semvec/pypi-packages huggingface dataset:
mcp-ocrai 0.3.0
A MCP server for OCR files using Mistral API
Requires Python: >=3.12

# MCP OCR AI

## Introduction

This repository contains an application that allows performing OCR (Optical Character Recognition) on various types of documents and images using the Mistral AI API. The application is designed to process files in formats such as PDF, Word, PowerPoint, images (JPEG, PNG, etc.), and other document formats.

### Main Features:
- Support for multiple document and image formats.
- Use of the Mistral AI API for OCR processing.
- Caching of results to improve performance.

## How to Set Up
1. Add the key to the environment variable:
```txt .profile
export MISTRAL_API_KEY="your_api_key_here"
```

2. Configure the MCP server on ZED:

```json
{
  /// The name of your MCP server
  "ocrai": {
    /// The command which runs the MCP server
    "command": "uvx",
    /// The arguments to pass to the MCP server
    "args": ["mcp_ocrai"],
    /// The environment variables to set
    "env": {}
  }
}

2. Alternatively, you can pass the API key directly via the command line:
```sh
mcp-ocrai --api-key "your_api_key_here"
```
litestar-flags 0.2.1
Production-ready feature flags library for Litestar
Requires Python: >=3.11
Keywords: litestar, feature-flags, feature-toggles, a/b-testing, rollouts

# litestar-flags

Production-ready feature flags for [Litestar](https://litestar.dev) applications.

[![PyPI version](https://badge.fury.io/py/litestar-flags.svg)](https://badge.fury.io/py/litestar-flags)
[![Python versions](https://img.shields.io/pypi/pyversions/litestar-flags.svg)](https://pypi.org/project/litestar-flags/)
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

## Installation

```bash
uv add litestar-flags

# With extras
uv add litestar-flags[redis]        # Redis backend
uv add litestar-flags[database]     # SQLAlchemy backend
uv add litestar-flags[workflows]    # Approval workflows
uv add litestar-flags[openfeature]  # OpenFeature SDK provider
uv add litestar-flags[prometheus]   # Prometheus metrics
uv add litestar-flags[observability] # OpenTelemetry + structlog
uv add litestar-flags[all]          # Everything
```

## Quick Start

```python
from litestar import Litestar, get
from litestar_flags import FeatureFlagsPlugin, FeatureFlagsConfig, FeatureFlagClient

config = FeatureFlagsConfig()
plugin = FeatureFlagsPlugin(config=config)

@get("/")
async def index(feature_flags: FeatureFlagClient) -> dict:
    if await feature_flags.is_enabled("new_checkout"):
        return {"flow": "new"}
    return {"flow": "legacy"}

app = Litestar(plugins=[plugin])
```

## Features

- **Multiple backends** - Memory, Redis, and SQLAlchemy storage
- **Percentage rollouts** - Gradual releases with consistent hashing
- **User targeting** - Rules based on user attributes
- **Segment-based targeting** - Reusable user groups for complex targeting
- **A/B testing** - Weighted variants for experiments
- **Time-based rules** - Scheduled launches and maintenance windows
- **Multi-environment** - Environment inheritance, promotion, and per-env configs
- **Admin API** - REST endpoints for flag management with RBAC and audit logging
- **Analytics** - Evaluation tracking, metrics, and Prometheus export
- **OpenFeature support** - Vendor-agnostic evaluation via OpenFeature SDK
- **Approval workflows** - Human-in-the-loop governance for enterprise use

## Documentation

Full docs at [flags.litestar.scriptr.dev](https://flags.litestar.scriptr.dev).

## Development

```bash
git clone https://github.com/JacobCoffee/litestar-flags.git
cd litestar-flags
uv sync --all-extras
uv run pytest
```

## License

MIT