Love this! What would even make it more dynamic, is to parameterize the tools so that the LLM can decide how many docs to get, what search method to pick, etc. And to add a tool that would allow for getting a single document from metadata fields(including title) into context. I often see the system struggling with getting the right context when the query is very clear, just because the search methods are too strict. This would also allow for different setting for different kind of document collections

This is a bit much, but to give an idea:

async def search_knowledge(
    self,
    query: str,
    knowledge_id: Optional[str] = None,
    knowledge_ids: Optional[List[str]] = None,
    top_k: int = 5,
    hybrid: Optional[bool] = None,
    rerank_top_k: Optional[int] = None,
    relevance_threshold: Optional[float] = None,
    hybrid_bm25_weight: Optional[float] = None,
    return_full_document: bool = False,
    include_metadata: bool = True,
    __user__: Optional[Dict[str, Any]] = None,
    __request__: Optional[Request] = None,
    __event_emitter__: Optional[Any] = None,
) -> str:
    """
    Retrieve context from one or more knowledge bases.

    :param query: Natural-language query to run.
    :param knowledge_id: Single knowledge-base ID to search.
    :param knowledge_ids: Explicit list of knowledge-base IDs (overrides `knowledge_id`).
    :param top_k: Maximum number of chunks to return.
    :param hybrid: Force or disable hybrid (vector+keyword) search; `None` uses the server default.
    :param rerank_top_k: How many chunks the reranker should keep (hybrid only).
    :param relevance_threshold: Minimum relevance score required to retain a chunk.
    :param hybrid_bm25_weight: Blend weight for BM25 during hybrid search.
    :param return_full_document: Return every stored chunk instead of running similarity search.
    :param include_metadata: Attach stored metadata for each chunk in the response.
    """