| Name | llama-index-embeddings-textembed JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | Integration of TextEmbed with llama-index for embeddings. |
| upload_time | 2024-08-22 04:23:03 |
| maintainer | None |
| docs_url | None |
| author | Keval Dekivadiya |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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).
Raw data
{
"_id": null,
"home_page": null,
"name": "llama-index-embeddings-textembed",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.8.1",
"maintainer_email": null,
"keywords": null,
"author": "Keval Dekivadiya",
"author_email": "kevaldekivadiya2415@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/4c/8d/9385957cd04f47b5f46c37d5d38b620ecc2af855a5a5c14c0567bc1466c7/llama_index_embeddings_textembed-0.1.0.tar.gz",
"platform": null,
"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",
"bugtrack_url": null,
"license": "MIT",
"summary": "Integration of TextEmbed with llama-index for embeddings.",
"version": "0.1.0",
"project_urls": null,
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "6a4f0439564d8695a30c8f248b64a040b5dbfc866a770d2d62b2cfb5b7d8ce42",
"md5": "81978e22a6db38dd529356ffd908337c",
"sha256": "be2461ba2ea50ba8533a93ebde735ec0bceaf57358e14fe56e61ab6f2500bc18"
},
"downloads": -1,
"filename": "llama_index_embeddings_textembed-0.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "81978e22a6db38dd529356ffd908337c",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4.0,>=3.8.1",
"size": 4280,
"upload_time": "2024-08-22T04:23:02",
"upload_time_iso_8601": "2024-08-22T04:23:02.209799Z",
"url": "https://files.pythonhosted.org/packages/6a/4f/0439564d8695a30c8f248b64a040b5dbfc866a770d2d62b2cfb5b7d8ce42/llama_index_embeddings_textembed-0.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4c8d9385957cd04f47b5f46c37d5d38b620ecc2af855a5a5c14c0567bc1466c7",
"md5": "9eb73f300208fd30ac4f5bd949ec3fc5",
"sha256": "130ad0395252f2dd19332139f7da61591e06f14d00359e9631dc8b91b213985c"
},
"downloads": -1,
"filename": "llama_index_embeddings_textembed-0.1.0.tar.gz",
"has_sig": false,
"md5_digest": "9eb73f300208fd30ac4f5bd949ec3fc5",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4.0,>=3.8.1",
"size": 3863,
"upload_time": "2024-08-22T04:23:03",
"upload_time_iso_8601": "2024-08-22T04:23:03.443584Z",
"url": "https://files.pythonhosted.org/packages/4c/8d/9385957cd04f47b5f46c37d5d38b620ecc2af855a5a5c14c0567bc1466c7/llama_index_embeddings_textembed-0.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-08-22 04:23:03",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "llama-index-embeddings-textembed"
}
