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**Lightweight recommender for teaching and small-scale production.**
## 🌟 Why Use vector-recsys-lite?
- **Purpose-built for teaching and learning:**
Interactive CLI, educator guides, and Jupyter demos—no need to reinvent the wheel for every class or workshop.
- **Robust, tested, and production-ready:**
Handles edge cases, bad input, and large/sparse data gracefully—unlike most one-off scripts.
- **Resource-light and offline-ready:**
<50MB install, runs on 2GB RAM, and works without internet after install.
- **Simple, reproducible production:**
One-command API deployment, clean CLI, and Docker support.
- **Multiple algorithms, modern tooling:**
SVD, ALS (implicit), KNN (cosine), bias handling, chunked processing, and more.
- **Extensible and community-focused:**
Easy to add new datasets, metrics, or algorithms. Contributor-friendly with templates and guides.
- **World-class documentation and examples:**
Notebooks, educator guides, and API docs for fast onboarding.
---
## 🚀 Getting Started
### Installation
```bash
pip install vector_recsys_lite
```
Offline: See guide below.
### Quick Example
```python
import numpy as np
from vector_recsys_lite import svd_reconstruct, top_n
ratings = np.array([[5, 3, 0, 1], [0, 0, 4, 5]], dtype=np.float32)
reconstructed = svd_reconstruct(ratings, k=2)
recommendations = top_n(reconstructed, ratings, n=2)
print(recommendations)
```
CLI: `vector-recsys --help`
## 🎓 For Educators & Students
Teach recommendation systems without fancy hardware.
- **Interactive Mode**: `vector-recsys teach --concept svd` (prompts, examples)
- **Notebooks**: `examples/svd_math_demo.ipynb` (math breakdowns, plots)
- **Low-Resource Demos**: Generate data and run in <1s
Full guide: [docs/educator_guide.md](docs/educator_guide.md)
## 🚀 For Small App Deployers
Build production recommenders for <10k users/items.
- **Deploy API**: `vector-recsys deploy model.pkl`
- **Offline Install**: Download wheel, install via USB
- **Resource Efficient**: Sparse matrices for low memory
Examples: Library book recommender, local e-commerce.
Full guide: [docs/deployment_guide.md](docs/deployment_guide.md)
## 🛠️ For Developers
Extend or contribute easily.
- **API**: Clean, typed functions (svd_reconstruct, RecommenderSystem)
- **Contributing**: `make dev` setup, tests, linting
- **Benchmarks**: `vector-recsys benchmark`
See [CONTRIBUTING.md](CONTRIBUTING.md) and [API Reference](#🔧-api-reference).
### ML Tooling Examples
**ALS (Implicit Feedback)**:
```python
from vector_recsys_lite import RecommenderSystem
ratings = np.array([[1, 0, 1], [0, 1, 0]], dtype=np.float32) # binary implicit
model = RecommenderSystem(algorithm="als")
model.fit(ratings, k=2)
preds = model.predict(ratings)
```
**KNN (Cosine Similarity)**:
```python
from vector_recsys_lite import RecommenderSystem
ratings = np.array([[5, 3, 0], [0, 0, 4]], dtype=np.float32)
model = RecommenderSystem(algorithm="knn")
model.fit(ratings, k=2)
preds = model.predict(ratings)
```
**Bias Handling (SVD)**:
```python
from vector_recsys_lite import RecommenderSystem
ratings = np.array([[5, 3, 0], [0, 0, 4]], dtype=np.float32)
model = RecommenderSystem(algorithm="svd")
model.fit(ratings, k=2)
preds = model.predict(ratings) # Includes global/user/item bias
```
**Chunked SVD**:
```python
from vector_recsys_lite import svd_reconstruct
large_mat = np.random.rand(10000, 500)
reconstructed = svd_reconstruct(large_mat, k=10, use_sparse=True)
```
**Metrics**:
```python
recs = [[1,2,3], [4,5]]
actual = [{1,3}, {4,6}]
print(precision_at_k(recs, actual, 3))
print(recall_at_k(recs, actual, 3))
print(ndcg_at_k(recs, actual, 3))
```
**CV Split**:
```python
train, test = train_test_split_ratings(mat, test_size=0.2)
```
**Pipeline**:
```python
from vector_recsys_lite import RecsysPipeline, RecommenderSystem
pipe = RecsysPipeline([('model', RecommenderSystem())])
pipe.fit(mat, k=2)
recs = pipe.recommend(mat, n=3)
```
**Grid Search**:
```python
result = grid_search_k(mat, [2,4], 'rmse')
print(result['best_k'])
```
Full details in API Reference.
## 📚 Use Cases
### Education
- **University courses**: Teach recommendation systems without expensive infrastructure
- **Self-learning**: Students can run everything on personal laptops
- **Workshops**: Quick demos that work offline
- **Research**: Simple baseline implementation for papers
### Small-Scale Production
- **School library**: Recommend books to students (500 books, 200 students)
- **Local business**: Product recommendations for small e-commerce
- **Community app**: Match local services to residents
- **Personal projects**: Add recommendations to your blog or app
### Development
- **Prototyping**: Test recommendation ideas quickly
- **Learning**: Understand SVD by reading clean, documented code
- **Benchmarking**: Compare against simple, fast baseline
- **Integration**: Easy to embed in larger systems
---
## 📈 Performance
### Resource Usage
Designed for resource-constrained environments:
| Dataset Size | RAM Usage | Time (old laptop) | Time (modern PC) |
|--------------|-----------|-------------------|------------------|
| 100 × 50 | < 10 MB | < 0.1s | < 0.01s |
| 1K × 1K | < 50 MB | < 1s | < 0.1s |
| 10K × 5K | < 500 MB | < 10s | < 2s |
### Tested On
- 10-year-old laptops (Core i3, 2GB RAM)
- Raspberry Pi 4
- Modern workstations
- Cloud containers (minimal resources)
### Memory Efficiency
- **Sparse matrix support**: Handles 90% sparse data efficiently
- **Chunked processing**: Works with limited RAM
- **Minimal dependencies**: ~50MB total install size
## 🔧 API Reference
### Core Functions
```python
def svd_reconstruct(
mat: FloatMatrix,
*,
k: Optional[int] = None,
random_state: Optional[int] = None,
use_sparse: bool = True,
) -> FloatMatrix:
"""Truncated SVD reconstruction for collaborative filtering. Supports chunked processing for large matrices."""
def top_n(
est: FloatMatrix,
known: FloatMatrix,
*,
n: int = 10
) -> np.ndarray:
"""Get top-N items for each user, excluding known items."""
def compute_rmse(predictions: FloatMatrix, actual: FloatMatrix) -> float:
"""Compute Root Mean Square Error between predictions and actual ratings."""
def compute_mae(predictions: FloatMatrix, actual: FloatMatrix) -> float:
"""Compute Mean Absolute Error between predictions and actual ratings."""
```
### I/O Functions
```python
def load_ratings(
path: Union[str, Path],
*,
format: Optional[str] = None,
sparse_format: bool = False,
**kwargs: Any,
) -> FloatMatrix:
"""Load matrix from file with format detection."""
def save_ratings(
matrix: Union[FloatMatrix, sparse.csr_matrix],
path: Union[str, Path],
*,
format: Optional[str] = None,
show_progress: bool = True,
**kwargs: Any,
) -> None:
"""Save rating matrix with automatic format detection."""
def create_sample_ratings(
n_users: int = 100,
n_items: int = 50,
sparsity: float = 0.8,
rating_range: tuple[float, float] = (1.0, 5.0),
random_state: Optional[int] = None,
sparse_format: bool = False,
) -> Union[FloatMatrix, sparse.csr_matrix]:
"""Create a sample rating matrix for testing."""
```
### RecommenderSystem Class
```python
class RecommenderSystem:
"""Production-ready recommender system with model persistence. Supports SVD, ALS (implicit), and KNN algorithms, with bias handling."""
def __init__(self, algorithm: str = "svd", ...):
"""algorithm: 'svd', 'als', or 'knn'"""
def fit(self, ratings: FloatMatrix, k: Optional[int] = None) -> "RecommenderSystem":
"""Fit the model to training data."""
def predict(self, ratings: FloatMatrix) -> FloatMatrix:
"""Generate predictions for the input matrix."""
def recommend(self, ratings: FloatMatrix, n: int = 10, exclude_rated: bool = True) -> np.ndarray:
"""Generate top-N recommendations for each user."""
def save(self, path: str) -> None:
"""Save model to file using secure joblib serialization."""
@classmethod
def load(cls, path: str) -> "RecommenderSystem":
"""Load model from file."""
```
## 🧩 Real-World Usage Examples
### Using with Pandas
```python
import pandas as pd
from vector_recsys_lite import svd_reconstruct, top_n
# Load ratings from a DataFrame
ratings_df = pd.read_csv('ratings.csv', index_col=0)
ratings = ratings_df.values.astype(float)
# SVD recommendations
reconstructed = svd_reconstruct(ratings, k=20)
recommendations = top_n(reconstructed, ratings, n=5)
print(recommendations)
```
### Integrating in a Web App (FastAPI Example)
```python
from fastapi import FastAPI
from vector_recsys_lite import svd_reconstruct, top_n
import numpy as np
app = FastAPI()
ratings = np.load('ratings.npy')
reconstructed = svd_reconstruct(ratings, k=10)
@app.get('/recommend/{user_id}')
def recommend(user_id: int, n: int = 5):
recs = top_n(reconstructed, ratings, n=n)
return {"user_id": user_id, "recommendations": recs[user_id].tolist()}
```
## 📚 Use Cases
### Education
- **University courses**: Teach recommendation systems without expensive infrastructure
- **Self-learning**: Students can run everything on personal laptops
- **Workshops**: Quick demos that work offline
- **Research**: Simple baseline implementation for papers
### Small-Scale Production
- **School library**: Recommend books to students (500 books, 200 students)
- **Local business**: Product recommendations for small e-commerce
- **Community app**: Match local services to residents
- **Personal projects**: Add recommendations to your blog or app
### Development
- **Prototyping**: Test recommendation ideas quickly
- **Learning**: Understand SVD by reading clean, documented code
- **Benchmarking**: Compare against simple, fast baseline
- **Integration**: Easy to embed in larger systems
---
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](../LICENSE) file for details.
## 🙏 Acknowledgments
- **NumPy/SciPy**: Core numerical computing
- **Numba**: JIT compilation for performance
- **Rich**: Beautiful terminal interface
- **Typer**: Modern CLI framework
---
> **"Fast, secure, and production-ready recommender systems for everyone."**
## 🗓️ Deprecation Policy
We strive to maintain backward compatibility and provide clear deprecation warnings. Deprecated features will:
- Be marked in the documentation and code with a warning.
- Remain available for at least one minor release cycle.
- Be removed only after clear notice in the changelog and release notes.
If you rely on a feature that is marked for deprecation, please open an issue to discuss migration strategies.
## 👩💻 Developer Quickstart
To get started as a contributor or to simulate CI locally:
```sh
# 1. Set up your full dev environment (Poetry, pre-commit, all dev deps)
make dev
# 2. Run all linters
make lint
# 3. Run all tests with coverage
make test
# 4. Run all pre-commit hooks
make precommit
# 5. Build the Docker image
make docker-build
# 6. Run tests inside Docker (as CI does)
make docker-test
# 7. Simulate the full CI pipeline (lint, test, coverage, Docker)
make ci
```
<!-- Trigger CI: workflow file updated -->
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"description": "\n[](https://github.com/Lunexa-AI/vector-recsys-lite/actions)\n[](https://python.org)\n[](https://opensource.org/licenses/MIT)\n[](https://pypi.org/project/vector-recsys-lite/)\n[](https://pre-commit.com/)\n\n**Lightweight recommender for teaching and small-scale production.**\n\n## \ud83c\udf1f Why Use vector-recsys-lite?\n\n- **Purpose-built for teaching and learning:**\n Interactive CLI, educator guides, and Jupyter demos\u2014no need to reinvent the wheel for every class or workshop.\n- **Robust, tested, and production-ready:**\n Handles edge cases, bad input, and large/sparse data gracefully\u2014unlike most one-off scripts.\n- **Resource-light and offline-ready:**\n <50MB install, runs on 2GB RAM, and works without internet after install.\n- **Simple, reproducible production:**\n One-command API deployment, clean CLI, and Docker support.\n- **Multiple algorithms, modern tooling:**\n SVD, ALS (implicit), KNN (cosine), bias handling, chunked processing, and more.\n- **Extensible and community-focused:**\n Easy to add new datasets, metrics, or algorithms. Contributor-friendly with templates and guides.\n- **World-class documentation and examples:**\n Notebooks, educator guides, and API docs for fast onboarding.\n\n---\n\n## \ud83d\ude80 Getting Started\n\n### Installation\n\n```bash\npip install vector_recsys_lite\n```\n\nOffline: See guide below.\n\n### Quick Example\n\n```python\nimport numpy as np\nfrom vector_recsys_lite import svd_reconstruct, top_n\n\nratings = np.array([[5, 3, 0, 1], [0, 0, 4, 5]], dtype=np.float32)\nreconstructed = svd_reconstruct(ratings, k=2)\nrecommendations = top_n(reconstructed, ratings, n=2)\nprint(recommendations)\n```\n\nCLI: `vector-recsys --help`\n\n## \ud83c\udf93 For Educators & Students\n\nTeach recommendation systems without fancy hardware.\n\n- **Interactive Mode**: `vector-recsys teach --concept svd` (prompts, examples)\n- **Notebooks**: `examples/svd_math_demo.ipynb` (math breakdowns, plots)\n- **Low-Resource Demos**: Generate data and run in <1s\n\nFull guide: [docs/educator_guide.md](docs/educator_guide.md)\n\n## \ud83d\ude80 For Small App Deployers\n\nBuild production recommenders for <10k users/items.\n\n- **Deploy API**: `vector-recsys deploy model.pkl`\n- **Offline Install**: Download wheel, install via USB\n- **Resource Efficient**: Sparse matrices for low memory\n\nExamples: Library book recommender, local e-commerce.\n\nFull guide: [docs/deployment_guide.md](docs/deployment_guide.md)\n\n## \ud83d\udee0\ufe0f For Developers\n\nExtend or contribute easily.\n\n- **API**: Clean, typed functions (svd_reconstruct, RecommenderSystem)\n- **Contributing**: `make dev` setup, tests, linting\n- **Benchmarks**: `vector-recsys benchmark`\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) and [API Reference](#\ud83d\udd27-api-reference).\n\n### ML Tooling Examples\n\n**ALS (Implicit Feedback)**:\n```python\nfrom vector_recsys_lite import RecommenderSystem\nratings = np.array([[1, 0, 1], [0, 1, 0]], dtype=np.float32) # binary implicit\nmodel = RecommenderSystem(algorithm=\"als\")\nmodel.fit(ratings, k=2)\npreds = model.predict(ratings)\n```\n\n**KNN (Cosine Similarity)**:\n```python\nfrom vector_recsys_lite import RecommenderSystem\nratings = np.array([[5, 3, 0], [0, 0, 4]], dtype=np.float32)\nmodel = RecommenderSystem(algorithm=\"knn\")\nmodel.fit(ratings, k=2)\npreds = model.predict(ratings)\n```\n\n**Bias Handling (SVD)**:\n```python\nfrom vector_recsys_lite import RecommenderSystem\nratings = np.array([[5, 3, 0], [0, 0, 4]], dtype=np.float32)\nmodel = RecommenderSystem(algorithm=\"svd\")\nmodel.fit(ratings, k=2)\npreds = model.predict(ratings) # Includes global/user/item bias\n```\n\n**Chunked SVD**:\n```python\nfrom vector_recsys_lite import svd_reconstruct\nlarge_mat = np.random.rand(10000, 500)\nreconstructed = svd_reconstruct(large_mat, k=10, use_sparse=True)\n```\n\n**Metrics**:\n```python\nrecs = [[1,2,3], [4,5]]\nactual = [{1,3}, {4,6}]\nprint(precision_at_k(recs, actual, 3))\nprint(recall_at_k(recs, actual, 3))\nprint(ndcg_at_k(recs, actual, 3))\n```\n\n**CV Split**:\n```python\ntrain, test = train_test_split_ratings(mat, test_size=0.2)\n```\n\n**Pipeline**:\n```python\nfrom vector_recsys_lite import RecsysPipeline, RecommenderSystem\npipe = RecsysPipeline([('model', RecommenderSystem())])\npipe.fit(mat, k=2)\nrecs = pipe.recommend(mat, n=3)\n```\n\n**Grid Search**:\n```python\nresult = grid_search_k(mat, [2,4], 'rmse')\nprint(result['best_k'])\n```\n\nFull details in API Reference.\n\n## \ud83d\udcda Use Cases\n\n### Education\n- **University courses**: Teach recommendation systems without expensive infrastructure\n- **Self-learning**: Students can run everything on personal laptops\n- **Workshops**: Quick demos that work offline\n- **Research**: Simple baseline implementation for papers\n\n### Small-Scale Production\n- **School library**: Recommend books to students (500 books, 200 students)\n- **Local business**: Product recommendations for small e-commerce\n- **Community app**: Match local services to residents\n- **Personal projects**: Add recommendations to your blog or app\n\n### Development\n- **Prototyping**: Test recommendation ideas quickly\n- **Learning**: Understand SVD by reading clean, documented code\n- **Benchmarking**: Compare against simple, fast baseline\n- **Integration**: Easy to embed in larger systems\n\n---\n\n## \ud83d\udcc8 Performance\n\n### Resource Usage\n\nDesigned for resource-constrained environments:\n\n| Dataset Size | RAM Usage | Time (old laptop) | Time (modern PC) |\n|--------------|-----------|-------------------|------------------|\n| 100 \u00d7 50 | < 10 MB | < 0.1s | < 0.01s |\n| 1K \u00d7 1K | < 50 MB | < 1s | < 0.1s |\n| 10K \u00d7 5K | < 500 MB | < 10s | < 2s |\n\n### Tested On\n- 10-year-old laptops (Core i3, 2GB RAM)\n- Raspberry Pi 4\n- Modern workstations\n- Cloud containers (minimal resources)\n\n### Memory Efficiency\n- **Sparse matrix support**: Handles 90% sparse data efficiently\n- **Chunked processing**: Works with limited RAM\n- **Minimal dependencies**: ~50MB total install size\n\n## \ud83d\udd27 API Reference\n\n### Core Functions\n\n```python\ndef svd_reconstruct(\n mat: FloatMatrix,\n *,\n k: Optional[int] = None,\n random_state: Optional[int] = None,\n use_sparse: bool = True,\n) -> FloatMatrix:\n \"\"\"Truncated SVD reconstruction for collaborative filtering. Supports chunked processing for large matrices.\"\"\"\n\ndef top_n(\n est: FloatMatrix,\n known: FloatMatrix,\n *,\n n: int = 10\n) -> np.ndarray:\n \"\"\"Get top-N items for each user, excluding known items.\"\"\"\n\ndef compute_rmse(predictions: FloatMatrix, actual: FloatMatrix) -> float:\n \"\"\"Compute Root Mean Square Error between predictions and actual ratings.\"\"\"\n\ndef compute_mae(predictions: FloatMatrix, actual: FloatMatrix) -> float:\n \"\"\"Compute Mean Absolute Error between predictions and actual ratings.\"\"\"\n```\n\n### I/O Functions\n\n```python\ndef load_ratings(\n path: Union[str, Path],\n *,\n format: Optional[str] = None,\n sparse_format: bool = False,\n **kwargs: Any,\n) -> FloatMatrix:\n \"\"\"Load matrix from file with format detection.\"\"\"\n\ndef save_ratings(\n matrix: Union[FloatMatrix, sparse.csr_matrix],\n path: Union[str, Path],\n *,\n format: Optional[str] = None,\n show_progress: bool = True,\n **kwargs: Any,\n) -> None:\n \"\"\"Save rating matrix with automatic format detection.\"\"\"\n\ndef create_sample_ratings(\n n_users: int = 100,\n n_items: int = 50,\n sparsity: float = 0.8,\n rating_range: tuple[float, float] = (1.0, 5.0),\n random_state: Optional[int] = None,\n sparse_format: bool = False,\n) -> Union[FloatMatrix, sparse.csr_matrix]:\n \"\"\"Create a sample rating matrix for testing.\"\"\"\n```\n\n### RecommenderSystem Class\n\n```python\nclass RecommenderSystem:\n \"\"\"Production-ready recommender system with model persistence. Supports SVD, ALS (implicit), and KNN algorithms, with bias handling.\"\"\"\n\n def __init__(self, algorithm: str = \"svd\", ...):\n \"\"\"algorithm: 'svd', 'als', or 'knn'\"\"\"\n def fit(self, ratings: FloatMatrix, k: Optional[int] = None) -> \"RecommenderSystem\":\n \"\"\"Fit the model to training data.\"\"\"\n def predict(self, ratings: FloatMatrix) -> FloatMatrix:\n \"\"\"Generate predictions for the input matrix.\"\"\"\n def recommend(self, ratings: FloatMatrix, n: int = 10, exclude_rated: bool = True) -> np.ndarray:\n \"\"\"Generate top-N recommendations for each user.\"\"\"\n def save(self, path: str) -> None:\n \"\"\"Save model to file using secure joblib serialization.\"\"\"\n @classmethod\n def load(cls, path: str) -> \"RecommenderSystem\":\n \"\"\"Load model from file.\"\"\"\n```\n\n## \ud83e\udde9 Real-World Usage Examples\n\n### Using with Pandas\n\n```python\nimport pandas as pd\nfrom vector_recsys_lite import svd_reconstruct, top_n\n\n# Load ratings from a DataFrame\nratings_df = pd.read_csv('ratings.csv', index_col=0)\nratings = ratings_df.values.astype(float)\n\n# SVD recommendations\nreconstructed = svd_reconstruct(ratings, k=20)\nrecommendations = top_n(reconstructed, ratings, n=5)\nprint(recommendations)\n```\n\n### Integrating in a Web App (FastAPI Example)\n\n```python\nfrom fastapi import FastAPI\nfrom vector_recsys_lite import svd_reconstruct, top_n\nimport numpy as np\n\napp = FastAPI()\nratings = np.load('ratings.npy')\nreconstructed = svd_reconstruct(ratings, k=10)\n\n@app.get('/recommend/{user_id}')\ndef recommend(user_id: int, n: int = 5):\n recs = top_n(reconstructed, ratings, n=n)\n return {\"user_id\": user_id, \"recommendations\": recs[user_id].tolist()}\n```\n\n## \ud83d\udcda Use Cases\n\n### Education\n- **University courses**: Teach recommendation systems without expensive infrastructure\n- **Self-learning**: Students can run everything on personal laptops\n- **Workshops**: Quick demos that work offline\n- **Research**: Simple baseline implementation for papers\n\n### Small-Scale Production\n- **School library**: Recommend books to students (500 books, 200 students)\n- **Local business**: Product recommendations for small e-commerce\n- **Community app**: Match local services to residents\n- **Personal projects**: Add recommendations to your blog or app\n\n### Development\n- **Prototyping**: Test recommendation ideas quickly\n- **Learning**: Understand SVD by reading clean, documented code\n- **Benchmarking**: Compare against simple, fast baseline\n- **Integration**: Easy to embed in larger systems\n\n---\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](../LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- **NumPy/SciPy**: Core numerical computing\n- **Numba**: JIT compilation for performance\n- **Rich**: Beautiful terminal interface\n- **Typer**: Modern CLI framework\n\n---\n\n> **\"Fast, secure, and production-ready recommender systems for everyone.\"**\n\n## \ud83d\uddd3\ufe0f Deprecation Policy\n\nWe strive to maintain backward compatibility and provide clear deprecation warnings. Deprecated features will:\n- Be marked in the documentation and code with a warning.\n- Remain available for at least one minor release cycle.\n- Be removed only after clear notice in the changelog and release notes.\n\nIf you rely on a feature that is marked for deprecation, please open an issue to discuss migration strategies.\n\n## \ud83d\udc69\u200d\ud83d\udcbb Developer Quickstart\n\nTo get started as a contributor or to simulate CI locally:\n\n```sh\n# 1. Set up your full dev environment (Poetry, pre-commit, all dev deps)\nmake dev\n\n# 2. Run all linters\nmake lint\n\n# 3. Run all tests with coverage\nmake test\n\n# 4. Run all pre-commit hooks\nmake precommit\n\n# 5. Build the Docker image\nmake docker-build\n\n# 6. Run tests inside Docker (as CI does)\nmake docker-test\n\n# 7. Simulate the full CI pipeline (lint, test, coverage, Docker)\nmake ci\n```\n\n<!-- Trigger CI: workflow file updated -->\n",
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