langchain/templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py

import os

from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
from langchain.vectorstores import Pinecone

if os.environ.get("PINECONE_API_KEY", None) is None:
    raise Exception("Missing `PINECONE_API_KEY` environment variable.")

if os.environ.get("PINECONE_ENVIRONMENT", None) is None:
    raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.")

PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test")

### Ingest code - you may need to run this the first time
# Load
# from langchain.document_loaders import WebBaseLoader
# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
# data = loader.load()

# # Split
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
# all_splits = text_splitter.split_documents(data)

# # Add to vectorDB
# vectorstore = Pinecone.from_documents(
#     documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME
# )
# retriever = vectorstore.as_retriever()

vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings())

# Get k=10 docs
retriever = vectorstore.as_retriever(search_kwargs={"k": 10})

# Re-rank
compressor = CohereRerank()
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=retriever
)

# RAG prompt
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)

# RAG
model = ChatOpenAI()
chain = (
    RunnableParallel(
        {"context": compression_retriever, "question": RunnablePassthrough()}
    )
    | prompt
    | model
    | StrOutputParser()
)