semvec/pypi-packages huggingface dataset:
globalMem 0.1.0
Gestion d'objets globaux accessibles sans import
Requires Python: >=3.8

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# globalMem

> Gestion automatique d'éléments globaux injectés dans `builtins`, accessibles sans import dans vos projets Python.

---

## 📦 Installation

Ajoutez `globalMem` comme dépendance dans votre projet :

```bash
pip install globalMem
```
ou, dans pyproject.toml :

```toml
[project]
dependencies = [
    "globalMem"
]
```

## 🚀 Utilisation rapide
1. Définir une fonction ou variable globale
Dans votre projet (exemple A), utilisez @Global pour rendre une fonction ou une variable accessible partout sans import :

```python
from globalMem import Global, register_global_module

@Global
def testA():
    """Fonction globale A."""
    pass

# Enregistre ce module pour l'initialisation automatique
register_global_module(__name__)
```
2. Initialiser automatiquement tous les globals
Dans votre projet principal (exemple B), au tout début de votre main.py :

```python
from globalMem import auto_initialize_globals

# Initialise tous les modules enregistrés
auto_initialize_globals()

# Maintenant vous pouvez utiliser directement testA() sans import !
```
## ⚙️ Fonctionnement Automatique sous VSCode
Chaque fois que vous enregistrez un fichier .py :

Le fichier global_builtins.pyi est mis à jour automatiquement.

Autocomplétion VSCode mise à jour immédiatement.

Pas besoin de lancer manuellement la commande make pyi.

✅ Fluide pour le développement
✅ Pas d'import manuel
✅ Compatible multi-projets

## 🛠️ Setup Environnement de Développement
Créer un environnement virtuel :

```bash
make setup
```

Générer les stubs .pyi au début :

```bash
make pyi
```

Compiler le package pour PyPI :

```bash
make build
```

Publier sur PyPI :

```bash
make publish
```

Incrémenter la version :

```bash
make bump-patch
make bump-minor
make bump-major
```

## 📄 Important pour VSCode
Installez l'extension gratuite :

Trigger Task on Save (éditeur : Gruntfuggly)

Cela permet à VSCode de lancer la mise à jour automatique des .pyi à chaque sauvegarde.

## 🧹 Nettoyer le projet
Supprimer build/, dist/, *.egg-info/, et __pycache__ :

```bash
make clean
```

## 📝 Licence
MIT - Guillaume Lefebvre
mcp-todoist-organizer 0.0.2
MCP server for Todoist integration
Requires Python: >=3.10

# Todoist MCP Server

A Model Context Protocol (MCP) server for Todoist integration. This server provides tools for interacting with Todoist, including managing tasks, projects, and tracking productivity.

## Features

- **Task Management**: Create, retrieve, update, and complete tasks
- **Project Management**: Create and retrieve projects
- **Label Management**: Retrieve labels for organization
- **Search**: Query for Todoist tasks using Todoist's filter syntax
- **Resources**: Access metadata about Todoist objects (tasks, projects, labels)
- **Prompts**: Templates for common Todoist workflows (task creation, project planning, productivity reports)

## Installation

```bash
pip install mcp-todoist-organizer
```

## Configuration

Set the following environment variable:

```bash
export TODOIST_API_TOKEN="your_api_token"
```

You can obtain a Todoist API token from the Todoist Integrations settings: [https://todoist.com/app/settings/integrations](https://todoist.com/app/settings/integrations)

## Usage

### Starting the server directly

```bash
mcp-todoist-organizer
```

### Using with UVX

```bash
uvx mcp-todoist-organizer
```

### Using with Claude Desktop

Add the following to your claude_desktop_config.json file:

```json
"mcp-todoist-organizer": {
  "command": "uvx",
  "args": [
    "mcp-todoist-organizer"
  ],
  "env": {
    "TODOIST_API_TOKEN": "your_api_token"
  }
}
```

Replace the environment variables with your actual Todoist credentials.

## Available Tools

* **get_tasks**: Get all active tasks
* **get_task**: Get details of a specific task
* **create_task**: Create a new task with optional project, due date, priority, and labels
* **complete_task**: Mark a task as complete
* **get_projects**: Get all projects
* **create_project**: Create a new project
* **get_labels**: Get all labels
* **search_tasks**: Search tasks using Todoist's filter syntax

## Available Resources

* **todoist://tasks**: List of all Todoist tasks
* **todoist://projects**: List of all Todoist projects
* **todoist://labels**: List of all Todoist labels

## Available Prompts

* **create_task**: Template for creating a new task
* **plan_project**: Template for planning a new project structure
* **productivity_report**: Template for generating a productivity report

## Version

0.0.1
metarag 0.1.7
MetaRAG: A multi-LLM ensemble Retrieval-Augmented Generation framework with cosine similarity ranking.
Requires Python: >=3.8
Keywords: llm, retrieval, augmented, generation, ai, nlp

MetaRAG is a Python framework for multi-model Retrieval-Augmented Generation. It queries multiple LLMs in parallel, scores the responses based on cosine similarity with the context, and aggregates the top responses for a more accurate and comprehensive answer.

## Features
- 🔍 Multi-LLM querying using Groq's LLMs (LLaMA3, Gemma, etc.)
- 🤝 Cosine similarity scoring of responses
- 🧠 Top-k response aggregation
- 📄 Works with PDFs and plain text
- ⚡ Fast execution with thread pooling

## Installation
```bash
pip install metarag

```
## Pre-requisites
Load your GROQ_API_KEY to the project from https://console.groq.com/keys and follow the sample example shown below.
## Example Usage (Local PC)
```python
import metarag
from dotenv import load_dotenv
load_dotenv()  
def test_metarag_functionality():
    
    document_paths = ["VectorDB_Paper.pdf"]  

    rag_instance = metarag.MetaRAG(document_paths=document_paths)

    query = "What is the methodology in 5 lines like a professor and extract 5 keywords?"

    result = rag_instance.run(query)

    print(f"Aggregated Response: {result}")

if __name__ == "__main__":
    test_metarag_functionality()

```
## On Google Colab
```
import metarag
import os

os.environ["GROQ_API_KEY"]="groq_api_key"

    
document_paths = ["/content/VectorDB_Paper.pdf"]  

rag_instance = metarag.MetaRAG(document_paths=document_paths)

query = "What is the methodology in 5 lines like a professor and extract 5 keywords?"

result = rag_instance.run(query)

print(f"Aggregated Response: {result}")


```
## Requirements
Python 3.8+

## License
MIT License - see LICENSE file for details.

## Author
**Nisharg Nargund**  
Founder @OpenRAG