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v0.20.x
v0.20.x
  • Introduction to AI Testing
  • Welcome to Distributional
  • Motivation
  • What is AI Testing?
  • Stages in the AI Software Development Lifecycle
    • Components of AI Testing
  • Distributional Testing
  • Getting Access to Distributional
  • Learning about Distributional
    • The Distributional Framework
    • Defining Tests in Distributional
      • Automated Production test creation & execution
      • Knowledge-based test creation
      • Comprehensive testing with Distributional
    • Reviewing Test Sessions and Runs in Distributional
      • Reviewing and recalibrating automated Production tests
      • Insights surfaced elsewhere on Distributional
      • Notifications
    • Data in Distributional
      • The flow of data
      • Components and the DAG for root cause analysis
      • Uploading data to Distributional
      • Living in your VPC
  • Using Distributional
    • Getting Started
    • Access
      • Organization and Namespaces
      • Users and Permissions
      • Tokens
    • Data
      • Data Objects
      • Run-Level Data
      • Data Storage Integrations
      • Data Access Controls
    • Testing
      • Creating Tests
        • Test Page
        • Test Drawer Through Shortcuts
        • Test Templates
        • SDK
      • Defining Assertions
      • Production Testing
        • Auto-Test Generation
        • Recalibration
        • Notable Results
        • Dynamic Baseline
      • Testing Strategies
        • Test That a Given Distribution Has Certain Properties
        • Test That Distributions Have the Same Statistics
        • Test That Columns Are Similarly Distributed
        • Test That Specific Results Have Matching Behavior
        • Test That Distributions Are Not the Same
      • Executing Tests
        • Manually Running Tests Via UI
        • Executing Tests Via SDK
      • Reviewing Tests
      • Using Filters
        • Filters in the Compare Page
        • Filters in Tests
    • Python SDK
      • Quick Start
      • Functions
        • login
        • Project
          • create_project
          • copy_project
          • export_project_as_json
          • get_project
          • get_or_create_project
          • import_project_from_json
        • Run Config
          • create_run_config
          • get_latest_run_config
          • get_run_config
          • get_run_config_from_latest_run
        • Run Results
          • get_column_results
          • get_scalar_results
          • get_results
          • report_column_results
          • report_scalar_results
          • report_results
        • Run
          • close_run
          • create_run
          • get_run
          • report_run_with_results
        • Baseline
          • create_run_query
          • get_run_query
          • set_run_as_baseline
          • set_run_query_as_baseline
        • Test Session
          • create_test_session
      • Objects
        • Project
        • RunConfig
        • Run
        • RunQuery
        • TestSession
        • TestRecalibrationSession
        • TestGenerationSession
        • ResultData
      • Experimental Functions
        • create_test
        • get_tests
        • get_test_sessions
        • wait_for_test_session
        • get_or_create_tag
        • prepare_incomplete_test_spec_payload
        • create_test_recalibration_session
        • wait_for_test_recalibration_session
        • create_test_generation_session
        • wait_for_test_generation_session
      • Eval Module
        • Quick Start
        • Application Metric Sets
        • How-To / FAQ
        • LLM-as-judge and Embedding Metrics
        • RAG / Question Answer Example
        • Eval Module Functions
          • Index of functions
          • eval
          • eval.metrics
    • Notifications
    • Release Notes
  • Tutorials
    • Instructions
    • Hello World (Sentiment Classifier)
    • Trading Strategy
    • LLM Text Summarization
      • Setting the Scene
      • Prompt Engineering
      • Integration testing for text summarization
      • Practical considerations
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  • Prerequisites
  • Instructions

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  1. Tutorials

Instructions

How to navigate the Tutorials section of the documentation

PreviousRelease NotesNextHello World (Sentiment Classifier)

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Prerequisites

  1. our Python client.

  2. Ensure pandas is : pip install "pandas[parquet]".

Instructions

The tutorials come with data and Jupyter notebooks that show you how to get your application data into dbnl and how to create projects and runs. To connect to dbnl, you will need to have the DBNL_API_TOKEN environment variable set. Some tutorials demonstrate how to use dbnl with proprietary data stores such as Snowflake — in that case, we provide example Run data so that you can execute the tutorial regardless.

  1. For each tutorial, download the relevant zip files at the beginning of the tutorial. The notebooks found in the zip files can be used to generate the project, runs, and tests within the dbnl platform.

  2. If you are simply interested in creating existing data on the platform, then execute the notebook within the extracted folder.

  3. To walk through the tutorial and learn the pieces of information we believe will be relevant for successful product usage, create a new notebook in the extracted folder and follow any of the tutorials in this section.

installed
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