semvec/pypi-packages huggingface dataset:
dtx-to-wif 4.5
Convert handweaving files from dtx format (FiberWorks) to wif
Requires Python: >=3.10

Convert FiberWorks .dtx weaving pattern files to WIF 1.1

This software can also convert WeavePoint .wpo files to WIF, though it cannot read fiber thickness or spacing information, so the conversion loses information.

This runs as a command-line script written in Python. See Installation and Usage for instructions.

This package can also read .wif, .dtx, and .wpo files into a standard in-memory model (`dtx_to_wif.PatternData`).
This may be used for writing weaving design or loom driver software.

This software is licensed under the MIT license; see license.txt for details.

Links:

* [Documentation](https://r-owen.github.io/dtx_to_wif/)
* [Source Code](https://github.com/r-owen/dtx_to_wif/)
* [PyPI](https://pypi.org/project/dtx-to-wif/)
genkit-plugin-ollama 0.5.0
Genkit Ollama Plugin
Requires Python: >=3.10
Keywords: ai, artificial-intelligence, generative-ai, genkit, llm, local, machine-learning, ollama, self-hosted

# Ollama Plugin

This Genkit plugin provides a set of tools and utilities for working with Ollama.

## Setup environment

```bash
uv venv
source .venv/bin/activate
```

## Serving Ollama Locally

### Ollama Service Installation

#### MacOS  (brew)

```bash
brew install ollama
```

#### Debian/Ubuntu (apt installer)

```bash
curl -fsSL https://ollama.com/install.sh | sh
```

Other installation options may be found [here](https://ollama.com/download)

### Start serving of Ollama locally

```bash
ollama serve
```

Ollama is served at `http://127.0.0.1:11434` by default.

### Installing Required Model

Once ollama service is serving - pull the required model version:

```bash
ollama pull <model-version>:<tag>
```

### Check installed models

Installed models can be reviewed with following command:

```bash
ollama list
```

## Examples

For examples check `./py/samples/ollama/README.md`
genkit-plugin-google-genai 0.5.0
Genkit Google GenAI Plugin
Requires Python: >=3.10
Keywords: ai, artificial-intelligence, gemini, generative-ai, genkit, google, imagen, llm, machine-learning, vertex-ai

# Google GenAI Plugin

This Genkit plugin provides a unified interface for Google AI (Gemini) and Vertex AI models, embedding, and other services.

## Setup environment

```bash
uv venv
source .venv/bin/activate
pip install genkit-plugins-google-genai
```

## Configuration

### Google AI (AI Studio)

To use Google AI models, obtain an API key from [Google AI Studio](https://aistudio.google.com/) and set it in your environment:

```bash
export GEMINI_API_KEY='<your-api-key>'
```

### Vertex AI (Google Cloud)

To use Vertex AI models, ensure you have a Google Cloud project and Application Default Credentials (ADC) set up:

```bash
gcloud auth application-default login
```

## Features

### Dynamic Models

The plugin automatically discovers available models from the API upon initialization. You can use any model name supported by the API (e.g., `googleai/gemini-2.0-flash-exp`, `vertexai/gemini-1.5-pro`).

### Dynamic Configuration

New or experimental parameters can be passed flexibly using `model_validate` to bypass strict schema checks:

```python
from genkit.plugins.google_genai import GeminiConfigSchema

config = GeminiConfigSchema.model_validate({
    'temperature': 1.0,
    'response_modalities': ['TEXT', 'IMAGE'],
})
```

## Examples

For comprehensive usage examples, see [`py/samples/google-genai-hello/README.md`](../../samples/google-genai-hello/README.md).