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>
44 lines
967 B
Python
44 lines
967 B
Python
"""Abstract base class for embedding providers."""
|
|
|
|
from abc import ABC, abstractmethod
|
|
|
|
|
|
class EmbeddingProvider(ABC):
|
|
"""Base class for embedding providers."""
|
|
|
|
@abstractmethod
|
|
async def embed(self, text: str) -> list[float]:
|
|
"""
|
|
Generate embedding vector for text.
|
|
|
|
Args:
|
|
text: Input text to embed
|
|
|
|
Returns:
|
|
Vector embedding as list of floats
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
async def embed_batch(self, texts: list[str]) -> list[list[float]]:
|
|
"""
|
|
Generate embeddings for multiple texts (optimized).
|
|
|
|
Args:
|
|
texts: List of texts to embed
|
|
|
|
Returns:
|
|
List of vector embeddings
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_dimension(self) -> int:
|
|
"""
|
|
Get embedding dimension for this provider.
|
|
|
|
Returns:
|
|
Vector dimension (e.g., 768 for nomic-embed-text)
|
|
"""
|
|
pass
|