SamuelTheophilus/kwame_ai_assesment

# Question and Answering System Interfaced with a Flask API ## This is an ML challenge project to build a system that answers legal questions based of information stored in an elasticsearch index. The project sets up an elasticsearch instance and indexes legal documents into the a new index. It has two endpoints that allow the user to ask a legal question and retrieves passages, metadata and relevance scores as a response. It can be run in a virtual environment, or as a docker container. ## How to install and run project. 1. Download the `docker-compose-dev.yml` 2. Run `docker compose -f 'path/to/docker-compose-dev.yml' up` * Wait for the container to start successfully, this is the message 'Connection Successful' appears on the terminal. ## Testing. ### - Question * Open an API testing tool, such as Postman/Insomnia * Create a post request to the endpoint `localhost` with a request body `{"question": your_question}` ### - File Upload * Open an API testing tool, such as Postman/Insomnia * Create a post request to the endpoint `localhost` with a mutipart file request of `{"file": your_file}` - File uploads take only files with `.pdf` or `.txt` extenstions.