RAG (Retrieval-Augmented Generation) Pattern
Build knowledge-powered AI systems that retrieve relevant context from your data before generating accurate, grounded responses.
What's Included#
- Document ingestion pipeline with text extraction and chunking
- Vector similarity search for retrieving relevant context
- Context-aware prompt construction with retrieved documents
- Source attribution for grounding answers in specific content
- Conversational RAG with chat history and follow-up questions
- Relevance scoring and filtering for retrieved results
Usage#
Via CLI#
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Via AI Assistant#
Ask your AI assistant:
- "Use the RAG pattern from Bootspring"
- "Apply the Bootspring RAG pattern to my project"
Key Considerations#
- Chunk documents thoughtfully with overlap to avoid losing context at boundaries
- Include source metadata with each chunk for transparent attribution in answers
- Set a similarity threshold to filter out low-relevance results before generation
- Limit context window size to stay within model token limits and control costs
- Re-index documents when content changes to keep the knowledge base current
Related Patterns#
- Embeddings - Vector embedding generation and storage
- OpenAI - OpenAI API integration
- Streaming - Stream RAG responses to users