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fix: Fix for case where nothing is selected for context.

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DESKTOP-RTLN3BA\$punk 2025-06-04 23:09:31 -07:00
parent 03aacc6d8b
commit 96f545f982
5 changed files with 185 additions and 71 deletions
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75
surfsense_backend/app/agents/researcher/qna_agent/nodes.py
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@ -3,7 +3,7 @@ from langchain_core.runnables import RunnableConfig
from .state import State
from typing import Any, Dict
from app.config import config as app_config
from .prompts import get_qna_citation_system_prompt
from .prompts import get_qna_citation_system_prompt, get_qna_no_documents_system_prompt
from langchain_core.messages import HumanMessage, SystemMessage
async def rerank_documents(state: State, config: RunnableConfig) -> Dict[str, Any]:
@ -73,7 +73,8 @@ async def answer_question(state: State, config: RunnableConfig) -> Dict[str, Any
This node takes the relevant documents provided in the configuration and uses
an LLM to generate a comprehensive answer to the user's question with
proper citations. The citations follow IEEE format using source IDs from the
documents.
documents. If no documents are provided, it will use chat history to generate
an answer.
Returns:
Dict containing the final answer in the "final_answer" key.
@ -87,55 +88,59 @@ async def answer_question(state: State, config: RunnableConfig) -> Dict[str, Any
# Initialize LLM
llm = app_config.fast_llm_instance
# If no documents were provided, return a message indicating this
if not documents or len(documents) == 0:
return {
"final_answer": "I don't have any relevant documents in your personal knowledge base to answer this question. Please try asking about topics covered in your saved content, or add more documents to your knowledge base."
}
# Check if we have documents to determine which prompt to use
has_documents = documents and len(documents) > 0
# Prepare documents for citation formatting
formatted_documents = []
for _i, doc in enumerate(documents):
# Extract content and metadata
content = doc.get("content", "")
doc_info = doc.get("document", {})
document_id = doc_info.get("id") # Use document ID
# Prepare documents for citation formatting (if any)
documents_text = ""
if has_documents:
formatted_documents = []
for _i, doc in enumerate(documents):
# Extract content and metadata
content = doc.get("content", "")
doc_info = doc.get("document", {})
document_id = doc_info.get("id") # Use document ID
# Format document according to the citation system prompt's expected format
formatted_doc = f"""
<document>
<metadata>
<source_id>{document_id}</source_id>
<source_type>{doc_info.get("document_type", "CRAWLED_URL")}</source_type>
</metadata>
<content>
{content}
</content>
</document>
"""
formatted_documents.append(formatted_doc)
# Format document according to the citation system prompt's expected format
formatted_doc = f"""
<document>
<metadata>
<source_id>{document_id}</source_id>
<source_type>{doc_info.get("document_type", "CRAWLED_URL")}</source_type>
</metadata>
<content>
{content}
</content>
</document>
# Create the formatted documents text
documents_text = f"""
Source material from your personal knowledge base:
<documents>
{"\n".join(formatted_documents)}
</documents>
"""
formatted_documents.append(formatted_doc)
# Create the formatted documents text
documents_text = "\n".join(formatted_documents)
# Construct a clear, structured query for the LLM
human_message_content = f"""
Source material from your personal knowledge base:
<documents>
{documents_text}
</documents>
{documents_text}
User's question:
<user_query>
{user_query}
</user_query>
Please provide a detailed, comprehensive answer to the user's question using the information from their personal knowledge sources. Make sure to cite all information appropriately and engage in a conversational manner.
{"Please provide a detailed, comprehensive answer to the user's question using the information from their personal knowledge sources. Make sure to cite all information appropriately and engage in a conversational manner." if has_documents else "Please provide a helpful answer to the user's question based on our conversation history and your general knowledge. Engage in a conversational manner."}
"""
# Choose the appropriate system prompt based on document availability
system_prompt = get_qna_citation_system_prompt() if has_documents else get_qna_no_documents_system_prompt()
# Create messages for the LLM, including chat history for context
messages_with_chat_history = state.chat_history + [
SystemMessage(content=get_qna_citation_system_prompt()),
SystemMessage(content=system_prompt),
HumanMessage(content=human_message_content)
]