semvec/pypi-packages huggingface dataset:
gemini-batch-processor 1.0.1
A Python module to process bulk data using Google's generative AI (Gemini).
Requires Python: >=3.8

# GeminiDataProcessor

## Overview

`GeminiDataProcessor` is a Python library that streamlines data processing through the use of Google's generative AI models (Gemini). It allows users to generate structured responses for tabular datasets using customizable prompts and schemas.

## Features

- **Integration with Gemini AI**: Process data through Google's generative AI models.
- **Asynchronous Processing**: Supports efficient handling of large datasets using `asyncio`.
- **Flexible Schema**: Define custom output schemas for structured JSON responses.
- **Error Handling**: Robust error management for API and data parsing issues.
- **Progress Tracking**: Real-time processing feedback with `tqdm`.

## Installation

You can install the module using pip:

```bash
pip install gemini_data_processor
```

```python
from gemini_batch_processor import GeminiBot

bot = GeminiBot(
    input_file="input_data.csv",
    prompt_template="Generate a summary for: {row[column name]}",
    key_names="summary,details",
    api_key="your_google_api_key",
    model="gemini-1.5-flash-002"
)
```
```python
from gemini_batch_processor import GeminiBot
GeminiBot.model_list(api_key="your_api_key")
```
mkdocs-sql 0.2.4
A MkDocs plugin for executing and displaying SQL queries
Requires Python: >=3.7

# mkdocs-sql

A MkDocs plugin for executing and embedding output of SQL queries in your documentation.

## Features

- Embed output of SQL queries in your markdown files
- Support for SQLite databases
- Display results as formatted tables
- Error handling and display
- Database configuration via mkdocs.yml
- Live updates - changes to database reflect immediately in documentation
- Toggle SQL queries on/off
- Toggle between formatted and raw markdown views

## Installation

```bash
pip install mkdocs-sql
```

## Usage

1. Add to mkdocs.yml:
```yaml
plugins:
  - sql:
      databasePath:
        type: sqlite
        path: ./path/to/database.file
```

2. In your markdown files:
```markdown
---
databasePath: ./relative/path/to/database.file
showQuery: true  # optional, defaults to true
---

```sql
SELECT * FROM users LIMIT 5;
```
```

## Example Database Management

The plugin comes with a sample population database and management script. You can use it to test live updates:

```bash
# Reset database to initial state
python docs/examples/create_sample_db.py --reset

# Update a city's population (±10% random change)
python docs/examples/create_sample_db.py --update-city "New York"

# Update a country's population (±5% random change)
python docs/examples/create_sample_db.py --update-country "United States"
```

Changes to the database are reflected immediately in the documentation - just refresh the page!

## Configuration

| Option | Description | Default |
|--------|-------------|---------|
| databasePath.type | Database type (currently only sqlite) | sqlite |
| databasePath.path | Path to database file | None |
| showQuery | Show SQL queries by default | true |

## License

MIT