For instructions on running the solution, click here.

Introducing Alplex, an AI-powered virtual law office designed to assist you with legal issues based on Swiss laws.

1. AI Legal Assistant - Dona:

   • Clarification & Summarization: Receive your case and help summarize it.
   • Technology: Powered by an Autogen Conversable Agent and a fine-tuned Mistral 7B model.

2. AI Paralegal - Rachel:

   • Case Classification: Classifies your case into the correct legal category.
   • RAG over Swiss Laws: Uses a large Mistral model to perform Retrieval-Augmented Generation over relevant Swiss laws.

We leveraged the Mistral fine-tuning API for two critical aspects:

1. Improving Dona: Enhanced guardrails and distilled from larger models (notebooks/04_dona_finetuning.ipynb)
2. Better Case Classification: Optimized classification accuracy for legal cases. (notebooks/05_classification_finetuning.ipynb)

1. Robust Client Interaction:

   • Good resilience against prompt hacking.
   • Created a dataset with a mix of legitimate replies and placeholders for prompt hacking scenarios.

2. Enhanced Responses:

   • Distilled from larger models to improve response quality.
   • Used GPT-4o outputs to inspire the Mistral 7B model for better summaries.

3. Cost and Performance Efficiency:

   • Autogen agent requiring multiple interactions.
   • Fine-tuned smaller model for efficiency and scalability.

We prepared a dataset of legal cases categorized under Civil, Public, or Criminal law and evaluated various models:

1. Baseline: Traditional ML (TFIDF+LGBM).
2. Mistral 7B: Prompting only.
3. Mistral 7B (Fine-tuned): Significant performance improvement, reduced hallucinations.

• TFIDF+LGBM: Accuracy 0.86
• Mistral 7B: Accuracy 0.55
• Mistral 7B (Fine-tuned): Accuracy 0.71

• Supports only Swiss Federal Laws.
• Handles only Civil, Public, or Criminal law cases.
• Case classification could be improved (class imbalance).
• The agentic RAG (Rachel) could make several iteration to improve the final answer.

git clone [email protected]:unit8co/mistral-hackathon-finetuning.git
cd mistral-hackathon-finetuning

# Ensure you have Python 3.11+ and Node.js + npm (tested with Node v22.1.0, npm 10.7.0) for the frontend.

# Install necessary assets:
# download chroma.zip at https://mistral-finetuning-hackathon-2024.s3.eu-central-1.amazonaws.com/chroma.zip
# move it into the root of the repository
# unzip it in the root of the repo

# Create a virtual environment
python -m venv .venv

# Install dependencies
pip install -r requirements.txt

# Create a .env file and enter your Mistral API key
cp .env.template .env

# Start the backend
PYTHONPATH=$(pwd) python src/backend/main.py

# In another terminal, navigate to the frontend folder and run the frontend
cd src/frontend
# Install Node.js dependencies
npm install
# Run the frontend
npm run dev

# Follow the localhost URL displayed to start interacting with Dona and Rachel.