Prompt Chain

Test Databricks Foundation Model APIs

Promptfoo example testing Databricks Foundation Model APIs, including Llama 3.3 text generation and vision models.

Works with databricks

80
Spark score
out of 100
Updated yesterday
Version 0.121.19
Models
llama 3

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Why it matters

Automate the testing of Databricks Foundation Model APIs. Ensure your models are performing as expected by integrating with promptfoo for robust evaluation.

Outcomes

What it gets done

01

Test Databricks Foundation Model APIs

02

Integrate promptfoo for automated testing

03

Evaluate model performance and accuracy

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/pfoo-provider-databricks | bash

Steps

Steps in the chain

01
Set up Databricks workspace and access token
02
Configure environment variables
03
Initialize the example project
04
Run the evaluation
05
View results in the web UI

Overview

Provider Databricks

A promptfoo example testing Databricks Foundation Model APIs, covering Llama 3.3 text generation with cost tracking and a separate vision-model configuration. Use when your models run on Databricks Foundation Model APIs and you need to validate text or vision output quality and cost through promptfoo.

What it does

This example tests Databricks Foundation Model APIs with promptfoo. It demonstrates using Databricks pay-per-token endpoints (Foundation Models), basic text generation with Llama 3.3, cost tracking with usage context, and simple assertions and quality checks. A dedicated configuration also covers vision-model capabilities.

When to use - and when NOT to

Use this example when your models run on Databricks Foundation Model APIs and you want to evaluate text generation quality and cost via promptfoo, including a separate vision-capable configuration. It is not a Databricks workspace setup guide - it assumes a Databricks workspace with Foundation Model APIs already enabled and a valid access token.

Inputs and outputs

Requires a Databricks workspace with Foundation Model APIs enabled and a Databricks access token, set as DATABRICKS_WORKSPACE_URL (e.g. https://your-workspace.cloud.databricks.com) and DATABRICKS_TOKEN, either in a .env file or the environment:

export DATABRICKS_WORKSPACE_URL=https://your-workspace.cloud.databricks.com
export DATABRICKS_TOKEN=your-databricks-token

Scaffold with npx promptfoo@latest init --example provider-databricks, run with npx promptfoo@latest eval, and view results with npx promptfoo@latest view. For vision capabilities, run the dedicated configuration with npx promptfoo@latest eval -c promptfooconfig.vision.yaml.

Integrations

Integrates Databricks Foundation Model APIs (pay-per-token endpoints) as a promptfoo provider, tested here with Llama 3.3 for text generation, with cost tracked via usage context and validated through simple assertions and quality checks.

Who it's for

Teams running models through Databricks Foundation Model APIs who want to evaluate text-generation quality, track per-request cost, and test vision-model configurations using promptfoo's standard eval workflow.

Source README

provider-databricks (Databricks Provider)

Test Databricks Foundation Model APIs with promptfoo.

Getting Started

You can run this example with:

npx promptfoo@latest init --example provider-databricks
cd provider-databricks

Prerequisites

  1. A Databricks workspace with Foundation Model APIs enabled
  2. A Databricks access token

Environment Variables

This example requires the following environment variables:

You can set these in a .env file or directly in your environment:

export DATABRICKS_WORKSPACE_URL=https://your-workspace.cloud.databricks.com
export DATABRICKS_TOKEN=your-databricks-token

Running the Example

### Run the evaluation
npx promptfoo@latest eval

### View results in the web UI
npx promptfoo@latest view

What This Example Demonstrates

  • Using Databricks pay-per-token endpoints (Foundation Models)
  • Basic text generation with Llama 3.3
  • Cost tracking with usage context
  • Simple assertions and quality checks

Vision Models

For vision capabilities, use the dedicated configuration:

npx promptfoo@latest eval -c promptfooconfig.vision.yaml

Learn More

FAQ

Common questions

Discussion

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