RedLite

An opinionated toolset for testing Conversational Language Models.

Documentation

https://innodatalabs.github.io/redlite/

Usage

Install required dependencies
```
pip install redlite[all]
```
Generate several runs (using Python scripting, see examples, and below)
Review and compare runs
```
redlite server --port <PORT>
```
Optionally, upload to Zeno
```
ZENO_API_KEY=zen_XXXX redlite upload
```

Python API

import os
from redlite import run, load_dataset
from redlite.model.openai_model import OpenAIModel
from redlite.metric import MatchMetric


model = OpenAIModel(api_key=os.environ["OPENAI_API_KEY"])
dataset = load_dataset("hf:innodatalabs/rt-gsm8k-gaia")
metric = MatchMetric(ignore_case=True, ignore_punct=True, strategy='prefix')

run(model=model, dataset=dataset, metric=metric)

Note: the code above uses OpenAI model via their API. You will need to register with OpenAI and get an API access key, then set it in the environment as OPENAI_API_KEY.

Goals

simple, easy-to-learn API
lightweight
only necessary dependencies
framework-agnostic (PyTorch, Tensorflow, Keras, Flax, Jax)
basic analytic tools included

Develop

python -m venv .venv
. .venv/bin/activate
pip install -e .[dev,all]

Make commands:

test
test-server
lint
wheel
docs
docs-server
black

Zeno integration

Benchmarks can be uploaded to Zeno interactive AI evaluation platform :

redlite upload --project my-cool-project

All tasks will be concatenated and uploaded as a single dataset, with extra fields:

task_id
dataset
metric

All models will be uploaded. If model was not tested on a specific task, a simulated zero-score dataframe is used instead.

Use task_id (or dataset as appropriate) to create task slices. Slices can be used to navigate data or create charts.

Serving as a static website

UI server data and code can be exported to a local directory that then can be served statically.

This is useful for publishing as a static website on cloud storage (S3, Google Storage).

redlite server-freeze /tmp/my-server
gsutil -m rsync -R /tmp/my-server gs://{your GS bucket}

Note that you have to configure cloud bucket in a special way, so that cloud provider serves it as a website. How to do this depends on the cloud provider.

TODO

[x] deps cleanup (randomname!)
[x] review/improve module structure
[x] automate CI/CD
[x] write docs
[x] publish docs automatically (CI/CD)
[x] web UI styling
[ ] better test server
[ ] tests
[x] Integrate HF models
[x] Integrate OpenAI models
[x] Integrate Anthropic models
[x] Integrate AWS Bedrock models
[ ] Integrate vLLM models
[x] Fix data format in HF datasets (innodatalabs/rt-* ones) to match standard
[ ] more robust backend API (future-proof)
[ ] better error handling for missing deps
[ ] document which deps we need when
[ ] export to CSV
[x] Upload to Zeno