Retriever (RAG)

Retriever (RAG) is the component in a Retrieval-Augmented Generation pipeline that locates the most relevant documents for a user query before a large language model (LLM) crafts the final answer. Depending on its type, it either matches the query lexically against an inverted index (BM25) or converts it into a vector embedding and searches a vector database with similarity metrics such as cosine or dot-product, returning a top-k list of passages. Popular retriever types include dense semantic search (Sentence-BERT, OpenAI embeddings), hybrid BM25-plus-vector search, and re-ranked cross-encoders for higher precision. A well-tuned retriever boosts factual accuracy, lowers hallucinations, and trims token costs by feeding only high-value context into the LLM’s context window. Key settings—embedding model, k value, max marginal relevance (MMR), and metadata filters—balance recall versus latency. Monitoring recall@k and hit-rate guards against drift as content grows. In essence, the Retriever is the “memory lookup” engine that grounds generative AI in trustworthy knowledge.

Related terms

• Graph RAG
• Hire Retrieval-Augmented Generation (RAG) developers
• IM-RAG (Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues)
• Retrieval-Augmented Generation RAG rewriter component
• Retriever-Reader Architecture

Related services: RAG development service, Agentic AI consulting.

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