textembed 0.0.5

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.
• Batch Processing: Supports batch processing for better and faster inference.
• OpenAI Compatible REST API Endpoint: Provides an OpenAI compatible REST API endpoint.

Getting Started

Prerequisites

Ensure you have Python 3.11 or higher installed. You will also need to install the required dependencies.

Installation

1. Install the required dependencies:

   pip install -U textembed
   

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 kevaldekivadiya/textembed:latest --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 kevaldekivadiya/textembed:latest --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.