What is Multi-Stage Retrieval
Multi-Stage Retrieval - A pipeline that chains discrete search operations to express complex information needs.
A composable pipeline architecture for search that chains discrete stages: filter candidates, sort by relevance, reduce duplicates, enrich with context, and apply business logic. Unlike single-query search, multi-stage retrieval lets you express complex information needs as a sequence of operations, similar to how SQL chains WHERE, ORDER BY, GROUP BY, and JOIN.
How It Works
Each stage in the pipeline receives a result set from the prior stage and applies a transformation: a feature search stage performs vector similarity lookup, a filter stage applies metadata predicates, a rerank stage re-scores results using a cross-encoder, and an enrichment stage appends additional fields. Stages are configured declaratively and executed in sequence.
Key Benefits
- Separates retrieval concerns so each stage can be tuned independently
- Supports hybrid search by combining dense vector, sparse keyword, and metadata filters
- Enables business logic (recency boosts, license filters, diversity constraints) without changing the underlying index
- Composable and version-controlled, making pipelines auditable and reproducible
When to Use It
- When a single nearest-neighbor query cannot express your ranking requirements
- When you need to combine results from multiple embedding spaces or modalities
- When business rules must be applied after initial retrieval (e.g., filter by rights, boost by recency)
- When you want to A/B test retrieval strategies without reindexing data