import os from langchain_community.chat_models import ChatOpenAI from langchain_community.embeddings import OpenAIEmbeddings from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnableParallel, RunnablePassthrough from langchain_pinecone import PineconeVectorStore 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_community.document_loaders import WebBaseLoader # loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") # data = loader.load() # # Split # from langchain_text_splitters import RecursiveCharacterTextSplitter # text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) # all_splits = text_splitter.split_documents(data) # # Add to vectorDB # vectorstore = PineconeVectorStore.from_documents( # documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME # ) # retriever = vectorstore.as_retriever() vectorstore = PineconeVectorStore.from_existing_index( PINECONE_INDEX_NAME, OpenAIEmbeddings() ) retriever = vectorstore.as_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": retriever, "question": RunnablePassthrough()}) | prompt | model | StrOutputParser() ) # Add typing for input class Question(BaseModel): __root__: str chain = chain.with_types(input_type=Question)