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Overview

This Flask application utilizes machine learning models from the Hugging Face transformers library to generate embeddings for input texts and find the most similar text in a predefined knowledge base. It leverages the power of pre-trained models for understanding and comparing text data, making it useful for applications requiring text similarity, question answering, or recommendation systems.

Features

• Generate Embeddings: Converts input texts into dense vector representations using a pre-trained model.
• Text Similarity: Finds the most similar text in the knowledge base to the input query.
• REST API: Offers a simple RESTful API endpoint for processing text similarity queries.

Installation

1. Clone the Repository

   git clone https://your-repository-url.git
   cd your-project-directory

2. Setup Environment

   Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies

   Install the required packages using pip:

   pip install -r requirements.txt

4. Run the Application

   Start the Flask application locally:

   python main.py

   Or use the provided start.sh script:

   ./start.sh

Usage

To test the application, use the /chat endpoint to send a POST request with a JSON body containing the text for which you want to find a similar match.

Testing with Postman

1. Method: POST
2. URL: http://localhost:5000/chat
3. Headers:
   • Content-Type: application/json
4. Body:

   {
       "text": ["Your text here"]
   }

Replace "Your text here" with the actual text you want to find similarities for.

Response

The response will be a JSON object containing the most similar text from the knowledge base:

{
    "response": ["Similar text response"]
}

Docker Deployment

Refer to the provided Dockerfile for containerizing and deploying the application using Docker.

Note

Ensure you have Docker installed and configured if you plan to deploy the application using a Docker container. For deploying on cloud platforms like AWS EC2, additional setup for security and environment variables might be required.

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