semvec/pypi-packages huggingface dataset:
llama-scan 0.1.9
A tool for converting PDFs to text files using Ollama.
Requires Python: >=3.10

# llama-scan

A tool for converting PDFs to text files using Ollama.

## Features

- Convert PDFs to text files locally, no token costs.
- Use the latest multimodal models supported by Ollama.
- Turn images and diagrams into detailed text descriptions.

## Requirements

- Python 3.10+
- Ollama installed and running locally

### Installing Ollama and the Default Model

1. Install [Ollama](https://ollama.com/)
2. Pull the default model:
```bash
ollama run qwen2.5vl:latest
```

## Installation

Install using pip:
```bash
pip install llama-scan
```

or uv:
```bash
uv tool install llama-scan
```

## Usage

Basic usage:
```bash
llama-scan path/to/your/file.pdf
```

### Options

- `--output`, `-o`: Output directory (default: "output")
- `--model`, `-m`: Ollama model to use (default: "qwen2.5vl:latest")
- `--start`, `-s`: Start page number (default: 0)
- `--end`, `-e`: End page number (default: 0)
- `--custom-instructions`, `-c`: Optional path to a text file containing additional instructions (default: None)
- `--server-url`, `-u`: Ollama server URL (default: "http://localhost:11434")
- `--width`, `-w`: Width of the resized images (0 to skip resizing; default: 0)
- `--keep-images`, `-k`: Keep the intermediate image files (default: False)
- `--stdout`, `-s`: Write merged output to stdout (default: False)

### Examples

Process specific pages:
```bash
llama-scan document.pdf --start 1 --end 5
```

Use a different Ollama model:
```bash
llama-scan document.pdf --model qwen2.5vl:3b
```
pydantic-ai-jupyter 0.1.2
Experiment with Pydantic AI Agents in your notebooks.
Requires Python: >=3.10
Keywords: agents, jupyter, llm, notebook, pydantic-ai

# pydantic-ai-jupyter

[![Tests](https://github.com/rgbkrk/pydantic-ai-jupyter/workflows/Tests/badge.svg)](https://github.com/rgbkrk/pydantic-ai-jupyter/actions)

Experiment with [Pydantic AI](https://github.com/pydantic/pydantic-ai) Agents interactively in Jupyter notebooks.

## Installation

```bash
pip install pydantic-ai-jupyter
```

Or with uv:

```bash
uv add pydantic-ai-jupyter
```

## Usage

```python
from pydantic_ai import Agent
from pydantic_ai_jupyter import run_in_jupyter

agent = Agent("openai:gpt-4o-mini")

@agent.tool_plain
def get_weather(city: str) -> str:
    return f"Sunny, 22°C in {city}"

# Run with rich display
result = await run_in_jupyter(agent, "What's the weather in Tokyo?")
```

### Features

- **Streaming text** - See the model's response as it generates
- **Streaming tool calls** - Watch tool arguments stream in (with providers that support it, like OpenAI)
- **Tool results** - Styled success and retry results
- **Thinking/reasoning** - Collapsible display of model thinking
- **Error handling** - Graceful display of exceptions with tracebacks

### Multi-turn conversations

```python
# First turn
result = await run_in_jupyter(agent, "What's the weather in Tokyo?")

# Continue the conversation
result = await run_in_jupyter(
    agent,
    "What about London?",
    message_history=result.all_messages(),
)
```

### Debug mode

Enable debug mode to see all events:

```python
result = await run_in_jupyter(agent, "Hello!", debug=True)
```

## Supported providers

Tool call argument streaming works best with **OpenAI**, which streams arguments token-by-token. Other providers like Groq and Ollama (via OpenAI-compat) buffer tool calls and send them all at once.

| Provider | Text streaming | Tool call streaming |
|----------|----------------|---------------------|
| OpenAI   | ✅ | ✅ |
| Groq     | ✅ | ❌ (buffered) |
| Ollama   | ✅ | ❌ (buffered) |

## Development

See [TESTING.md](TESTING.md) for testing guidelines and [PUBLISHING.md](PUBLISHING.md) for release instructions.

```bash
# Install dependencies
uv sync --extra dev

# Run tests
uv run pytest tests/ -v

# Run linting
uv run ruff check .
uv run ruff format .

# Type checking
uv run mypy pydantic_ai_jupyter/
```

## License

BSD 3-Clause
kasheesh-tools 0.1.9
Standard Tools
Requires Python: >=3.11

# kasheesh-tools

This is a package for Kasheesh python tools
## Install from:
https://pypi.org/project/kasheesh-tools/

## To upgrade:
`pip install --upgrade kasheesh-tools`

## Currently supports:

* tool_logger
* tool_file_rw
* tool_cmd_parser
* tool_stats