qevals

qEvals

Table of Contents

• What is evals?
• Getting Started
• Prerequisites
• Compatibility
• Running Tests
• Contributing
• Release Notes

What is evals?

Evals is a synthetic data generation and evaluation framework for LLMs and RAG applications.

It has 2 main modules:

• datagen
• eval

A high level architecture diagram of evals is the following:

Architecture diagram

Getting Started

To get started with evals, follow these steps:

1. Clone the repository to your local machine.
2. Install the necessary dependencies by running pip install -r requirements.txt in the project directory.
3. Create a copy of config/config.toml.template and name it config/config.toml.
4. Update 2 sections in the config.toml file:
   1. MISC
      • Configure your SSL cert file location.
   2. DATAGEN
      • Set DATA_DIR variable controls the location of the data corpus to generate synthetic data from, it’s relative to the datagen/data/ directory. In other words, add your data directories in there and specify their name in the variable.
      • The GEN_PROVIDER variable allows choosing between azure or vertex.
      • Add in the rest variables desired for generative purposes.
   3. DATAEVAL
      • EVAL_TESTS offers a list of evaluation tests supported by the framework. The possible options are AnswerRelevancy, Hallucination, Faithfulness, Bias, Toxicity, Correctness, Coherence, PromptInjection, PromptBreaking, PromptLeakage.
      • The EVAL_RPVODER variable allows choosing between azure or vertex.
      • Add in the rest of variables required for the model desired to use as judge for evaluations.

To run the synthetic data generation module:

1. Modify/adapt the sample client provided (datagen/client.py)

2. Run python -m datagen.client

3. The synthetically generated data will be stored in the datagen/qa_out/ directory as a CSV file with the format:

    ```csv
    question,context,ground_truth
    ```
   

To run the eval module:

1. Modify/adapt the sample client provided (eval/client.py)
   1. The input data needs to match the format of the data produced by the synthetic data generation (question,context,ground_truth).
   2. The ground_truth may or may not be used depending on the setting use_answers_from_dataset. When set to False it will ignore that data column and generate new outputs using the configured generative model.
2. Start MlFlow by running:

   mlflow ui --port 5000

3. Run python -m eval.client
4. Monitor and analyse the eval results on your local MlFlow interface here: http://localhost:5000