ModelRefs / Retrieval Pipeline — AI Glossary

Retrieval Pipeline — AI Glossary

The end-to-end process of transforming a user query into retrieved documents: query processing, indexing, search, reranking, and filtering.

Overview

A complete retrieval pipeline includes: query normalization and expansion, vector/sparse search, metadata filtering, cross-encoder reranking, context compression, and result deduplication. Each stage can be tuned independently. LlamaIndex, LangChain, Haystack, and Weaviate provide pipeline-as-code abstractions.

Reference details

Topic	rag
Last reviewed	2026-06-24

Example: Where quality is actually lost

A pipeline is query rewriting → search → filter → rerank → compress → generate. When answers are poor, the instinct is to change the model, but the loss is usually upstream: chunks split mid-sentence, a metadata filter excluding valid documents, or reranking absent so the model reads whatever the index ranked first. Measure each stage before touching the last one.

Commonly confused with

The pipeline is not the vector database. The database is one stage — storage and nearest-neighbour search. Chunking, query processing, filtering, reranking and compression sit around it, and most retrieval quality problems live in those stages rather than in the index.

When to use it

Reach for it when:

Retrieval quality is inconsistent and you need to isolate which stage is failing
You are combining several retrieval strategies and need one order
Different document types need different chunking or filtering

Reach for something else when:

The corpus is small and simple — stages add latency and failure modes
You are adding stages without measuring each one; complexity hides the real fault

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Frequently asked questions

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