Upload 2 files

app.py

@@ -15,7 +15,7 @@ from chat_chains import (
     parse_context_and_question,
     ai_response_format,
 )
-from autoqa_chains import auto_qa_chain, auto_qa_output_parser
+from autoqa_chains import auto_qa_chain, auto_qa_output_parser, followup_qa_chain
 from chain_of_density import chain_of_density_chain
 from insights_bullet_chain import insights_bullet_chain
 from insights_mind_map_chain import insights_mind_map_chain
@@ -292,23 +292,17 @@ def auto_qa_chain_wrapper(inputs):
         raise InvalidArgumentError("Please provide snippet ids")
     document = "\n\n".join([st.session_state.documents[c].page_content for c in inputs])
     llm = ChatOpenAI(model=st.session_state.model, temperature=0)
-    auto_qa_conversation = []
     with get_openai_callback() as cb:
-        auto_qa_response = auto_qa_chain(llm).invoke({"paper": document})
-        auto_qa_response_parsed = auto_qa_output_parser.invoke(auto_qa_response)[
-            "questions"
-        ]
-        auto_qa_conversation = [
-            (f'/auto {qa["question"]}', qa["answer"], "identity")
-            for qa in auto_qa_response_parsed
-        ]
+        auto_qa_response = auto_qa_output_parser.invoke(
+            auto_qa_chain(llm).invoke({"paper": document})
+        )["questions"]
+        formated_response = "\n\n".join(
+            f"#### {qa['question']}\n\n{qa['answer']}" for qa in auto_qa_response
+        )
         stats = cb
     st.session_state.messages.append(
-        (f"/auto-insight {inputs}", "Auto Convervation Generated", "identity")
+        (f"/auto-insight {inputs}", formated_response, "identity")
     )
-    for qa in auto_qa_conversation:
-        st.session_state.messages.append((qa[0], qa[1], "identity"))
-
     st.session_state.costing.append(
         {
             "prompt tokens": stats.prompt_tokens,
@@ -317,10 +311,7 @@ def auto_qa_chain_wrapper(inputs):
         }
     )
     return (
-        "\n\n".join(
-            f"Q: {qa['question']}\n\nA: {qa['answer']}"
-            for qa in auto_qa_response_parsed
-        ),
+        formated_response,
         "identity",
     )
 

autoqa_chains.py

@@ -19,7 +19,7 @@ The focus should be on the paper's objectives, methodology, key findings, and im
 The answers must be based on the content of the research paper, offering clear and comprehensive insights for readers.
 Ensure that the questions cover a broad range of topics related to the paper, including but not limited to the introduction, literature review, \
 methodology, results, discussion, and conclusions. 
-The goal is to capture the essence of the paper in a way that is accessible to a broad audience.
+The goal is to capture the essence of the paper in a way that is accessible to an expert audience.
 Your response should be recorded in the following json format: {format_instructions}.
 
 here is the research paper: ####{paper}####
@@ -48,10 +48,10 @@ here is the research paper: ####{paper}####
 
 followup_prompt = PromptTemplate(
     template=followup_prompt_template,
-    input_variables=["paper"],
+    input_variables=["paper", "question", "answer"],
     partial_variables={
         "format_instructions": auto_qa_output_parser.get_format_instructions()
     },
 )
 
-followup_qa_chain = lambda model: qa_prompt | model
+followup_qa_chain = lambda model: followup_prompt | model