mirror of
https://github.com/hwchase17/langchain
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fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import os
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from langchain.prompts import ChatPromptTemplate
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from langchain.retrievers.multi_query import MultiQueryRetriever
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores import Pinecone
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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if os.environ.get("PINECONE_API_KEY", None) is None:
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raise Exception("Missing `PINECONE_API_KEY` environment variable.")
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if os.environ.get("PINECONE_ENVIRONMENT", None) is None:
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raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.")
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PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test")
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### Ingest code - you may need to run this the first time
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# Load
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# from langchain_community.document_loaders import WebBaseLoader
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# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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# data = loader.load()
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# # Split
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# from langchain.text_splitter import RecursiveCharacterTextSplitter
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# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
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# all_splits = text_splitter.split_documents(data)
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# # Add to vectorDB
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# vectorstore = Pinecone.from_documents(
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# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME
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# )
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# retriever = vectorstore.as_retriever()
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# Set up index with multi query retriever
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vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings())
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model = ChatOpenAI(temperature=0)
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retriever = MultiQueryRetriever.from_llm(
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retriever=vectorstore.as_retriever(), llm=model
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)
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG
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model = ChatOpenAI()
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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