RAG Context Builder
Format search results as context for LLM generation with citation tracking
Why do anything?
RAG applications need properly formatted context from search results. Manual formatting is error-prone.
Why now?
RAG is the dominant pattern for grounded LLM applications.
Why this feature?
Format search results into LLM context with source citations, relevance ordering, and token management.
How It Works
RAG preparation formats search results for LLM consumption.
1
Content Selection
Select relevant content from documents
2
Citation Assignment
Assign citation identifiers
3
Context Formatting
Format into context string with citations
4
Token Management
Truncate to fit token budget
Why This Approach
Standardized RAG preparation ensures consistent context formatting. Citation tracking enables attribution.
Where This Is Used
Integration
retriever = client.retrievers.create(stages=[{"type": "rag_prepare", "max_tokens": 4000}])