semvec/pypi-packages huggingface dataset:
fasthtml-sqlalchemy 0.1.0
SQLAlchemy integration for FastHTML
Requires Python: >=3.6

# fasthtml-sqlalchemy
IntegraciÃ³n de SqlAlchemy con el framework fastHTML

## InstalaciÃ³n
pip install fasthtml-sqlalchemy

## Ejemplo de uso:

```python:
from fasthtml import FastHTML
from fasthtml_sqlalchemy import SQLAlchemy, BaseModel

app = FastHTML()
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'

db = SQLAlchemy(app)

class User(BaseModel):
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True, nullable=False)

db.create_all()

@app.route("/")
def get():
    users = db.session.query(User).all()
    return str(users)

@app.route("/add/<username>")
def add(username):
    user = User(username=username)
    user.save()
    return f"User {username} added!"

if __name__ == "__main__":
    app.serve()
```

y la ayuda sigue por aquÃ­ ...
GenIText 0.1.2
A CLI tool for generating high-quality image-text pairs for AI training
Requires Python: >=3.8

# GenIText: Generative Image-Text Automated package

<p align="center">
  <img src="resources/demo.gif" alt="Demonstration video of GenIText tool">
</p>

## Overview
This repository is independently developed as a felxible framework to generate high-quality Image-Text pairs for finetuning Image-Generation models, such as Stable Diffusion, DALL-E, and other generative models. By leveraging open-source captioning models, GenIText automates the process of generating diverse captions for corresponding images, ensuring that the text data is well-suited for downstream applications such as style-specific generations or domain adaptation. This framework is designed to complement contemporary repositories or modules in the field, offering an additional option for flexibility and automation to create customized datasets.

GenIText will become distributable as a CLI tool once package is ready for testing across systems. Please support in any way you see fit!

## Table of Contents
- [Installation](#installation)
- [Results](#results)
- [Usage](#usage)
  - [Inference](#inference)
  - [Training](#training)
  - [Dataset Preparation](#dataset-preparation)

## Installation
GenIText is available as a Python package and can be installed easily using `pip`. 

To install GenIText, simply run:
```bash
pip install genitext
```
After installation, you can verify that the CLI tool is accessible by running:
```bash 
genitext --help
```
To initiate the CLI tool, run: 
```bash
genitext
```
open-deepsearch 0.1.6
Deep Research but Open-Sourced, called open-deepsearch
Requires Python: >=3.9

# open-deepsearch

open-deepsearch ( Deep Research but Open-Sourced )

Q&A for more details, research, and report generation.

## How to install in DEV environment after git clone

```bash
python3 -m venv .venv
source .venv/bin/activate
#modify .env file and put in OPENAI_KEY and FIRECRAWL_KEY
cp .env.example .env
pip install -r requirements.txt
pip install -e .
deepsearch
```

## How to install in PROD environment after pip install open-deepsearch

```bash
python3 -m venv .venv
source .venv/bin/activate
#modify .env file and put in OPENAI_KEY and FIRECRAWL_KEY
cp .env.example .env
pip install open-deepsearch
deepsearch
```

## ⭐ A python port from node.js version

<https://github.com/dzhng/deep-research>

## It only uses OpenAI_KEY and FIRECRAWL_KEY to produce output.md

## Future work

Fix some minor bugs
DE-DataBaseConnect 0.0.91
Conector com varias base de dados
Requires Python: >=3.9

# DE_DataBaseConnect
ConexÃ£o com os mais variados banco de dados.
De forma simples estabeleÃ§a uma conexÃ£o com sua base de dados.

Oracle
Mysql
SQLite
PostGres

######Obtenha tambÃ©m o metadados para uma tabela especifica, independente da base de dados onde esta localizada

#### Dependencias
Python: 3.9 (recomendado) [python.org](https://www.python.org/)
setuptools, wheel e twine [PyPI](https://pypi.org)
mysql.connector
psycopg2
cx_Oracle
sqlite3
pandas
```
pip install setuptools
pip install wheel
pip install twine
pip install mysql.connector
pip install psycopg2
pip install cx_Oracle
pip install sqlite3
pip install pandas
```
#### InstalaÃ§Ã£o
Para clonar este repositorio em sua maquina local use:
```
git clone https://github.com/almirjgomes/DE_DataBaseConnect.git
- License MIT
```
archivyx 0.1
A powerful collection of utilities for terminal applications.
Requires Python: >=3.7

# ![Archivyx](assets/archivyx.svg)
# 🏛️ Archivyx – The Grand Library of Utilities

> **A growing vault of essential tools, utilities, and CLI magic** 🔮  

Archivyx is your **all-in-one Python utility package**. Designed to hold **everything useful**, 
it contains interactive CLI tools, helper functions, and **anything else that makes coding easier**.

---

## ✨ Features  
✅ **Interactive CLI prompts** (Vertical & Horizontal selections)  
✅ **Rich-powered text formatting** 🖋️  
✅ **Utility functions for common tasks**  
✅ **Modular & Expandable** – More features will be added as the library grows  

---

## 📦 Installation
```sh
pip install archivyx
```
MilvusModel.py 0.1.0
SQLModel-like library for Milvus
Requires Python: >=3.11

# PyMilvusModel

Inspired by [SQLModel](https://sqlmodel.tiangolo.com/), PyMilvusModel:

> is a library for interacting with [Milvus](https://milvus.io/) databases from Python code, with Python objects.

and follows the same paradigm, using Python type annotations and powered by [Pydantic](https://docs.pydantic.dev/latest/)

## Example Usage

```py
from typing import List, Optional
from typing_extensions import Annotated
from pymilvus import MilvusClient, DataType
from pymilvusmodel import MilvusIndexParam, MilvusField, MilvusModel


class ExampleModel(MilvusModel):
    indexes: list[MilvusIndexParam] = [
        MilvusIndexParam("vector", "IVF_FLAT", "vector_index", metric_type="COSINE", params={
            "nlist": 128
        })
    ]
    id: Annotated[
        Optional[int],
        MilvusField(name="id", dtype=DataType.INT64,
                    is_primary=True, auto_id=True)
    ] = None
    vector: Annotated[
        List[float],
        MilvusField(name="vector", dtype=DataType.FLOAT_VECTOR, dim=2)
    ]


MILVUS_CLIENT = MilvusClient("http://localhost:19530")
MilvusModel.metadata.create_all(MILVUS_CLIENT)
print(MILVUS_CLIENT.list_collections()) # ['ExampleModel']
```