categoryestablishedmedium complexity

Retrieval & Search

Retrieval systems are AI and information-retrieval architectures that locate, filter, and rank relevant items from large data collections such as documents, web pages, logs, or databases. They transform both queries and content into searchable representations (keywords, embeddings, or structured fields), index them for fast lookup, and apply ranking algorithms to surface the most relevant results. Modern retrieval systems often blend lexical, semantic, and metadata signals, and they are foundational for semantic search, RAG (retrieval-augmented generation), and enterprise knowledge access.

0implementations
0industries
1sub-patterns
Sub-patterns
Router-Gateway
01

When to Use

  • You need to search or navigate large collections of documents, logs, or records where manual browsing is infeasible.
  • You are building a RAG system and must reliably fetch relevant context for an LLM from a knowledge base.
  • Your users express information needs in natural language and expect semantically relevant results, not just keyword matches.
  • You have heterogeneous data sources (files, databases, APIs) and want a unified search experience across them.
  • You need to support complex filtering and ranking based on metadata, recency, or business rules.
02

When NOT to Use

  • Your dataset is very small and easily loaded into memory for direct scanning or prompting an LLM without a dedicated index.
  • You only need deterministic lookups by exact ID or key (e.g., primary-key database queries) rather than relevance-based search.
  • You do not have a reasonably clean or text-extractable corpus; most of your data is unstructured media without metadata or transcripts.
  • You cannot implement or enforce access control and your corpus contains sensitive information that must not be exposed via search.
  • Your application requires strict, formally verifiable reasoning over structured data (e.g., financial ledgers) where SQL or graph queries are more appropriate.
03

Key Components

  • Data ingestion and connectors (file systems, APIs, databases, web crawlers)
  • Document parsing and normalization (text extraction, cleaning, segmentation)
  • Indexing engine (inverted index, vector index, or hybrid index)
  • Representation layer (tokenization, keyword features, embeddings, metadata fields)
  • Query processing (normalization, expansion, rewriting, intent detection)
  • Retrieval algorithms (BM25, dense vector search, hybrid retrieval, ANN search)
  • Ranking and re-ranking layer (learning-to-rank, cross-encoder rerankers, LLM rerankers)
  • Metadata and filtering layer (facets, access control, time filters, business rules)
  • Evaluation and analytics (relevance metrics, A/B testing, query logs analysis)
  • Caching and performance layer (result caching, index sharding, replication)
Common Tools
ElasticsearchOpenSearchApache SolrWeaviatePineconeQdrantMilvusFAISSpgvector (PostgreSQL)ChromaVespaAzure AI SearchAmazon OpenSearch ServiceGoogle Vertex AI SearchLangChain
Top Industries
04

Best Practices

  • Start with a simple baseline (e.g., BM25 or a basic vector index) and measure relevance before adding complexity like hybrid retrieval or reranking.
  • Segment large documents into smaller, semantically coherent chunks to improve recall and reduce irrelevant context in downstream systems like RAG.
  • Normalize and clean text consistently (lowercasing, Unicode normalization, removing boilerplate) while preserving important structure such as headings and lists.
  • Use hybrid retrieval (lexical + vector) when you need both exact keyword matching and semantic similarity, especially for long-tail or domain-specific queries.
  • Leverage metadata and filters (e.g., document type, date, language, access level) to narrow search space and enforce business rules and permissions.
05

Common Pitfalls

  • Indexing raw, unstructured documents without segmentation, leading to poor recall and noisy results for downstream systems like RAG.
  • Relying solely on vector search and ignoring lexical signals, which can hurt performance on exact-match or rare keyword queries (e.g., IDs, codes, names).
  • Using default embedding models that are not suited to the domain, resulting in semantically plausible but practically irrelevant matches.
  • Failing to enforce access control at the retrieval layer, which can leak sensitive or confidential information in multi-tenant or enterprise environments.
  • Over-engineering the retrieval stack (multiple indexes, complex rerankers) before establishing a strong baseline and clear evaluation metrics.
06

Learning Resources

tutorialHow to Build an Over-Engineered Retrieval SystemtutorialThe Complete Guide to Embeddings and RAG: From Theory to ProductiontutorialRetrieval-Augmented Generation (RAG) in AI: Technical Implementation GuidetutorialThe Guide to Retrieval-Augmented Generation (RAG)tutorialBuilding an Enhanced RAG System with Query Expansion and Reranking in PythoncourseModern Information Retrieval: A Brief Overview
07

Example Use Cases

01Enterprise knowledge search that lets employees query internal documents, wikis, tickets, and emails using natural language.
02Customer support assistant that retrieves relevant help center articles, past tickets, and FAQs to answer user questions.
03Legal document search that finds similar cases, clauses, or contracts based on semantic similarity and legal-specific terminology.
04Clinical decision support tool that retrieves relevant medical literature, guidelines, and patient records for a given case description.
05E-commerce product search that combines keyword and vector search to match user intent, including vague or descriptive queries.
08

Solutions Using Retrieval & Search

0 FOUND

No solutions found for this pattern.

Browse all patterns