Corpus
A corpus is an organized collection of documents, texts, or other language data assembled for processing, retrieval, analysis, training, or evaluation.
Software Testing with Generative AI
A corpus is a curated collection of documents or other information assembled for retrieval and analysis.
- How it is used
- Supplies searchable context that RAG retrieves and inserts into prompts for more accurate testing-oriented LLM responses.
- How deep it goes
- Explains corpus contents, relevance selection, context-window rationale, file loading, and cosine-distance matching.
- Examples
- Uses labeled HTML pages, user-story files, booking API requirements, and Java resource-folder loading as concrete corpus examples.
- What you can do after
- Build it from scratch by loading documents, matching a query with cosine distance, and placing the closest document into a prompt.
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Natural Language Processing in Action, Second Edition
A corpus is a structured collection of documents or language samples used for linguistic analysis or model development.
- How it is used
- Supports statistical pattern-finding across documents for linguistic analysis and language-model development.
- How deep it goes
- Defines document collection, indexing, counting, and vectorization, while deferring deeper language-model mechanics to Word2Vec.
- Examples
- Uses German and English greeting messages, document indexes, token-sorting analogies, and Python Counter outputs.
- What you can do after
- Apply it via word counts and document indexes to inspect recurring language patterns.
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Generative AI in Action
A corpus is a collection of documents or passages assembled for information retrieval or language processing.
- How it is used
- Supports retrieval-augmented generation by supplying documents or passages for retrieval and grounded response generation.
- How deep it goes
- Explains sparse and dense retrieval mechanisms, trade-offs, selection criteria, and production quality controls.
- Examples
- Uses proprietary enterprise documents, diverse sources, current information, internal policies, and industry regulations as corpus contents.
- What you can do after
- Operate/configure a retrieval corpus and choose sparse or dense retrieval according to query, domain, and resource requirements.
AI-Powered Search
A document corpus is a collection of documents assembled for linguistic analysis or information retrieval.
- How it is used
- Frames documents as a searchable collection and supplies corpus evidence for query interpretation, concept extraction, and autocomplete.
- How deep it goes
- Explains corpus structure, text-analysis stages, and practical extraction workflows, but does not develop a full knowledge graph.
- Examples
- Uses phrase lookup, behavioral co-occurrence, and an outdoors dataset processed with spaCy for frequent concept extraction.
- What you can do after
- Apply it via spaCy and search data to interpret queries, extract vocabulary, and filter low-frequency autocomplete candidates.
Related concepts
- JSONhas part · JSON is a standardized, language-independent text format for representing structured data using objects, arrays, strings, numbers, Boolean values, and null.
- language modelused by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- 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.
- evaluation datasetpart of · An evaluation dataset is a collection of examples or test cases, often with expected or judged outcomes, designated to assess a model or system against defined criteria.
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