What is Multi-Stage Retrieval

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