semvec/pypi-packages huggingface dataset:
ai-robot-position-finder 0.1.1
Python app to control the 16 Servo positions

# AI Robot Position Finder
[PyPI Page](https://pypi.org/project/ai-robot-position-finder)

![alt text](https://raw.githubusercontent.com/aruncs31s/ai_robot_position_finder_app/main/images/image.png)

![alt text](https://raw.githubusercontent.com/aruncs31s/ai_robot_position_finder_app/refs/heads/main/images/image2.png)

## Installation

```bash
pip install ai-robot-position-finder
```


**First Time Only**
This app requires a config.json file present in the working directory 
*Which should contain the following* or it can be obtained from [config.json](config.json)

```json
{
    "initial_position_url": "https://github.com/AI-Robot-GCEK/robo-initial-positions/blob/main/src/initial-positions.h",
    "servo_count": 16,
    "control_api": "setServo",
    "ip": "192.168.180.110"
}
```
> **Some Things to note**
> - I have provided the initial_positions as a URL, and it is `scraped` using a module called `ai-robot-position-scraper` you can find it [here](https://pypi.org/project/ai-robot-position-scraper/). If you want to change that and want to do something like, provide a list inside the JSON or something create an issue or update the app and send a PR.

## Usage

```bash
ai-ps
```

## update
```bash
pip install --upgrade ai-robot-position-finder
```

## Features
- Initializes the robot position from a URL ✅
- Values can be saved to a file ✅


## Contributing
Contributions are welcome! Please read the [contributing guidelines](CONTRIBUTING.md) for details on how to contribute to this project.
fibonacci-Enoch12 0.0.1
Method to calculate Faculty
Requires Python: >=3.0

Sure! Here's a tailored `README.md` for your recursive Fibonacci calculator, based on the example you provided:

---

```markdown
# 🌀 Recursive Fibonacci Calculator - `fibonacci_Enoch12`

[![Python Version](https://img.shields.io/badge/Python-3.6%2B-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

A simple Python script to calculate Fibonacci numbers using recursion.  
Perfect for learning how recursion works in Python and understanding Fibonacci sequences.

---

## 🚀 Features

- Recursive implementation of Fibonacci logic
- Beginner-friendly structure and code
- Interactive command-line input
- No external dependencies

---

## 📂 File Structure
```

📁 fibonacci-recursive/
│
├── fibonacci_Enoch12.py # Main Python script
└── README.md # Project documentation

````

---

## 🧾 Code Overview

```python
def fibonacci(n):
    if n <= 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n - 1) + fibonacci(n - 2)

def main():
    try:
        count = int(input("How many Fibonacci numbers should be printed? "))
        print("Fibonacci sequence:")
        for i in range(count):
            print(f"fibonacci({i}) = {fibonacci(i)}")
    except ValueError:
        print("Please enter a valid integer.")

if __name__ == "__main__":
    main()
````

---

## 🧪 Sample Output

```
How many Fibonacci numbers should be printed? 7
Fibonacci sequence:
fibonacci(0) = 0
fibonacci(1) = 1
fibonacci(2) = 1
fibonacci(3) = 2
fibonacci(4) = 3
fibonacci(5) = 5
fibonacci(6) = 8
```

---

## 🔧 How to Use

1. **Clone the repository**:

```bash
git clone https://github.com/Enoch1234455/fibonacci-recursive.git
cd fibonacci-recursive
```

2. **Run the script**:

```bash
python fibonacci_Enoch12.py
```

3. **Enter how many Fibonacci numbers you want** when prompted.

---

## 💡 Enhancements (Optional Ideas)

Expand this project by:

- Adding **memoization** or **dynamic programming** to optimize performance
- Implementing **unit tests** with `unittest` or `pytest`
- Providing **graphical output** using `matplotlib`
- Creating a **CLI** using `argparse` for more flexible input

---

## 📜 License

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

---

## 🙌 Contributing

Feel free to fork the repo, open issues, or submit pull requests. Contributions are welcome!

---

## 📬 Contact

Made with ❤️ by [Enoch](https://github.com/Enoch1234455)

```

---

Let me know if you'd like help generating a `LICENSE` file or optimizing the recursive code with memoization.
```
adk-client 0.1.0
Add your description here
Requires Python: >=3.13

# LangDB LLM for Google ADK

A Google ADK (Agent Development Kit) model implementation for LangDB's LLM API. This package provides seamless integration of LangDB's LLM capabilities into the Google ADK framework.

## Features

- Native integration with Google ADK's `BaseLlm` interface
- Support for both streaming and non-streaming completions
- Built-in function calling support
- Integration with MCP (Model Control Protocol) servers
- Asynchronous API compatible with ADK's execution model
- Automatic handling of tool execution and responses

## Installation

```bash
pip install -e .
```

## Usage with Google ADK

### Basic Usage

```python
import os
import asyncio
from google.adk import Agent
from langdb_llm import LangDBLlm
from google.adk.runners import InMemoryRunner

async def main():
    # Initialize the LangDB LLM with your credentials
    llm = LangDBLlm(
        model_name="anthropic/claude-sonnet-4",
        api_key=os.getenv("LANGDB_API_KEY"),
        project_id=os.getenv("LANGDB_PROJECT_ID")
    )
    
    # Create an ADK agent with the LangDB LLM
    agent = Agent(llm=llm)
    
    # Create a runner to execute the agent
    runner = InMemoryRunner()
    
    # Run the agent with a prompt
    response = await runner.run(agent, "Hello, how are you?")
    print(response.text)

if __name__ == "__main__":
    asyncio.run(main())
```

## With MCP Servers

```python
# Configure MCP servers for LangDB
mcp_servers = [
    {
        "server_url": "server_url",
        "type": "sse",
        "name": "search",
        "description": "Web search capabilities via DuckDuckGo"
    }
]

async def main():
    # Initialize the LangDB LLM with MCP servers
    llm = LangDBLlm(
        model_name="anthropic/claude-sonnet-4",
        api_key=os.getenv("LANGDB_API_KEY"),
        project_id=os.getenv("LANGDB_PROJECT_ID"),
        mcp_servers=mcp_servers
    )
    
    # Create an ADK agent with the LangDB LLM
    agent = Agent(llm=llm)
    
    # Create a runner to execute the agent
    runner = InMemoryRunner()
    
    # Run the agent with a prompt that can use MCP tools
    response = await runner.run(agent, "Search for the latest news about AI")
    print(response.text)
```

## Configuration

### Environment Variables

- `LANGDB_API_KEY`: Your LangDB API key (required)
- `LANGDB_PROJECT_ID`: Your LangDB project ID (required)

### LangDBLlm Parameters

- `model_name`: The name of the model to use (e.g., "anthropic/claude-sonnet-4")
- `api_key`: Your LangDB API key
- `project_id`: Your LangDB project ID
- `extra_headers`: Additional headers to include in requests
- `mcp_servers`: List of MCP server configurations for extended capabilities


## License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.

## Acknowledgements

- Google ADK Team for the Agent Development Kit
textminer-pro-asddzxqw 0.1.0
텍스트 분석을 위한 Python 패키지
Requires Python: >=3.7

markdown# TextMiner Pro

고급 텍스트 분석을 위한 Python 패키지입니다.

## 기능

- **불용어 제거** (`remove_stopwords`): NLTK를 사용한 불용어 제거
- **키워드 추출** (`extract_keywords`): TF-IDF 기반 핵심 키워드 추출 
- **텍스트 요약** (`summarize`): Gensim을 활용한 텍스트 요약
- **언어 감지** (`detect_language`): langdetect를 사용한 언어 감지

## 설치

```bash
pip install textminer-pro-asddzxqw
사용법
pythonfrom textminer import remove_stopwords, extract_keywords, summarize, detect_language

# 불용어 제거
text = "This is a sample text with some common stopwords"
clean_text = remove_stopwords(text)
print(clean_text)

# 키워드 추출 (상위 5개)
keywords = extract_keywords(text, top_n=5)
print(keywords)

# 텍스트 요약 (20% 길이로)
long_text = "여기에 긴 텍스트를 입력..."
summary = summarize(long_text, ratio=0.2)
print(summary)

# 언어 감지
language = detect_language(text)
print(f"감지된 언어: {language}")
라이선스
MIT License
저자
asddzxqw (kk020929@naver.com)

## 5단계: VSCode에서 테스트 실행

**VSCode 터미널에서 다음 명령어로 테스트:**
```bash
# 현재 위치 확인 (프로젝트 루트에 있어야 함)
pwd

# 패키지가 제대로 import되는지 테스트
python -c "from textminer import remove_stopwords; print('Import 성공!')"

# 단위 테스트 실행
python -m pytest tests/ -v

# 또는 unittest로 실행
python -m unittest discover tests/