade --quiet yfinance') import os os.environ["OPENAI_API_KEY"] = "..." from langchain.agents import AgentType, initialize_agent from langchain_community.tools.yahoo_finance_news import YahooFinanceNewsTool from langchain_openai import ChatOpenAI llm = ChatOpenAI(temperature=0.0) tools = [YahooFinanceNewsTool()] agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, ) agent_chain.run( "What happens today with Microsoft stocks?", ) agent_chain.run( "How does Microsoft feels today comparing with Nvidia?", ) tool =
YahooFinanceNewsTool()
langchain_community.tools.yahoo_finance_news.YahooFinanceNewsTool
get_ipython().run_line_magic('pip', 'install --upgrade --quiet opencv-python scikit-image') import os from langchain_openai import OpenAI os.environ["OPENAI_API_KEY"] = "<your-key-here>" from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper from langchain_openai import OpenAI llm = OpenAI(temperature=0.9) prompt = PromptTemplate( input_variables=["image_desc"], template="Generate a detailed prompt to generate an image based on the following description: {image_desc}", ) chain =
LLMChain(llm=llm, prompt=prompt)
langchain.chains.LLMChain
import json from pprint import pprint from langchain.globals import set_debug from langchain_community.llms import NIBittensorLLM set_debug(True) llm_sys =
NIBittensorLLM( system_prompt="Your task is to determine response based on user prompt.Explain me like I am technical lead of a project" )
langchain_community.llms.NIBittensorLLM
get_ipython().run_line_magic('pip', 'install --upgrade --quiet redis redisvl langchain-openai tiktoken') import getpass import os os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() redis_url = "redis://localhost:6379" redis_url = "redis://:secret@redis:7379/2" redis_url = "redis://joe:secret@redis/0" redis_url = "redis+sentinel://localhost:26379" redis_url = "redis+sentinel://joe:secret@redis" redis_url = "redis+sentinel://redis:26379/zone-1/2" redis_url = "rediss://localhost:6379" redis_url = "rediss+sentinel://localhost" metadata = [ { "user": "john", "age": 18, "job": "engineer", "credit_score": "high", }, { "user": "derrick", "age": 45, "job": "doctor", "credit_score": "low", }, { "user": "nancy", "age": 94, "job": "doctor", "credit_score": "high", }, { "user": "tyler", "age": 100, "job": "engineer", "credit_score": "high", }, { "user": "joe", "age": 35, "job": "dentist", "credit_score": "medium", }, ] texts = ["foo", "foo", "foo", "bar", "bar"] from langchain_community.vectorstores.redis import Redis rds = Redis.from_texts( texts, embeddings, metadatas=metadata, redis_url="redis://localhost:6379", index_name="users", ) rds.index_name get_ipython().system('rvl index listall') get_ipython().system('rvl index info -i users') get_ipython().system('rvl stats -i users') results = rds.similarity_search("foo") print(results[0].page_content) results = rds.similarity_search("foo", k=3) meta = results[1].metadata print("Key of the document in Redis: ", meta.pop("id")) print("Metadata of the document: ", meta) results = rds.similarity_search_with_score("foo", k=5) for result in results: print(f"Content: {result[0].page_content} --- Score: {result[1]}") results = rds.similarity_search_with_score("foo", k=5, distance_threshold=0.1) for result in results: print(f"Content: {result[0].page_content} --- Score: {result[1]}") results = rds.similarity_search_with_relevance_scores("foo", k=5) for result in results: print(f"Content: {result[0].page_content} --- Similiarity: {result[1]}") results = rds.similarity_search_with_relevance_scores("foo", k=5, score_threshold=0.9) for result in results: print(f"Content: {result[0].page_content} --- Similarity: {result[1]}") new_document = ["baz"] new_metadata = [{"user": "sam", "age": 50, "job": "janitor", "credit_score": "high"}] rds.add_texts(new_document, new_metadata) results = rds.similarity_search("baz", k=3) print(results[0].metadata) results = rds.max_marginal_relevance_search("foo") results = rds.max_marginal_relevance_search("foo", lambda_mult=0.1) rds.write_schema("redis_schema.yaml") new_rds = Redis.from_existing_index( embeddings, index_name="users", redis_url="redis://localhost:6379", schema="redis_schema.yaml", ) results = new_rds.similarity_search("foo", k=3) print(results[0].metadata) new_rds.schema == rds.schema index_schema = { "tag": [{"name": "credit_score"}], "text": [{"name": "user"}, {"name": "job"}], "numeric": [{"name": "age"}], } rds, keys = Redis.from_texts_return_keys( texts, embeddings, metadatas=metadata, redis_url="redis://localhost:6379", index_name="users_modified", index_schema=index_schema, # pass in the new index schema ) from langchain_community.vectorstores.redis import RedisText is_engineer =
RedisText("job")
langchain_community.vectorstores.redis.RedisText
from langchain.agents import create_spark_sql_agent from langchain_community.agent_toolkits import SparkSQLToolkit from langchain_community.utilities.spark_sql import SparkSQL from langchain_openai import ChatOpenAI from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() schema = "langchain_example" spark.sql(f"CREATE DATABASE IF NOT EXISTS {schema}") spark.sql(f"USE {schema}") csv_file_path = "titanic.csv" table = "titanic" spark.read.csv(csv_file_path, header=True, inferSchema=True).write.saveAsTable(table) spark.table(table).show() spark_sql = SparkSQL(schema=schema) llm = ChatOpenAI(temperature=0) toolkit =
SparkSQLToolkit(db=spark_sql, llm=llm)
langchain_community.agent_toolkits.SparkSQLToolkit
from langchain.chains import HypotheticalDocumentEmbedder, LLMChain from langchain.prompts import PromptTemplate from langchain_openai import OpenAI, OpenAIEmbeddings base_embeddings = OpenAIEmbeddings() llm = OpenAI() embeddings = HypotheticalDocumentEmbedder.from_llm(llm, base_embeddings, "web_search") result = embeddings.embed_query("Where is the Taj Mahal?") multi_llm = OpenAI(n=4, best_of=4) embeddings = HypotheticalDocumentEmbedder.from_llm( multi_llm, base_embeddings, "web_search" ) result = embeddings.embed_query("Where is the Taj Mahal?") prompt_template = """Please answer the user's question about the most recent state of the union address Question: {question} Answer:""" prompt =
PromptTemplate(input_variables=["question"], template=prompt_template)
langchain.prompts.PromptTemplate
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langkit langchain-openai langchain') from langchain.callbacks import WhyLabsCallbackHandler from langchain_openai import OpenAI whylabs =
WhyLabsCallbackHandler.from_params()
langchain.callbacks.WhyLabsCallbackHandler.from_params
get_ipython().run_line_magic('pip', 'install -qU chromadb langchain langchain-community langchain-openai') from langchain_community.document_loaders import TextLoader from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter loader = TextLoader("../../state_of_the_union.txt") documents = loader.load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) for i, doc in enumerate(texts): doc.metadata["page_chunk"] = i embeddings = OpenAIEmbeddings() vectorstore =
Chroma.from_documents(texts, embeddings, collection_name="state-of-union")
langchain_community.vectorstores.Chroma.from_documents