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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.