llama-index-embeddings-textembed 0.2.0

Integration of TextEmbed with llama-index for embeddings.

TextEmbed - Embedding Inference Server

Maintained by Keval Dekivadiya, TextEmbed is licensed under the Apache-2.0 License.

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 via PyPI

Install the required dependencies:

pip install -U textembed

Start the TextEmbed Server

Start the TextEmbed server with your desired models:

python -m textembed.server --models sentence-transformers/all-MiniLM-L12-v2 --workers 4 --api-key TextEmbed

Example Usage with llama-index

Here's a simple example to get you started with llama-index:

from llama_index.embeddings.textembed import TextEmbedEmbedding

# Initialize the TextEmbedEmbedding class
embed = TextEmbedEmbedding(
    model_name="sentence-transformers/all-MiniLM-L12-v2",
    base_url="http://0.0.0.0:8000/v1",
    auth_token="TextEmbed",
)

# Get embeddings for a batch of texts
embeddings = embed.get_text_embedding_batch(
    [
        "It is raining cats and dogs here!",
        "India has a diverse cultural heritage.",
    ]
)

print(embeddings)

For more information, please read the documentation.