214 lines
7.4 KiB
Markdown
214 lines
7.4 KiB
Markdown
# Story 6.5: Implement Vector Storage Integration
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## Story Information
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- **Epic/Task**: Task 6 - Develop AI Integration Layer
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- **Story Number**: 6.5
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- **Title**: Implement Vector Storage Integration
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- **Status**: Ready
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- **Complexity**: High
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- **Priority**: Medium
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- **Dependencies**: Tasks 6.1, 6.2, 6.3 (6.1 completed, 6.2 ready, 6.3 draft)
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## Story Statement
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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.
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## Acceptance Criteria
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1. [ ] Pinecone vector database is configured and connected
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2. [ ] Text embedding generation works with OpenAI ada-002 model
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3. [ ] Vector indexing processes tasks, projects, and documentation
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4. [ ] Semantic search achieves 90% relevance accuracy
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5. [ ] Similarity search returns related tasks within 200ms
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6. [ ] RAG pipeline integrates with existing AI services
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7. [ ] Incremental indexing handles new/updated content
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8. [ ] Vector store maintains synchronization with primary database
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9. [ ] Hybrid search combines vector and keyword search
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10. [ ] Integration tests validate vector operations
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## Dev Notes
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### Architecture Context References
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- **[Source: architecture.md#Vector Storage]** - Pinecone integration requirements
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- **[Source: Story 6.1]** - OpenAI service for embeddings
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- **[Source: Story 6.2]** - NLP outputs need vectorization
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- **[Source: Story 6.3]** - Context retrieval via vectors
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### Previous Story Insights
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- OpenAI service can generate embeddings
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- LangChain supports vector store integration
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- Redis available for caching frequent queries
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- Task and project data ready for indexing
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### Vector Storage Architecture
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**System Components**:
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```typescript
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interface VectorStorageSystem {
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// Core vector operations
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vectorStore: PineconeVectorStore;
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embeddingGenerator: EmbeddingService;
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// Indexing and retrieval
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indexingPipeline: VectorIndexingService;
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retrievalEngine: SemanticRetrievalService;
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// Search and recommendations
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semanticSearch: SemanticSearchService;
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recommendationEngine: SimilarityRecommendationService;
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// RAG integration
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ragPipeline: RAGPipelineService;
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contextAugmenter: ContextAugmentationService;
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}
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```
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**Vector Collections**:
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- Tasks Collection (task descriptions, requirements)
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- Projects Collection (project contexts, goals)
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- Documents Collection (documentation, guides)
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- Conversations Collection (chat history, Q&A)
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- Knowledge Base (domain knowledge, best practices)
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### Indexing Strategy
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1. **Initial Indexing**: Bulk process existing data
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2. **Incremental Updates**: Real-time indexing of changes
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3. **Scheduled Re-indexing**: Nightly optimization
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4. **Selective Indexing**: Only meaningful content
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### File Locations
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- **Vector Services**: `src/services/vector/` - Vector storage services
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- **Embedding Services**: `src/services/vector/embeddings/` - Embedding generation
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- **Search Services**: `src/services/vector/search/` - Search implementations
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- **RAG Services**: `src/services/vector/rag/` - RAG pipeline
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- **Tests**: `tests/services/vector/` - Vector service tests
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### Technical Constraints
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- Pinecone free tier: 100K vectors, 1 index
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- OpenAI ada-002: 1536 dimensions
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- Batch processing: 100 vectors per request
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- Rate limiting: 20 requests per second
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- Index size: Monitor for scaling needs
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## Tasks / Subtasks
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### Task 1: Set up Pinecone Infrastructure (AC: 1)
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- [ ] Create `src/services/vector/pinecone.service.ts`
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- [ ] Configure Pinecone client with API keys
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- [ ] Create vector index with proper dimensions
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- [ ] Set up namespace strategy for collections
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- [ ] Implement connection health checks
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### Task 2: Implement Embedding Generation Service (AC: 2)
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- [ ] Create `src/services/vector/embeddings/embedding.service.ts`
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- [ ] Integrate OpenAI ada-002 for text embeddings
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- [ ] Add batch processing for multiple texts
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- [ ] Implement caching for common embeddings
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- [ ] Create embedding validation and normalization
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### Task 3: Build Vector Indexing Pipeline (AC: 3, 7)
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- [ ] Create `src/services/vector/indexing/indexing-pipeline.service.ts`
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- [ ] Implement task vectorization process
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- [ ] Add project and document indexing
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- [ ] Create incremental update mechanism
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- [ ] Build error recovery for failed indexing
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### Task 4: Develop Semantic Search Service (AC: 4, 5)
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- [ ] Create `src/services/vector/search/semantic-search.service.ts`
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- [ ] Implement vector similarity search
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- [ ] Add relevance scoring algorithms
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- [ ] Create result ranking and filtering
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- [ ] Optimize query performance
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### Task 5: Build Hybrid Search System (AC: 9)
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- [ ] Create `src/services/vector/search/hybrid-search.service.ts`
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- [ ] Combine vector and keyword search
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- [ ] Implement result fusion strategies
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- [ ] Add weight balancing for search types
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- [ ] Create fallback mechanisms
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### Task 6: Implement RAG Pipeline (AC: 6)
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- [ ] Create `src/services/vector/rag/rag-pipeline.service.ts`
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- [ ] Build context retrieval system
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- [ ] Integrate with LangChain for augmentation
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- [ ] Create prompt enhancement with context
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- [ ] Add source attribution for retrieved content
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### Task 7: Create Recommendation Engine (AC: 5)
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- [ ] Create `src/services/vector/recommendations/similarity.service.ts`
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- [ ] Implement task similarity recommendations
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- [ ] Add collaborative filtering logic
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- [ ] Create personalized recommendations
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- [ ] Build recommendation explanations
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### Task 8: Implement Synchronization Service (AC: 8)
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- [ ] Create `src/services/vector/sync/vector-sync.service.ts`
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- [ ] Monitor database changes for updates
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- [ ] Implement two-way sync logic
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- [ ] Add conflict resolution
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- [ ] Create sync status tracking
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### Task 9: Build Vector Management Tools (AC: 7, 8)
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- [ ] Create `src/services/vector/management/vector-admin.service.ts`
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- [ ] Implement index optimization utilities
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- [ ] Add vector count monitoring
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- [ ] Create cleanup and maintenance tools
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- [ ] Build migration utilities
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### Task 10: Create API Endpoints
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- [ ] Add POST `/api/vector/search`
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- [ ] Add GET `/api/vector/similar/:taskId`
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- [ ] Add POST `/api/vector/index`
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- [ ] Add GET `/api/vector/recommendations`
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- [ ] Add DELETE `/api/vector/clear`
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### Task 11: Write Tests (AC: 10)
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- [ ] Create unit tests for embedding service
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- [ ] Test vector indexing pipeline
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- [ ] Validate search accuracy
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- [ ] Test RAG pipeline integration
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- [ ] Achieve 80% coverage
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### Task 12: Performance Optimization
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- [ ] Implement query caching strategies
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- [ ] Optimize batch processing
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- [ ] Add connection pooling
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- [ ] Create performance monitoring
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- [ ] Document optimization techniques
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### Task 13: Documentation and Examples
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- [ ] Create vector search guide
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- [ ] Document RAG implementation
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- [ ] Add semantic search examples
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- [ ] Create troubleshooting guide
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- [ ] Write scaling recommendations
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## Project Structure Notes
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- Integrates with all previous AI stories
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- Uses existing OpenAI and LangChain services
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- Maintains TypeORM patterns for metadata
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- Leverages Redis for caching
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## Dev Agent Record
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*To be filled by implementing agent*
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### File List
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**Created:**
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**Modified:**
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### Implementation Notes
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*To be filled during implementation*
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### Challenges Encountered
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*To be filled during implementation*
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### Technical Decisions
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*To be filled during implementation*
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### Completion Notes
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*To be filled upon completion*
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---
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*Story created by: Bob (Scrum Master)
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Date: 2025-08-12* |