Function answer_question

agents/wiki-agent/retrieve/answer.py:220–253

Return an LLM answer with local wiki citations.

(
    user_id: str,
    question: str,
    *,
    limit: int = 8,
    pinned_nodes: Optional[List[Dict[str, Any]]] = None,
    allowed_node_ids: Optional[Iterable[str]] = None,
    response_language: str = "zh",
)

def answer_question(
    user_id: str,
    question: str,
    *,
    limit: int = 8,
    pinned_nodes: Optional[List[Dict[str, Any]]] = None,
    allowed_node_ids: Optional[Iterable[str]] = None,
    response_language: str = "zh",
) -> Dict[str, Any]:
    """Return an LLM answer with local wiki citations."""
    language = _normalize_response_language(response_language)
    started = time.time()
    hits, embedding_error = _prepare_hits(user_id, question, limit, pinned_nodes, allowed_node_ids=allowed_node_ids)
    if not hits:
        return _empty_answer(started, response_language=language)

    prompt = _build_prompt(question, hits)
    llm = build_llm_provider()
    llm_error = None
    response = None
    try:
        response = llm.generate(prompt, system=_system_prompt(language), temperature=0.0, max_tokens=_answer_max_tokens())
        answer_text = response.text
    except Exception as exc:
        llm_error = str(exc)
        answer_text = _extractive_fallback(hits, llm_error, response_language=language)
    citations = _build_citations(user_id, hits)
    return {
        "text": answer_text,
        "citations": citations,
        "elapsed_ms": int((time.time() - started) * 1000),
        "response_language": language,
        "token_usage": _token_usage(llm, response, embedding_error, llm_error),
    }


def answer_question_stream(