# MelowRAG
MelowRAG is a modular Python framework for semantic search, vector indexing, and retrieval-augmented generation (RAG). It provides a unified interface for embedding, indexing, searching, and managing data using dense, sparse, and hybrid vector models.
## Features
- **Embeddings Management**: Transform data into embeddings using various backends.
- **Flexible Indexing**: Build, update, and search indexes with support for dense, sparse, and hybrid models.
- **Database Integration**: Store and retrieve content using pluggable database backends.
- **Graph Algorithms**: Advanced graph-based search and topic modeling.
- **Pipelines**: Modular pipelines for text, audio, and image processing.
- **Remote Storage**: Archive and load indexes from local or cloud storage.
- **Extensible**: Easily add new models, scoring functions, or storage backends.
## Quick Start
```python
from melowrag import Embeddings
# Initialize embeddings
embedding = Embeddings()
# Index some texts
texts = ["The cat sat on the mat.", "Dogs are wonderful companions."]
embedding.index(texts)
# Search for similar content
results = embedding.search("animal companions", 1)
for result in results:
print(f"Index: {result.index}, Score: {result.score}")
```
## Installation
```bash
pip install -e .
```
## License
This project is licensed under the terms of the MIT license.
Raw data
{
"_id": null,
"home_page": "https://github.com/erfanzar/melowrag",
"name": "melowrag",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.14,>=3.10",
"maintainer_email": null,
"keywords": "Deep Learning, Machine Learning",
"author": "Erfan Zare Chavoshi",
"author_email": "Erfanzare810@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/06/93/65d2b743c26696240dbb49660c542d42a831c623cef2ed5f22826795735e/melowrag-0.0.1.tar.gz",
"platform": null,
"description": "# MelowRAG\n\nMelowRAG is a modular Python framework for semantic search, vector indexing, and retrieval-augmented generation (RAG). It provides a unified interface for embedding, indexing, searching, and managing data using dense, sparse, and hybrid vector models.\n\n## Features\n\n- **Embeddings Management**: Transform data into embeddings using various backends.\n- **Flexible Indexing**: Build, update, and search indexes with support for dense, sparse, and hybrid models.\n- **Database Integration**: Store and retrieve content using pluggable database backends.\n- **Graph Algorithms**: Advanced graph-based search and topic modeling.\n- **Pipelines**: Modular pipelines for text, audio, and image processing.\n- **Remote Storage**: Archive and load indexes from local or cloud storage.\n- **Extensible**: Easily add new models, scoring functions, or storage backends.\n\n## Quick Start\n\n```python\nfrom melowrag import Embeddings\n\n# Initialize embeddings\nembedding = Embeddings()\n\n# Index some texts\ntexts = [\"The cat sat on the mat.\", \"Dogs are wonderful companions.\"]\nembedding.index(texts)\n\n# Search for similar content\nresults = embedding.search(\"animal companions\", 1)\nfor result in results:\n print(f\"Index: {result.index}, Score: {result.score}\")\n```\n \n\n## Installation\n\n```bash\npip install -e .\n```\n\n## License\n\nThis project is licensed under the terms of the MIT license.\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "a modular Python framework for semantic search, vector indexing, and retrieval-augmented generation",
"version": "0.0.1",
"project_urls": {
"Documentation": "https://melowrag.readthedocs.io/en/latest/",
"Homepage": "https://github.com/erfanzar/melowrag",
"Repository": "https://github.com/erfanzar/melowrag"
},
"split_keywords": [
"deep learning",
" machine learning"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "d3640eeb712742ffc2eb069281ff1fd2d65bcf2ab17a4c1b0811e407e0fefc0c",
"md5": "bad15d05441a9d111e0533b4fb5d9de7",
"sha256": "829d3e42462addba9a98066f32ee6214889f5bd59fc1e558613d31ce2580945f"
},
"downloads": -1,
"filename": "melowrag-0.0.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "bad15d05441a9d111e0533b4fb5d9de7",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.14,>=3.10",
"size": 257946,
"upload_time": "2025-07-16T13:22:01",
"upload_time_iso_8601": "2025-07-16T13:22:01.914472Z",
"url": "https://files.pythonhosted.org/packages/d3/64/0eeb712742ffc2eb069281ff1fd2d65bcf2ab17a4c1b0811e407e0fefc0c/melowrag-0.0.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "069365d2b743c26696240dbb49660c542d42a831c623cef2ed5f22826795735e",
"md5": "bf4103608a5ddc6af4f0fb25ae756201",
"sha256": "fc1272f571358f0cda2175688a845c83325173f5e65fb63735b22bf674133675"
},
"downloads": -1,
"filename": "melowrag-0.0.1.tar.gz",
"has_sig": false,
"md5_digest": "bf4103608a5ddc6af4f0fb25ae756201",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.14,>=3.10",
"size": 123075,
"upload_time": "2025-07-16T13:22:03",
"upload_time_iso_8601": "2025-07-16T13:22:03.210858Z",
"url": "https://files.pythonhosted.org/packages/06/93/65d2b743c26696240dbb49660c542d42a831c623cef2ed5f22826795735e/melowrag-0.0.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-16 13:22:03",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "erfanzar",
"github_project": "melowrag",
"github_not_found": true,
"lcname": "melowrag"
}
JSON
