Setup Guide

Follow these steps to set up the project:

Installation Steps

1. Clone the Repository

git clone https://github.com/klinucsd/wenokn

2. Download and Setup Vector Databases

Data Commons Vector DB

Download and unzip the Data Commons vector database:

# Download from: https://hubbub.sdsc.edu/.test/data_commons.zip
# Extract the downloaded file to your project directory

WENOKN Vector DB

Download and extract the WENOKN vector database:

# Download from: https://hubbub.sdsc.edu/.test/wenokn.zip
# Extract the downloaded file to your project directory

3. Install Dependencies

Install the required Python libraries:

pip install -r requirements.txt

4. Configure Environment

Edit the .env file to configure your environment settings. Make sure to include:

OPENAI_KEY=your_openai_api_key_here

5. Test Installation

Run the test to verify everything is working correctly:

python -m smart_query.test.data_system_test.py

Running as FastAPI (Optional)

You can optionally run the system as a web API using FastAPI:

Prerequisites

Ensure you have completed all the installation steps above, particularly:

• Vector databases are downloaded and extracted
• Dependencies are installed
• Environment variables are configured

Starting the FastAPI Server

Option 1: Direct Python execution

python main.py

Option 2: Using uvicorn directly

uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Accessing the API

Once the server is running, you can:

• Interactive API Documentation: Visit http://localhost:8000/docs for Swagger UI
• Query Endpoint: http://localhost:8000/api/v1/query?query=your_question_here

Example API Usage

Using curl:

# Example query
curl "http://localhost:8000/api/v1/query?query=What is the population of California?"

Using Python requests:

import requests

# Make a query
response = requests.get(
    "http://localhost:8000/api/v1/query",
    params={"query": "What is the population of California?"}
)
print(response.json())

API Response Format

The API returns JSON responses in the following format:

{
  "query": "Your original query",
  "result": "JSON formatted data result",
  "status": "success"
}

Production Deployment

For production deployment, consider:

• Using a production ASGI server like Gunicorn with Uvicorn workers
• Setting up proper logging and monitoring
• Configuring SSL/TLS certificates
• Setting up load balancing if needed

Notes

• Make sure you have Python and pip installed on your system
• Ensure you have sufficient storage space for the vector databases
• Check that all file paths are correctly configured in your .env file
• For FastAPI deployment, ensure your OpenAI API key is properly configured in the environment