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    Semantic Search for Knowledge Bases

    Deploy semantic search across knowledge bases, wikis, and documentation. Users ask questions in natural language and get precise answers from your content.

    Who It's For

    Knowledge management teams, internal documentation owners, customer support organizations, and EdTech platforms maintaining 10K+ articles, documents, and multimedia resources

    Problem Solved

    Knowledge bases grow large and disorganized over time. Users cannot find answers because they do not know the exact terminology used in documentation. Keyword search returns irrelevant results or nothing at all, forcing users to browse hierarchical category structures or ask colleagues directly.

    Ready to implement?

    Schedule a DemoView Documentation
    One-click templatesStart from a templateEach template deploys a working search app on your own files.Browse templates

    Why Mixpeek

    Multimodal understanding indexes the content of diagrams, screenshots, and videos alongside text, making visual documentation searchable by concept. RAG preparation structures retrieved content for downstream LLM synthesis. Handles PDFs, wikis, Confluence pages, and any document format.

    Overview

    Semantic search for knowledge bases replaces keyword matching with conceptual understanding. Users describe what they need in their own words and get answers drawn from across the entire repository, regardless of how the original content was titled or categorized.

    Challenges This Solves

    Terminology Barrier

    Users searching a knowledge base use different terms than the authors who created the documentation, especially when authors are domain experts and users are not

    Impact: 40-50% of support tickets and internal questions have answers in existing documentation that the user could not find

    Content Fragmentation

    Answers to common questions span multiple documents, requiring users to discover and synthesize information across articles

    Impact: Users give up after checking 2-3 documents and escalate to human support even when the answer exists

    Multimedia Knowledge Gaps

    Diagrams, screenshots, video tutorials, and embedded images contain critical information that text search cannot access

    Impact: Visual documentation (architecture diagrams, workflow screenshots, tutorial videos) is invisible to traditional knowledge base search

    Recipe Composition

    This use case is composed of the following recipes, connected as a pipeline.

    1
    Semantic Multimodal Search

    Find anything across video, image, audio, and documents

    2
    Multimodal RAG

    LLMs that cite real clips, frames, and documents

    3
    Document Intelligence Search

    Search through PDFs with OCR and semantic retrieval

    Feature Extractors Used

    Text Embedding

    Extract semantic embeddings from documents, transcripts and text content

    document graph extractor

    multimodal extractor

    Retriever Stages Used

    feature-search

    rerank

    Rerank documents using cross-encoder models for accurate relevance

    rag-prepare

    Expected Outcomes

    85% of queries answered on first search vs. 40% baseline

    First-search success rate

    35% reduction in tickets with existing knowledge base answers

    Support ticket deflection

    3x more articles surfaced and read per month

    Knowledge base utilization

    Build this in the docs

    The exact stages and extractors this use case runs on, with API reference and worked examples.

    Semantic search tutorialA worked semantic search build over your own content.Feature search stageThe stage that turns a question into ranked matches.

    Add Semantic Search to Your Knowledge Base

    Clone the semantic search pipeline and connect your documentation repository or wiki.

    Estimated setup: 45 min
    Run this on your own data, from $25/moBook a demo

    Frequently Asked Questions

    How does semantic knowledge base search differ from Elasticsearch or Algolia?
    Traditional search engines match keywords and synonyms. Semantic search understands the meaning of a query and matches it to conceptually relevant content, even when no keywords overlap. Asking "how do I handle authentication errors" finds content about "troubleshooting 401 responses" without requiring keyword configuration.
    Can semantic search power a question-answering chatbot over our knowledge base?
    Yes. The RAG-prepare retriever formats retrieved content for downstream LLM synthesis. This enables chatbot and Q&A interfaces that generate natural language answers sourced from your knowledge base, with citations pointing users to the original documents for full context.
    Does semantic search work with technical documentation containing code snippets?
    Yes. The text extractor handles code blocks, API references, and technical specifications alongside prose. Queries like "how to paginate API results" match relevant code examples and API documentation, not just conceptual articles about pagination.
    How does the system handle knowledge base content in multiple formats?
    Mixpeek processes Markdown, HTML, PDF, Word documents, Confluence pages, and multimedia content through format-specific extractors. All content is indexed into a unified semantic space, so a single query searches across all formats and sources simultaneously.

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    Ready to Implement This Use Case?

    Our team can help you get started with Semantic Search for Knowledge Bases in your organization.

    Schedule a DemoRead the Docs