Flask API for Similarity Search

This Flask application provides an API for performing similarity searches within a dataset using embeddings. The application uses OpenAI's API to generate embeddings for text inputs and compares these with a pre-computed embeddings dataset to find and return the most similar items. This application is intended for experimental purposes only and should not be used in production environments.

Features

• Environment Variable Management: Utilizes dotenv for secure API key management.
• OpenAI Integration: Leverages OpenAI's API to create text embeddings.
• Similarity Search: Implements cosine similarity to find the most similar items based on embeddings.
• Pandas and NumPy: Uses Pandas for data manipulation and NumPy for numerical operations.

Setup

Requirements

• Python
• Flask
• Pandas
• NumPy
• openai
• python-dotenv

Installation

1. Clone the repository:

   git clone <repository-url>
   

2. Navigate to the app directory:

   cd <app-directory>
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Create a .env file in the root directory of the application and add your OpenAI API key:

   OPENAI_API_KEY=your_api_key_here
   

5. Load the pre-computed embeddings CSV file (data/<file-name>.csv) into the application's data directory.

Running the Application

To start the application, run:

python app.py

The application will be available at http://localhost:5000. Try your first request by navigating to http://localhost:5000/sim_search/<text> in your browser or using a tool like Postman.

API Endpoints

Similarity Search

• URL: /sim_search/<text>
• Method: GET
• URL Params: text=[string]
• Success Response: A JSON array of the top 5 most similar items based on the text input.
• Error Response: Error message in case of failure.

Hyde Search (TODO)

• URL: /hyde_search/<term>
• Method: GET
• Description: This endpoint is planned for future implementation.

Contributing

Contributions to this project are welcome. Please fork the repository, make your changes, and submit a pull request.