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Chunk-first retrieval starts with similarity or full-text search over segmented source documents. The best matching text chunks directly form the answer context or serve as starting points for subsequent graph enrichment. The pattern suits text-centric questions and provides a simple baseline for more advanced GraphRAG approaches.
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Chunk-first retrieval selects the most relevant text chunks from segmented source documents first and uses them directly as answer context or as the starting point for further enrichment. The pattern combines text-centric search with a clear, inspectable context window.
Chunk-first retrieval emerged from RAG and GraphRAG practice as a way to obtain a small, dependable answer context quickly from segmented source documents. Instead of resolving entities or whole graphs first, the best text chunks are selected through similarity or full-text search and then used directly or enriched with graph context later.
Think of it as a search gate: documents are split into chunks and made discoverable in an index. A query pulls the best matches forward until a compact context window is formed. Only when text proximity is not enough does a second step load matching entities and relationships.
Source documents are split into smaller sections so they can be found and loaded into context precisely.
A vector index makes semantically similar chunks quickly retrievable.
The query looks for content-near matches, usually based on embeddings or word overlap.
Only the highest-ranked chunks are kept, which keeps the context small and focused.
A later step can pull in entities, relationships, or extra knowledge from the matching chunks.
The pattern is useful for text-centric questions, fast prototyping, and as a baseline for comparing entity-first or graph-driven approaches. It depends on workable chunking and good index quality; overly coarse chunks dilute hits, while overly fine chunks increase spread and context loss. If answers are distributed across many documents or relationships matter most, chunk-first alone is often not enough.
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