Create index.py

index.py

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+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+import os
+os.environ["OPENAI_API_KEY"]
+
+loader = PyPDFLoader("2023_GPT4All_Technical_Report.pdf")
+pages = loader.load_and_split()
+# print(len(pages), pages)
+
+embeddings = OpenAIEmbeddings()
+# Create DB
+# db = FAISS.from_documents(pages, embeddings)
+
+# Save the DB in your local
+# db.save_local("faiss_index")
+
+# load the DB 
+new_db = FAISS.load_local("faiss_index", embeddings)
+
+# Init LLM
+llm = ChatOpenAI()
+qa_chain = RetrievalQA.from_chain_type(llm, retriever=new_db.as_retriever())
+
+def ask(user_query):
+    res=qa_chain({"query": user_query})
+    return res["result"]