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Redis AI Integration Documentation
Complete guide to connecting Redis AI with AI/LLM/Agent systems
📚 Documentation Overview
This documentation set provides comprehensive coverage of Redis AI integration patterns, architectures, and best practices for building AI/LLM/agent systems.
Files in This Collection
- redis-ai-integration.md - Primary Reference
- Complete integration guide (37KB, ~45 min read)
- What can connect to Redis AI
- Architecture patterns
- 5 complete implementation examples
- Best practices and use cases
-
Performance expectations
-
redis-ai-quick-reference.md - Quick Start
- Quick reference guide (13.5KB, ~10 min read)
- Connection matrix
- Code snippets (Python, Node.js, Go)
- Troubleshooting guide
-
Configuration recommendations
-
redis-ai-architecture-diagrams.md - Visual Guide
- Architecture diagrams (29.5KB, ~20 min read)
- System integration overview
- Flow diagrams
- Deployment topologies
- Technology stack visualization
🚀 Quick Start
For Beginners
Start with the Quick Reference → Review Architecture Diagrams → Deep dive into Integration Guide
For Experienced Developers
Start with Quick Reference for patterns → Reference Integration Guide for specific implementations
For Architects
Start with Architecture Diagrams → Review Integration Guide sections → Use Quick Reference for decisions
🎯 What You'll Learn
Core Concepts
- How Redis AI connects to LLMs (OpenAI, Anthropic, etc.)
- Using Redis for AI agent state management
- Vector similarity search with RediSearch
- Real-time model inference with RedisAI
- Multi-agent coordination patterns
Practical Skills
- Implementing LLM response caching (50-90% cost reduction)
- Building RAG (Retrieval-Augmented Generation) systems
- Managing conversational AI state
- Coordinating multiple AI agents
- Serving ML models with low latency
System Design
- Architecture patterns for production systems
- Performance optimization techniques
- Security best practices
- Scaling strategies
- Monitoring and observability
🔧 Technologies Covered
Redis Components
- RedisAI: Model serving and inference
- RediSearch: Vector similarity search
- Core Redis: Caching, state management, Pub/Sub, Streams
- Redis Cluster: High availability and scaling
AI/ML Frameworks
- LangChain, AutoGPT, CrewAI (Agent frameworks)
- TensorFlow, PyTorch, ONNX (ML models)
- OpenAI, Anthropic, Cohere (LLM APIs)
- HuggingFace (Embedding models)
Languages
- Python (primary examples)
- Node.js/JavaScript
- Go
- General patterns applicable to any language
📊 Key Integration Patterns
| Pattern |
Use Case |
Expected Performance |
| LLM Caching |
Reduce API costs |
50-90% cost reduction, <1ms cache hit |
| Vector Search |
Semantic search, RAG |
Sub-5ms for millions of vectors |
| Agent State |
Conversational AI |
<1ms state retrieval |
| Multi-Agent |
Distributed AI systems |
Real-time coordination |
| Model Inference |
ML model serving |
10,000+ inferences/sec |
💡 Use Cases
- Conversational AI: Chatbots with persistent memory
- Semantic Search: Document search with natural language
- Recommendation Systems: Real-time personalized recommendations
- Content Moderation: Automated ML-based moderation
- RAG Systems: Enhanced LLM responses with retrieval
- Multi-Agent Systems: Coordinated AI agent workflows
Official Documentation
This Repository
- [[SKYNET.md]] - Technology research overview
- [[ai scenarios simulator]] - AI simulation scenarios
- [[textgenerator]] - AI text generation tools
📝 Document Status
- Created: 2025-11-17
- Status: Complete ✓
- Version: 1.0
- Maintained By: SKYNET Research Division
🎓 Learning Path
Beginner Path (2-3 hours)
- Read Quick Reference - Connection Matrix (15 min)
- Review Architecture Diagrams - Overview (20 min)
- Study Integration Guide - Core Concepts (30 min)
- Try Example 1: LLM Caching (45 min)
- Try Example 2: Agent State (45 min)
- Complete Beginner Path
- Deep dive into Vector Search pattern (1 hour)
- Study Multi-Agent coordination (1 hour)
- Implement RAG pipeline example (2 hours)
- Review best practices and optimization (1 hour)
Advanced Path (8-12 hours)
- Complete Intermediate Path
- Study all architecture patterns (2 hours)
- Implement all 5 examples (4 hours)
- Design custom integration for your use case (2 hours)
- Implement monitoring and observability (2 hours)
🤝 Contributing
This documentation is part of the NNAMED research repository. For improvements or corrections:
- Review the content thoroughly
- Open an issue with specific suggestions
- Follow the repository's contribution guidelines
📄 License
This documentation is part of the NNAMED repository and follows the same MIT License.
Part of SKYNET Research Division - Technology & Systems Research