About EU AI Act Compliance Chatbot
EU AI Act Compliance Chatbot answers questions about the EU AI Act using vector search and a knowledge graph. It is intended for people seeking information from the Act’s original text.
Key features
- 01EU AI Act question answering
- 02Vector search
- 03Knowledge graph retrieval
- 04Document processing pipeline
- 05FastAPI application
Use cases
- 01Finding information in the EU AI Act
- 02Querying the Act’s processed document
- 03Serving a local question-answering API
MCP support
The project is described as an MCP server, but the README does not specify MCP tools or other exposed capabilities.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEEU AI Act Compliance Chatbot
A chatbot designed to answer questions about the EU AI Act using a hybrid retrieval approach (vector search + knowledge graph).
Setup
- Install Poetry: If you don't have Poetry installed, follow the instructions here.
- Clone the repository:
git clone <your-repo-url> cd eu-ai-act-chatbot - Install dependencies:
poetry install - Set up environment variables:
- Copy the
.env.examplefile to.env:cp .env .env # Or .env.example if you rename the template - Fill in your API keys and service details in the
.envfile.
- Copy the
- Download the EU AI Act PDF: Place the PDF file in a
data/directory (you might need to create it) and name iteu_ai_act.pdf.
Processing Data
To process the EU AI Act document and populate the vector store and knowledge graph:
poetry shell
python -m scripts.process_eu_ai_act
Running the API
To run the FastAPI application locally:
poetry shell
uvicorn src.eu_ai_act_chatbot.api.main:app --reload
The API will be available at http://127.0.0.1:8000. You can access the interactive documentation at http://127.0.0.1:8000/docs.
Running Tests
poetry shell
pytest
Deployment (AWS Lambda)
- Create a deployment package:
# Ensure dependencies are installed in the project's virtual env poetry install --no-dev # Create the package directory mkdir package # Install dependencies into the package directory poetry run pip install --upgrade pip poetry run pip install -r <(poetry export -f requirements.txt --without-hashes) --target ./package # Copy source code and Lambda handler cp -r ./src ./package/ cp lambda_function.py ./package/ # Create the zip file cd package zip -r ../deployment.zip . cd .. - Upload
deployment.zipto your AWS Lambda function. - Ensure the Lambda function's handler is set to
lambda_function.lambda_handler. - Configure necessary environment variables in the Lambda function settings.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.