import os from langchain.document_loaders.csv_loader import CSVLoader from langchain.embeddings.openai import OpenAIEmbeddings from langchain.embeddings import CacheBackedEmbeddings from langchain_community.vectorstores import FAISS from langchain.storage import LocalFileStore from langchain.chains import RetrievalQA from langchain_openai import ChatOpenAI from langchain_core.callbacks import StdOutCallbackHandler def create_index(): # Load the data from CSV file data_loader = CSVLoader(file_path="data.csv") data = data_loader.load() # Create the embeddings model embeddings_model = OpenAIEmbeddings() # Create the cache backed embeddings in vector store store = LocalFileStore("./cache") cached_embedder = CacheBackedEmbeddings.from_bytes_store( embeddings_model, store, namespace=embeddings_model.model ) # Create FAISS vector store from documents vector_store = FAISS.from_documents(data, embeddings_model) return vector_store.as_retriever() def setup_openai(openai_key): # Set the API key for OpenAI os.environ["OPENAI_API_KEY"] = openai_key # Create index retriever retriever = create_index() # Initialize ChatOpenAI model chat_openai_model = ChatOpenAI(model="gpt-4") return retriever, chat_openai_model def ai_doctor_chat(openai_key, query): # Setup OpenAI environment retriever, chat_model = setup_openai(openai_key) # Create the QA chain handler = StdOutCallbackHandler() qa_with_sources_chain = RetrievalQA.from_chain_type( llm=chat_model, retriever=retriever, callbacks=[handler], return_source_documents=True ) # Ask a question/query res = qa_with_sources_chain({"query": query}) return res['result']