enable rag q&a

langchain-streamlit-demo/app.py

@@ -26,7 +26,7 @@ from langchain.vectorstores import FAISS
 from langsmith.client import Client
 from streamlit_feedback import streamlit_feedback
 
-from qagen import get_qa_gen_chain, combine_qa_pair_lists
+from qagen import combine_qa_pair_lists, get_rag_qa_gen_chain
 from summarize import get_summarization_chain
 
 __version__ = "0.0.10"
@@ -353,9 +353,11 @@ if st.session_state.llm:
         if document_chat_chain_type == "Summarization":
             st.session_state.doc_chain = "summarization"
         elif document_chat_chain_type == "Q&A Generation":
-            st.session_state.doc_chain = get_qa_gen_chain(st.session_state.llm)
-            # from qagen import get_rag_qa_gen_chain
-            # st.session_state.doc_chain = get_rag_qa_gen_chain(st.session_state.retriever, st.session_state.llm)
+            # st.session_state.doc_chain = get_qa_gen_chain(st.session_state.llm)
+            st.session_state.doc_chain = get_rag_qa_gen_chain(
+                st.session_state.retriever,
+                st.session_state.llm,
+            )
         else:
             st.session_state.doc_chain = RetrievalQA.from_chain_type(
                 llm=st.session_state.llm,
@@ -426,14 +428,14 @@ if st.session_state.llm:
                         )
                         if st.session_state.provider == "Anthropic":
                             config["max_concurrency"] = 5
-                        raw_results = st.session_state.doc_chain.batch(
-                            [
-                                {"input": doc.page_content, "prompt": prompt}
-                                for doc in st.session_state.texts
-                            ],
-                            config,
-                        )
-                        # raw_results = st.session_state.doc_chain.invoke(prompt, config)
+                        # raw_results = st.session_state.doc_chain.batch(
+                        #     [
+                        #         {"input": doc.page_content, "prompt": prompt}
+                        #         for doc in st.session_state.texts
+                        #     ],
+                        #     config,
+                        # )
+                        raw_results = st.session_state.doc_chain.invoke(prompt, config)
                         results = combine_qa_pair_lists(raw_results).QuestionAnswerPairs
 
                         def _to_str(idx, qap):

langchain-streamlit-demo/qagen.py

@@ -64,10 +64,10 @@ def combine_qa_pair_lists(
     )
 
 
-def get_qa_gen_chain(llm: BaseLanguageModel) -> RunnableSequence:
-    return (
-        CHAT_PROMPT | llm | OutputFixingParser.from_llm(llm=llm, parser=PYDANTIC_PARSER)
-    )
+# def get_qa_gen_chain(llm: BaseLanguageModel) -> RunnableSequence:
+#     return (
+#         CHAT_PROMPT | llm | OutputFixingParser.from_llm(llm=llm, parser=PYDANTIC_PARSER)
+#     )
 
 
 def get_rag_qa_gen_chain(