semvec/pypi-packages huggingface dataset:
lollmsvectordb 1.3.8
A modular text-based database manager for retrieval-augmented generation (RAG), seamlessly integrating with the LoLLMs ecosystem.
Requires Python: >=3.6

# LoLLMsVectorDB

**LoLLMsVectorDB**: A modular text-based database manager for retrieval-augmented generation (RAG), seamlessly integrating with the LoLLMs ecosystem. Supports various vectorization methods and directory bindings for efficient text data management.

## Features

- **Flexible Vectorization**: Supports multiple vectorization methods including TF-IDF and Word2Vec.
- **Directory Binding**: Automatically updates the vector store with text data from a specified directory.
- **Efficient Search**: Provides fast and accurate search results with metadata to locate the original text chunks.
- **Modular Design**: Easily extendable to support new vectorization methods and functionalities.

## Installation

```bash
pip install lollmsvectordb
```

## Usage

### Example with TFIDFVectorizer

```python
from lollmsvectordb import TFIDFVectorizer, VectorDatabase, DirectoryBinding

# Initialize the vectorizer
tfidf_vectorizer = TFIDFVectorizer()
tfidf_vectorizer.fit(["This is a sample text.", "Another sample text."])

# Create the vector database
db = VectorDatabase("vector_db.sqlite", tfidf_vectorizer)

# Bind a directory to the vector database
directory_binding = DirectoryBinding("path_to_your_directory", db)

# Update the vector store with text data from the directory
directory_binding.update_vector_store()

# Search for a query in the vector database
results = directory_binding.search("This is a sample text.")
print(results)
```

### Adding New Vectorization Methods

To add a new vectorization method, create a subclass of the `Vectorizer` class and implement the `vectorize` method.

```python
from lollmsvectordb import Vectorizer

class CustomVectorizer(Vectorizer):
    def vectorize(self, data):
        # Implement your custom vectorization logic here
        pass
```

## Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

## License

This project is licensed under the MIT License.

## Contact

For any questions or suggestions, feel free to reach out to the author:

- **Twitter**: [@ParisNeo_AI](https://twitter.com/ParisNeo_AI)
- **Discord**: [Join our Discord](https://discord.gg/BDxacQmv)
- **Sub-Reddit**: [r/lollms](https://www.reddit.com/r/lollms/)
- **Instagram**: [spacenerduino](https://www.instagram.com/spacenerduino/)

## Acknowledgements

Special thanks to the LoLLMs community for their continuous support and contributions.
genefeast 1.0.1
Gene-centric functional enrichment analysis summarisation tool
Requires Python: ==3.12.*

# GeneFEAST

GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarisation and visualisation tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to identify sets of genes putatively driving multiple enrichments and to explore gene-level quantitative data first used to identify input genes. Further, GeneFEAST can juxtapose FEA results from multiple studies, making it possible to highlight patterns of gene expression amongst genes commonly differentially expressed in one or more of multiple conditions, and giving rise to shared enrichments under those conditions. Thus, GeneFEAST offers a novel, effective way to address the complexities of linking up many overlapping FEA results to their underlying genes and data, advancing gene-centric hypotheses, and providing pivotal information for downstream validation experiments.

<br>

Please see the [User Guide](https://avigailtaylor.github.io/GeneFEAST/user_guide.html) for installation and usage instructions.

<br>

## Quick-start with example input data and output report

After installing GeneFEAST or downloading the docker container (see [User Guide](https://avigailtaylor.github.io/GeneFEAST/user_guide.html)), you can test it using the example data below.

The following links contain example input data, instructions on how to run GeneFEAST on them, and example output GeneFEAST reports.
- [Single FEA](https://avigailtaylor.github.io/GeneFEAST/example_usage.html)
- [Multi FEA](https://avigailtaylor.github.io/GeneFEAST/example_usage_multi.html)
  
---

### Citation

If you use GeneFEAST in your research, please cite the paper:
- Taylor, A., Macaulay, V.M., Maurya, A.K., Miossec, M.J., Buffa, F.M. GeneFEAST: the pivotal, gene-centric step in functional enrichment
  analysis interpretation. [arXiv:2309.00061](https://doi.org/10.48550/arXiv.2309.00061)

If you use an upset plot generated by GeneFEAST, please also cite: 
- Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., Pfister, R. UpSet: Visualization of Intersecting Sets. IEEE Transactions on Visualization and Computer Graphics (InfoVis ‘14), vol. 20, no. 12, pp. 1983–1992, 2014. doi.org/10.1109/TVCG.2014.2346248

If you use a GO hierarchy image generated by GeneFEAST, please also cite:
- Klopfenstein, D.V., Zhang, L., Pedersen, B.S. et al. GOATOOLS: A Python library for Gene Ontology analyses. Sci Rep 8, 10872 (2018). 
https://doi.org/10.1038/s41598-018-28948-z
intrepydd 0.1.15
An implementation of the Intrepydd compiler
Requires Python: >=3.6

Intrepydd compiles Python code with type annotations to optimized C++ code. Check out our paper to learn more: [Intrepydd: performance, productivity, and portability for data science application kernels](https://dl.acm.org/doi/10.1145/3426428.3426915).

A web tool is now available to try Intrepydd online: [https://tongzhou80.github.io/intrepydd-web/index.html](https://tongzhou80.github.io/intrepydd-web/index.html).

# Install
Anaconda (Python3) is required to run Intrepydd. Install intrepydd using `pip`:

```bash
pip install intrepydd
```

# Usage Example

```bash
import intrepydd
from intrepydd import float64

def foo() -> float64:
    a = 1.0
    b = 2.0
    c = (a + b) * 3.0 
    return c

foo1 = intrepydd.compile(foo)
print(foo1())
```
hypercli 2.0.8
Generate enhanced menu-driven CLI programs with ease.
Requires Python: <4.0,>=3.12

<h1 align="center">hypercli</h1>

<p align="center">
<img alt="GitHub Actions Workflow Status" src="https://img.shields.io/github/actions/workflow/status/HYP3R00T/hypercli/pypi_publish.yml?style=for-the-badge&labelColor=%2324273a&color=%23b7bdf8">
<a src="https://pypi.org/project/hypercli/" target="_blank">
<img alt="Pypi versions" src="https://img.shields.io/pypi/v/hypercli?style=for-the-badge&labelColor=%2324273a&color=%23b7bdf8">
</a>
</p>

**hypercli** is a Python package that provides an elegant solution for interacting with command line tools. It offers a menu-based command line interface (CLI) that allows users to navigate through different options and execute functions based on their choices.

## Features

- Generate menu-driven CLI programs with ease using decorators
- Customize the menus, options, and visual styles according to your requirements
- Supports nested menus and navigation between menus

## Installation

You can install **hypercli** using pip. Open your terminal and run the following command:

```bash
pip install hypercli
```

## Usage

Checkout [documentation](https://hypercli.hyperoot.dev) for more information on the configs, methods, and resources available in **hypercli**.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.
plusnethubtwo 0.0.4
A Python Package that can control and fetch data from the Plusnet Hub Two router
Requires Python: >=3.8

# Plusnet Hub Two

A Python Package that can control and fetch data from the Plusnet Hub Two router.

## Current Features

- Turn the hub light **on** or **off**
- Retrieve the current hub light status
- Retrieve list of devices in hub memory
- Retrieve list of currently connected devices


## Installation

```bash
pip install plusnethubtwo
```
goat-sdk-plugin-spl-tokens 0.1.0
Goat plugin for spl_token
Requires Python: <4.0,>=3.10
Keywords: goat, sdk, agents, ai, spl_token

# spl-token Plugin for GOAT SDK

A plugin for the GOAT SDK that provides spl-token functionality.

## Installation

```bash
# Install the plugin
poetry add goat-sdk-plugin-spl-token

# Install required wallet dependency
poetry add goat-sdk-wallet-solana
```

## Usage

```python
from goat_plugins.spl-token import spl_token, SplTokenPluginOptions

# Initialize the plugin
options = SplTokenPluginOptions(
    api_key="your-api-key"
)
plugin = spl_token(options)
```

## Features

- Example query functionality
- Example action functionality
- Solana chain support

## License

This project is licensed under the terms of the MIT license.
goat-plugin-nansen 0.1.0
Nansen plugin for GOAT SDK
Requires Python: <4.0,>=3.10
Keywords: goat, sdk, web3, agents, ai, nansen

# GOAT SDK Nansen Plugin

This plugin provides tools for interacting with the Nansen API, allowing you to:
- Get token details and trades
- Retrieve NFT collection details and trades
- Access smart money wallet analysis
- Get trading signals based on on-chain data

## Installation

```bash
pip install goat-sdk-plugin-nansen
```

## Usage

```python
from goat_plugins.nansen import nansen, NansenPluginOptions

# Initialize the plugin with your API key
plugin = nansen(NansenPluginOptions(
    api_key="your-nansen-api-key"
))

## Features

- Token Analysis:
  - Get detailed token information
  - Track token trading activity
  
- NFT Analytics:
  - Fetch NFT collection details
  - Monitor NFT trading activity
  
- Smart Money Tracking:
  - Analyze smart money wallet behavior
  - Track token flows
  
- Trading Signals:
  - Get trading signals based on on-chain data
  - Filter by date ranges and specific tokens
goat-plugin-1inch 0.1.0
1inch plugin for GOAT SDK
Requires Python: <4.0,>=3.10
Keywords: goat, sdk, web3, agents, ai, 1inch

# 1inch Plugin for GOAT

This plugin provides integration with the 1inch API for the GOAT SDK.

## Features

- Get token balances and allowances for a wallet address on a specific chain

## Installation

```bash
pip install goat-plugin-1inch
```

## Usage

```python
from goat_plugins.inch1 import OneInchService

# Initialize the service with your API key
service = OneInchService(api_key="your_api_key")

# Get balances for a wallet
balances = await service.get_aggregated_balances({
    "wallet_address": "0x...",
    "chain_id": 1  # Ethereum mainnet
})
```

## Configuration

The plugin requires a 1inch API key to function. You can obtain one from the [1inch Developer Portal](https://portal.1inch.dev/).

Set your API key when initializing the service:

```python
service = OneInchService(api_key="your_api_key")
```

## Development

To set up the development environment:

1. Clone the repository
2. Install dependencies with Poetry:
   ```bash
   poetry install
   ```
3. Run tests:
   ```bash
   poetry run pytest
   ```
VIIRS-swath-granules 1.0.1
Visible Infrared Imaging Radiometer Suite (VIIRS) Swath Product Search and Download Utility
Requires Python: >=3.11

# Visible Infrared Imaging Radiometer Suite (VIIRS) Swath Product Search and Download Utility

This tool uses the `earthaccess` library to search and download swath remote sensing data products from the Visible Infrared Imaging Radiometer Suite (VIIRS).

[Gregory H. Halverson](https://github.com/gregory-halverson-jpl) (they/them)<br>
[gregory.h.halverson@jpl.nasa.gov](mailto:gregory.h.halverson@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 329G

[Evan W. Davis](https://github.com/evandjpl) (he/him)<br>
[evan.w.davis@jpl.nasa.gov](mailto:evan.w.davis@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 397K

## Installation

Install the `VIIRS-Swath-Granules` package with dashes in the name from PyPi using pip:

```
pip install VIIRS-Swath-Granules
```

## Usage

Import the `VIIRS_swath_granules` package with underscores in the name:

```
import VIIRS_swath_granules
```
deepseek-local 0.1.1
A Python library for running DeepSeek-Coder locally using Ollama.
Requires Python: >=3.8

# DeepSeek-Local

🚀 **DeepSeek-Local** is a Python package that allows you to run **DeepSeek-Coder** locally using **Ollama**. With this package, you can generate and modify code using **DeepSeek-Coder**, all without requiring an internet connection!

---

## ✨ Features
- **Offline AI Coding Assistance** – Run DeepSeek-Coder locally via Ollama.
- **Simple Installation** – Easy setup with `pip install`.
- **Seamless Integration** – Use the package in any Python project.
- **No Internet Required** – Works entirely on your local machine.

---

## 📥 Installation

### 1️⃣ Prerequisites
Ensure you have the following installed on your system:
- **Python 3.8+**
- **Ollama** ([Download Here](https://ollama.ai))

### 2️⃣ Install DeepSeek-Local
```bash
pip install deepseek-local
```

---

## 🚀 Usage

### ✅ Check if Ollama is Installed
```python
from deepseek_local.utils import is_ollama_installed

if is_ollama_installed():
    print("Ollama is installed and ready to use!")
else:
    print("Please install Ollama first.")
```

### 🔄 Install DeepSeek-Coder Model
```python
from deepseek_local import install_deepseek

install_deepseek()
```

### 🤖 Run DeepSeek-Coder
```python
from deepseek_local import run_deepseek

response = run_deepseek("Write a Python function to reverse a string.")
print(response)
```

---

## ⚡ Troubleshooting

If you encounter issues, try the following:
- Ensure **Ollama** is installed and accessible from the terminal:
  ```bash
  ollama list
  ```
- If `run_deepseek()` doesn’t return a response, verify that **DeepSeek-Coder** is installed:
  ```bash
  ollama run deepseek-coder "Test query"
  ```
- If problems persist, **restart your terminal** and ensure Python can detect Ollama.

---

## 🌍 Contributing
We welcome contributions! To contribute:
1. **Fork** this repository 🍴
2. **Create a feature branch** (`git checkout -b feature-name`)
3. **Commit your changes** (`git commit -m "Added feature XYZ"`)
4. **Push to GitHub** (`git push origin feature-name`)
5. **Submit a Pull Request** 🛠️

---

## 📄 License
This project is licensed under the **MIT License**.

---

## 📬 Contact
Have questions or suggestions? Open an **issue** or reach out!

💻 **Email**: [saadaibani3@gmail.com]

---

⭐ If you find this project useful, **give it a star!** ⭐
friendly_names 0.1.2
Generate friendly, readable names by combining adjectives, nouns and verbs
Requires Python: >=3.8
Keywords: friendly, generator, names, readable

# friendly_names

[![PyPI - Version](https://img.shields.io/pypi/v/friendly_names.svg)](https://pypi.org/project/friendly_names)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/friendly_names.svg)](https://pypi.org/project/friendly_names)

A super simple random friendly name generator that creates readable, hyphenated names like "red-loop-bounty".

-----

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Development](#development)
- [Release Process](#release-process)
- [License](#license)

## Installation

```console
pip install friendly_names
```

## Usage

```python
import friendly_names

# Generate a friendly name like "red-loop-bounty"
name = friendly_names.generate()

# Customize number of words
name = friendly_names.generate(words=4)  # e.g., "happy-blue-running-fox"

# Use different separator
name = friendly_names.generate(separator="_")  # e.g., "green_swift_river"
```

## Development

To set up the development environment:

```console
pip install hatch
```

Common development tasks:

```console
# Run tests
hatch run test:test

# Run tests with coverage
hatch run test:coverage

# Run type checking
hatch run types:check

# Check code style
hatch run lint:style

# Format code
hatch run lint:fmt
```

The project uses:
- [Hatch](https://hatch.pypa.io/) for development environment and build management
- [pytest](https://docs.pytest.org/) for testing
- [mypy](https://mypy.readthedocs.io/) for type checking
- [Ruff](https://docs.astral.sh/ruff/) for linting and formatting
- [Black](https://black.readthedocs.io/) style rules (via Ruff)

## Release Process

To release a new version:

1. Update version in `src/friendly_names/__about__.py`
2. Commit the change:
   ```console
   git add src/friendly_names/__about__.py
   git commit -m "Bump version to x.y.z"
   ```
3. Tag the release:
   ```console
   git tag -a vx.y.z -m "Release version x.y.z"
   ```
4. Push changes and tag:
   ```console
   git push origin master
   git push origin vx.y.z
   ```

The GitHub Actions workflow will automatically build and publish to PyPI when the tag is pushed.

## License

`friendly_names` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.