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  • Tonic Validate guide
  • About Tonic Validate
    • What is Tonic Validate?
    • Validate components and tools
    • Validate workflows
  • Getting started with Validate
    • Starting your Validate account
    • Setting up the Validate SDK
    • Quickstart example - create a run and log responses
    • Creating and revoking Validate API keys
  • About RAG metrics
    • About using metrics to evaluate a RAG system
    • RAG components summary
    • RAG metrics summary
    • RAG metrics reference
  • Benchmarks and projects
    • Managing benchmarks in Validate
    • Managing projects in Validate
  • Runs
    • Starting a Validate run
    • Viewing and managing runs
  • Production monitoring
    • Configuring your RAG system to send questions to Validate
    • Viewing the metric scores and logged questions
  • Code examples
    • End-to-end example using LlamaIndex
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  1. Production monitoring

Configuring your RAG system to send questions to Validate

Last updated 1 year ago

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To configure your RAG system to log questions and answers to Tonic Validate, whenever your RAG system answers a question from a user, you add a call to the Validate log function.

The call to log includes:

  • The identifier of the Validate production monitoring project to send the question to

  • The text of the question from the user to the RAG system

  • The answer that the RAG system provided to the user

  • The context that the RAG system used to answer the question

from tonic_validate import ValidateMonitorer
monitorer = ValidateMonitorer()
monitorer.log(
    project_id="<project identifier>",
    question="<question text>",
    answer="<answer>",
    context_list=["<context used to answer the question"]
)

As your RAG system sends questions to the Validate production managing project, Validate by default generates the following metrics scores for each question:

You can also request additional metrics. For information about the available metrics, go to RAG metrics reference.

Answer consistency
Retrieval precision
Augmentation precision