Recommendica

Recommendica is a research recommendation API powered by AI and Retrieval-Augmented Generation (RAG). It helps users find the most relevant research papers based on their input queries. This project is set up to create a virtual environment, install dependencies, and run a Django server for the Recommendica.

Setup Instructions

1. Create a Virtual Environment:

   python -m venv env

2. Activate the Virtual Environment:

   • On macOS and Linux:

     source env/bin/activate

   • On Windows:

     .\env\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Configure Environment Variables:

   • Copy the example environment file to create your own environment configuration:

     cp env.example .env

5. Import Research Data:

   python manage.py import_research

6. Apply Migrations:

   python manage.py migrate

7. Run the Django Development Server:

   python manage.py runserver

Input Prompt Configuration

• localhost:8000/api/v1/prompt/

{
    "input_prompt": "What is the impact of COVID-19 on the economy?"
}

The return response will be the top 5 most relevant research papers to the input prompt.

{
    "status": 200,
    "message": "Success",
    "data": {
        "response": "ai response",
        "research_results": [
            {
                "title": "title",
                "category": "category",
                "summary": "summary",
                "authors": "authors"
            }
        ]
    }
}

API Documentation

• localhost:8000/docs/