Karini AI Documentation
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  • Introduction
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  • User Management
    • User Invitations
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    • Embeddings Models
    • Large Language Models (LLMs)
  • Prompt Management
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    • Create Prompt
    • Test Prompt
      • Test & Compare
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      • Create Agent Prompt
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    • Prompt Task Types
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  • Datasets
  • Recipes
    • QnA Recipe
      • Data Storage Connectors
      • Connector Credential Setup
      • Vector Stores
      • Create Recipe
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      • Test Recipe
      • Evaluate Recipe
      • Export Recipe
      • Recipe Runs
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    • Agent Recipe
      • Agent Recipe Configuration
      • Set up Agentic Recipe
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    • 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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  1. Recipes
  2. QnA Recipe

Run Recipe

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Last updated 8 months ago

Once a recipe is created and saved, you need to publish it to assign it a version number. A Run button is enabled after the recipe has been published.

Recipe "run" process starts the data ingestion pipeline, executing tasks to connect to data source, pre-process the data and create a knowledge base with vector embedding as per the configurations in the recipe elements.

Configurations related to the prompt and output elements are not relevant for the recipe run.

Following processing metrics are displayed on the recipe dashboard, highlighting each task in the data ingestion process.

  • X-axis: Processing tasks

    • OCR (Optical Character Recognition): Extraction of text from images or scanned documents.

    • PII (Personally Identifiable Information): Identification and handling of sensitive personal data such as names, addresses, or social security numbers.

    • Chunking: Division of text into smaller, meaningful parts or "chunks" for analysis or processing.

    • Embeddings: Conversion of text data into numerical format for machine learning algorithms by mapping words or phrases to vectors in a high-dimensional space

  • Y-axis: Count of processed items

If errors occur during the recipe run, error messages are displayed in the recipe panel and can also be visualized as error counts in the dashboard.

You can also review the summary of the run, including a list of connectors with embedded items and chunks.

Recipe Run Dashboard