mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
import os
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from langchain.retrievers import ContextualCompressionRetriever
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from langchain.retrievers.document_compressors import CohereRerank
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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_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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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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from langchain_pinecone import PineconeVectorStore
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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_splitters 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 = PineconeVectorStore.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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vectorstore = PineconeVectorStore.from_existing_index(
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PINECONE_INDEX_NAME, OpenAIEmbeddings()
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)
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# Get k=10 docs
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retriever = vectorstore.as_retriever(search_kwargs={"k": 10})
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# Re-rank
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compressor = CohereRerank()
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compression_retriever = ContextualCompressionRetriever(
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base_compressor=compressor, base_retriever=retriever
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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(
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{"context": compression_retriever, "question": RunnablePassthrough()}
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)
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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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