import os from langchain.chat_models import ChatOpenAI from langchain.document_loaders import WebBaseLoader from langchain.embeddings import OpenAIEmbeddings from langchain.prompts import ChatPromptTemplate from langchain.pydantic_v1 import BaseModel from langchain.schema.output_parser import StrOutputParser from langchain.schema.runnable import RunnableParallel, RunnablePassthrough from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import Weaviate if os.environ.get("WEAVIATE_API_KEY", None) is None: raise Exception("Missing `WEAVIATE_API_KEY` environment variable.") if os.environ.get("WEAVIATE_ENVIRONMENT", None) is None: raise Exception("Missing `WEAVIATE_ENVIRONMENT` environment variable.") WEAVIATE_INDEX_NAME = os.environ.get("WEAVIATE_INDEX", "langchain-test") ### Ingest code - you may need to run this the first time # Load loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") data = loader.load() # # Split text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) all_splits = text_splitter.split_documents(data) # # Add to vectorDB # vectorstore = Weaviate.from_documents( # documents=all_splits, embedding=OpenAIEmbeddings(), index_name=WEAVIATE_INDEX_NAME # ) # retriever = vectorstore.as_retriever() vectorstore = Weaviate.from_existing_index(WEAVIATE_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)