Vector Storage Integration

Vector Storage Integration will enable embedding-based retrieval for documents, memories, and other data types, enhancing AgentDock's capabilities for contextual and semantic search.

Current Status

Status: In Progress

We are actively developing the Vector Storage system as a critical component of AgentDock's advanced features, particularly for RAG (Retrieval Augmented Generation) and memory capabilities.

Feature Overview

The Vector Storage Integration will provide:

• Embedding Generation: Creating vector representations of text and other data
• Similarity Search: Finding related content based on semantic meaning
• Provider Abstraction: Support for multiple vector database backends
• Hybrid Search: Combining vector search with traditional search methods
• Metadata Filtering: Filtering search results by metadata attributes
• Chunking Strategies: Smart document segmentation for optimal retrieval
• Index Management: Creating and maintaining vector indexes

Architecture Diagrams

Vector Storage Core Components

Provider Interfaces and Integrations

Implementation Details

The vector storage system includes these key components:

// Vector storage provider interface
interface VectorStorageProvider {
  // Store vectors with their content and metadata
  store(
    vectors: number[][], 
    content: string[], 
    metadata: Record<string, any>[]
  ): Promise<string[]>;
  
  // Search for similar vectors
  search(
    query: number[], 
    limit: number, 
    filters?: Record<string, any>
  ): Promise<VectorSearchResult[]>;
  
  // Delete vectors by ID
  delete(ids: string[]): Promise<void>;
  
  // Create a new index
  createIndex(
    name: string, 
    dimensions: number, 
    options?: IndexOptions
  ): Promise<void>;
}

// Vector search result
interface VectorSearchResult {
  id: string;
  content: string;
  metadata: Record<string, any>;
  score: number; // Similarity score
}

Current Implementation Status

Currently, we have implemented:

• Interface Design: Core interface for vector storage operations
• Embedding Integration: Connections to embedding model providers
• Basic Vector Operations: Foundation for similarity search and operations

Supported Providers

We're implementing support for these vector database providers:

1. Local Vector Store: Simple in-memory vector store for development
2. PostgreSQL + pgvector: Production-ready vector database solution
3. Qdrant: High-performance vector database
4. Pinecone: Managed vector database service
5. Chroma: Open-source embedding database

Integration with LLM Providers

The system will support multiple embedding models:

• OpenAI Embeddings: text-embedding-3-small and text-embedding-3-large
• Anthropic Embeddings: claude-3 embeddings
• Open Source Models: Support for Hugging Face sentence transformers

Dependency on Storage Abstraction Layer

The Vector Storage system builds on the Storage Abstraction Layer:

• Storage Backend: Uses the storage layer for persisting vector data
• Provider Flexibility: Leverages storage providers for different deployment scenarios
• Metadata Storage: Stores vector metadata through the storage layer
• Optimized Access: Uses specialized vector storage where available

Connection to Advanced Memory Systems

Vector Storage is tightly integrated with Advanced Memory Systems:

• Memory Retrieval: Provides semantic search for memory items
• Contextual Relevance: Helps identify memories related to current context
• Concept Relationships: Identifies related concepts and information
• Memory Embedding: Generates and manages embeddings for memory items

RAG Implementation

Vector Storage is a key component of our RAG (Retrieval Augmented Generation) pipeline:

1. Document Processing: Chunk and process documents
2. Embedding Generation: Create vector representations
3. Storage: Store embeddings with metadata
4. Query Understanding: Process user queries
5. Retrieval: Find relevant content
6. Context Integration: Include retrieved content in LLM context
7. Response Generation: Generate augmented responses

Use Cases

Vector storage enables several key capabilities:

1. Document Q&A: Answer questions based on document contents
2. Knowledge Bases: Create searchable knowledge repositories
3. Semantic Memory: Store and retrieve agent memories by meaning
4. Similarity Matching: Find similar content or queries
5. Content Organization: Group related content automatically
6. Contextual Recommendations: Suggest relevant information

Performance Considerations

The vector storage system is designed with performance in mind:

• Batched Operations: Efficient batch processing for embeddings
• Caching: Caching frequent queries and embeddings
• Index Optimization: Performance tuning for different use cases
• Dimension Reduction: Techniques to reduce vector dimensions while preserving meaning
• Hybrid Search: Combining vector search with keyword search for better results

Future Enhancements

After the initial implementation, we plan to add:

• Multi-modal Vectors: Support for image and audio embeddings
• Incremental Updates: Efficient handling of document updates
• Hierarchical Indexing: Advanced indexing for improved performance
• Cross-Encoder Reranking: Improve result relevance with reranking
• Sparse + Dense Vectors: Combining traditional and neural search approaches

Timeline

Connection to Other Roadmap Items

Vector Storage has strong connections to:

• Storage Abstraction Layer: Provides the foundation for persisting vector data
• Advanced Memory Systems: Enables semantic search for agent memories
• Evaluation Framework: Supports testing and evaluation of retrieval quality
• AgentDock Pro: Offers enterprise-grade vector search capabilities

Documentation

Comprehensive documentation will cover:

• Setting up vector storage for different providers
• Implementing custom vector storage providers
• Best practices for chunking and embedding
• Performance optimization techniques
• Scaling vector storage for production use