semvec/pypi-packages huggingface dataset:
deep-semantic-search 0.1.1
A library for embedding, indexing, and applying semantic search for text and image data
Requires Python: >=3.8

# Deep Semantic Search

A Python library for embedding, indexing, and applying semantic search for text and image data.

## Features

- **Multi-modal Semantic Search**:
  - Embedding and indexing text data using the nli-mpnet-base-v2 model
  - Embedding and indexing image data using the CLIP model
  - Semantic search for both text and image data
  - Search images by both image and text queries

- **Clustering and Image Captioning**:
  - Cluster image embeddings using PyTorch KMeans (with GPU support)
  - Caption images using the BLIP model

- **Retrieval-Augmented Generation (RAG)**:
  - Answer questions based on search results
  - Summarize search results
  - Generate topics for image captions

## Installation

```bash
pip install deep-semantic-search
```

## Quick Start

### Text Search

```python
from deep_semantic_search import LoadTextData, TextEmbedder, TextSearch

# Load text data
loader = LoadTextData()
corpus_dict = loader.from_folder("path/to/text/files")

# Embed the text data
embedder = TextEmbedder()
embedder.embed(corpus_dict)

# Search for similar texts
search = TextSearch()
results = search.find_similar("your search query", top_n=5)

for result in results:
    print(f"Score: {result['score']}, Text: {result['text'][:100]}...")
```

### Image Search

```python
from deep_semantic_search import LoadImageData, ImageSearch

# Load image data
loader = LoadImageData()
image_paths = loader.from_folder("path/to/images")

# Set up image search
searcher = ImageSearch(image_paths)

# Search for similar images to a text query
results = searcher.get_similar_images_to_text("cat on a sofa", number_of_images=5)

# Display results
for path, score in results.items():
    print(f"Score: {score}, Image: {path}")
```

### RAG (Retrieval-Augmented Generation)

```python
from deep_semantic_search import ask_question

# Ask a question based on provided text data
texts = ["Text document 1...", "Text document 2..."]
answer = ask_question(texts, "What is the main topic discussed?")
print(answer)
```

## Requirements

- Python 3.8+
- PyTorch
- Sentence Transformers
- Hugging Face Transformers
- FAISS
- LangChain

## License

MIT
vmdcube 1.0.0
A Python package for rendering cube files with VMD.
Requires Python: >=3.8
Keywords: vmd, cube, rendering

# VMDCube

![GitHub](https://img.shields.io/github/license/fevangelista/vmdcube)
![GitHub issues](https://img.shields.io/github/issues/fevangelista/vmdcube)
![GitHub pull requests](https://img.shields.io/github/issues-pr/fevangelista/vmdcube)
![GitHub last commit](https://img.shields.io/github/last-commit/fevangelista/vmdcube)
![GitHub contributors](https://img.shields.io/github/contributors/fevangelista/vmdcube)
![GitHub repo size](https://img.shields.io/github/repo-size/fevangelista/vmdcube)
![GitHub stars](https://img.shields.io/github/stars/fevangelista/vmdcube)
![GitHub forks](https://img.shields.io/github/forks/fevangelista/vmdcube)

## Description

A simple pip-installable Python module to generate pretty 3D visualizations of molecular orbitals like this the one below:

<p align="center">
<img src="title.png" alt="VMDCube Header" width="450"/>
</p>

VMDCube is designed to work with VMD (Visual Molecular Dynamics) and is compatible with the VMD `cube` file format. It can be used to visualize molecular orbitals, electron density, and other volumetric data.

## Features

VMDCube can be used in Python scripts and Jupyter notebooks to render cube files. Here is an example of how to use it in Python:

```python
from vmdcube import VMDCube
vmd = VMDCube() # by default render all cube files in the current directory
vmd.run()
```

The following showcases VMDCube's visualization capabilities in Jupyter notebooks:

<p align="center">
<img src="example.png" alt="Example Visualization" width="450"/>
</p>

## Installation

Clone the repository, then run:

```bash
git clone git@github.com:fevangelista/VMDCube.git
cd VMDCube
pip install -e .
```

## Tutorials

See [the VMDCube introductory tutorial](tutorials/vmdcube_tutorial.ipynb) for how to use VMDCube in Jupyter notebooks.
llamapublisher 0.1.0
A powerful library for AI-powered search and data processing
Requires Python: >=3.8

# llamapublisher

## Overview
llamapublisher is a powerful library for the LlamaSearch AI ecosystem. It provides developers with robust tools for working with AI-powered search and data processing.

## Features
- High-performance data processing
- Seamless integration with other Llama libraries
- Comprehensive API for AI-powered search
- Scalable architecture for enterprise applications
- MLX support for optimized performance on Apple Silicon
- Extensive customization options

## Installation

### Prerequisites
- Python 3.8+
- pip

### Install from PyPI
```bash
pip install llamapublisher
```

### Install from source
```bash
git clone https://github.com/llamasearchai/llamapublisher.git
cd llamapublisher
pip install -e .
```

## Quick Start
```python
import llamapublisher

# Initialize the client
client = llamapublisher.Client()

# Process data
results = client.process("Your query here")
print(results)
```

## Documentation
For full documentation, visit [our documentation site](https://llamasearchai.github.io/llamapublisher/).

## Examples
See the `examples/` directory for more usage examples.

## Contributing
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Related Projects
- [llamaindex](https://github.com/llamasearchai/llamaindex)
- [llamasearch](https://github.com/llamasearchai/llamasearch)
- [llamagraph](https://github.com/llamasearchai/llamagraph)

## Roadmap
- Advanced integration with vector databases
- Enhanced MLX support
- Additional language models
- Improved documentation and examples
- Performance optimizations

## Community
Join our community on [Discord](https://discord.gg/llamasearch)
confidant-py 0.2.0
Multi platform scalable environment variables manager.
Requires Python: >=3.7

# Confidant-Py

Multi platform scalable environment variables manager

## Installation

```sh
pip install confidant-py
```

## Usage

```python
from confidant_py.loader import ConfidantLoader

loader = ConfidantLoader()
envs = loader.get_envs()
print(envs)
```


## **4. Publish to PyPI**
1. Install necessary tools:
```sh
pip install setuptools wheel twine
```

2. Build the package

```sh
python setup.py sdist bdist_wheel
```

3. Upload to PyPI:

```sh
twine upload dist/*
```
openfiles 1.1.1
Python SDK for Openfiles API
Requires Python: >=3.7
Keywords: openfiles, ton, storage, api, sdk

# Openfiles Python SDK

A Python SDK for interacting with the Openfiles API.

## Installation

```bash
pip install openfiles
```

## Usage

```python
from openfiles import OpenfilesClient

# Method 1: Initialize the client with your API token
client = OpenfilesClient(api_token="your_api_token")

# Method 2: Initialize using OPENFILES_API_TOKEN environment variable
# export OPENFILES_API_TOKEN="your_api_token"
client = OpenfilesClient()

# Upload a file
response = client.upload_file(
    file_path="test/file.txt", description="My file description"
)
file_bag_id = response.bag_id
print(f"File uploaded with bag_id: {file_bag_id}")

# Get user information
user_info = client.get_user_info()
print(f"Space left: {user_info.space_left} / {user_info.capacity}")

# List user files
files = client.get_user_files_list()
for file in files:
    print(f"File: {file.filename}, Size: {file.size}, Bag ID: {file.bag_id}")

# Download a file
client.download_file(bag_id=file_bag_id, destination="test/file_copy.txt")

# Delete a file
client.delete_file(bag_id=file_bag_id)
```

## Features

-   Upload files
-   Download files
-   Delete files
-   Get user information
-   List user files

## License

[MIT License](LICENSE)
proxup 0.1.1
Get proxies for free and automatically without any hassle.
Requires Python: >=3.6



-----

### <p align="center">👻 ProxUp 👻</p>

<br><br>
<p align="center">
<strong>
First of all, if you're looking for a good Python Module For Proxies Rotating, welcome to ProxUp!
<br><br>
  
-----

### <p align="center">✔ Example ✔</p>

```
import proxup

print(requests.get('https://google.com', proxies=proxup.GetProxy()).status_code)
```

-----

### <p align="center">📌 Disclaimer 📌</p>

<br><br>
* ***Please use this program only for educational purposes.***
* ***It is not meant to be used in any malicious way, and I decline any responsibility for what you do with it.***
<br><br>

-----

### <p align="center">Zarby</p>
proxUP 0.1.0
Get proxies for free and automatically without any hassle.
Requires Python: >=3.6



-----

### <p align="center">👻 ProxUp 👻</p>

<br><br>
<p align="center">
<strong>
First of all, if you're looking for a good Python Module For Proxies Rotating, welcome to ProxUp!
<br><br>
  
-----

### <p align="center">✔ Example ✔</p>

```
from ProxUp import *

print(requests.get('https://google.com', proxies=GetProxy()).status_code)
```

-----

### <p align="center">📌 Disclaimer 📌</p>

<br><br>
* ***Please use this program only for educational purposes.***
* ***It is not meant to be used in any malicious way, and I decline any responsibility for what you do with it.***
<br><br>

-----

### <p align="center">Zarby</p>