mcp-nextcloud/nextcloud_mcp_server/vector/collection_metadata.py

"""Per-collection metadata: embedding identity + chunking config (design §10.1).

The query path needs to know which embedding produced a collection's vectors
(``embedding_identity``) and how it was chunked (``chunking_config``) so it can
request the matching embedding at lookup time. Two sources, selected by
``COLLECTION_METADATA_SOURCE``:

- ``qdrant`` — a sentinel point (deterministic UUID, normalisable non-zero dense
  vector) stored inside the collection. Works for any Qdrant deployment, so
  self-hosters benefit even without a control plane.
- ``api`` — an HTTP GET against the control plane
  (``/v1/qdrant-collections/{name}/metadata``).

On a missing/unreadable sentinel the query path logs a warning and falls back to
the environment-configured defaults — matching today's monolith behavior, so
query availability is preserved (design §10.1).
"""

from __future__ import annotations

import logging
from typing import Any

import httpx
from qdrant_client import AsyncQdrantClient, models

from ..config import Settings, get_settings
from .payload_keys import EMBEDDING_IDENTITY

logger = logging.getLogger(__name__)

# Deterministic sentinel point id (design §10.1). Carries collection metadata
# and never matches a search (no user_id/doc_id/doc_type payload to match).
SENTINEL_POINT_ID = "00000000-0000-0000-0000-000000000000"

# Sentinel payload keys.
CHUNKING_CONFIG = "chunking_config"
IS_SENTINEL = "is_sentinel"


def build_embedding_identity(settings: Settings | None = None) -> str:
    """The embedding identity for locally-produced vectors: the model name.

    The gateway and query path route on this name; for the monolith it is the
    active embedding model (matching the collection-name derivation).
    """
    s = settings or get_settings()
    return s.get_embedding_model_name()


def env_default_metadata(settings: Settings | None = None) -> dict[str, Any]:
    """Metadata derived purely from environment config — the fallback when no
    sentinel/API metadata is available."""
    s = settings or get_settings()
    return {
        "embedding_identity": build_embedding_identity(s),
        "chunking_config": {
            "chunk_size": s.document_chunk_size,
            "chunk_overlap": s.document_chunk_overlap,
        },
    }


def _sentinel_dense(dimension: int) -> list[float]:
    # Cosine distance is undefined for the zero vector (and Qdrant Cloud strict
    # mode rejects it), so use one tiny non-zero element — mirrors the doc-id
    # backfill sentinel in qdrant_client.py.
    return [1e-9] + [0.0] * (dimension - 1)


async def upsert_sentinel(
    client: AsyncQdrantClient,
    collection_name: str,
    *,
    embedding_identity: str,
    chunking_config: dict[str, Any],
    dimension: int,
) -> None:
    """Idempotently write the metadata sentinel point for a collection."""
    point = models.PointStruct(
        id=SENTINEL_POINT_ID,
        vector={
            "dense": _sentinel_dense(dimension),
            "sparse": models.SparseVector(indices=[], values=[]),
        },
        payload={
            EMBEDDING_IDENTITY: embedding_identity,
            CHUNKING_CONFIG: chunking_config,
            IS_SENTINEL: True,
        },
    )
    await client.upsert(collection_name=collection_name, points=[point], wait=True)
    logger.debug("Upserted metadata sentinel on '%s'", collection_name)


async def _read_from_qdrant(
    client: AsyncQdrantClient, collection_name: str
) -> dict[str, Any] | None:
    points = await client.retrieve(
        collection_name=collection_name,
        ids=[SENTINEL_POINT_ID],
        with_payload=True,
    )
    if not points:
        return None
    payload = points[0].payload or {}
    if EMBEDDING_IDENTITY not in payload:
        return None
    return {
        "embedding_identity": payload.get(EMBEDDING_IDENTITY),
        "chunking_config": payload.get(CHUNKING_CONFIG),
    }


async def _read_from_api(api_url: str, collection_name: str) -> dict[str, Any] | None:
    url = f"{api_url.rstrip('/')}/v1/qdrant-collections/{collection_name}/metadata"
    async with httpx.AsyncClient(timeout=httpx.Timeout(10.0, connect=5.0)) as client:
        resp = await client.get(url)
        if resp.status_code == 404:
            return None
        resp.raise_for_status()
        return resp.json()


async def read_collection_metadata(
    client: AsyncQdrantClient,
    collection_name: str,
    settings: Settings | None = None,
) -> dict[str, Any]:
    """Read collection metadata from the configured source, falling back to env
    defaults on any miss/error (preserves query availability — §10.1)."""
    s = settings or get_settings()
    meta: dict[str, Any] | None = None
    try:
        if s.collection_metadata_source == "api":
            assert s.collection_metadata_api_url is not None
            meta = await _read_from_api(s.collection_metadata_api_url, collection_name)
        else:
            meta = await _read_from_qdrant(client, collection_name)
    except Exception:
        logger.warning(
            "Collection metadata read failed for '%s' (source=%s); using env defaults",
            collection_name,
            s.collection_metadata_source,
            exc_info=True,
        )

    if not meta or not meta.get("embedding_identity"):
        logger.warning(
            "Collection metadata missing for '%s' (source=%s); using env defaults",
            collection_name,
            s.collection_metadata_source,
        )
        return env_default_metadata(s)
    return meta