| Name | textembed JSON |
| Version |
0.0.8
JSON |
| download |
| home_page | https://github.com/kevaldekivadiya2415/textembed |
| Summary | 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. |
| upload_time | 2024-06-13 03:05:31 |
| maintainer | None |
| docs_url | None |
| author | Keval Dekivadiya |
| requires_python | >=3.10.0 |
| license | Apache License 2.0 |
| keywords |
embedding
rag
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/kevaldekivadiya2415/textembed/graphs/contributors)
[](https://github.com/kevaldekivadiya2415/textembed/issues)
[](https://github.com/kevaldekivadiya2415/textembed/blob/main/LICENSE)
[](https://pepy.tech/project/textembed)
[](https://hub.docker.com/r/kevaldekivadiya/textembed)
[](https://pypi.org/project/textembed/)
[](https://sonarcloud.io/summary/new_code?id=kevaldekivadiya2415_textembed)
[](https://sonarcloud.io/summary/new_code?id=kevaldekivadiya2415_textembed)
# 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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