semvec/pypi-packages huggingface dataset:
stonefish 0.7.0a2
Python code protection and licensing
Requires Python: >=3.10

<p align="center">
  <a href="https://github.com/stonefish-dev/code-shield"><img alt="stonefish" src="https://raw.githubusercontent.com/stonefish-dev/code-shield/main/images/stonefish-logo.svg" width="60%"></a>

Industry-grade Python code protection and licensing.

</p>
multimodalsim-viewer 0.1.6.0
Multimodal simulation viewer
Requires Python: >=3.10
Keywords: flask angular ui multimodal server

# multimodalsim-viewer

This package provides an interface to the [multimodalsim simulation project](https://pypi.org/project/multimodalsim/), allowing you to run and visualize simulations easily through a web interface.

## Usage

You have access to several commands that will allow you to run the project easily. You can use the `--help` flag to see a list of available options.

```bash
viewer start 

viewer stop

viewer simulate
```

## `DataCollector`

The `SimulationVisualizationDataCollector` class is used to collect data from the simulation and visualize it. You can pass an instance of this class to the simulation to collect data during the simulation. This might be useful if you work on the multimodalsim package and want to visualize the simulation data in real-time.

## Input data

To run a simulation, you need to provide input data. You can upload input data folders through the web interface. Some basic input data folders are available [here](https://github.com/lab-core/multimodal-data). You can also clone the repository and use the data from there : 

```bash
git clone https://github.com/lab-core/multimodal-data.git
```

## Environment variables

Some environment variables are available to customize the application. You can find a detailed list of these variables in the [README.md](https://github.com/lab-core/multimodal-simulator/blob/main/README.md#environment-variables) file of the multimodal-simulator repository.
secure-passgenx 0.1.3
Basit şifre üretici ve hash hesaplayıcı
Requires Python: >=3.8
Keywords: hash, password, security

# secure_passgen

Basit bir Python şifre üretici ve hash hesaplayıcı kütüphanedir

## Kullanım
```python
from secure_passgenx import password_generator
password, hash_value = password_generator(12, "sha256", True, True, True, True)
print(password)
print(hash_value)
qualrepair 1.0.0
Update the FASTQ quality scores from a subsequence FASTQ.
Requires Python: >=3.11

# qualrepair

[![PyPI Release](https://badge.fury.io/py/qualrepair.svg)](https://badge.fury.io/py/qualrepair)
[![CI](https://github.com/clintval/qualrepair/actions/workflows/python_package.yml/badge.svg?branch=main)](https://github.com/clintval/qualrepair/actions/workflows/python_package.yml?query=branch%3Amain)
[![Python Versions](https://img.shields.io/badge/python-3.11_|_3.12_|_3.13-blue)](https://github.com/clintval/qualrepair)
[![MyPy Checked](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://docs.astral.sh/ruff/)

Update the FASTQ quality scores from a subsequence FASTQ.

```console
pip install qualrepair
```

## Example

Repair the qualities in a sub-sequence FASTQ file from its original FASTQ file.

```bash
❯ qualrepair --in-source "r1.fastq.gz" --in-subseq "r1.subseq.fastq.gz"
```

This tool is primarily used to mitigate an unsolved issue in the [`splitcode`](https://github.com/pachterlab/splitcode/issues/18) tool.

## Development and Testing

See the [contributing guide](./CONTRIBUTING.md) for more information.
zencognify 0.1.2
Document ingestion and processing package with vector store and graph capabilities
Requires Python: >=3.12

# Zencognify

A Python package for document ingestion and processing with vector store capabilities.

## Installation

```bash
pip install zencognify
```

## Features

- Document processing from URLs (PDF, DOC, etc.)
- Vector storage with Qdrant
- Bulk document upload
- Document chunking and metadata management
- OpenAI embeddings integration

## Usage

### Basic Setup

```python
from cognify import VectorStore

vector_store = VectorStore(qdrant_url="http://localhost:6333")
```

### Create Collection

```python
vector_store.create_collection("my_documents")
```

### Bulk Upload Documents

```python
urls = [
    "https://example.com/document1.pdf",
    "https://example.com/document2.pdf"
]

results = vector_store.bulk_url_upload("my_documents", urls)
print(f"Uploaded {results['successful_uploads']} documents")
```

### Retrieve Document Chunks

```python
chunks = vector_store.get_document_chunks("my_documents", "document_id")
```

### Semantic Search

```python
results = vector_store.search("my_documents", "machine learning algorithms", top_k=5)
for doc, score in results:
    print(f"Score: {score:.3f}")
    print(f"Content: {doc.page_content[:200]}...")
    print(f"Source: {doc.metadata['source']}")
    print("---")

document_id = "your-document-id"
results = vector_store.search_by_document("my_documents", "neural networks", document_id, top_k=3)
```

### Delete Documents

```python
chunk_ids = ["chunk_id1", "chunk_id2"]
vector_store.delete_documents("my_documents", chunk_ids)
```

## Requirements

- Python 3.12+
- Qdrant vector database
- OpenAI API key (for embeddings)

## Dependencies

- langchain
- langchain-openai
- langchain-qdrant
- qdrant-client
- docling
- python-dotenv
- openai
- tiktoken

## Environment Variables

Set your OpenAI API key:

```bash
export OPENAI_API_KEY="your-api-key-here"
```

## License

MIT License
vstransitions 0.1.4
VapourSynth functions for making transitions and reanimating static shots
Requires Python: >=3.10

# vs-transitions, a collection of functions for making transitions and reanimating static shots.

For support you can check out the [JET Discord server](https://discord.gg/XTpc6Fa9eB). <br><br>

## How to install

Install `vstransitions` with the following command:

```sh
pip install vstransitions
```

Or if you want the latest git version, install it with this command:

```sh
pip install git+https://github.com/Jaded-Encoding-Thaumaturgy/vs-transitions.git
```

<br>