semvec/pypi-packages huggingface dataset:
raga-once 0.0.3
A one-step Ragas cli tool to evaluate RAG apps
Requires Python: >=3.8

# Ragas CLI

A one-step Ragas cli tool to evaluate QCAG testsets generated by RAG apps. (Q = Question, C = Contexts, A = Answer, G = Ground_truth)

## Install with pip

```bash
pip install raga-once
```

## Arguments

- `--model`: Specifies the model to use for evaluation.
    - Default value is "gpt-3.5-turbo". Langchain compatible.
- `--apibase`: Specifies the base URL for the API.
    - Default value is "https://api.openai.com/v1".
- `--apikey`: Specifies the API key to authenticate requests. 
    - Not required if using psuedo-openai API server, e.g. vLLM, Fastchat, etc.
- `--embeddings`: Specifies the Huggingface embeddings model to use for evaluation. 
    - Embeddings will run **locally**.
    - Will use OpenAI embeddings if not set.
    - Better set if using psuedo-openai API server.
- `--metrics`: Specifies the metrics to use for evaluation.
    - Will use Ragas default metrics if not set.
    - Default metrics: `["answer_relevancy", "context_precision", "faithfulness", "context_recall", "context_relevancy"]`
    - Other metrics: `"answer_similarity", "answer_correctness"`
- `--dataset`: Specifies the path to the dataset for evaluation.    
    - Dataset format must meet RAGAS requirements.
    - Will use fiqa dataset as demo if not set.

## Usage

### Fiqa dataset demo:

```bash
python3 -m ragas_once.cli --apikey "YOUR_OPENAI_API_KEY"
```

### Evaluate with GPT-4 and `BAAI/bge-small-en` embeddings

The huggingface embeddings will run locally, so **Make sure your machine works and have [sentence-transformers](https://pypi.org/project/sentence-transformers/) installed:**

```bash
pip install sentence-transformers
```
Then run:

```bash
python3 -m ragas_once.cli --model "gpt-4" --apikey "YOUR_OPENAI_API_KEY" --embeddings "BAAI/bge-small-en" --dataset "path/to/dataset.csv"
```

### Prepare Dataset

See [**Ragas documentation**](https://docs.ragas.io/en/stable/howtos/applications/data_preparation.html)
codelink 0.0.26
Node-based visual programming environment
Requires Python: >=3.10

# Codelink

Codelink is a node-based visual programming environment that can be easily and flexibly integrated into 
[PySide2](https://pypi.org/project/PySide2/) applications as a widget. With Codelink, functions (so-called nodes) can 
be logically linked to each other via wires, as in flowcharts, to create directed node graphs that can be used to 
perform complex calculations or control other programs. A main application area is node-based modeling of 3D geometries 
with the parametric CAD software [FreeCAD](https://www.freecad.org/).

![Startup image](https://github.com/j8sr0230/codelink/raw/main/img/start_up_image.png)


It provides:
* customizable nodes, sockets and data types
* framing and commenting nodes
* sub-graphs and group nodes
* socket options
* validation node connections (links)
* full undo/redo implementation
* framework for node developer
* easy integration in [PySide2](https://pypi.org/project/PySide2/) applications
* made for [FreeCAD](https://www.freecad.org/)

## Installation
Binary wheels for [codelink](https://pypi.org/project/codelink/) are available on 
[PyPI](https://pypi.org/).  
```
pip install codelink
```

## Run
After installing Codelink via [PyPI](https://pypi.org/) start your Python interpreter and run the following commands:
```
>>> import codelink.path_loader
>>> from codelink.app import main
>>> main()
```

If you want to run Codelink from within [FreeCAD](https://www.freecad.org/), execute the following commands in the 
integrated Python interpreter:
```
>>> import codelink.path_loader
>>> from codelink.app_fc import main
>>> main()
```