semvec/pypi-packages huggingface dataset:
pycon-co-2025-geopozo 0.9.3
Talk for pycon-co-2025
Requires Python: >=3.13


Este cuaderno de [marimo](https://marimo.io/) está activado para usar la versión
nueva de python: free-threaded (hilo-libre).

No es necesario, pero para aprovechar las funciones, debes usar una versión
de python > `3.15t`.

Lastimosamente, está versión no está disponible para Windows.

Puedes verificar las versiones disponibles con:

```sh
uv python list
```

salida:
```sh
cpython-3.14.0a6+freethreaded-windows-x86_64-none              <download available>
cpython-3.14.0a6-windows-x86_64-none                           <download available>
cpython-3.13.2+freethreaded-windows-x86_64-none                <download available>
cpython-3.13.2+freethreaded-windows-x86_64-none                <download available>
cpython-3.9.21-windows-x86_64-none                             <download available>
cpython-3.8.20-windows-x86_64-none                             <download available>
cpython-3.7.9-windows-x86_64-none                              <download available>
```

## Linux

`PYTHON_GIL=0 uv run marimo edit`

## Windows

`uv run --python 3.14 marimo edit`

## Mac

Tienes que mirar a `uv list python`.
autodw 0.1.1.post3
Database schema serialization with LLM integration
Requires Python: <4,>=3.8


<img src="./images/autodw.png" width="960" height="450" alt="AutoDW">

# AutoDW: Data Warehouse Intelligent Agent 🚀  
> **Automated schema processing bridge connecting data warehouses with LLMs**  

AutoDW is an LLM-based intelligent agent that enhances data warehouse development efficiency through automated database schema processing, metadata optimization, and ETL generation.  

---  

## ✨ Core Features  

### Schema Parsing Engine  
| Capability             | Description                                    |  
|------------------------|-----------------------------------------------|  
| **Multi-format Export** | Exports to JSON/Spider-JSON/MSchema formats    |  
| **Intelligent Sampling** | Random value extraction + frequency-based optimization |  
| **Precision Filtering** | Table/column-level data filtering support      |  
| **Currently Supported** | SQLite • More DB types coming soon             |  

### Metadata Serializer  
```python  
# Complete metadata serialization in 3 steps  
1. Initialize database connection  
2. Select serialization format (mschema/json)  
3. Retrieve structured metadata  
```  

---  

## 🚧 Development Roadmap  

### ✅ Implemented Features  
- Database schema interaction engine  
- DB Schema ↔ JSON bidirectional conversion  
- DB Schema → MSchema serialization  

### 🚀 Coming Soon  
- SQL → JSON intelligent converter  
- LLM-powered query optimization engine  
- Automated ETL script generator  
- Schema diff visualization tool  
- Cloud service support: BigQuery • Snowflake • Redshift  
- REST API remote access interface  

---  

## ⚙️ Quick Installation  
```bash  
pip install autodw  
```  

## 🎯 Usage Examples  

### Schema Parsing  
```python  
from autodw.connectors.sqlite import SQLiteConnector  

db = SQLiteConnector("your_db.sqlite")  
with db:  
    schema = db.get_database_schema(  
        format="json",  
        sample_type="random",  
        max_samples=3  
    )  
    print(schema)  # Get structured schema data  
```  

### Metadata Serialization  
```python  
from autodw.serializers import DatabaseSchemaSerializer  

db = SQLiteConnector("your_db.sqlite")  
with db:  
    serializer = DatabaseSchemaSerializer(  
        connector=db,  
        serializer_type="mschema",  
        exclude_tables=["temp_*"]  
    )  
    print(serializer.generate())  # Output MSchema format  
```  

---  

## 📚 Advanced Documentation  
Visit `docs/` directory for:  
- Detailed API reference  
- Schema parsing configuration guide  
- LLM integration best practices  

> **Technical Support**: Questions? Submit a GitHub Issue  
> **Project Lead**: d7inshi@outlook.com  
> **Project Status**: v0.1.1 (Actively Developed)