Information retrieval

As explained in Elasticsearch in Action, Second Edition

Searching textual content using tokenization, matching, relevance scoring, or related linguistic analysis.

Information retrieval finds relevant content for a user or application. Elasticsearch supports search across varied providers such as Slack, Confluence, and Google Drive, and can back web, mobile, or server-side applications. section 1.4.1

Retrieval begins when analyzed data is stored in Elasticsearch. A search query is analyzed similarly to the indexed field, tokenized and normalized, then matched against the inverted index to return results. section 3.1.3

Useful retrieval capabilities include:

• Match more than exact words by handling root words, synonyms, and spelling mistakes through advanced full-text queries. section 3.1.3
• Issue queries through REST APIs over HTTP, using Kibana as a visual editor or another REST-capable client. section 2.0
• Retrieve matching documents separately from analytical queries, which gather summarized statistics. section 3.1.3

Analyzer-consistent search

Suppose a field uses a French analyzer. Elasticsearch applies that same analyzer when searching the field, so query tokens are normalized consistently with the tokens stored in the inverted index. section 3.1.3

Figure 1.2 Core application areas of Elastic, the company behind Elasticsearch

Use the figure to place search alongside observability and security as major Elasticsearch application areas. section 1.4.1

How Elasticsearch in Action, Second Edition covers it

How deep it goes

Explains analysis, tokenization, normalization, and inverted-index matching, while deferring detailed query construction.

Examples

Uses enterprise content providers, application search, French analyzers, root words, synonyms, and spelling mistakes as examples.

What it assumes

Assumes running Elasticsearch and Kibana with analyzed data loaded before executing search queries.

What you can do after

Operate retrieval through Kibana or REST clients to execute queries and return matching data from Elasticsearch.

How it connects

Connects search with full-text queries, analyzers, tokenization, inverted indexes, analytics, and Kibana.

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Also explained in

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  Introduction to Generative AI, Second EditionRetrieval is the process of finding relevant information from an external collection in response to a query or representation.
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  Effective Conversational AIThe retrieval phase is the stage of a retrieval-augmented generation process that finds and obtains information relevant to an input.
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  Grokking AI ApplicationsThe process of finding and obtaining relevant information from a data source for use in an AI application.
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  MongoDB 8.0 in Action, Third EditionInformation retrieval is the process of finding relevant information from a collection of stored data in response to an information need.
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  Natural Language Processing in Action, Second EditionFull-text search retrieves documents by searching their complete textual contents rather than only structured fields.
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  Microsoft Azure in ActionRetrieval is the process of selecting relevant stored information to provide as context for a language model.
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  Data Storytelling with Altair and AIInformation retrieval is the process of searching for and returning relevant information from a collection or external knowledge source.
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  Build a Website with ChatGPTSearch is the process of finding records whose data matches a user-supplied word or phrase.
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  Ensemble Methods for Machine LearningInformation retrieval is the field concerned with finding and ranking relevant information items in response to queries.
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  Building Reliable AI SystemsRetrieval is the process of finding relevant information from an external collection to supply as model context.

Compare how 11 books explain information retrieval →

Related concepts

• retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
• hybrid searchused by · Hybrid search is an information-retrieval technique that combines multiple retrieval methods or relevance signals to find or rank relevant results.
• similarity searchincludes · 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.
• chunkingused by · Chunking is the process of dividing content or data into smaller units for processing, retrieval, or organization.
• agentic RAGused by · Agentic RAG is a retrieval-augmented generation approach in which an agent dynamically decides how and when to retrieve information, often iterating or using tools before generating a response.

This explainer was generated by Manning's knowledge graph from the text of Elasticsearch in Action, Second Edition. The section links point to the parts of the book it draws on.