Chris Coutinho
6812e1aca7
This fixes dimension mismatch errors when using embedding models with non-standard dimensions (e.g., qwen3-embedding:4b produces 2560-dim vectors instead of the hardcoded 768). Changes: - OllamaEmbeddingProvider: Detect dimensions dynamically by generating test embedding instead of hardcoding to 768 - qdrant_client: Call dimension detection before collection creation - app.py: Initialize Qdrant collection before starting background tasks in streamable-http transport path - tests: Fix integration tests to properly mock EmbeddingService wrapper Fixes dimension mismatch error: "could not broadcast input array from shape (2560,) into shape (768,)" All integration tests passing (6/6). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
129 lines
5.2 KiB
Python
129 lines
5.2 KiB
Python
"""Qdrant client wrapper."""
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import logging
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from qdrant_client import AsyncQdrantClient
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from qdrant_client.models import Distance, VectorParams
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from nextcloud_mcp_server.config import get_settings
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logger = logging.getLogger(__name__)
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# Singleton instance
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_qdrant_client: AsyncQdrantClient | None = None
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async def get_qdrant_client() -> AsyncQdrantClient:
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"""
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Get singleton Qdrant client instance.
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Automatically creates collection on first use if it doesn't exist.
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Supports three Qdrant modes:
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- Network mode: QDRANT_URL set (e.g., http://qdrant:6333)
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- In-memory mode: QDRANT_LOCATION=:memory: (default if nothing configured)
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- Persistent local mode: QDRANT_LOCATION=/path/to/data
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Returns:
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Configured AsyncQdrantClient instance
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Raises:
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Exception: If Qdrant connection fails or collection creation fails
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"""
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global _qdrant_client
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if _qdrant_client is None:
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settings = get_settings()
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# Detect mode and initialize client accordingly
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if settings.qdrant_url:
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# Network mode
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logger.info(f"Using Qdrant network mode: {settings.qdrant_url}")
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_qdrant_client = AsyncQdrantClient(
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url=settings.qdrant_url,
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api_key=settings.qdrant_api_key,
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timeout=30,
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)
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elif settings.qdrant_location:
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# Local mode (either :memory: or persistent path)
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if settings.qdrant_location == ":memory:":
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logger.info("Using Qdrant in-memory mode: :memory:")
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_qdrant_client = AsyncQdrantClient(":memory:")
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else:
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# Persistent local mode - use path parameter
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logger.info(f"Using Qdrant persistent mode: {settings.qdrant_location}")
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_qdrant_client = AsyncQdrantClient(path=settings.qdrant_location)
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else:
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# Should not happen due to __post_init__ validation, but handle gracefully
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logger.warning("No Qdrant mode configured, defaulting to :memory:")
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_qdrant_client = AsyncQdrantClient(":memory:")
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# Get collection name (auto-generated from deployment ID + model)
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collection_name = settings.get_collection_name()
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# Import here to avoid circular dependency
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from nextcloud_mcp_server.embedding import get_embedding_service
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embedding_service = get_embedding_service()
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# Detect dimension dynamically (for OllamaEmbeddingProvider)
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if hasattr(embedding_service.provider, "_detect_dimension"):
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await embedding_service.provider._detect_dimension() # type: ignore[call-non-callable]
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expected_dimension = embedding_service.get_dimension()
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# Explicitly check if collection exists
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logger.debug(f"Checking if collection '{collection_name}' exists...")
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collections = await _qdrant_client.get_collections()
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collection_names = [c.name for c in collections.collections]
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if collection_name in collection_names:
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# Collection exists - validate dimensions
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logger.debug(
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f"Collection '{collection_name}' found, validating dimensions..."
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)
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collection_info = await _qdrant_client.get_collection(collection_name)
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actual_dimension = collection_info.config.params.vectors.size
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# Validate dimension matches
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if actual_dimension != expected_dimension:
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raise ValueError(
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f"Dimension mismatch for collection '{collection_name}':\n"
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f" Expected: {expected_dimension} (from embedding model '{settings.ollama_embedding_model}')\n"
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f" Found: {actual_dimension}\n"
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f"This usually means you changed the embedding model.\n"
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f"Solutions:\n"
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f" 1. Delete the old collection: Collection will be recreated with new dimensions\n"
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f" 2. Set QDRANT_COLLECTION to use a different collection name\n"
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f" 3. Revert OLLAMA_EMBEDDING_MODEL to the original model"
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)
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logger.info(
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f"Using existing Qdrant collection: {collection_name} "
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f"(dimension={actual_dimension}, model={settings.ollama_embedding_model})"
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)
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else:
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# Collection doesn't exist - create it
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logger.info(
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f"Collection '{collection_name}' not found, creating with "
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f"dimension={expected_dimension}, model={settings.ollama_embedding_model}..."
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)
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await _qdrant_client.create_collection(
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collection_name=collection_name,
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vectors_config=VectorParams(
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size=expected_dimension,
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distance=Distance.COSINE,
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),
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)
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logger.info(
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f"Created Qdrant collection: {collection_name}\n"
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f" Dimension: {expected_dimension}\n"
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f" Model: {settings.ollama_embedding_model}\n"
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f" Distance: COSINE\n"
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f"Background sync will index all documents with this embedding model."
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)
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return _qdrant_client
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