astronomeramber/phoenix

AI Observability & Evaluation

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

• Tracing - Trace your LLM application's runtime using using OpenTelemetry-based instrumentation.
• Evaluation - Leverage LLMs to benchmark your application's performance using response and retrieval evals.
• Inference Analysis - Visualize inferences and embeddings using dimensionality reduction and clustering to identify drift and performance degradation.

Phoenix is vendor and language agnostic with out-of-the-box support for popular frameworks (🦙LlamaIndex, 🦜⛓LangChain, 🧩DSPy) and LLM providers (OpenAI, Bedrock, and more). For details on auto-instrumentation, check out the 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 along with extra dependencies for running evals:

pip install 'arize-phoenix[evals]'

Phoenix container images are available via Docker Hub and can be deployed using Docker or Kubernetes.

Community

Join our community to connect with thousands of AI builders.

• 🌍 Join our Slack community.
• 💡 Ask questions and provide feedback in the #phoenix-support channel.
• 🌟 Leave a star on our GitHub.
• 🐞 Report bugs with GitHub Issues.
• 𝕏 Follow us on 𝕏.
• 💌️ Sign up for our mailing list.
• 🗺️ Check out our roadmap to see where we're heading next.

Thanks

• UMAP For unlocking the ability to visualize and reason about embeddings
• HDBSCAN For providing a clustering algorithm to aid in the discovery of drift and performance degradation

Breaking Changes

See the migration guide 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.

This software is licensed under the terms of the Elastic License 2.0 (ELv2). See LICENSE.