textembed 0.0.2

TextEmbed provides a robust and scalable REST API for generating vector embeddings from text. Built for performance and flexibility, it supports various sentence-transformer models, allowing users to easily integrate state-of-the-art NLP techniques into their applications. Whether you need embeddings for search, recommendation, or other NLP tasks, TextEmbed delivers with high efficiency.

TextEmbed - Embedding Inference Server

TextEmbed is a high-throughput, low-latency REST API designed for serving vector embeddings. It supports a wide range of sentence-transformer models and frameworks, making it suitable for various applications in natural language processing.

Features

• High Throughput & Low Latency: Designed to handle a large number of requests efficiently.
• Flexible Model Support: Works with various sentence-transformer models.
• Scalable: Easily integrates into larger systems and scales with demand.
• REST API: Simple and accessible API endpoints.

Getting Started

Prerequisites

Ensure you have Python 3.10 or higher installed. You will also need to install the required dependencies.

Installation

1. Clone the repository:

   git clone https://github.com/kevaldekivadiya2415/textembed.git
   cd textembed
   

2. Install the dependencies:

   pip install -r requirements.txt
   

Running the Server

1. Set up the package in development mode:

   python3 setup.py develop
   

2. Start the TextEmbed server with your desired model:

   python3 -m textembed.server --model <Model Name>
   

   Replace <Model Name> with the name of the model you want to use.

3. For more information and additional options, run:

   python3 -m textembed.server --help
   

Running with Docker (Recommended)

You can also run TextEmbed using Docker. The Docker image is available on Docker Hub.

docker run keval2415/textembed:0.0.1 --help

This command will show the help message for the TextEmbed server, detailing the available options and usage.

For Example:

docker run -p 8000:8000 keval2415/textembed:0.0.1 --model sentence-transformers/all-MiniLM-L6-v2 --port 8000

Accessing the API

Once the server is running, you can access the API documentation via Swagger UI.