https://github.com/arize-ai/phoenix

AI Observability & Evaluation
https://github.com/arize-ai/phoenix

ai-monitoring ai-observability ai-roi aiengineering datasets llm-eval llmops ml-observability mlops model-observability

Last synced: 2 days ago
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AI Observability & Evaluation

• Host: GitHub
• URL: https://github.com/arize-ai/phoenix
• Owner: Arize-ai
• License: other
• Created: 2022-11-09T23:44:35.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-29T03:58:42.000Z (about 1 month ago)
• Last Synced: 2024-08-29T11:07:12.847Z (about 1 month ago)
• Topics: ai-monitoring, ai-observability, ai-roi, aiengineering, datasets, llm-eval, llmops, ml-observability, mlops, model-observability
• Language: Jupyter Notebook
• Homepage: https://docs.arize.com/phoenix
• Size: 86 MB
• Stars: 3,361
• Watchers: 26
• Forks: 244
• Open Issues: 207
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Security: SECURITY.md

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Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. It provides:

- **_Tracing_** - Trace your LLM application's runtime using OpenTelemetry-based instrumentation.

- **_Evaluation_** - Leverage LLMs to benchmark your application's performance using response and retrieval evals.

- **_Datasets_** - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.

- **_Experiments_** - Track and evaluate changes to prompts, LLMs, and retrieval.

Phoenix is vendor and language agnostic with out-of-the-box support for popular frameworks (🦙LlamaIndex, 🦜⛓LangChain, Haystack, 🧩DSPy) and LLM providers (OpenAI, Bedrock, and more). For details on auto-instrumentation, check out the [OpenInference](https://github.com/Arize-ai/openinference) project.

Phoenix runs practically anywhere, including your Jupyter notebook, local machine, containerized deployment, or in the cloud.



## Installation

Install Phoenix via `pip` or `conda`

```shell

pip install arize-phoenix

```

Phoenix container images are available via [Docker Hub](https://hub.docker.com/r/arizephoenix/phoenix) and can be deployed using Docker or Kubernetes.

## Community

Join our community to connect with thousands of AI builders.

- 🌍 Join our [Slack community](https://join.slack.com/t/arize-ai/shared_invite/zt-1px8dcmlf-fmThhDFD_V_48oU7ALan4Q).

- 📚 Read our [documentation](https://docs.arize.com/phoenix).

- 💡 Ask questions and provide feedback in the _#phoenix-support_ channel.

- 🌟 Leave a star on our [GitHub](https://github.com/Arize-ai/phoenix).

- 🐞 Report bugs with [GitHub Issues](https://github.com/Arize-ai/phoenix/issues).

- 𝕏 Follow us on [𝕏](https://twitter.com/ArizePhoenix).

- 💌️ Sign up for our [mailing list](https://phoenix.arize.com/#updates).

- 🗺️ Check out our [roadmap](https://github.com/orgs/Arize-ai/projects/45) to see where we're heading next.

## Breaking Changes

See the [migration guide](./MIGRATION.md) for a list of breaking changes.

## Copyright, Patent, and License

Copyright 2023 Arize AI, Inc. All Rights Reserved.

Portions of this code are patent protected by one or more U.S. Patents. See [IP_NOTICE](https://github.com/Arize-ai/phoenix/blob/main/IP_NOTICE).

This software is licensed under the terms of the Elastic License 2.0 (ELv2). See [LICENSE](https://github.com/Arize-ai/phoenix/blob/main/LICENSE).