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40 lines
1.3 KiB
40 lines
1.3 KiB
# RAG Examples Overview
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Enhance your agents with Retrieval-Augmented Generation (RAG). Connect to vector databases and knowledge bases to give agents access to your custom data.
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## What You'll Learn
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| Topic | Description |
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|-------|-------------|
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| **RAG Fundamentals** | Understanding retrieval-augmented generation |
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| **Vector Databases** | Connecting to Qdrant, Pinecone, and more |
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| **Document Processing** | Ingesting and indexing documents |
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| **Semantic Search** | Finding relevant context for queries |
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## RAG Examples
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| Example | Description | Vector DB | Link |
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|---------|-------------|-----------|------|
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| **RAG with Qdrant** | Complete RAG implementation with Qdrant | Qdrant | [View Example](../swarms/RAG/qdrant_rag.md) |
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## Use Cases
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| Use Case | Description |
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|----------|-------------|
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| **Document Q&A** | Answer questions about your documents |
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| **Knowledge Base** | Query internal company knowledge |
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| **Research Assistant** | Search through research papers |
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| **Code Documentation** | Query codebase documentation |
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| **Customer Support** | Access product knowledge |
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## Related Resources
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- [Memory Documentation](../swarms/memory/diy_memory.md) - Building custom memory
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- [Agent Long-term Memory](../swarms/structs/agent.md#long-term-memory) - Agent memory configuration
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