# Story 6.5: Implement Vector Storage Integration

## Story Information
- **Epic/Task**: Task 6 - Develop AI Integration Layer
- **Story Number**: 6.5
- **Title**: Implement Vector Storage Integration
- **Status**: Ready
- **Complexity**: High
- **Priority**: Medium
- **Dependencies**: Tasks 6.1, 6.2, 6.3 (6.1 completed, 6.2 ready, 6.3 draft)

## Story Statement
As the Directus Task Management system, I need a vector storage system integrated with Pinecone that enables semantic search across tasks, provides similarity-based recommendations, supports RAG (Retrieval Augmented Generation) workflows, and enables intelligent task discovery so that users can find relevant information through meaning rather than just keywords.

## Acceptance Criteria
1. [ ] Pinecone vector database is configured and connected
2. [ ] Text embedding generation works with OpenAI ada-002 model
3. [ ] Vector indexing processes tasks, projects, and documentation
4. [ ] Semantic search achieves 90% relevance accuracy
5. [ ] Similarity search returns related tasks within 200ms
6. [ ] RAG pipeline integrates with existing AI services
7. [ ] Incremental indexing handles new/updated content
8. [ ] Vector store maintains synchronization with primary database
9. [ ] Hybrid search combines vector and keyword search
10. [ ] Integration tests validate vector operations

## Dev Notes

### Architecture Context References
- **[Source: architecture.md#Vector Storage]** - Pinecone integration requirements
- **[Source: Story 6.1]** - OpenAI service for embeddings
- **[Source: Story 6.2]** - NLP outputs need vectorization
- **[Source: Story 6.3]** - Context retrieval via vectors

### Previous Story Insights
- OpenAI service can generate embeddings
- LangChain supports vector store integration
- Redis available for caching frequent queries
- Task and project data ready for indexing

### Vector Storage Architecture

**System Components**:
```typescript
interface VectorStorageSystem {
  // Core vector operations
  vectorStore: PineconeVectorStore;
  embeddingGenerator: EmbeddingService;
  
  // Indexing and retrieval
  indexingPipeline: VectorIndexingService;
  retrievalEngine: SemanticRetrievalService;
  
  // Search and recommendations
  semanticSearch: SemanticSearchService;
  recommendationEngine: SimilarityRecommendationService;
  
  // RAG integration
  ragPipeline: RAGPipelineService;
  contextAugmenter: ContextAugmentationService;
}
```

**Vector Collections**:
- Tasks Collection (task descriptions, requirements)
- Projects Collection (project contexts, goals)
- Documents Collection (documentation, guides)
- Conversations Collection (chat history, Q&A)
- Knowledge Base (domain knowledge, best practices)

### Indexing Strategy
1. **Initial Indexing**: Bulk process existing data
2. **Incremental Updates**: Real-time indexing of changes
3. **Scheduled Re-indexing**: Nightly optimization
4. **Selective Indexing**: Only meaningful content

### File Locations
- **Vector Services**: `src/services/vector/` - Vector storage services
- **Embedding Services**: `src/services/vector/embeddings/` - Embedding generation
- **Search Services**: `src/services/vector/search/` - Search implementations
- **RAG Services**: `src/services/vector/rag/` - RAG pipeline
- **Tests**: `tests/services/vector/` - Vector service tests

### Technical Constraints
- Pinecone free tier: 100K vectors, 1 index
- OpenAI ada-002: 1536 dimensions
- Batch processing: 100 vectors per request
- Rate limiting: 20 requests per second
- Index size: Monitor for scaling needs

## Tasks / Subtasks

### Task 1: Set up Pinecone Infrastructure (AC: 1)
- [ ] Create `src/services/vector/pinecone.service.ts`
- [ ] Configure Pinecone client with API keys
- [ ] Create vector index with proper dimensions
- [ ] Set up namespace strategy for collections
- [ ] Implement connection health checks

### Task 2: Implement Embedding Generation Service (AC: 2)
- [ ] Create `src/services/vector/embeddings/embedding.service.ts`
- [ ] Integrate OpenAI ada-002 for text embeddings
- [ ] Add batch processing for multiple texts
- [ ] Implement caching for common embeddings
- [ ] Create embedding validation and normalization

### Task 3: Build Vector Indexing Pipeline (AC: 3, 7)
- [ ] Create `src/services/vector/indexing/indexing-pipeline.service.ts`
- [ ] Implement task vectorization process
- [ ] Add project and document indexing
- [ ] Create incremental update mechanism
- [ ] Build error recovery for failed indexing

### Task 4: Develop Semantic Search Service (AC: 4, 5)
- [ ] Create `src/services/vector/search/semantic-search.service.ts`
- [ ] Implement vector similarity search
- [ ] Add relevance scoring algorithms
- [ ] Create result ranking and filtering
- [ ] Optimize query performance

### Task 5: Build Hybrid Search System (AC: 9)
- [ ] Create `src/services/vector/search/hybrid-search.service.ts`
- [ ] Combine vector and keyword search
- [ ] Implement result fusion strategies
- [ ] Add weight balancing for search types
- [ ] Create fallback mechanisms

### Task 6: Implement RAG Pipeline (AC: 6)
- [ ] Create `src/services/vector/rag/rag-pipeline.service.ts`
- [ ] Build context retrieval system
- [ ] Integrate with LangChain for augmentation
- [ ] Create prompt enhancement with context
- [ ] Add source attribution for retrieved content

### Task 7: Create Recommendation Engine (AC: 5)
- [ ] Create `src/services/vector/recommendations/similarity.service.ts`
- [ ] Implement task similarity recommendations
- [ ] Add collaborative filtering logic
- [ ] Create personalized recommendations
- [ ] Build recommendation explanations

### Task 8: Implement Synchronization Service (AC: 8)
- [ ] Create `src/services/vector/sync/vector-sync.service.ts`
- [ ] Monitor database changes for updates
- [ ] Implement two-way sync logic
- [ ] Add conflict resolution
- [ ] Create sync status tracking

### Task 9: Build Vector Management Tools (AC: 7, 8)
- [ ] Create `src/services/vector/management/vector-admin.service.ts`
- [ ] Implement index optimization utilities
- [ ] Add vector count monitoring
- [ ] Create cleanup and maintenance tools
- [ ] Build migration utilities

### Task 10: Create API Endpoints
- [ ] Add POST `/api/vector/search`
- [ ] Add GET `/api/vector/similar/:taskId`
- [ ] Add POST `/api/vector/index`
- [ ] Add GET `/api/vector/recommendations`
- [ ] Add DELETE `/api/vector/clear`

### Task 11: Write Tests (AC: 10)
- [ ] Create unit tests for embedding service
- [ ] Test vector indexing pipeline
- [ ] Validate search accuracy
- [ ] Test RAG pipeline integration
- [ ] Achieve 80% coverage

### Task 12: Performance Optimization
- [ ] Implement query caching strategies
- [ ] Optimize batch processing
- [ ] Add connection pooling
- [ ] Create performance monitoring
- [ ] Document optimization techniques

### Task 13: Documentation and Examples
- [ ] Create vector search guide
- [ ] Document RAG implementation
- [ ] Add semantic search examples
- [ ] Create troubleshooting guide
- [ ] Write scaling recommendations

## Project Structure Notes
- Integrates with all previous AI stories
- Uses existing OpenAI and LangChain services
- Maintains TypeORM patterns for metadata
- Leverages Redis for caching

## Dev Agent Record
*To be filled by implementing agent*

### File List
**Created:**
- 

**Modified:**
- 

### Implementation Notes
*To be filled during implementation*

### Challenges Encountered
*To be filled during implementation*

### Technical Decisions
*To be filled during implementation*

### Completion Notes
*To be filled upon completion*

---
*Story created by: Bob (Scrum Master)
Date: 2025-08-12*