Karini AI Documentation
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  • Introduction
  • Installation
  • Getting Started
  • Organization
  • User Management
    • User Invitations
    • Role Management
  • Model Hub
    • Embeddings Models
    • Large Language Models (LLMs)
  • Prompt Management
    • Prompt Templates
    • Create Prompt
    • Test Prompt
      • Test & Compare
      • Prompt Observability
      • Prompt Runs
    • Agentic Prompts
      • Create Agent Prompt
      • Test Agent Prompt
    • Prompt Task Types
    • Prompt Versions
  • Datasets
  • Recipes
    • QnA Recipe
      • Data Storage Connectors
      • Connector Credential Setup
      • Vector Stores
      • Create Recipe
      • Run Recipe
      • Test Recipe
      • Evaluate Recipe
      • Export Recipe
      • Recipe Runs
      • Recipe Actions
    • Agent Recipe
      • Agent Recipe Configuration
      • Set up Agentic Recipe
      • Test Agentic Recipe
      • Agentic Evaluation
    • Databricks Recipe
  • Copilots
  • Observability
  • Dashboard Overview
    • Statistical Overview
    • Cost & Usage Summary
      • Spend by LLM Endpoint
      • Spend by Generative AI Application
    • Model Endpoints & Datasets Distribution
    • Dataset Dashboard
    • Copilot Dashboard
    • Model Endpoints Dashboard
  • Catalog Schemas
    • Connectors
    • Catalog Schema Import and Publication Process
  • Prompt Optimization Experiments
    • Set up and execute experiment
    • Optimization Insights
  • Generative AI Workshop
    • Agentic RAG
    • Intelligent Document Processing
    • Generative BI Agentic Assistant
  • Release Notes
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Catalog Schemas

Karini AI supports integration with a broad range of databases via catalog schemas, including Glue, MySQL, PostgreSQL, Snowflake, Redshift, MS SQL, and Oracle, ensuring streamlined data management across diverse sources. Utilizing its Generative BI capabilities, Karini AI enables users to effectively organize, access, and analyze data, optimizing business intelligence workflows. The platform facilitates the extraction of insights, creation of dashboards, identification of data anomalies, and generation of forecasts, while enhancing data processing and query performance. This integration simplifies data management, offering users advanced, accessible analytics across multiple data silos.

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Last updated 1 month ago