This repository contains a reproducible workflow setup using DVC backed by a JASMIN object store. Before working with the repository please contact Matt Coole to request access to the Jasmin object store llm-eval-o. Then follow the instructions below.

• Ollama (llama3.1 and mistral-nemo models)
• Python 3.9+
• uv

This project uses uv to manage python version and dependency. If you haven't got uv installed, the easiest way to it is using pip:

pip install uv

This will allow you to use uv across projects. You can verify that the installation is successful by running:

uv --version

Once uv is installed you can use it to automatically download the appropriate version of python and create a virtual environment for running the project code. This can be done using:

uv sync

This will create a virtual environment in .venv and installed the necessary dependencies from pyproject.toml. Within the project, any commands that you wish to run can be preceeded by uv run to ensure that they run with the correct version of python and using the correct virtual environment.

Note: The remainder of this readme assume you have either activated the virtual environment created using source .venv/bin/activate or that you are prepending all commands with uv run.

Next setup your local DVC configuration with your Jasmin object store access key:

dvc remote modify --local jasmin access_key_id '<ACCES_KEY_ID>'
dvc remote modify --local jasmin secret_access_key '<KEY_SECRET>'

Pull the data from the object store using DVC:

dvc pull

You should now be ready to run the pipeline:

dvc repro

This should only reproduce the pipeline, but only stages that have been modified will actually be re-run (see output whilst running). If you want to check that all stages of the pipeline are running correctly you can either user the -f flag with the above command to force DVC to re-run all stages of the pipeline or (as re-running with all the data can take several hours) run the convenience script test-pipeline.sh. This script will run the pipeline with a tiny subset of data as an experiment which should only take a copule of minutes:

./test-pipeline.sh

This pipeline is defined in dvc.yaml and can be viewed with the command:

dvc dag

or it can be output to mermaid format to display in markdown:

dvc dag -md

flowchart TD
	node1["chunk-data"]
	node2["create-embeddings"]
	node3["evaluate"]
	node4["extract-metadata"]
	node5["fetch-metadata"]
	node6["fetch-supporting-docs"]
	node7["generate-testset"]
	node8["run-rag-pipeline"]
	node9["upload-to-docstore"]
	node1-->node2
	node2-->node9
	node4-->node1
	node5-->node4
	node5-->node6
	node6-->node1
	node7-->node8
	node8-->node3
	node9-->node8
	node10["data/evaluation-sets.dvc"]
	node11["data/synthetic-datasets.dvc"]

Note: To re-run the fetch-supporting-docs stage of the pipeline you will need to request access to the Legilo service from the EDS dev team and provide your username and password in a .env file.

The pipeline by default will run using the parameters defind in params.yaml. To experiment with varying these paramaters you can change them directly, or use DVC experiments.

To run an experiment varying a particual parameter:

dvc exp run -S hp.chunk-size=1000

This will re-run the pipeline but override the value of the hp.chunk-size parameter in params.yaml and set it to 1000. Only the necessary stages of the pipeline should be re-run and the result should appear in your workspace.

You can compare the results of your experiment to the results of the baseline run of the pipeline using:

dvc exp diff

Path               Metric              HEAD      workspace    Change
data/metrics.json  answer_correctness  0.049482  0.043685     -0.0057974
data/metrics.json  answer_similarity   0.19793   0.17474      -0.02319
data/metrics.json  context_recall      0.125     0            -0.125
data/metrics.json  faithfulness        0.75      0.69375      -0.05625

Path         Param          HEAD    workspace    Change
params.yaml  hp.chunk-size  300     1000         700

It is also possible to compare the results of all experiments:

dvc exp show --only-changed

Experiments can be remove using (-A flag removes all experiment, but individually experiment can be removed using their name or ID):

dvc exp remove -A

The repository includes a simple shell script that can be used as an experiment runner to test various different models:

./run-experiments.sh

This will run the dvc pipeline with various different llm model (check the shell scripts for details) and save the results as experiments.

An experiment for each model defined will be queued and run in the background. To check the status of the experiments:

dvc queue status

To check the output for an experiment currently running use:

dvc queue log $EXPERIMENT_NAME

Notes on the use of Data Version Control and Continuous Machine Learning:

• DVC
• CML

Notes on running models with vLLM:

• vLLM