def web_research()

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

• 25 lines of code
• 6 McCabe index (conditional complexity)

def web_research(state: WebSearchState, config: RunnableConfig) -> OverallState:
    """LangGraph node that performs web research using the native Google Search API tool.

    Executes a web search using the native Google Search API tool in combination with Gemini 2.0 Flash.

    Args:
        state: Current graph state containing the search query and research loop count
        config: Configuration for the runnable, including search API settings

    Returns:
        Dictionary with state update, including sources_gathered, research_loop_count, and web_research_results
    """
    # Configure
    configurable = Configuration.from_runnable_config(config)
    formatted_prompt = web_searcher_instructions.format(
        current_date=get_current_date(),
        research_topic=state["search_query"],
    )

    # Uses the google genai client as the langchain client doesn't return grounding metadata
    response = genai_client.models.generate_content(
        model=configurable.query_generator_model,
        contents=formatted_prompt,
        config={
            "tools": [{"google_search": {}}],
            "temperature": 0,
        },
    )
    # resolve the urls to short urls for saving tokens and time
    resolved_urls = resolve_urls(
        response.candidates[0].grounding_metadata.grounding_chunks, state["id"]
    )
    # Gets the citations and adds them to the generated text
    citations = get_citations(response, resolved_urls)
    modified_text = insert_citation_markers(response.text, citations)
    sources_gathered = [item for citation in citations for item in citation["segments"]]

    return {
        "sources_gathered": sources_gathered,
        "search_query": [state["search_query"]],
        "web_research_result": [modified_text],
    }