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Contributing to TruLens

Interested in contributing to TruLens? Here's how to get started!

What can you work on?

  1. ๐Ÿ’ช Add new feedback functions
  2. ๐Ÿค Add new feedback function providers.
  3. ๐Ÿ› Fix bugs
  4. ๐ŸŽ‰ Add usage examples
  5. ๐Ÿงช Add experimental features
  6. ๐Ÿ“„ Improve code quality & documentation

Also, join the AI Quality Slack community for ideas and discussions.

๐Ÿ’ช Add new feedback functions

Feedback functions are the backbone of TruLens, and evaluating unique LLM apps may require new evaluations. We'd love your contribution to extend the feedback functions library so others can benefit!

  • To add a feedback function for an existing model provider, you can add it to an existing provider module. You can read more about the structure of a feedback function in this guide.
  • New methods can either take a single text (str) as a parameter or two different texts (str), such as prompt and retrieved context. It should return a float, or a dict of multiple floats. Each output value should be a float on the scale of 0 (worst) to 1 (best).
  • Make sure to add its definition to this list.

๐Ÿค Add new feedback function providers.

Feedback functions often rely on a model provider, such as OpenAI or HuggingFace. If you need a new model provider to utilize feedback functions for your use case, we'd love if you added a new provider class, e.g. Ollama.

You can do so by creating a new provider module in this folder.

Alternatively, we also appreciate if you open a GitHub Issue if there's a model provider you need!

๐Ÿ› Fix Bugs

Most bugs are reported and tracked in the Github Issues Page. We try our best in triaging and tagging these issues:

Issues tagged as bug are confirmed bugs. New contributors may want to start with issues tagged with good first issue. Please feel free to open an issue and/or assign an issue to yourself.

๐ŸŽ‰ Add Usage Examples

If you have applied TruLens to track and evalaute a unique use-case, we would love your contribution in the form of an example notebook: e.g. Evaluating Pinecone Configuration Choices on Downstream App Performance

All example notebooks are expected to:

  • Start with a title and description of the example
  • Include a commented out list of dependencies and their versions, e.g. # ! pip install trulens==0.10.0 langchain==0.0.268
  • Include a linked button to a Google colab version of the notebook
  • Add any additional requirements

๐Ÿงช Add Experimental Features

If you have a crazy idea, make a PR for it! Whether if it's the latest research, or what you thought of in the shower, we'd love to see creative ways to improve TruLens.

๐Ÿ“„ Improve Code Quality & Documentation

We would love your help in making the project cleaner, more robust, and more understandable. If you find something confusing, it most likely is for other people as well. Help us be better!


We try to respect various code, testing, and documentation standards outlined in trulens_eval/