Inverted index
An inverted index is a data structure that maps terms or tokens to the documents, records, or locations in which they occur.
Elasticsearch in Action, Second Edition
An inverted index is an index that maps each token or term to the documents containing it.
- How it is used
- Accelerates full-text search by mapping analyzed terms to matching document identifiers.
- How deep it goes
- Explains tokenization, normalization, frequencies, and document associations, while deferring deeper analyzer internals.
- Examples
- Uses greeting documents and The Godfather title queries to contrast indexed tokens with query values.
- What you can do after
- Evaluate/understand why analyzed text searches match or fail against stored terms.
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AI-Powered Search
An inverted index is a data structure that maps terms or tokens to the documents and locations where they occur.
- How it is used
- Enables fast lexical retrieval and supplies term-to-document links for relationship discovery.
- How deep it goes
- Explains field-specific postings, positions, query lookup, and the forward-index counterpart, but not low-level index storage algorithms.
- Examples
- Uses job documents with job-title, description, and skills fields to show field-level indexing and its forward-index counterpart.
- What you can do after
- Operate/configure it for lexical lookup, and enable doc values when a Lucene-based field needs a forward index for faceting.
Natural Language Processing in Action, Second Edition
An inverted index maps terms to the documents and positions in which they occur to support efficient text retrieval.
- How it is used
- Supports efficient full-text retrieval and grounds keyword or document searches in larger information systems.
- How deep it goes
- Explains forward-index inversion, document positions, sparse-vector behavior, and trigram limitations, while deferring engine internals.
- Examples
- Uses a textbook index analogy, Hacker Public Radio show notes, TF–IDF vectors, and the query “where is the lost audio.”
- What you can do after
- Apply it via scikit-learn, Whoosh, or database full-text search to retrieve documents without scanning every document.
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MongoDB 8.0 in Action, Third Edition
An inverted index is a search data structure that maps terms to the documents or positions in which they occur.
- How it is used
- Supports fast, relevant full-text retrieval across document collections and complex search interfaces.
- How deep it goes
- Explains term mapping, tokenization, normalization, scoring, and query types, but stops short of index-construction internals.
- Examples
- Uses two inspection documents and a table mapping names, locations, dates, and inspection terms to matching documents.
- What you can do after
- Evaluate/understand how Lucene and Atlas Search retrieve and rank documents through indexed terms.
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AI Agents and Applications
An inverted index maps terms to the documents or locations in which they occur to support lexical search.
- How it is used
- Supports sparse, keyword-driven retrieval of document chunks within retrieval-augmented applications.
- How deep it goes
- Explains tokenization, postings, query matching, ranking, and scalability limits without detailing index internals.
- Examples
- Uses document-chunk term matching, Boolean must-or-must-not queries, and combined BM25 and vector-search results.
- What you can do after
- Apply it via BM25Retriever for in-memory lexical search and combine its results with vector retrieval using RRF.
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Related concepts
- Luceneimplemented by · Apache Lucene is an open-source Java library for indexing, searching, and retrieving textual and other content.
- lexical searchused by · Lexical search retrieves or ranks items by matching query terms or other surface-text features against indexed content, often using sparse representations and statistical weighting.
- similarity searchused by · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
- knowledge graphused by · A knowledge graph is a graph-structured representation of entities and their semantically meaningful relationships, often including associated attributes or facts.
- segmentpart of · A segment is a distinct portion or bounded region of a larger structure, system, or sequence, with boundaries or properties determined by its context.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.