| Name | llama-index-embeddings-textembed JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | Integration of TextEmbed with llama-index for embeddings. |
| upload_time | 2024-11-05 21:31:44 |
| maintainer | None |
| docs_url | None |
| author | Keval Dekivadiya |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# TextEmbed - Embedding Inference Server
Maintained by Keval Dekivadiya, TextEmbed is licensed under the [Apache-2.0 License](https://opensource.org/licenses/Apache-2.0).
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:
```bash
pip install -U textembed
```
### Start the TextEmbed Server
Start the TextEmbed server with your desired models:
```bash
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:
```python
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](https://github.com/kevaldekivadiya2415/textembed/blob/main/docs/setup.md).
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"description": "# TextEmbed - Embedding Inference Server\n\nMaintained by Keval Dekivadiya, TextEmbed is licensed under the [Apache-2.0 License](https://opensource.org/licenses/Apache-2.0).\n\nTextEmbed 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.\n\n## Features\n\n- **High Throughput & Low Latency**: Designed to handle a large number of requests efficiently.\n- **Flexible Model Support**: Works with various sentence-transformer models.\n- **Scalable**: Easily integrates into larger systems and scales with demand.\n- **Batch Processing**: Supports batch processing for better and faster inference.\n- **OpenAI Compatible REST API Endpoint**: Provides an OpenAI compatible REST API endpoint.\n- **Single Line Command Deployment**: Deploy multiple models via a single command for efficient deployment.\n- **Support for Embedding Formats**: Supports binary, float16, and float32 embeddings formats for faster retrieval.\n\n## Getting Started\n\n### Prerequisites\n\nEnsure you have Python 3.10 or higher installed. You will also need to install the required dependencies.\n\n### Installation via PyPI\n\nInstall the required dependencies:\n\n```bash\npip install -U textembed\n```\n\n### Start the TextEmbed Server\n\nStart the TextEmbed server with your desired models:\n\n```bash\npython -m textembed.server --models sentence-transformers/all-MiniLM-L12-v2 --workers 4 --api-key TextEmbed\n```\n\n### Example Usage with llama-index\n\nHere's a simple example to get you started with llama-index:\n\n```python\nfrom llama_index.embeddings.textembed import TextEmbedEmbedding\n\n# Initialize the TextEmbedEmbedding class\nembed = TextEmbedEmbedding(\n model_name=\"sentence-transformers/all-MiniLM-L12-v2\",\n base_url=\"http://0.0.0.0:8000/v1\",\n auth_token=\"TextEmbed\",\n)\n\n# Get embeddings for a batch of texts\nembeddings = embed.get_text_embedding_batch(\n [\n \"It is raining cats and dogs here!\",\n \"India has a diverse cultural heritage.\",\n ]\n)\n\nprint(embeddings)\n```\n\nFor more information, please read the [documentation](https://github.com/kevaldekivadiya2415/textembed/blob/main/docs/setup.md).\n",
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