qa attempt

app.py

@@ -40,50 +40,20 @@ metadata_field_info=[
     ),
 ]
 
-
-# metadata_field_info=[
-#     AttributeInfo(
-#         name="author",
-#         description="The author of the excerpt", 
-#         type="string or list[string]", 
-#     ),
-#     AttributeInfo(
-#         name="chapter_number",
-#         description="The chapter number of excerpt", 
-#         type="integer", 
-#     ),
-#     AttributeInfo(
-#         name="chapter_name",
-#         description="The chapter name of the excerpt", 
-#         type="string", 
-#     ),
-# ]
-
-# document_content_description = "Excerpt's from Reid Hoffman's book Impromptu"
-# embeddings = OpenAIEmbeddings()
-
-# index_name = str(os.environ['PINECONE_INDEX_NAME'])
-
 def load_chain():
-    # vectorstore = Pinecone.from_existing_index(index_name, embeddings)
-    # llm = OpenAI(temperature=0)
     retriever = SelfQueryRetriever.from_llm(
-         llm, 
-         vectorstore, 
-         document_content_description, 
-         metadata_field_info, 
-         verbose=True)
-    return retriever
+        llm, 
+        vectorstore, 
+        document_content_description, 
+        metadata_field_info, 
+        verbose=True)
+    qa = RetrievalQA.from_chain_type(
+        llm=OpenAI(), 
+        chain_type="stuff",
+        retriever=retriever, 
+        return_source_documents=False)
+    return qa
     
- # docsearch = Pinecone.from_existing_index(index_name, embeddings)
- # return docsearch
-
-# def load_chain():
-#     """Logic for loading the chain you want to use should go here."""
-#     llm = OpenAI(temperature=0)
-#     chain = ConversationChain(llm=llm)
-#     return chain
-
 chain = load_chain()
 
 # From here down is all the StreamLit UI.