semvec/pypi-packages huggingface dataset:
aiojito 0.1.1
An async Python library for interacting with the Jito JSON-RPC
License: MIT
Requires Python: >=3.10

# aiojito

`aiojito` is an async Python library for interacting with the Jito via JSON-RPC.

## Installation

```bash
pip install aiojito
```
    
## Usage

### 1.Gets the block engine url

You can access the block engine information using the interface below

```python
from aiojito.api.block_engine import BlockEngine

engines = BlockEngine.get_block_engines(network='mainnet')
for k, v in engines.items():
    print(k)
    print(v['block_engine_url'])
```

You can get the address of the block engine from jito's website

- [Mainnet Addresses | Jito (gitbook.io)](https://jito-labs.gitbook.io/mev/searcher-resources/block-engine/mainnet-addresses)
- [Testnet Addresses | Jito (gitbook.io)](https://jito-labs.gitbook.io/mev/searcher-resources/block-engine/testnet-addresses)

### 2.Interact with Jito as a searcher asynchronously

```python  
import asyncio
import aiohttp
from aiojito.async_api.searcher import AsyncSearcher


async def main():
    # Create a session context manager
    async with aiohttp.ClientSession() as session:
        # Create a searcher instance  
        block_engine_url = "https://ny.mainnet.block-engine.jito.wtf"
        searcher = AsyncSearcher(block_engine_url=block_engine_url, session=session)

        # Get tip accounts  
        tip_accounts = await searcher.get_tip_accounts()
        print("Tip Accounts:", tip_accounts)

        # Get bundle statuses  
        bundle_ids = ["your_bundle_id_here"]
        bundle_statuses = await searcher.get_bundle_statuses(bundle_ids)
        print("Bundle Statuses:", bundle_statuses)

        # Send a bundle  
        transactions = ["your_base58_encoded_transaction_here"]
        bundle_id = await searcher.send_bundle(transactions)
        print("Sent Bundle ID:", bundle_id)

        # Send a transaction  
        transaction = "your_base58_encoded_transaction_here"
        transaction_id = await searcher.send_transaction(transaction)
        print("Sent Transaction ID:", transaction_id)


asyncio.run(main())
```

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
wav-finder 0.1.0
A tool to find WAV files from a given URL or path
License: MIT
Requires Python: >=3.7

# WAV Finder

一个简单的Python工具，用于从指定地址（URL或本地路径）中查找WAV文件。

## 功能特性

- 支持从HTTP/HTTPS URL查找WAV文件
- 支持从本地文件系统路径查找WAV文件
- 递归搜索子目录
- 支持多种WAV文件扩展名（.wav, .WAV）
- 提供命令行接口和Python API

## 安装

```bash
pip install wav-finder
```

## 使用方法

### 命令行使用

```bash
# 从URL查找WAV文件
wav-finder https://example.com/audio-files/

# 从本地路径查找WAV文件
wav-finder /path/to/audio/directory

# 显示帮助信息
wav-finder --help
```

### Python API使用

```python
from wav_finder import WavFinder

# 创建WAV查找器实例
finder = WavFinder()

# 从URL查找WAV文件
wav_files = finder.find_wav_files("https://example.com/audio-files/")
print(wav_files)

# 从本地路径查找WAV文件
wav_files = finder.find_wav_files("/path/to/audio/directory")
print(wav_files)
```

## 输出格式

工具会返回一个包含WAV文件路径的列表：

```python
[
    "https://example.com/audio-files/song1.wav",
    "https://example.com/audio-files/song2.wav",
    "/path/to/audio/file3.wav"
]
```

## 依赖

- Python 3.7+
- requests
- beautifulsoup4
- urllib3

## 许可证

MIT License
doc2code-assistant 0.1.0
Your own AI coding assistant based on your documentation using open models (like Ollama) + Streamlit
License: MIT
Requires Python: >=3.10

# 🧠 Doc2Code Assistant

Your own **AI coding assistant** powered by **local language models** — no internet, no OpenAI, just raw power on your machine 💻⚡

---

## ✨ Features

✅ Runs entirely **locally** using `phi3:mini` (or any Ollama-supported model)  
✅ Uses your **GPU** for fast inference *(if available)*  
✅ Clean and interactive **Streamlit UI** for querying docs and generating code  
✅ **No dependencies** on OpenAI, APIs, or external services  
✅ Automatically checks for **GPU usage** 💪

---

## 🚀 Getting Started

### 1. 📦 Install Ollama

#### Linux/macOS

```bash
curl -fsSL https://ollama.com/install.sh | sh
```

#### Windows

Download the installer and follow the setup instructions.

### 2. 📥 Download a Model

#### Pull your preferred local model (default: phi3:mini):

```bash
ollama pull phi3:mini
```

You can also try others like `codellama:7b` or `mistral:7b`.

### 3. 🧪 (Optional) Test the Model in Terminal

```bash
ollama run phi3:mini
```

### 4. 🎛️ Run the App

```bash
streamlit run app.py
```

Then open your browser at http://localhost:8501

### 📝 Notes
Your documents should be placed in the docs/ folder. The assistant will index and learn from them automatically.

If you want to switch models, update the model name in app.py:

```bash
    llm = Ollama(model="your-model-name-here")
```

### ⚙️ Requirements

Python 3.10+
Ollama installed and running
Streamlit:

```bash
pip install streamlit llama-index llama-index-llms-ollama llama-index-embeddings-huggingface
```

### 🤝 Contributing
Ideas, improvements, or bugs? Open an issue or submit a PR — contributions are welcome!

### 🔐 License
MIT License