README.md

metadata

title: Montreal Chabo ChatUI
emoji: 🤖
colorFrom: yellow
colorTo: gray
sdk: docker
app_port: 3000
pinned: false

About Montreal Agent

It helps you find and summarize specific decisions and annexes, and can also answer general questions about the Montreal treaty. For transparency, it provides references with links to the relevant decisions and annexes, so you can easily verify the sources. While this chatbot was developed with care, we recommend double-checking the links to gain a deeper understanding of the material.

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💬 How to Ask Effective Questions

❌ Less Effective ✅ More Effective "What is Treaty?" "What are the article 5 countries for hcfc phase out in Montreal Treaty?" "Tell me about compliance" "What requirements apply to developing countries in terms of refrigeration substances usage?"

🔍 Using Data Sources Talk to Database: Eiher ask borad questions or ask Meeting/Decision specific questions

⭐ Best Practices

• Be specific about focus of question
• Ask one question at a time for clearer answers
• Use follow-up questions to explore topics deeper
• Provide context when possible

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Important Disclaimers

⚠️ Scope & Limitations:

This tool is designed for Montreal database Q&A Responses should not be considered official legal or compliance advice Always consult qualified professionals for official compliance decisions

⚠️ Data & Privacy:

• We collect usage statistics to improve the tool
• Files are processed temporarily and not permanently stored

⚠️ AI Limitations:

• Responses are AI-generated and may contain inaccuracies
• The tool is a prototype under continuous development
• Always verify important information with authoritative sources

Data Collection: We collect questions, answers, feedback, and anonymized usage statistics to improve tool performance based on legitimate interest in service enhancement.By using this chatbot, you agree to these terms and acknowledge that you are solely responsible for any reliance on or actions taken based on its responses.

Technical Information: User can read more about the technical information about the tool in section below.

This is just a prototype and being tested and worked upon, so its not perfect and may sometimes give irrelevant answers. If you are not satisfied with the answer, please ask a more specific question or report your feedback to help us improve the system.

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Technical Documentation of the system in accordance with EU AI Act

System Name: Montreal Chatbot

Provider / Supplier: GIZ Data Service Center and GIZ Data Lab

As of: September 2025

1. General Description of the System

Montreal Bot is an AI-powered conversational assistant designed to help you interact with Montreal treaty database. This tool leverages advanced language models to help you get clear and structured answers about Montreal meetings/decisions.

It combines a generative language assistant with a knowledge base implemented via Retrieval-Augmented Generation (RAG).

2. Models Used

Generative LLM

• Model Name: meta-llama/Llama-3.1-8B-Instruct
• Model Source API: Nebius AI

Retriever/Embedding

• Model Name: BAAI/bge-m3
• Model Source: Local Instance

Re-ranker

• Model Name: BAAI/bge-reranker-v2-m3
• Model Source: Local Instance

External API's

• Whisp Info: https://openforis.org/solutions/whisp/
• API Endpoint https://whisp.openforis.org/documentation/api-guide

3. Model Training Data

All the models mentioned above are being consumed without any fine-tuning or training being performed by the developer team of Montreal Bot. And hence there is no training data which had been used by the development team of Montreal Bot.

4. Knowledge Base (Retrieval Component)

• Data Sources: Public Montreal database
• Embedding Model: BAAI/bge-m3
• Embedding Dimension: 1024
• Vector Database: Qdrant (via API)
• Framework: Langchain (custom RAG pipeline)
• Top-k: 10 relevant text segments per query

5. System Limitations and Non-Purposes

• The system does not make autonomous decisions.
• No processing of personal data except for the usage statistics as mentioned in Disclaimer.
• Results are intended for orientation only – not for legal or regulatory compliance advice.
• Users should consult official EU documentation and legal experts for definitive compliance guidance.

6. Transparency Towards Users

• The user interface clearly indicates the use of a generative AI model.
• An explanation of the RAG method is included.
• We collect usage statistics as detailed in Disclaimer tab of the app along with the explicit display in the user interface of the tool.
• Feedback mechanism available (via https://huggingface.co/spaces/GIZ/mlf_chabo_prototype/discussions/new).

7. Monitoring, Feedback, and Incident Reporting

• User can provide feedback via UI by giving (Thumbs-up or down to AI-Generated answer). Alternatively for more detailed feedback please use https://huggingface.co/spaces/GIZ/mlf_chabo_prototype/discussions/new to report any issue.
• Technical development is carried out by the GIZ Data Service Center and Data Lab
• No automated bias detection – but low risk due to content restrictions.

8. Contact

For any questions, please contact via https://huggingface.co/spaces/GIZ/mlf_chabo_prototype/discussions/new or send us email to dataservicecenter@giz.de