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 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.
Ensure you have Python 3.10 or higher installed. You will also need to install the required dependencies.
Install the required dependencies:
pip install -U textembed
Start the TextEmbed server with your desired models:
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.
FAQs
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.
We found that textembed demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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