semvec/pypi-packages huggingface dataset:
track-flow 0.0.1
ETL Para banco macro, extrae pdf desde la url que le pongas, extrae datos y crea chunks que sube a s3 (aws)
License: MIT
Requires Python: >=3.8

# Macro Flow / Flujo de Procesamiento de PDFs  

**ES / Español 🇪🇸**  

`macro_flow` es una librería modular en Python diseñada para pipelines de **ETL sobre documentos PDF**. Permite extraer un PDF desde una URL, transformarlo en chunks de texto y almacenarlo en un formato optimizado para su posterior uso en embeddings o flujos de RAG (Retrieval Augmented Generation).  

---

## 🚀 Características  

- Descarga de **PDFs desde URLs externas**.  
- Transformación de PDFs en **chunks de texto estructurados**.  
- Exportación en **Parquet** para análisis eficiente o entrenamiento de modelos.  
- Carga opcional en **Amazon S3** u otros destinos.  
- Configuración flexible mediante `.env`.  
- Uso como **librería Python** o desde **línea de comandos**.  

---

## ⚙️ Variables de entorno necesarias  

Debes definir un archivo `.env` en la raíz de tu proyecto con las siguientes variables:  

```env
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_DEFAULT_REGION=us-east-1 
AWS_BUCKET_NAME=chunks-data-parquet-2025
AWS_S3_PREFIX=chunks-s3
```


## macroflow --url https://example.com/documento.pdf

from macro_flow.main import MacroEtl

etl = MacroEtl(url="https://example.com/documento.pdf")
etl.run()


##🧠 Requirements

Python >= 3.9

Configured .env file

Internet connection to download PDFs

Valid AWS credentials (if using S3)

##🔮 Use cases

Preprocessing documents for embeddings.

Building RAG pipelines from PDF corpora.

Integration with cloud storage systems.

##🪪 License

MIT © Facu Vega
https://github.com/facuvegaingenieer
sherlock-project 0.16.0
Hunt down social media accounts by username across social networks
License: MIT
Requires Python: <4.0,>=3.9
Keywords: osint, reconnaissance, information gathering

<!-- This README should be a mini version at all times for use on pypi -->

<p align=center>
  <br>
  <a href="https://sherlock-project.github.io/" target="_blank"><img src="https://www.kali.org/tools/sherlock/images/sherlock-logo.svg" width="25%"/></a>
  <br>
  <strong><span>Hunt down social media accounts by username across <a href="https://github.com/sherlock-project/sherlock/blob/master/sites.md">400+ social networks</a></span></strong>
  <br><br>
  <span>Additional documentation can be found at our <a href="https://github.com/sherlock-project/sherlock/">GitHub repository</a></span>
  <br>
</p>

## Usage

```console
$ sherlock --help
usage: sherlock [-h] [--version] [--verbose] [--folderoutput FOLDEROUTPUT]
                [--output OUTPUT] [--tor] [--unique-tor] [--csv] [--xlsx]
                [--site SITE_NAME] [--proxy PROXY_URL] [--json JSON_FILE]
                [--timeout TIMEOUT] [--print-all] [--print-found] [--no-color]
                [--browse] [--local] [--nsfw]
                USERNAMES [USERNAMES ...]
```

To search for only one user:
```bash
$ sherlock user123
```

To search for more than one user:
```bash
$ sherlock user1 user2 user3
```
<br>

___

<br>
<p align="center">
<img width="70%" height="70%" src="https://user-images.githubusercontent.com/27065646/219638267-a5e11090-aa6e-4e77-87f7-0e95f6ad5978.png"/>
</a>
</p>
memorious-mcp 0.0.2
Persistent semantic key-value memory MCP server with ChromaDB backend
License: MIT
Requires Python: >=3.12

# memorious-mcp

Memorious-MCP is a minimal Model Context Protocol (MCP) server that provides a persistent key-value memory store with vector-similarity lookup using ChromaDB.

## Features
- FastMCP-based MCP stdio server exposing three tools: `store`, `recall`, and `forget`.
- ChromaDB backend (`ChromaMemoryBackend`) persisting data on disk using Chroma's PersistentClient.
- Tests (integration) that exercise store/recall/forget behavior.
- Standard Python package layout for easy installation and distribution.

## Getting Started

1. Install the package (recommended in a virtual environment):

```bash
# Install in development mode
uv sync

# Or install from source
pip install .
```

2. Run the server (stdio transport, suitable for local/CLI integrations):

```bash
# Using uv
uv run memorious-mcp --collection memories

# Or if installed globally
memorious-mcp --collection memories
```

3. Call tools using an MCP client (FastMCP client or a compatible MCP client) over stdio.

## Example Tool Signatures
- `store(key: str, value: str) -> {"id": str}`
- `recall(key: str, top_k: int = 3) -> {"results": [...]}` where each result includes id, key, value, distance, timestamp
- `forget(key: str, top_k: int = 3) -> {"deleted_ids": [...]}`

## Testing

Run tests with:

```bash
# Using uv
uv run python -m pytest tests/ -v

# Or if pytest is available globally
pytest tests/ -v
```

## Package Structure

The project follows the standard Python package layout:

```
memorious-mcp/
├── src/
│   └── memorious_mcp/
│       ├── __init__.py
│       ├── main.py                 # MCP server entry point
│       └── backends/
│           ├── __init__.py
│           ├── memory_backend.py   # Abstract base class
│           └── chroma_backend.py   # ChromaDB implementation
├── tests/
│   └── test_chroma_backend.py      # Integration tests
├── pyproject.toml                  # Package configuration
└── README.md
```

## Notes

- By default memory persistence is enabled and stored under `./.memorious`. You can override `persist_directory` when creating `ChromaMemoryBackend` in code.
- The backend uses Chroma's default embedding function when available.
- The package is configured with a console script entry point for easy command-line usage.

## Contributing

Contributions are welcome. Open a PR with tests.