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TextEmbed is a REST API crafted for high-throughput and low-latency embedding inference. It accommodates a wide variety of embedding models and frameworks, making it ideal for various natural language processing applications.

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# 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.
- **Single Line Command Deployment:** Deploy multiple models via a single command for efficient deployment.
- **Support for Embedding Formats:** Supports binary, float16, and float32 embeddings formats for faster retrieval.

## Getting Started

### Prerequisites

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

### Installation

1. Install the required dependencies:
    ```bash
    pip install -U textembed
    ```

2. Start the TextEmbed server with your desired models:
    ```bash
    python3 -m textembed.server --models <Model1>, <Model2> --port <Port>
    ```

Replace `<Model1>` and `<Model2>` with the names of the models you want to use, separated by commas. Replace `<Port>` with the port number on which you want to run the server.

For more information about the Docker deployment and configuration, please refer to the documentation [setup.md](docs/setup.md).

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