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You can now configure and run Evals directly in the OpenAI Dashboard. Get started →
Evals provide a framework for evaluating large language models (LLMs) or systems built using LLMs. We offer an existing registry of evals to test different dimensions of OpenAI models and the ability to write your own custom evals for use cases you care about. You can also use your data to build private evals which represent the common LLMs patterns in your workflow without exposing any of that data publicly.
If you are building with LLMs, creating high quality evals is one of the most impactful things you can do. Without evals, it can be very difficult and time intensive to understand how different model versions might affect your use case. In the words of OpenAI's President Greg Brockman:
To run evals, you will need to set up and specify your OpenAI API key. After you obtain an API key, specify it using the
environment variable. Please be aware of the costs associated with using the API when running evals. You can also run and create evals using Weights & Biases.OPENAI_API_KEY
Minimum Required Version: Python 3.9
Our evals registry is stored using Git-LFS. Once you have downloaded and installed LFS, you can fetch the evals (from within your local copy of the evals repo) with:
cd evals git lfs fetch --all git lfs pull
This will populate all the pointer files under
evals/registry/dataYou may just want to fetch data for a select eval. You can achieve this via:
git lfs fetch --include=evals/registry/data/${your eval} git lfs pull
If you are going to be creating evals, we suggest cloning this repo directly from GitHub and installing the requirements using the following command:
pip install -e .
Using
-eOptionally, you can install the formatters for pre-committing with:
pip install -e .[formatters]
Then run
pre-commit installIf you want to manually run all pre-commit hooks on a repository, run
pre-commit run --all-filespre-commit run <hook_id>If you don't want to contribute new evals, but simply want to run them locally, you can install the evals package via pip:
pip install evals
You can find the full instructions to run existing evals in
and our existing eval templates in run-evals.md
. For more advanced use cases like prompt chains or tool-using agents, you can use our Completion Function Protocol.eval-templates.md
We provide the option for you to log your eval results to a Snowflake database, if you have one or wish to set one up. For this option, you will further have to specify the
SNOWFLAKE_ACCOUNTSNOWFLAKE_DATABASESNOWFLAKE_USERNAMESNOWFLAKE_PASSWORDWe suggest getting starting by:
build-eval.mdcustom-eval.mdcompletion-fns.mdPlease note that we are currently not accepting evals with custom code! While we ask you to not submit such evals at the moment, you can still submit model-graded evals with custom model-graded YAML files.
If you think you have an interesting eval, please open a pull request with your contribution. OpenAI staff actively review these evals when considering improvements to upcoming models.
Do you have any examples of how to build an eval from start to finish?
examplesbuild-eval.mdDo you have any examples of evals implemented in multiple different ways?
evals/registry/evals/coqa.yamlWhen I run an eval, it sometimes hangs at the very end (after the final report). What's going on?
There's a lot of code, and I just want to spin up a quick eval. Help? OR,
I am a world-class prompt engineer. I choose not to code. How can I contribute my wisdom?
examplesBy contributing to evals, you are agreeing to make your evaluation logic and data under the same MIT license as this repository. You must have adequate rights to upload any data used in an eval. OpenAI reserves the right to use this data in future service improvements to our product. Contributions to OpenAI evals will be subject to our usual Usage Policies: https://platform.openai.com/docs/usage-policies.