astra-logs


Nameastra-logs JSON
Version 4.2.8 PyPI version JSON
download
home_pageNone
SummaryAI Observability and Evaluation
upload_time2024-06-07 14:02:04
maintainerNone
docs_urlNone
authorNone
requires_python<3.13,>=3.8
licenseElastic-2.0
keywords explainability monitoring observability
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
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        <img src="https://img.shields.io/static/v1?message=Docs&logo=data:image/png;base64,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&labelColor=grey&color=blue&logoColor=white&label=%20"/>
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</p>

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](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` along with extra dependencies for running evals:

```shell
pip install 'arize-phoenix[evals]'
```

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).
-   💡 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.

## Thanks

-   [UMAP](https://github.com/lmcinnes/umap) For unlocking the ability to visualize and reason about embeddings
-   [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) For providing a clustering algorithm to aid in the discovery of drift and performance degradation

## 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).

            

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It provides:\n\n-   **_Tracing_** - Trace your LLM application's runtime using using OpenTelemetry-based instrumentation.\n-   **_Evaluation_** - Leverage LLMs to benchmark your application's performance using response and retrieval evals.\n-   **_Inference Analysis_** - Visualize inferences and embeddings using dimensionality reduction and clustering to identify drift and performance degradation.\n\nPhoenix is vendor and language agnostic with out-of-the-box support for popular frameworks (\ud83e\udd99LlamaIndex, \ud83e\udd9c\u26d3LangChain, \ud83e\udde9DSPy) and LLM providers (OpenAI, Bedrock, and more). For details on auto-instrumentation, check out the [OpenInference](https://github.com/Arize-ai/openinference) project.\n\nPhoenix runs practically anywhere, including your Jupyter notebook, local machine, containerized deployment, or in the cloud.\n\n## Installation\n\nInstall Phoenix via `pip` or `conda` along with extra dependencies for running evals:\n\n```shell\npip install 'arize-phoenix[evals]'\n```\n\nPhoenix container images are available via [Docker Hub](https://hub.docker.com/r/arizephoenix/phoenix) and can be deployed using Docker or Kubernetes.\n\n## Community\n\nJoin our community to connect with thousands of AI builders.\n\n-   \ud83c\udf0d Join our [Slack community](https://join.slack.com/t/arize-ai/shared_invite/zt-1px8dcmlf-fmThhDFD_V_48oU7ALan4Q).\n-   \ud83d\udca1 Ask questions and provide feedback in the _#phoenix-support_ channel.\n-   \ud83c\udf1f Leave a star on our [GitHub](https://github.com/Arize-ai/phoenix).\n-   \ud83d\udc1e Report bugs with [GitHub Issues](https://github.com/Arize-ai/phoenix/issues).\n-   \ud835\udd4f Follow us on [\ud835\udd4f](https://twitter.com/ArizePhoenix).\n-   \ud83d\udc8c\ufe0f Sign up for our [mailing list](https://phoenix.arize.com/#updates).\n-   \ud83d\uddfa\ufe0f Check out our [roadmap](https://github.com/orgs/Arize-ai/projects/45) to see where we're heading next.\n\n## Thanks\n\n-   [UMAP](https://github.com/lmcinnes/umap) For unlocking the ability to visualize and reason about embeddings\n-   [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) For providing a clustering algorithm to aid in the discovery of drift and performance degradation\n\n## Breaking Changes\n\nSee the [migration guide](./MIGRATION.md) for a list of breaking changes.\n\n## Copyright, Patent, and License\n\nCopyright 2023 Arize AI, Inc. 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