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.
Knowledge management teams, internal documentation owners, customer support organizations, and EdTech platforms maintaining 10K+ articles, documents, and multimedia resources
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.
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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.
Feature Extractors Used
text extractor
document graph extractor
multimodal extractor
Retriever Stages Used
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
Add Semantic Search to Your Knowledge Base
Clone the semantic search pipeline and connect your documentation repository or wiki.
Frequently Asked Questions
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Our team can help you get started with Semantic Search for Knowledge Bases in your organization.