What is Retrieval Pipeline?
A retrieval pipeline is the sequence of processing steps that transforms a user query into final retrieved results, encompassing query preprocessing, retrieval execution, and result postprocessing. Rather than a single monolithic retrieval operation, production systems typically implement multi-stage pipelines where each stage refines or filters results, balancing accuracy, efficiency, and relevance. Understanding pipeline architecture is essential for building high-quality RAG systems and agent memory.
A typical pipeline might include: query preprocessing (normalization, expansion, or rewriting), initial retrieval using fast methods (vector search or keyword search) to get candidates, hybrid merging if using multiple retrieval methods, re-ranking the top candidates with more sophisticated models, postprocessing (deduplication, filtering, excerpt extraction), and formatting for consumption by the LLM. Each stage can be optimized independently, and the pipeline can be instrumented for monitoring and debugging.
Pipeline design involves important tradeoffs. Adding stages generally improves quality but increases latency and complexity. Fast approximate methods for initial retrieval enable handling large corpora, while slower accurate methods in later stages refine a smaller candidate set. The pipeline architecture affects both retrieval quality and system performance. Modern frameworks like LlamaIndex and Haystack provide abstractions for building retrieval pipelines, making it easier to compose, test, and optimize multi-stage retrieval systems. Effective pipeline design considers the specific requirements of the application, data characteristics, and performance constraints.