semvec/pypi-packages huggingface dataset:
sylla-films 0.0.3
SDK Python pour accéder à l’API MovieLens (compatible Data Analysts/Data Scientists)
Requires Python: >=3.12

# 🎬 MovieLens SDK - `sylla_films`

Un SDK Python moderne permettant d’accéder à l’API MovieLens  
déployée sur Render.  
Idéal pour les **Data Analysts**, **Data Scientists**, étudiants, et projets ML.

[![PyPI version](https://img.shields.io/pypi/v/sylla-films.svg)](https://pypi.org/project/sylla-films/)
![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)

---

## 🚀 Installation

```bash
pip install sylla_films
```

---

## ⚙️ Configuration

```python
from sylla_films.film_client import MovieClient
from sylla_films.film_config import MovieConfig

config = MovieConfig(
    movie_base_url="https://projet-api-et-deploiement-2.onrender.com"
)

client = MovieClient(config=config)
```

Ou via une variable d’environnement :

```
MOVIE_API_BASE_URL=https://projet-api-et-deploiement-2.onrender.com
```

Puis :

```python
client = MovieClient()
```

---

## 🩺 Tester le SDK

### 🔍 1. Health Check

```python
client.health_check()
# → {"message": "API MovieLens opérationnelle"}
```

---

## 🎞️ 2. Récupérer un film

```python
movie = client.get_movie(1)
print(movie.title)
```

Résultat :

```
Toy Story (1995)
```

---

## 📋 3. Liste de films dans plusieurs formats

### a) Format Pydantic (par défaut)

```python
movies = client.list_movies(limit=5)
print(movies[0].title)
```

### b) Format dictionnaire

```python
movies_dict = client.list_movies(limit=5, output_format="dict")
```

### c) Format Pandas DataFrame

```python
df = client.list_movies(limit=5, output_format="pandas")
print(df.head())
```

---

## ⭐ Formats supportés

| Format demandé           | Retour |
|-------------------------|--------|
| `"pydantic"` (défaut)  | Liste d’objets Pydantic |
| `"dict"`               | Liste de dictionnaires |
| `"pandas"`             | Pandas DataFrame |

Exemple :

```python
client.list_ratings(limit=10, output_format="pandas")
```

---

## 🧩 Travailler avec les autres endpoints

### 🎭 Rating

```python
rating = client.get_rating(user_id=1, movie_id=1)
print(rating.rating)
```

### 🏷️ Tags

```python
tags = client.list_tags(limit=5, output_format="dict")
```

### 🔗 Liens IMDB / TMDB

```python
link = client.get_link(1)
print(link.imdbId)
```

### 📊 Analytics global

```python
analytics = client.get_analytics()
print(analytics.movie_count)
```

---

## 🧪 Tester avec une API locale

```python
config = MovieConfig(movie_base_url="http://localhost:8000")
client = MovieClient(config=config)
```

---

## 👥 Public cible

- Data Analysts  
- Data Scientists  
- Étudiants  
- Développeurs Python  
- Projets ML / IA  

---

## 📄 Licence

MIT License

---

## 🔗 Liens utiles

- API Render : https://projet-api-et-deploiement-2.onrender.com  
- Documentation interactive : https://projet-api-et-deploiement-2.onrender.com/docs  
- PyPI : https://pypi.org/project/sylla-films/
types-flake8-bugbear 25.11.29.20251201
Typing stubs for flake8-bugbear
Requires Python: >=3.9

## Typing stubs for flake8-bugbear

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`flake8-bugbear`](https://github.com/PyCQA/flake8-bugbear) package. It can be used by type checkers
to check code that uses `flake8-bugbear`. This version of
`types-flake8-bugbear` aims to provide accurate annotations for
`flake8-bugbear==25.11.29`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/flake8-bugbear`](https://github.com/python/typeshed/tree/main/stubs/flake8-bugbear)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.407

It was generated from typeshed commit
[`ef2b90c67e5c668b91b3ae121baf00ee5165c30b`](https://github.com/python/typeshed/commit/ef2b90c67e5c668b91b3ae121baf00ee5165c30b).
knot0-types 2.0.34
Prebuilt Pydantic models generated from Knot0 JSON Schemas
Requires Python: >=3.9

knot0-types (Python)

Prebuilt Pydantic models generated from the monorepo JSON Schemas.

Note: The generator writes modules under `knot0-src/packages-v2/types-py/knot0_types_v2/`.
You can import models directly after running the generator.

Build
```bash
node knot0-src/schemas-v2/scripts/build.mjs
python3 knot0-src/scripts/generate-pydantic-models.py
```
knot0-schemas 2.0.34
Knot0 JSON Schemas and validation (Python)
Requires Python: >=3.9

knot0-schemas (Python)

Simple helpers to load Knot0 v2 JSON Schemas from the monorepo and validate manifests.

Usage
```bash
# Build schemas first
node knot0-src/schemas-v2/scripts/build.mjs
```

```python
from knot0_schemas import validator

ok, errors = validator.validate('Component', {
    'kind': 'Component',
    'schema_version': '2.0',
    'metadata': {'name': 'hello'},
    'contract': {},
    'body': {'execution': {'runner': 'python', 'run': 'print(1)'}}
})
print(ok, errors)
```
knot0-event-types 2.0.34
Pydantic models for Knot0 CloudEvents payloads (generated from JSON Schemas)
Requires Python: >=3.9

knot0-event-types (Python)

Pydantic models for Knot0 CloudEvents payloads, generated from JSON Schemas in `schemas-v2/events/**`.

Install
```
pip install knot0_event_types
```

Usage
```
from knot0_event_types_v2.cloud_event import CloudEvent
# from knot0_event_types_v2.events.v1.orchestration.workflow_started import OrchestrationWorkflowStartedPayload

evt = CloudEvent[type(None)](
    specversion='1.0',
    type='v1.orchestration.workflow.started',
    source='orchestration-service',
    id='...',
    data={}
)
```

Notes
- Generated event payload models live under `knot0_event_types_v2.events.*`.
- CloudEvent is provided for envelope typing; runtime validation should use JSON Schemas.
knot0-event-schemas 2.0.34
Knot0 event JSON Schemas (Python package) with convenience loader
Requires Python: >=3.9

knot0-event-schemas (Python)

JSON Schemas for Knot0 platform events, packaged for Python with a convenience loader.

Install
```
pip install knot0_event_schemas
```

Usage
```
from knot0_event_schemas import load_schema_for_envelope
ok, schema = load_schema_for_envelope({
  'specversion': '1.0',
  'type': 'v1.orchestration.workflow.started',
  'source': 'orchestration-service',
  'id': '...',
  'data': {}
})
```
aliyun-mcp-server-rabbitmq 0.4.0
A Model Context Protocol server for Aliyun RabbitMQ
Requires Python: >=3.12
Keywords: aliyun, llm, mcp, rabbitmq

# Aliyun RabbitMQ MCP Server

一个用于阿里云 RabbitMQ 的 Model Context Protocol (MCP) 服务器，允许 LLM 通过 MCP 协议管理和操作阿里云 RabbitMQ 资源。


## 如何运行 MCP Server

本文档说明如何将 `server.py` 作为 MCP server 运行起来。

## 前置条件

1. **安装依赖**（如果还没有安装）：
   ```bash
   # 使用 uv（推荐）
   uv sync
   
   # 或使用 pip
   pip install -e .
   ```

2. **配置阿里云凭据**（通过环境变量）：
   ```bash
   export ALIBABA_CLOUD_ACCESS_KEY_ID="你的AccessKeyID"
   export ALIBABA_CLOUD_ACCESS_KEY_SECRET="你的AccessKeySecret"
   ```


## MCP 客户端配置
 
### Cursor 配置

#### 通过配置文件配置

在项目根目录下创建 `.cursor/mcp.json` 文件（如果目录不存在，需要先创建），或在用户目录下创建 `~/.cursor/mcp.json`：

**方案 1：使用 uv 运行（需要设置 PYTHONPATH）**

```json
{
  "mcpServers": {
    "aliyun-rabbitmq": {
      "command": "/pathTo/aliyun-mcp-server-rabbitmq/.venv/bin/python",
      "args": ["-m", "src.server"],
      "cwd": "/pathTo/aliyun-mcp-server-rabbitmq" 
    }
  }
}
```

**方案 2：使用已安装的命令（推荐，最简单）**

项目已经发布到阿里内部仓库，可以使用下列方式直接依赖。

```json
{
  "mcpServers": {
    "aliyun-rabbitmq": {
      "command": "uvx",
      "args": [
        "aliyun-mcp-server-rabbitmq"
      ],
      "env": {
        "CONNECTION_STRING": ""
      }
    }
  }
}
```