RAG Examples Overview - swarms

Enhance your agents with Retrieval-Augmented Generation (RAG). Swarms does not bundle a vector database — you wire your own retrieval code into an agent as a tool, which keeps you free to use any store you already run.

What You’ll Learn

Topic	Description
RAG Fundamentals	Understanding retrieval-augmented generation
Retrieval Tools	Exposing your own store to an agent as a callable tool
Document Processing	Ingesting and indexing documents
Semantic Search	Finding relevant context for queries

RAG Examples

Example	Description	Vector DB	Link
Full Agent RAG	End-to-end retrieval and generation over a document folder	LlamaIndex	View Example
Qdrant Agent	Agent backed by a Qdrant collection	Qdrant	View Example
Pinecone	Semantic search over a Pinecone index	Pinecone	View Example

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

Agent Memory - Persistent memory, compression, and conversation history
External Knowledge - Wiring a retrieval tool into an agent
Agent Tools - The tool interface retrieval code plugs into