Chris Coutinho
8f45e996e8
Implements background vector database synchronization using anyio TaskGroups for BasicAuth mode with single-user credentials. Scanner Implementation: - Periodic document discovery (hourly, configurable) - Timestamp-based change detection (Nextcloud vs Qdrant) - Wake event for immediate scanning on-demand - Supports both initial sync (all docs) and incremental sync (changes only) - Detects deleted documents and queues for removal Processor Implementation: - Concurrent document processing pool (3 workers default) - I/O-bound embedding generation via Ollama API - Retry logic with exponential backoff (3 retries) - Document chunking (512 words, 50-word overlap) - Handles both index and delete operations - Upserts vectors to Qdrant with rich metadata App Lifespan Integration: - Extended AppContext with background task state - Modified app_lifespan_basic() to start tasks via anyio TaskGroups - Graceful shutdown with coordinated task cancellation - Only activates when VECTOR_SYNC_ENABLED=true Embedding Service: - OllamaEmbeddingProvider with TLS support - Singleton pattern for shared client instances - Batch embedding support for efficiency - Auto-detects embedding dimension (768 for nomic-embed-text) Qdrant Client: - Async client wrapper with singleton pattern - Auto-creates collection on first use - COSINE distance metric for semantic similarity - Integrates with embedding service for dimension detection Health Check Enhancement: - Added Qdrant status check to /health/ready endpoint - Only checks when VECTOR_SYNC_ENABLED=true - 2-second timeout for health probe - Reports connection errors with details Configuration: - VECTOR_SYNC_ENABLED: Enable background sync - VECTOR_SYNC_SCAN_INTERVAL: Scanner frequency (3600s default) - VECTOR_SYNC_PROCESSOR_WORKERS: Concurrent processors (3 default) - QDRANT_URL, QDRANT_API_KEY, QDRANT_COLLECTION: Vector DB config - OLLAMA_BASE_URL, OLLAMA_EMBEDDING_MODEL: Embedding service config Dependencies Added: - qdrant-client>=1.7.0: Vector database client Docker Compose: - Added Qdrant service with health check - Exposed ports 6333 (REST) and 6334 (gRPC) - Configured MCP service with vector sync environment - Added qdrant-data volume for persistence Known Issue: - FastMCP lifespan not triggering for streamable-http transport - Background tasks will start once lifespan integration is complete - Lifespan triggers on MCP session establishment, not server startup Related: ADR-007 Background Vector Database Synchronization 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
86 lines
2.4 KiB
Python
86 lines
2.4 KiB
Python
"""Ollama embedding provider."""
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import logging
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import httpx
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from .base import EmbeddingProvider
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logger = logging.getLogger(__name__)
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class OllamaEmbeddingProvider(EmbeddingProvider):
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"""Ollama embedding provider with TLS support."""
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def __init__(
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self,
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base_url: str,
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model: str = "nomic-embed-text",
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verify_ssl: bool = True,
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):
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"""
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Initialize Ollama embedding provider.
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Args:
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base_url: Ollama API base URL (e.g., https://ollama.internal.coutinho.io:443)
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model: Embedding model name (default: nomic-embed-text)
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verify_ssl: Verify SSL certificates (default: True)
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"""
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self.base_url = base_url.rstrip("/")
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self.model = model
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self.verify_ssl = verify_ssl
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self.client = httpx.AsyncClient(verify=verify_ssl, timeout=30.0)
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self._dimension = 768 # nomic-embed-text default
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logger.info(
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f"Initialized Ollama provider: {base_url} (model={model}, verify_ssl={verify_ssl})"
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)
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async def embed(self, text: str) -> list[float]:
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"""
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Generate embedding vector for text.
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Args:
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text: Input text to embed
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Returns:
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Vector embedding as list of floats
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"""
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response = await self.client.post(
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f"{self.base_url}/api/embeddings",
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json={"model": self.model, "prompt": text},
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)
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response.raise_for_status()
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return response.json()["embedding"]
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async def embed_batch(self, texts: list[str]) -> list[list[float]]:
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"""
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Generate embeddings for multiple texts (batched requests).
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Note: Ollama doesn't have native batch API, so we send requests sequentially.
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For better performance with large batches, consider using asyncio.gather().
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Args:
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texts: List of texts to embed
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Returns:
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List of vector embeddings
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"""
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embeddings = []
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for text in texts:
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embedding = await self.embed(text)
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embeddings.append(embedding)
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return embeddings
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def get_dimension(self) -> int:
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"""
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Get embedding dimension.
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Returns:
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Vector dimension (768 for nomic-embed-text)
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"""
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return self._dimension
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async def close(self):
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"""Close HTTP client."""
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await self.client.aclose()
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