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https://github.com/MODSetter/SurfSense.git
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feat: Added Follow Up Qns Logic
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
d611bd6303
commit
f7fe20219b
6 changed files with 466 additions and 10 deletions
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@ -9,7 +9,7 @@ from langchain_core.runnables import RunnableConfig
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from sqlalchemy.ext.asyncio import AsyncSession
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from .configuration import Configuration, SearchMode
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from .prompts import get_answer_outline_system_prompt
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from .prompts import get_answer_outline_system_prompt, get_further_questions_system_prompt
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from .state import State
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from .sub_section_writer.graph import graph as sub_section_writer_graph
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from .sub_section_writer.configuration import SubSectionType
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@ -924,8 +924,11 @@ async def process_sections(state: State, config: RunnableConfig, writer: StreamW
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# Skip the final update since we've been streaming incremental updates
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# The final answer from each section is already shown in the UI
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# Use the shared documents for further question generation
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# Since all sections used the same document pool, we can use it directly
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return {
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"final_written_report": final_written_report
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"final_written_report": final_written_report,
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"reranked_documents": all_documents
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}
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@ -1194,6 +1197,7 @@ async def handle_qna_workflow(state: State, config: RunnableConfig, writer: Stre
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# Track streaming content for real-time updates
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complete_content = ""
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captured_reranked_documents = []
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# Call the QNA agent with streaming
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async for _chunk_type, chunk in qna_agent_graph.astream(qna_state, qna_config, stream_mode=["values"]):
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@ -1214,6 +1218,10 @@ async def handle_qna_workflow(state: State, config: RunnableConfig, writer: Stre
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answer_lines = complete_content.split("\n")
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streaming_service.only_update_answer(answer_lines)
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writer({"yeild_value": streaming_service._format_annotations()})
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# Capture reranked documents from QNA agent for further question generation
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if "reranked_documents" in chunk:
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captured_reranked_documents = chunk["reranked_documents"]
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# Set default if no content was received
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if not complete_content:
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@ -1222,9 +1230,10 @@ async def handle_qna_workflow(state: State, config: RunnableConfig, writer: Stre
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streaming_service.only_update_terminal("🎉 Q&A answer generated successfully!")
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writer({"yeild_value": streaming_service._format_annotations()})
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# Return the final answer in the expected state field
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# Return the final answer and captured reranked documents for further question generation
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return {
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"final_written_report": complete_content
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"final_written_report": complete_content,
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"reranked_documents": captured_reranked_documents
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}
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except Exception as e:
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@ -1238,3 +1247,166 @@ async def handle_qna_workflow(state: State, config: RunnableConfig, writer: Stre
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}
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async def generate_further_questions(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]:
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"""
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Generate contextually relevant follow-up questions based on chat history and available documents.
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This node takes the chat history and reranked documents from sub-agents (qna_agent or sub_section_writer)
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and uses an LLM to generate follow-up questions that would naturally extend the conversation
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and provide additional value to the user.
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Returns:
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Dict containing the further questions in the "further_questions" key for state update.
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"""
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from app.services.llm_service import get_user_fast_llm
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# Get configuration and state data
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configuration = Configuration.from_runnable_config(config)
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chat_history = state.chat_history
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user_id = configuration.user_id
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streaming_service = state.streaming_service
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# Get reranked documents from the state (will be populated by sub-agents)
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reranked_documents = getattr(state, 'reranked_documents', None) or []
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streaming_service.only_update_terminal("🤔 Generating follow-up questions...")
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writer({"yeild_value": streaming_service._format_annotations()})
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# Get user's fast LLM
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llm = await get_user_fast_llm(state.db_session, user_id)
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if not llm:
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error_message = f"No fast LLM configured for user {user_id}"
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print(error_message)
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streaming_service.only_update_terminal(f"❌ {error_message}", "error")
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# Stream empty further questions to UI
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streaming_service.only_update_further_questions([])
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writer({"yeild_value": streaming_service._format_annotations()})
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return {"further_questions": []}
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# Format chat history for the prompt
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chat_history_xml = "<chat_history>\n"
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for message in chat_history:
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if hasattr(message, 'type'):
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if message.type == "human":
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chat_history_xml += f"<user>{message.content}</user>\n"
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elif message.type == "ai":
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chat_history_xml += f"<assistant>{message.content}</assistant>\n"
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else:
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# Handle other message types if needed
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chat_history_xml += f"<message>{str(message)}</message>\n"
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chat_history_xml += "</chat_history>"
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# Format available documents for the prompt
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documents_xml = "<documents>\n"
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for i, doc in enumerate(reranked_documents):
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document_info = doc.get("document", {})
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source_id = document_info.get("id", f"doc_{i}")
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source_type = document_info.get("document_type", "UNKNOWN")
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content = doc.get("content", "")
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documents_xml += f"<document>\n"
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documents_xml += f"<metadata>\n"
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documents_xml += f"<source_id>{source_id}</source_id>\n"
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documents_xml += f"<source_type>{source_type}</source_type>\n"
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documents_xml += f"</metadata>\n"
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documents_xml += f"<content>\n{content}</content>\n"
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documents_xml += f"</document>\n"
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documents_xml += "</documents>"
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# Create the human message content
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human_message_content = f"""
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{chat_history_xml}
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{documents_xml}
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Based on the chat history and available documents above, generate 3-5 contextually relevant follow-up questions that would naturally extend the conversation and provide additional value to the user. Make sure the questions can be reasonably answered using the available documents or knowledge base.
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Your response MUST be valid JSON in exactly this format:
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{{
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"further_questions": [
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{{
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"id": 0,
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"question": "further qn 1"
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}},
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{{
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"id": 1,
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"question": "further qn 2"
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}}
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]
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}}
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Do not include any other text or explanation. Only return the JSON.
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"""
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streaming_service.only_update_terminal("🧠 Analyzing conversation context to suggest relevant questions...")
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writer({"yeild_value": streaming_service._format_annotations()})
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# Create messages for the LLM
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messages = [
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SystemMessage(content=get_further_questions_system_prompt()),
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HumanMessage(content=human_message_content)
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]
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try:
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# Call the LLM
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response = await llm.ainvoke(messages)
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# Parse the JSON response
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content = response.content
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# Find the JSON in the content
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json_start = content.find('{')
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json_end = content.rfind('}') + 1
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if json_start >= 0 and json_end > json_start:
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json_str = content[json_start:json_end]
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# Parse the JSON string
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parsed_data = json.loads(json_str)
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# Extract the further_questions array
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further_questions = parsed_data.get("further_questions", [])
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streaming_service.only_update_terminal(f"✅ Generated {len(further_questions)} contextual follow-up questions!")
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# Stream the further questions to the UI
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streaming_service.only_update_further_questions(further_questions)
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writer({"yeild_value": streaming_service._format_annotations()})
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print(f"Successfully generated {len(further_questions)} further questions")
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return {"further_questions": further_questions}
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else:
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# If JSON structure not found, return empty list
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error_message = "Could not find valid JSON in LLM response for further questions"
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print(error_message)
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streaming_service.only_update_terminal(f"⚠️ {error_message}", "warning")
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# Stream empty further questions to UI
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streaming_service.only_update_further_questions([])
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writer({"yeild_value": streaming_service._format_annotations()})
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return {"further_questions": []}
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except (json.JSONDecodeError, ValueError) as e:
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# Log the error and return empty list
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error_message = f"Error parsing further questions response: {str(e)}"
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print(error_message)
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streaming_service.only_update_terminal(f"⚠️ {error_message}", "warning")
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# Stream empty further questions to UI
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streaming_service.only_update_further_questions([])
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writer({"yeild_value": streaming_service._format_annotations()})
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return {"further_questions": []}
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except Exception as e:
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# Handle any other errors
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error_message = f"Error generating further questions: {str(e)}"
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print(error_message)
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streaming_service.only_update_terminal(f"⚠️ {error_message}", "warning")
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# Stream empty further questions to UI
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streaming_service.only_update_further_questions([])
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writer({"yeild_value": streaming_service._format_annotations()})
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return {"further_questions": []}
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