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| 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(temperature=0) | |
| 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'] | |