Retrieval-Augmented Generation (RAG)
Mar 21, 2026
5 min read
RAG (Retrieval-Augmented Generation) patterns enable agents to access and use external knowledge bases when generating responses. This is particularly useful for:
- Answering questions about specific documentation
- Maintaining context across long conversations
- Accessing proprietary or domain-specific knowledge
- Reducing hallucinations by grounding responses in factual data
How RAG Works in Mule
The RAG system in Mule consists of three main components:
flowchart LR
Query[User Query] --> Embed[Generate Query Embedding]
Embed --> Search[Vector Search]
Search --> Retrieve[Retrieve Relevant Documents]
Retrieve --> Generate[LLM Generation]
Generate --> Response[Augmented Response]
subgraph Knowledge["Knowledge Base"]
Docs[Documents]
EmbedDB[(Vector DB)]
end
Search -.-> EmbedDB
EmbedDB <-.-> Docs
The RAG Pipeline
- Ingestion: Documents are chunked, embedded, and stored in the vector database
- Query Processing: User queries are converted to embeddings
- Retrieval: Similar documents are retrieved based on vector similarity
- Augmentation: Retrieved context is added to the prompt
- Generation: The LLM generates responses using the augmented context
RAG Implementation in Mule
Memory Tool as RAG Foundation
The memory tool in Mule provides the foundation for RAG functionality:
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