def reflection()

in backend/src/agent/graph.py [0:0]

• 24 lines of code
• 2 McCabe index (conditional complexity)

def reflection(state: OverallState, config: RunnableConfig) -> ReflectionState:
    """LangGraph node that identifies knowledge gaps and generates potential follow-up queries.

    Analyzes the current summary to identify areas for further research and generates
    potential follow-up queries. Uses structured output to extract
    the follow-up query in JSON format.

    Args:
        state: Current graph state containing the running summary and research topic
        config: Configuration for the runnable, including LLM provider settings

    Returns:
        Dictionary with state update, including search_query key containing the generated follow-up query
    """
    configurable = Configuration.from_runnable_config(config)
    # Increment the research loop count and get the reasoning model
    state["research_loop_count"] = state.get("research_loop_count", 0) + 1
    reasoning_model = state.get("reasoning_model", configurable.reflection_model)

    # Format the prompt
    current_date = get_current_date()
    formatted_prompt = reflection_instructions.format(
        current_date=current_date,
        research_topic=get_research_topic(state["messages"]),
        summaries="\n\n---\n\n".join(state["web_research_result"]),
    )
    # init Reasoning Model
    llm = ChatGoogleGenerativeAI(
        model=reasoning_model,
        temperature=1.0,
        max_retries=2,
        api_key=os.getenv("GEMINI_API_KEY"),
    )
    result = llm.with_structured_output(Reflection).invoke(formatted_prompt)

    return {
        "is_sufficient": result.is_sufficient,
        "knowledge_gap": result.knowledge_gap,
        "follow_up_queries": result.follow_up_queries,
        "research_loop_count": state["research_loop_count"],
        "number_of_ran_queries": len(state["search_query"]),
    }