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78 lines
3.3 KiB
78 lines
3.3 KiB
# RAG Vector Databases
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## Overview
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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.
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## Available Vector Database Integrations
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### Cloud-Based Solutions
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- **[Pinecone](pinecone.md)** - Serverless vector database with auto-scaling and high availability
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- **[Weaviate Cloud](weaviate-cloud.md)** - Multi-modal vector database with GraphQL API
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- **[Milvus Cloud](milvus-cloud.md)** - Enterprise-grade managed vector database service
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### Self-Hosted Solutions
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- **[Qdrant](qdrant.md)** - High-performance vector similarity search engine
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- **[ChromaDB](chromadb.md)** - Simple, fast vector database for AI applications
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- **[FAISS](faiss.md)** - Facebook's efficient similarity search library
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- **[Weaviate Local](weaviate-local.md)** - Self-hosted Weaviate with full control
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- **[Milvus Local](milvus-local.md)** - Local Milvus deployment for development
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### Specialized Solutions
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- **[SingleStore](singlestore.md)** - SQL + Vector hybrid database for complex queries
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- **[Zyphra RAG](zyphra-rag.md)** - Specialized RAG system with advanced features
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## Key Features Across All Integrations
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### Unified LiteLLM Embeddings
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All guides use the standardized LiteLLM approach with `text-embedding-3-small` for consistent embedding generation across different vector databases.
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### Swarms Agent Integration
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Each integration demonstrates how to:
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- Initialize vector database connections
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- Add documents with rich metadata
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- Perform semantic search queries
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- Integrate with Swarms agents for RAG operations
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### Common Capabilities
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- **Semantic Search**: Vector similarity matching for relevant document retrieval
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- **Metadata Filtering**: Advanced filtering based on document properties
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- **Batch Operations**: Efficient bulk document processing
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- **Real-time Updates**: Dynamic knowledge base management
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- **Scalability**: Solutions for different scale requirements
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## Choosing the Right Vector Database
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### For Development & Prototyping
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- **ChromaDB**: Simple setup, good for experimentation
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- **FAISS**: High performance, good for research
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- **Milvus Local**: Feature-rich local development
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### For Production Cloud Deployments
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- **Pinecone**: Serverless, auto-scaling, managed
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- **Weaviate Cloud**: Multi-modal, GraphQL API
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- **Milvus Cloud**: Enterprise features, high availability
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### For Self-Hosted Production
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- **Qdrant**: High performance, clustering support
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- **Weaviate Local**: Full control, custom configurations
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- **SingleStore**: SQL + Vector hybrid capabilities
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### For Specialized Use Cases
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- **SingleStore**: When you need both SQL and vector operations
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- **Zyphra RAG**: For advanced RAG-specific features
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- **FAISS**: When maximum search performance is critical
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## Getting Started
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1. Choose a vector database based on your requirements
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2. Follow the specific integration guide
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3. Install required dependencies
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4. Configure embeddings with LiteLLM
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5. Initialize your Swarms agent with the vector database memory
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6. Add your documents and start querying
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Each guide provides complete code examples, setup instructions, and best practices for production deployment. |