directus-task-management/stories/6.5.story.md

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:

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

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