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RAG Vector Databases

Overview

This section provides comprehensive guides for integrating various vector databases with Swarms agents for Retrieval-Augmented Generation (RAG) operations. Each guide demonstrates how to use unified LiteLLM embeddings with different vector database systems to create powerful, context-aware AI agents.

Available Vector Database Integrations

Cloud-Based Solutions

  • Pinecone - Serverless vector database with auto-scaling and high availability
  • Weaviate Cloud - Multi-modal vector database with GraphQL API
  • Milvus Cloud - Enterprise-grade managed vector database service

Self-Hosted Solutions

  • Qdrant - High-performance vector similarity search engine
  • ChromaDB - Simple, fast vector database for AI applications
  • FAISS - Facebook's efficient similarity search library
  • Weaviate Local - Self-hosted Weaviate with full control
  • Milvus Local - Local Milvus deployment for development

Specialized Solutions

  • SingleStore - SQL + Vector hybrid database for complex queries
  • Zyphra RAG - Specialized RAG system with advanced features

Key Features Across All Integrations

Unified LiteLLM Embeddings

All guides use the standardized LiteLLM approach with text-embedding-3-small for consistent embedding generation across different vector databases.

Swarms Agent Integration

Each integration demonstrates how to: - Initialize vector database connections - Add documents with rich metadata - Perform semantic search queries - Integrate with Swarms agents for RAG operations

Common Capabilities

  • Semantic Search: Vector similarity matching for relevant document retrieval
  • Metadata Filtering: Advanced filtering based on document properties
  • Batch Operations: Efficient bulk document processing
  • Real-time Updates: Dynamic knowledge base management
  • Scalability: Solutions for different scale requirements

Choosing the Right Vector Database

For Development & Prototyping

  • ChromaDB: Simple setup, good for experimentation
  • FAISS: High performance, good for research
  • Milvus Local: Feature-rich local development

For Production Cloud Deployments

  • Pinecone: Serverless, auto-scaling, managed
  • Weaviate Cloud: Multi-modal, GraphQL API
  • Milvus Cloud: Enterprise features, high availability

For Self-Hosted Production

  • Qdrant: High performance, clustering support
  • Weaviate Local: Full control, custom configurations
  • SingleStore: SQL + Vector hybrid capabilities

For Specialized Use Cases

  • SingleStore: When you need both SQL and vector operations
  • Zyphra RAG: For advanced RAG-specific features
  • FAISS: When maximum search performance is critical

Getting Started

  1. Choose a vector database based on your requirements
  2. Follow the specific integration guide
  3. Install required dependencies
  4. Configure embeddings with LiteLLM
  5. Initialize your Swarms agent with the vector database memory
  6. Add your documents and start querying

Each guide provides complete code examples, setup instructions, and best practices for production deployment.