"""
embedding_database_search

Semantic search over a per-collection datastore using FAISS (cosine via L2-normalized vectors). S...
"""

from typing import Any, Optional, Callable
from ._shared_client import get_shared_client


def embedding_database_search(
    database_name: str,
    query: str,
    action: Optional[str] = None,
    top_k: Optional[int] = 5,
    filters: Optional[dict[str, Any]] = None,
    provider: Optional[str] = None,
    model: Optional[str] = None,
    *,
    stream_callback: Optional[Callable[[str], None]] = None,
    use_cache: bool = False,
    validate: bool = True,
) -> dict[str, Any]:
    """
    Semantic search over a per-collection datastore using FAISS (cosine via L2-normalized vectors). S...

    Parameters
    ----------
    action : str

    database_name : str
        Collection/database name to search
    query : str
        Query text to embed and search with
    top_k : int
        Number of most similar documents to return
    filters : dict[str, Any]
        Optional metadata filters ('$gte', '$lte', '$in', '$contains', exact match)
    provider : str
        Embedding backend for the query vector. Defaults to collection/env.
    model : str
        Embedding model/deployment id for the query vector. Defaults to collection/env.
    stream_callback : Callable, optional
        Callback for streaming output
    use_cache : bool, default False
        Enable caching
    validate : bool, default True
        Validate parameters

    Returns
    -------
    dict[str, Any]
    """
    # Handle mutable defaults to avoid B006 linting error
    if filters is None:
        filters = {}
    return get_shared_client().run_one_function(
        {
            "name": "embedding_database_search",
            "arguments": {
                "action": action,
                "database_name": database_name,
                "query": query,
                "top_k": top_k,
                "filters": filters,
                "provider": provider,
                "model": model,
            },
        },
        stream_callback=stream_callback,
        use_cache=use_cache,
        validate=validate,
    )


__all__ = ["embedding_database_search"]
