semvec/pypi-packages huggingface dataset:
berag 0.1.0
A modular RAG pipeline library for your own data
Requires Python: >=3.8

# 🧠 BeRAG

BeRAG is a modular Python library for Retrieval-Augmented Generation (RAG) systems built from your own data.

## Features
- Load and chunk documents
- Generate embeddings
- Store and retrieve using vector DBs
- Ask questions via LLMs

## Installation
```bash
pip install berag
```

## Usage
```python
from berag import RAGPipeline
rag = RAGPipeline(config="config.yaml")
rag.ask("What is BeRAG?")
```
apisani-test-project2 0.2.0
A short description of the project
Requires Python: <4.0,>=3.10

# apisani-test-project2

A short description of the project

## Overview

This documentation covers the apisani-test-project2 library.

## Installation

```bash
pip install apisani-test-project2
```

Or, if you use Poetry:

```bash
poetry add apisani-test-project2
```

## Quick Start

```python
from apisani_test_project2 import Example

# Initialize
example = Example()

# Use the library
result = example.run()
print(result)
```
scpygen 1.2.1
A python project structure generator
Requires Python: >=3.7

# SCPYGEN 🚀
*V1.2.1*

SCPYGEN is a Python CLI tool that instantly generates production-ready Flask project skeletons. Whether you need a minimal API or a full-stack Flask app (with templates, static files, and blueprints), SCPYGEN saves you time creating your project.

## Features ✨

- Interactive CLI: Choose between a Flask API or a full-stack Flask app with a single command.
- Best Practices: Projects include blueprints, config, models, migrations, .env, and more.
- Customizable: Generated code is modular and easy to extend.
- Fast Start: Go from zero to a working Flask project in seconds.

## Installation ⚙️

Install via pip:

```bash
pip install scpygen
```

## Usage 🖥️

Run the CLI in your terminal:

```bash
scpygen
```

You’ll be prompted to choose:

```bash
1: Flask API Skeleton (RESTful, no frontend)
2: Flask Full Stack Application (with templates, static, and database models)
```

Then enter your project name. scpygen will generate a new folder with all required files and folders.

Example Output Structure

For a Flask API Skeleton:
```bash
my-flask-api/
├── run.py
├── requirements.txt
├── .env
├── api/
│   ├── __init__.py
│   ├── config/
│   ├── models/
│   ├── routes/
│   └── ...
├── tests/
└── ...
```

For a Full Stack Flask App:
```bash
my-flask-app/
├── run.py
├── requirements.txt
├── .env
├── app/
│   ├── __init__.py
│   ├── templates/
│   ├── static/
│   ├── models/
│   ├── routes/
│   └── ...
├── tests/
└── ...
```

## Change Log 📄
You can view the Change Log [HERE](https://github.com/xShadowCodingx/scpygen/blob/prod/changelog.md).

## License 🪪
This project is licensed under the MIT License - see the [LICENSE](https://github.com/xShadowCodingx/scpygen/blob/prod/LICENSE) file for details.
hwc-bgr-888-image-to-base64-uri 0.1.0a0
A specialized utility for converting OpenCV-compatible HWC BGR 888 images to Base64-encoded data URIs. Can be used with the OpenAI Chat Completions API.
Requires Python: >=2

# HWC BGR 888 Image to Base64 URI

A specialized utility for converting OpenCV-compatible HWC BGR 888 images to Base64-encoded data URIs. Can be used with the OpenAI Chat Completions API.

## Usage with OpenAI Chat Completions API

```python
from __future__ import print_function
import cv2

from hwc_bgr_888_image_to_base64_uri import hwc_bgr_888_image_to_base64_uri
from openai import OpenAI


# Initialize client
client = OpenAI(
    # For Ollama local endpoint
    base_url='http://localhost:11434/v1/',
    api_key='ollama', # Required parameter (ignored by Ollama)
)

# 1. Load image using OpenCV (native BGR format)
image = cv2.imread('input.jpg')

# 2. Convert to API-compliant URI (auto-resizes to 512px longest edge)
uri = hwc_bgr_888_image_to_base64_uri(
    image,
    max_long_edge=512, # Recommended for most vision models
    output_format_extension=u'.jpg' # Or u'.png' for quality
)

# 3. Structure chat completions request
response = client.chat.completions.create(
    model=u"llava",
    messages=[
        {
            u"role": u"user",
            u"content": [
                {u"type": u"text", u"text": u"What's in this image?"},
                {
                    u"type": u"image_url",
                    u"image_url": {
                        u"url": uri # Our generated URI
                    }
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)
```

For more information:

- https://platform.openai.com/docs/api-reference/chat/create
- https://github.com/ollama/ollama/blob/main/docs/openai.md#curl

## Contributing

Contributions are welcome! Please submit pull requests or open issues on the GitHub repository.

## License

This project is licensed under the [MIT License](LICENSE).