What is Multi-Stage Retrieval Pipeline

How It Works

A pipeline is defined as an ordered list of stages. The first stage (usually a filter) searches across an embedding space to produce an initial candidate set. Subsequent stages narrow, reorder, sample, or enrich those candidates. For example: face search (847 candidates) → logo filter (23) → sentiment sort (23 reordered) → top-k reduce (5) → brand context enrich (5 enriched results).

The SQL Analogy

• Filter stages are like WHERE clauses: they narrow the candidate set by embedding similarity or metadata
• Sort stages are like ORDER BY: they re-rank results by weighted combinations of scores
• Reduce stages are like LIMIT and DISTINCT: they control output size via sampling or deduplication
• Enrich stages are like JOIN: they attach data from other collections (the semantic join)
• Apply stages are like INSERT INTO...SELECT: they write results or trigger side effects

Best Practices

• Start with broad filter stages and narrow progressively; don't over-constrain early
• Use sort stages to combine multiple ranking signals with explicit weights
• Always include a reduce stage to control output size
• Use enrich stages for cross-collection context instead of client-side merging
• Test pipelines with different stage orders, since the sequence affects both quality and latency

Related Pages

• Retriever documentation: /docs/retrieval/retrievers
• Retrieval Cookbook: /docs/retrieval/cookbook
• Blog: Multi-Stage Retrieval Pipelines - /blog/multi-stage-retrieval-pipelines