We develop ELQM, a RAG-based question answering system for Eurpopean energy law acquired from EUR-Lex. ELQM comprises a full end-to-end pipeline, including data scraping, preprocessing, splitting, vectorization, storage, retrieval, and answer generation with chat-based LLMs. Our work also focuses on usability, providing three access points and linking to source documents for transparency.

• 16 GB RAM
• 12 GB VRAM
  • by default CUDA is used
• 25 GB storage space
  • 6 GB cache for all configurations
  • 7 GB environment
  • ~5 GB for each llama2 and mistral model

• Python 3.10
• pip >= 24.0
• Ollama
• Ubuntu >= 22.04 (optional, for GPT4AllEmbeddings which requires glibc)
• For SparkNLP: Java OpenJDK or similar (see https://pypi.org/project/spark-nlp/)

git clone https://github.com/psaegert/elqm-INLPT-WS2023
cd elqm-INLPT-WS2023

Optional: Create a virtual environment:

conda:

conda create -n elqm python=3.10 [ipykernel]
conda activate elqm

Optional: Install ipykernel to use the environment in Jupyter Notebook

venv:

python3 -m venv elqmVenv
source elqmVenv/bin/activate

Then, install the package via

pip install --upgrade pip
pip install -e .

Scrape the EUR-Lex data with

elqm scrape-data

Alternatively, you can download the scraped data from our Huggingface dataset and move its contents into /data

1. Run the Ollama backend via

ollama serve

2. Pull the desired Ollama model, e.g. mistral

ollama pull mistral

To generate the oracle dataset, we use the llama2 model:

ollama pull llama2

Gradio Frontend

elqm gui -c configs/prompts/256_5_5_nlc_bge_fn_mistral_h2.yaml

CLI

elqm run -c configs/prompts/256_5_5_nlc_bge_fn_mistral_h2.yaml

Python API

from dynaconf import Dynaconf
import os

from elqm import ELQMPipeline
from elqm.utils import get_dir

config = Dynaconf(settings_files=os.path.join(get_dir("configs", "prompts"), "256_5_5_nlc_bge_fn_mistral_h2.yaml"))
elqm = ELQMPipeline(config)

print(elqm.answer("Which CIE LUV does a model supporting greater than 99 % of the sRGB colour space translate to?"))

To set up the development environment, run the following commands:

pip install -e .[dev]
pre-commit install

To run the tests locally, run the following commands:

ollama serve
pytest tests --cov src

If you use ELQM: Energy-Law Query-Master for your research, please cite it using the following

@software{elqm_2024,
    author = {Daniel Knorr and Paul Saegert and Nikita Tatsch},
    title = {ELQM: Energy-Law Query-Master},
    month = mar,
    year = 2024,
    publisher = {GitHub},
    version = {1.0.0},
    url = {https://github.com/psaegert/elqm}
}