Knowledge base
A knowledge base is an organized collection of information that can be accessed or queried to support reference, retrieval, or reasoning.
Build Python Web Apps with Streamlit
A knowledge base is an organized collection of information that a system can consult to answer questions about a defined subject.
- How it is used
- Supplies company and product information for a customer-support chatbot to answer relevant questions.
- How deep it goes
- Works through article ingestion, chunking, retrieval, and prompt augmentation, but treats embedding internals only conceptually.
- Examples
- Uses product descriptions and fountain-pen maintenance articles as customer-support information.
- What you can do after
- Apply it via Pinecone and LangChain to retrieve support-article excerpts for chatbot answers.
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Natural Language Processing in Action, Second Edition
A knowledge base is an organized collection of facts, entities, and relationships representing information about a domain or the world.
- How it is used
- Supports NLP interpretation, chatbot business logic, question answering, retrieval, and decisions with organized domain knowledge.
- How deep it goes
- Descends from organizational purpose to graph inference and executable SPARQL queries, while deferring broader commonsense compilation challenges.
- Examples
- Uses Wikidata identifiers, SPARQL queries, scholarly authorship, and a military-rank inference graph.
- What you can do after
- Apply it via SPARQL to retrieve linked entities and answer questions requiring multiple relational hops.
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Grokking AI Applications
An organized collection of information that can be searched or supplied to an application.
- How it is used
- Equips LLM applications with external or company-specific information so responses can use relevant context.
- How deep it goes
- Explains prompt attachment, document search, chunking, embeddings, and vector storage, while deferring consistent retrieval to RAG.
- Examples
- Uses office information and Langflow document chunks as examples of retrieved knowledge supplied to an LLM.
- What you can do after
- Operate/configure a Langflow Knowledge Base by uploading documents, previewing chunks, saving them, and retrieving them in a flow.
Domain-Specific Small Language Models
Retrieved outside information supplied to a model at inference time to ground its output.
- How it is used
- Serves as retrieved context for RAG when domain-specific answers need grounding beyond the model’s parameters.
- How deep it goes
- Explains the two-phase RAG flow and a worked PDF retrieval pipeline, but stops short of dedicated framework coverage.
- Examples
- Uses a PDF query example, sentence chunking, cosine similarity search, and a blocked KV-cache retrieval result snippet.
- What you can do after
- Apply it via RAG to retrieve relevant passages and generate more accurate, up-to-date answers.
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Build AI-Enhanced Web Apps
A knowledge base is an organized collection of information representing questions, answers, facts, or other domain knowledge.
- How it is used
- Deploys organized domain content as searchable context for support tools, corporate assistants, interview feedback, and conversational AI.
- How deep it goes
- Explains document workflows and embedding-based retrieval, including query encoding and cosine similarity, while deferring deeper vector-store implementation.
- Examples
- Uses IT support questions, interview best practices, PDF and DOCX uploads, multi-tenant collections, and conversational corporate knowledge bases.
- What you can do after
- Build it from scratch as a JavaScript retrieval workflow or operate it as a secure multi-knowledge-base chat application.
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Effective Conversational AI
A knowledge base is an organized collection of information that can be searched to support answering questions.
- How it is used
- Supports conversational search by supplying data that a large language model searches before generating an answer.
- How deep it goes
- Descends to source documents, URLs, enrichments, and ingestion schedules, but stops short of specifying storage or retrieval implementation.
- Examples
- Uses source documents, URLs, enrichments, ingestion schedules, and hybrid intent-to-search routing as concrete artifacts and patterns.
- What you can do after
- Diagnose weak understanding by auditing knowledge-base sources, enrichments, and ingestion schedules for relevance and freshness.
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Knowledge Graphs and LLMs in Action
A knowledge base is a repository that stores structured representations of facts, entities, relationships, and rules for computational use.
- How it is used
- Supports domain-specific autonomous advisors by supplying established knowledge for reasoning and feedback-driven updates.
- How deep it goes
- Explains acquisition, graph representation, reasoning use, and completeness limitations, but stops short of implementation syntax or storage technology.
- Examples
- Uses medical diagnosis, disease–symptom probabilities, patient preferences, action costs, treatment outcomes, and logical decision statements.
- What you can do after
- Evaluate how deductive reasoning uses structured domain knowledge to propose actions, while recognizing completeness and accuracy requirements.
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AI Agents and Applications
An internal knowledge base is information encoded in a language model's learned parameters and available without consulting external data sources.
- How it is used
- Grounds LLM answers in trusted information retrieved before generation.
- How deep it goes
- Describes document ingestion, chunking, embedding, and vector storage, but does not provide implementation code here.
- Examples
- Uses local documents and enterprise intranet content to illustrate retrieval for question answering.
- What you can do after
- Build it from scratch by loading documents, splitting text, embedding chunks, and storing them for retrieval with LangChain components.
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Inside AI
A knowledge base is a repository of domain-specific facts and rules that represents accumulated expert knowledge for an intelligent system.
- How it is used
- Supports expert systems by storing domain rules separately from the procedures that execute them.
- How deep it goes
- Defines rule storage and component separation, then relates rules to inference and maintenance without detailing implementation.
- Examples
- Uses gardening advice, MYCIN’s infection diagnosis, and industrial emergency-pump rules as concrete applications.
- What you can do after
- Evaluate how expert systems store, update, and apply domain expertise through explicit rules.
Machine Learning System Design
A knowledge base is an organized repository of structured facts, entities, relationships, or domain-specific information.
- How it is used
- Uses organized entity information to correct model inputs promptly when retraining cannot meet the required release timeline.
- How deep it goes
- Explains the pre-inference lookup and hinting workflow, but stops short of specifying repository design or matching algorithms.
- Examples
- Uses missing customer entities and rapid, nontechnical additions as the central operational example.
- What you can do after
- Apply it to augment model inputs and resolve selected recognition failures without retraining.
Read how Machine Learning System Design explains knowledge base →
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.
- 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.
- Retrieverused by · A retriever is a software component that searches or queries an information source and returns items relevant to a query for use by downstream processes.
- entity linkingused by · Entity linking is the task of mapping a textual mention to the corresponding canonical entity or identifier in a knowledge base.
- information retrievalused by · Information retrieval is the process of locating and selecting relevant information from an available collection in response to a query or information need.
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.