<div align="center">
<h3 align="center">embedbase-qdrant</h3>
<p align="center">
<p align="center">
<a href="https://github.com/different-ai/embedbase">Embedbase</a> + <a href="https://qdrant.tech">Qdrant</a>
Advanced and high-performant vector similarity search technology in your AI applications
</p>
</p>
<br>
⚠️ Status: Alpha release ⚠️
<br>
<br>
<a href="https://discord.gg/pMNeuGrDky"><img alt="Discord" src="https://img.shields.io/discord/ 1066022656845025310?color=black&style=for-the-badge"></a>
<a href="https://badge.fury.io/py/embedbase-qdrant"><img alt="PyPI" src="https://img.shields.io/pypi/v/embedbase-qdrant?color=black&style=for-the-badge"></a>
<br>
<div align="center">
<p align="center">
If you have any feedback or issues, please let us know by opening an issue or contacting us on <a href="https://discord.gg/pMNeuGrDky">discord</a>.
</p>
<p align="center">
Please refer to the <a href="https://docs.embedbase.xyz">documentation</a>
</p>
</div>
</div>
## Getting started
To install the Embedbase Qdrant library, run the following command:
```bash
pip install embedbase-qdrant
```
## Quick tour
Let's try Embedbase + Qdrant with an OpenAI `embedder`:
```bash
pip install openai uvicorn
```
```python
import os
import uvicorn
from embedbase import get_app
from embedbase.embedding.openai import Openai
from embedbase_qdrant import Qdrant
# here we use openai to create embeddings and qdrant to store the data
app = get_app().use_embedder(Openai(os.environ["OPENAI_API_KEY"])).use_db(Qdrant()).run()
if __name__ == "__main__":
uvicorn.run(app)
```
Start a local Qdrant:
```bash
docker-compose up -d
```
Run Embedbase:
```bash
python3 main.py
```
Check out other [examples](./examples/main.py) and [documentation](https://docs.embedbase.xyz) for more details.
Raw data
{
"_id": null,
"home_page": "https://github.com/different-ai/embedbase-qdrant",
"name": "embedbase-qdrant",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8,<3.12",
"maintainer_email": "",
"keywords": "embeddings,machine learning,artificial intelligence",
"author": "Different AI",
"author_email": "louis@embedbase.xyz",
"download_url": "https://files.pythonhosted.org/packages/71/f3/d0a0618b7938aa9b21d6911206cae5fa03470e7f4fb8c3f889a7c8dc67e8/embedbase_qdrant-1.0.1.tar.gz",
"platform": null,
"description": "\n<div align=\"center\">\n <h3 align=\"center\">embedbase-qdrant</h3>\n <p align=\"center\">\n <p align=\"center\">\n <a href=\"https://github.com/different-ai/embedbase\">Embedbase</a> + <a href=\"https://qdrant.tech\">Qdrant</a>\n Advanced and high-performant vector similarity search technology in your AI applications \n </p>\n </p>\n <br>\n \u26a0\ufe0f Status: Alpha release \u26a0\ufe0f\n <br>\n <br>\n <a href=\"https://discord.gg/pMNeuGrDky\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/ 1066022656845025310?color=black&style=for-the-badge\"></a>\n <a href=\"https://badge.fury.io/py/embedbase-qdrant\"><img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/embedbase-qdrant?color=black&style=for-the-badge\"></a>\n <br>\n <div align=\"center\">\n <p align=\"center\">\n If you have any feedback or issues, please let us know by opening an issue or contacting us on <a href=\"https://discord.gg/pMNeuGrDky\">discord</a>.\n </p>\n <p align=\"center\">\n Please refer to the <a href=\"https://docs.embedbase.xyz\">documentation</a>\n </p>\n </div>\n\n</div>\n\n\n## Getting started\n\nTo install the Embedbase Qdrant library, run the following command:\n\n```bash\npip install embedbase-qdrant\n```\n\n## Quick tour\n\nLet's try Embedbase + Qdrant with an OpenAI `embedder`:\n\n```bash\npip install openai uvicorn\n```\n\n```python\nimport os\nimport uvicorn\nfrom embedbase import get_app\nfrom embedbase.embedding.openai import Openai\nfrom embedbase_qdrant import Qdrant\n\n# here we use openai to create embeddings and qdrant to store the data\napp = get_app().use_embedder(Openai(os.environ[\"OPENAI_API_KEY\"])).use_db(Qdrant()).run()\n\nif __name__ == \"__main__\":\n uvicorn.run(app)\n```\n\nStart a local Qdrant:\n\n```bash\ndocker-compose up -d\n```\n\nRun Embedbase:\n\n```bash\npython3 main.py\n```\n\n\n\nCheck out other [examples](./examples/main.py) and [documentation](https://docs.embedbase.xyz) for more details.\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Embedbase + Qdrant - Advanced and high-performant vector similarity search technology in your AI applications.",
"version": "1.0.1",
"project_urls": {
"Homepage": "https://github.com/different-ai/embedbase-qdrant",
"Repository": "https://github.com/different-ai/embedbase-qdrant"
},
"split_keywords": [
"embeddings",
"machine learning",
"artificial intelligence"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "b39c3f05b3ee392e738373df92abae52446b8362df7b9b39672e9cb8fc1d194c",
"md5": "ee3965f3deabc2f1c5326e237f506d04",
"sha256": "f0445db8799feb3cb99584845d267e219cccd9ac5277f3bf63dfa50c8b8aeb23"
},
"downloads": -1,
"filename": "embedbase_qdrant-1.0.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "ee3965f3deabc2f1c5326e237f506d04",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8,<3.12",
"size": 4967,
"upload_time": "2023-05-07T10:49:50",
"upload_time_iso_8601": "2023-05-07T10:49:50.904391Z",
"url": "https://files.pythonhosted.org/packages/b3/9c/3f05b3ee392e738373df92abae52446b8362df7b9b39672e9cb8fc1d194c/embedbase_qdrant-1.0.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "71f3d0a0618b7938aa9b21d6911206cae5fa03470e7f4fb8c3f889a7c8dc67e8",
"md5": "7954b3cb883597ded2a4d4737a901636",
"sha256": "e673a898f25d434e44c299235ec677a26adfbef24e5b0e3b6678c31b87671f4e"
},
"downloads": -1,
"filename": "embedbase_qdrant-1.0.1.tar.gz",
"has_sig": false,
"md5_digest": "7954b3cb883597ded2a4d4737a901636",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8,<3.12",
"size": 5389,
"upload_time": "2023-05-07T10:49:52",
"upload_time_iso_8601": "2023-05-07T10:49:52.552423Z",
"url": "https://files.pythonhosted.org/packages/71/f3/d0a0618b7938aa9b21d6911206cae5fa03470e7f4fb8c3f889a7c8dc67e8/embedbase_qdrant-1.0.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-05-07 10:49:52",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "different-ai",
"github_project": "embedbase-qdrant",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "embedbase-qdrant"
}
JSON
