# RAG Examples Overview

Enhance your agents with Retrieval-Augmented Generation (RAG). Connect to vector databases and knowledge bases to give agents access to your custom data.

## What You'll Learn

| Topic | Description |
|-------|-------------|
| **RAG Fundamentals** | Understanding retrieval-augmented generation |
| **Vector Databases** | Connecting to Qdrant, Pinecone, and more |
| **Document Processing** | Ingesting and indexing documents |
| **Semantic Search** | Finding relevant context for queries |

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## RAG Examples

| Example | Description | Vector DB | Link |
|---------|-------------|-----------|------|
| **RAG with Qdrant** | Complete RAG implementation with Qdrant | Qdrant | [View Example](../swarms/RAG/qdrant_rag.md) |

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## Use Cases

| Use Case | Description |
|----------|-------------|
| **Document Q&A** | Answer questions about your documents |
| **Knowledge Base** | Query internal company knowledge |
| **Research Assistant** | Search through research papers |
| **Code Documentation** | Query codebase documentation |
| **Customer Support** | Access product knowledge |

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## Related Resources

- [Memory Documentation](../swarms/memory/diy_memory.md) - Building custom memory
- [Agent Long-term Memory](../swarms/structs/agent.md#long-term-memory) - Agent memory configuration