chore: import upstream snapshot with attribution
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# provider-atlascloud (Atlas Cloud Example)
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This directory contains an example configuration for using [Atlas Cloud](https://www.atlascloud.ai/) with promptfoo.
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Atlas Cloud exposes an OpenAI-compatible LLM API, so it is a good fit for prompt and model evaluation workflows across multiple model families behind a single provider account.
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## Prerequisites
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1. Create an Atlas Cloud API key from the [Atlas Cloud docs](https://www.atlascloud.ai/docs/en/models/get-start).
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2. Set the environment variable:
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```bash
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export ATLASCLOUD_API_KEY=your_api_key_here
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```
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## Quick Start
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```bash
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npx promptfoo@latest init --example provider-atlascloud
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cd provider-atlascloud
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npx promptfoo eval -c promptfooconfig.yaml
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```
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## Example Config
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The included `promptfooconfig.yaml` demonstrates:
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- Multiple Atlas Cloud-hosted chat models
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- Standard OpenAI-style generation parameters
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- A mix of deterministic and rubric-based assertions
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## Provider Syntax
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```yaml
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providers:
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- id: atlascloud:deepseek-ai/DeepSeek-V3-0324
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- id: atlascloud:qwen/qwen3-32b
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```
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## Custom Gateway Example
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```yaml
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providers:
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- id: atlascloud:deepseek-ai/DeepSeek-V3-0324
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config:
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apiBaseUrl: https://proxy.example.com/atlas/v1
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apiKeyEnvar: MY_ATLASCLOUD_TOKEN
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```
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## Resources
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- [Atlas Cloud Provider Docs](https://www.promptfoo.dev/docs/providers/atlascloud/)
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- [Atlas Cloud Docs](https://www.atlascloud.ai/docs)
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- [Promptfoo Docs](https://www.promptfoo.dev/docs/)
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# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
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description: Atlas Cloud Provider Example
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prompts:
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- |
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You are a helpful assistant. Answer the following question concisely:
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{{question}}
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providers:
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- id: atlascloud:deepseek-ai/DeepSeek-V3-0324
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label: 'DeepSeek V3 0324 via Atlas Cloud'
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config:
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temperature: 0.7
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max_tokens: 500
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- id: atlascloud:qwen/qwen3-32b
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label: 'Qwen 3 32B via Atlas Cloud'
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config:
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temperature: 0.3
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max_tokens: 500
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tests:
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- description: 'Basic knowledge test'
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vars:
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question: 'What is the capital of France?'
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assert:
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- type: contains
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value: 'Paris'
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- description: 'Technical explanation'
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vars:
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question: 'Explain machine learning in simple terms'
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assert:
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- type: icontains
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value: 'machine learning'
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