google-workspace-unlimited

# Qdrant Integration Comprehensive Summary ## Overview This document provides a complete overview of all Qdrant-related resources, tools, middleware, and integrations in the FastMCP Google MCP Server. **Status**: ✅ **FULLY OPERATIONAL** - All critical issues resolved as of September 2024 ## Architecture The Qdrant integration follows a unified architecture with modular components: - **Unified Middleware**: Single `QdrantUnifiedMiddleware` for all vector operations - **Modular Core**: Separated into specialized managers in `middleware/qdrant_core/` - **Deferred Initialization**: Components load only when first used to prevent startup blocking - **Auto-Discovery**: Automatically discovers Qdrant instances on multiple ports - **Compression**: Automatic compression for large payloads (>5KB) - **Template Integration**: Works with email templates and other cached data - **Resource System**: Exposes cached data through FastMCP resource system - **Pydantic Integration**: Full type safety with Pydantic models throughout ## Core Components ### 1. Middleware Layer #### QdrantUnifiedMiddleware (`middleware/qdrant_middleware.py`) - **Purpose**: Central middleware orchestrating all Qdrant operations - **Architecture**: Delegates to specialized managers in `qdrant_core/` - **Features**: - Auto-discovery of Qdrant instances (ports 6333, 6334, 6335) - Deferred initialization of embeddings model (sentence-transformers/all-MiniLM-L6-v2) - Automatic compression for payloads >5KB - Support for multiple query types (ID lookup, filtered search, semantic search) - FastMCP Google MCP middleware integration for automatic tool response storage - Enhanced user email extraction with priority-based fallbacks - Execution time tracking and performance monitoring - **Key Hooks**: - `on_call_tool()`: Intercepts and stores tool responses - `on_read_resource()`: Handles qdrant:// resource URIs - `on_list_tools()`: Triggers background initialization #### Qdrant Core Managers (`middleware/qdrant_core/`) ##### QdrantClientManager (`client.py`) - **Purpose**: Connection management and model loading - **Features**: - Auto-discovery across multiple ports - Embedding model loading with caching - SSL/cloud instance support - Health checking and diagnostics ##### QdrantStorageManager (`storage.py`) - **Purpose**: Data storage and persistence operations - **Features**: - Async response storage with embeddings - Compression for large payloads - Collection management and optimization - Batch operations support ##### QdrantSearchManager (`search.py`) - **Purpose**: Search operations and analytics - **Features**: - Advanced query parsing (supports `user:email`, `tool:name` syntax) - Semantic search with relevance scoring - Analytics and reporting capabilities - Field mapping for user-friendly queries ##### QdrantResourceHandler (`resource_handler.py`) - **Purpose**: Handles qdrant:// URI processing - **Features**: - Resource URI parsing and routing - Context caching integration - Pydantic model responses - Error handling and validation ### 2. Resource Layer #### Qdrant Resources (`middleware/qdrant_core/resources.py`) The following Qdrant-specific resources are exposed: ##### Collection Resources - **`qdrant://collections/list`** ✅ - List all collections with detailed statistics - **`qdrant://collection/{name}/info`** ✅ - Collection metadata and configuration - **`qdrant://collection/{name}/responses/recent`** ✅ - Recent responses from specific collection ##### Search Resources - **`qdrant://search/{query}`** ✅ - Global semantic search across all collections - **`qdrant://search/{collection}/{query}`** ✅ - Collection-specific semantic search ##### Status Resources - **`qdrant://status`** ✅ - Middleware health and diagnostics #### Tool Output Resources (`resources/tool_output_resources.py`) General cached data resources: ##### Cached Data Resources - **`spaces://list`** - Cached Google Chat spaces list (5min TTL) - **`drive://files/recent`** - Recent Google Drive files (5min TTL) - **`gmail://messages/recent`** - Recent Gmail messages from last 7 days (5min TTL) - **`calendar://events/today`** - Today's calendar events (5min TTL) ##### Cache Management Resources - **`cache://status`** - Cache status and analytics - **`cache://clear`** - Clear cache for current user ### 3. Tools Layer #### Enhanced Qdrant Tools (`middleware/qdrant_core/tools.py`) - **`search`**: Advanced search through Qdrant with natural language queries - Supports semantic search, filters, and direct point lookup - Returns structured search results with relevance scores and metadata - **`fetch`**: Retrieve complete document content from Qdrant by point ID - Returns full document with structured metadata and formatted content - **`search_tool_history`**: Advanced search through historical tool responses - Supports ID lookup (`id:12345`) - Filtered search (`user_email:test@gmail.com query`) - Combined filters (`tool_name:search user_email:test@gmail.com documents`) - Natural language queries - **`get_tool_analytics`**: Comprehensive analytics on tool usage and performance - **`get_response_details`**: Retrieve full response details by unique ID - **`cleanup_qdrant_data`**: Manual cleanup of stale data with retention policies #### Configuration Tools - **`verify_qdrant_config.py`**: Environment and configuration verification - **`test_qdrant_env.py`**: Environment testing utilities ### 4. Type System #### Pydrant Types (`middleware/qdrant_types.py`) Complete Pydantic model definitions for type safety: - **QdrantCollectionInfo**: Collection metadata and statistics - **QdrantCollectionsListResponse**: List of collections with status - **QdrantSearchResponse**: Search results with relevance scores - **QdrantDocumentResponse**: Full document retrieval responses - **QdrantErrorResponse**: Standardized error responses - **QdrantStatusResponse**: Health and diagnostic information ### 5. Integration Layer #### Email Templates (`gmail/templates.py`) - **Purpose**: Email template management with Qdrant storage - **Features**: - Store HTML email templates with metadata - Semantic search for templates by content/tags - Template assignment tracking - Integration with unified Qdrant middleware #### Module Wrapper (`adapters/module_wrapper.py`) - **Purpose**: Index Python modules for semantic search - **Features**: - Automatic component discovery and indexing - FastEmbed integration for efficient embeddings - Deterministic IDs to prevent duplicates - **Collection**: `card_framework_components_fastembed` ## Configuration ### Environment Variables ```bash QDRANT_URL=https://your-cloud-instance.qdrant.io:6333 QDRANT_HOST=localhost # For local instances QDRANT_PORT=6333 # Primary port QDRANT_API_KEY=your_api_key # Required for cloud instances QDRANT_OPTIMIZATION_PROFILE=cloud_low_latency # Performance profile ``` ### Optimization Profiles - **`cloud_low_latency`** ⚡ (Default): Optimized for small datasets with fast access - **`cloud_balanced`** ⚖️: Medium datasets with balanced performance - **`cloud_large_scale`** 📈: Large datasets with efficiency focus - **`local_development`** 🛠️: Fast startup for development ### Default Configuration - **Collection Name**: `mcp_tool_responses` - **Vector Size**: 384 (sentence-transformers/all-MiniLM-L6-v2) - **Distance Metric**: Cosine - **Compression Threshold**: 5120 bytes (5KB) - **Default Search Limit**: 10 results - **Score Threshold**: 0.0 (configurable) ## Search Capabilities ### Query Types Supported 1. **Direct ID Lookup**: `id:12345-abcd-5678-efgh` 2. **Filtered Search**: `user_email:test@gmail.com semantic query` 3. **Multiple Filters**: `tool_name:search user_email:test@gmail.com documents` 4. **Pure Semantic Search**: `natural language query` ### Field Mapping System User-friendly aliases map to actual field names: - `user` → `user_email` - `service` → `tool_name` - `tool` → `tool_name` - `email` → `user_email` - `session` → `session_id` - `type` → `payload_type` ### Filtering Fields - `tool_name`: Filter by specific tool - `user_email`: Filter by user - `timestamp`: Filter by time range - `session_id`: Filter by session - `payload_type`: Filter by payload type - `execution_time_ms`: Filter by performance metrics ## Recent Fixes & Improvements ### ✅ Critical Issues Resolved (September 2024) #### 1. Validation Error Resolution - **Problem**: `vectors_count: Input should be a valid integer [type=int_type, input_value=None]` - **Solution**: Enhanced validation in `resource_handler.py` with explicit None checking - **Impact**: All collection statistics now properly validate and display #### 2. Resource Handler Integration - **Problem**: `'tuple' object has no attribute 'content'` errors in resource access - **Solution**: Complete Pydantic model integration throughout resource handlers - **Impact**: Type-safe resource responses with proper error handling #### 3. Initialization Timing - **Problem**: Resources failing due to uninitialized middleware - **Solution**: Added initialization triggers to `on_list_resources` and `on_read_resource` hooks - **Impact**: Reliable resource access regardless of initialization order #### 4. Context Caching Implementation - **Problem**: No communication between middleware processing and resource handlers - **Solution**: Implemented FastMCP context state management system - **Impact**: Efficient data sharing and caching between components #### 5. Architecture Refactoring - **Problem**: Monolithic middleware with mixed concerns - **Solution**: Modular `qdrant_core` architecture with specialized managers - **Impact**: Better maintainability, testability, and separation of concerns ## Collections in Use 1. **`mcp_tool_responses`**: Main collection for tool response storage 2. **`card_framework_components_fastembed`**: Module wrapper components 3. **`email_templates`**: Email template storage (if using templates) 4. **Various user collections**: Dynamic collections for specific use cases ## Performance Characteristics ### Caching - **Resource TTL**: Context-based caching with intelligent invalidation - **Compression**: Automatic for payloads >5KB using gzip - **Lazy Loading**: Models and connections load only when needed ### Cloud Optimizations - **Vector Storage**: In-memory for fastest access (cloud_low_latency profile) - **HNSW Configuration**: `ef_construct: 200, m: 32` for high search quality - **Optimizer Settings**: Immediate indexing at 1000 vectors (vs default 20000) - **Segment Management**: Smaller segments (10MB) for faster operations ### Scalability - **Vector Size**: 384 dimensions (optimized for performance) - **Batch Operations**: Supported for multiple responses - **Connection Pooling**: Auto-discovery across multiple ports - **Field Indexing**: Comprehensive keyword indexes for fast filtering ## Testing and Diagnostics ### Health Checks - Connection status verification via `qdrant://status` resource - Collection information and point counts - Search functionality testing with sample queries - Configuration validation and optimization profile verification ### Test Coverage - Complete test suite in `tests/client/test_qdrant_middleware_refactored.py` - Resource access validation with Pydantic model verification - Error handling and edge case coverage - Performance benchmarking capabilities ### Monitoring - Active user tracking through session context - Tool usage analytics with execution time metrics - Response time and performance monitoring - Comprehensive error tracking and logging ## Integration Points ### FastMCP Google MCP Integration - Middleware hooks for automatic response storage - Resource system for cached data access with type safety - Tool registration for search and analytics capabilities - Context management for state sharing ### Google Workspace Integration - Gmail template management with semantic search - Chat card component indexing and retrieval - Calendar event caching with time-based filtering - Drive file metadata storage and search ### Development Workflow - Automatic tool response indexing for development insights - Search-driven debugging and development assistance - Performance analytics for optimization guidance - Historical analysis of API usage patterns ## API Reference ### Resource URIs ``` qdrant://collections/list # List all collections qdrant://collection/{name}/info # Collection details qdrant://collection/{name}/responses/recent # Recent responses qdrant://search/{query} # Global search qdrant://search/{collection}/{query} # Collection search qdrant://status # Health status ``` ### Tool Names ``` search # Advanced semantic search fetch # Retrieve by point ID search_tool_history # Historical tool response search get_tool_analytics # Usage and performance analytics get_response_details # Detailed response retrieval cleanup_qdrant_data # Data maintenance and cleanup ``` ## File Locations Summary ### Core Architecture - `middleware/qdrant_middleware.py` - Main unified middleware orchestrator - `middleware/qdrant_core/` - Modular core components - `client.py` - Connection and model management - `storage.py` - Data storage operations - `search.py` - Search and analytics - `resource_handler.py` - Resource URI processing - `config.py` - Configuration and optimization profiles - `tools.py` - Tool definitions and setup - `resources.py` - Resource definitions and setup ### Type System - `middleware/qdrant_types.py` - Complete Pydantic model definitions ### Integration Files - `gmail/templates.py` - Email template management - `adapters/module_wrapper.py` - Module indexing - `resources/tool_output_resources.py` - General resource definitions ### Documentation - `docs/qdrant-middleware-architecture.md` - Comprehensive technical documentation - `documentation/middleware/QDRANT_COMPREHENSIVE_SUMMARY.md` - This summary ### Server Integration - `server.py` lines 152-167 - Middleware registration and setup ## Troubleshooting Guide ### Common Issues #### "No Qdrant client available" - **Cause**: Auto-discovery failed or configuration invalid - **Solution**: Check `QDRANT_URL` environment variable or network connectivity #### "Collection not found" - **Cause**: Collection hasn't been created or wrong name - **Solution**: Use `qdrant://collections/list` to verify available collections #### "Embedding model not loaded" - **Cause**: First-time model download or network issue - **Solution**: Wait for background initialization or check internet connection #### "Invalid query syntax" - **Cause**: Malformed filter syntax in search query - **Solution**: Use format `field:value query text` for filtered searches ### Performance Tuning #### For Small Datasets (<10K responses) - Use `cloud_low_latency` profile (default) - Enable in-memory storage for vectors - Set aggressive cleanup thresholds #### For Large Datasets (>100K responses) - Switch to `cloud_large_scale` profile - Enable disk storage for vectors - Increase batch sizes for bulk operations #### For Development - Use `local_development` profile - Enable verbose logging for debugging - Use smaller embedding models if needed ## Future Roadmap ### Planned Enhancements 1. **Multi-modal Support**: Integration with vision and audio embeddings 2. **Advanced Analytics**: Time-series analysis and trend detection 3. **Custom Models**: Support for domain-specific embedding models 4. **Clustering**: Automatic response clustering and organization 5. **Real-time Search**: WebSocket-based live search capabilities ### Performance Improvements 1. **Batch Indexing**: Bulk operations for better throughput 2. **Index Optimization**: Advanced HNSW parameter tuning 3. **Caching Strategies**: Multi-level caching with intelligent eviction 4. **Parallel Processing**: Concurrent search and storage operations This comprehensive summary reflects the current state of the Qdrant integration with all recent fixes and improvements. The system is now fully operational and production-ready.