Glossary

LazyGraphRAG

What is LazyGraphRAG?

LazyGraphRAG is an approach to graph-based retrieval-augmented generation presented by Microsoft Research in 2024. Unlike GraphRAG, it does not have a language model create summaries during indexing; instead, it identifies concepts and their co-occurrences using classic natural language processing, and the language model is only used at query time. According to Microsoft, this brings indexing costs down to the level of basic vector RAG. It is particularly suited to questions that require an overview of large document collections.

Topics

RAG
Knowledge graph
Retrieval

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Related terms

Retrieval-augmented generation (RAG)

Retrieval-augmented generation combines search with text generation: before answering, the system searches a knowledge collection – such as manuals, policies or FAQs – for relevant passages and passes them to the language model as context. The answer is therefore based on current, company-specific content rather than only on the model's training knowledge. This reduces invented statements, makes sources traceable and allows updates without retraining.

RAG
knowledge search
+1

Multi-hop reasoning

Multi-hop reasoning is an AI system’s ability to answer a question through several successive inferences, often combining information from different sources. Example: “Which department is headed by the person responsible for project X?” first requires identifying the responsible person and then their department. Such questions are demanding for search and RAG systems because every intermediate step must be found and linked correctly.

reasoning
question answering
+1

Information Retrieval

Information retrieval covers methods that find the documents or passages in a large body of text that match a query and rank them by relevance. Classic approaches such as BM25 weight search terms by how often and how rarely they occur; newer ones add semantic similarity. It underpins search engines, enterprise search and retrieval-augmented generation, in which a language model uses the retrieved passages as the basis for its answer.

Search
BM25
+1

Retrieval pipeline

A retrieval pipeline is the sequence of processing steps a system uses to find the right information for a question. Typical steps are: ingesting and cleaning documents, splitting them into chunks, building a search index, optionally rewriting the query, retrieving matches, ranking them (reranking) and passing the best ones to the language model. It is the foundation of every RAG application, and its quality largely determines how good the resulting answers are.