# SPEC-009: Advanced Vector Database

## Overview
Complete semantic discovery system with embedded vector database for efficient RabbitMQ operation search and documentation retrieval.

## Components

### Vector Database Engine (Constitution Mandated)
- **ChromaDB Local Mode**: File-based embedded vector database (recommended for Python - best option)
- **Alternative**: sqlite-vec for lightweight vector storage
- **Storage Location**: `./data/vectors/rabbitmq.db` (pre-packaged with application)
- **Portability**: Cross-platform database files with no external dependencies beyond application runtime
- **Pre-generated**: Database files MUST be packaged with application - no external database installations required
- **Version Control**: Vector database files MUST be committed to repository

### Embedding System (Build-Time Generation)
- **Pre-computed Embeddings**: All RabbitMQ operations from OpenAPI specification, generated at build-time and committed to repository
- **Embedding Model**: sentence-transformers for text embeddings
- **Content Indexing**: Operation descriptions, parameters, use cases, troubleshooting from OpenAPI
- **Generation Trigger**: Embeddings regenerated ONLY when:
  1. `.specify/memory/rabbitmq-http-api-openapi.yaml` is modified
  2. `python scripts/generate_embeddings.py` is manually executed
  3. Initial repository setup/clone
- **NOT Runtime**: Embeddings NOT generated at runtime or during server startup

### Search Capabilities
- **Semantic Search**: Natural language queries for RabbitMQ operations
- **Similarity Search**: Vector similarity with relevance scoring
- **Pagination Support**: Efficient pagination with relevance preservation
- **Performance**: Sub-100ms search response time per page

### Search Architecture
- **Two-tier System**: Lightweight search index + rich content storage
- **Discovery Layer**: Fast operation discovery with metadata
- **Detail Layer**: Complete operation information and schemas
- **Caching**: Intelligent caching for frequently accessed operations

## Technical Requirements

### Performance
- Search response time < 100ms per page
- Database memory footprint < 50MB
- Support for 10,000+ operations
- Efficient pagination with relevance preservation

### Content Coverage
- All RabbitMQ Management API operations
- Operation descriptions and parameter documentation
- Use cases and troubleshooting scenarios
- Code examples and integration patterns

### Database Features
- Incremental updates for documentation changes
- Backup and restore capabilities
- Cross-platform compatibility
- No external database server required

### Search Features
- Natural language query processing
- Relevance scoring and ranking
- Pagination with cursor support
- Search result caching

## Acceptance Criteria

### Functional Requirements
- [ ] Semantic search returns relevant operations
- [ ] Search response time under 100ms per page
- [ ] Pagination preserves relevance scores
- [ ] Database updates automatically with documentation changes
- [ ] All RabbitMQ operations are indexed and searchable

### Performance Requirements
- [ ] Search queries complete within 100ms
- [ ] Database memory usage under 50MB
- [ ] Supports 10,000+ operations efficiently
- [ ] Pagination is fast and efficient

### Quality Requirements
- [ ] Search results are highly relevant
- [ ] Database is portable across platforms
- [ ] No external dependencies required
- [ ] Backup and restore functions work correctly

### Content Requirements
- [ ] All RabbitMQ operations are documented and indexed
- [ ] Use cases and examples are comprehensive
- [ ] Troubleshooting scenarios are covered
- [ ] Documentation is kept current

## Dependencies
- ChromaDB or sqlite-vec for vector storage
- sentence-transformers for embeddings
- numpy for vector operations
- asyncio for concurrent operations

## Implementation Notes
- **ChromaDB Local Mode**: Best Python option for file-based vector database
- **OpenAPI Source**: Index content extracted from `.specify/memory/rabbitmq-http-api-openapi.yaml`
- **Build-Time Generation**: Run `python scripts/generate_embeddings.py` when OpenAPI changes
- **Pre-generated Artifacts**: Vector database indices committed to repository
- **CI/CD Validation**: Pipeline verifies indices are up-to-date (does NOT regenerate)
- Cache frequently accessed embeddings for performance
- Use efficient vector similarity algorithms
- Implement proper database backup and recovery
- Set up monitoring for search performance
- **Two-tier Architecture**: Lightweight search index + rich content storage
- Create search analytics and usage tracking
- **AMQP Operations**: Manually index AMQP operations (not in OpenAPI) alongside HTTP API operations