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KnowledgeBase

KnowledgeBase are a powerful feature of the NeuralTrust platform that allow you to create and manage specialized knowledge repositories for AI model testing and evaluation. They provide a structured way to organize domain-specific information that can be used to enhance your model testing capabilities.

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Knowledge bases typically serve as vector databases used for Retrieval-Augmented Generation (RAG).

With Knowledge Bases, you can:

  • Create specialized knowledge repositories for different domains
  • Seed knowledge bases with initial topics and data
  • Use knowledge bases to generate more targeted and relevant test cases
  • Manage credentials and access controls for different knowledge sources
  • Integrate with various data sources and knowledge systems

Knowledge Bases are particularly useful for:

  • Domain-specific testing of AI models
  • Generating contextually relevant test cases
  • Maintaining consistent testing knowledge across teams
  • Supporting specialized compliance and security testing scenarios

Supported Knowledge Base Types​

NeuralTrust currently supports the following knowledge base types:

  • Upstash - Vector database for real-time data storage and retrieval
  • Azure AI Search - Cognitive search service for fast and sophisticated data indexing
  • Company Documents - Support for PDF documents and other company materials
    • Automatically indexes and processes PDF content
    • Maintains document hierarchy and relationships
    • Extracts key information and metadata

For more information on how to use Knowledge Bases, please refer to the Knowledge Bases documentation.