semvec/pypi-packages huggingface dataset:
sjaicom-google-patents 0.1.10
A MCP server for Google Patents search and analysis
Requires Python: >=3.10

# Google Patents MCP Service

A Model Context Protocol (MCP) server that provides patent search capabilities using Google Patents (via SerpApi).

Designed for AI Agents to perform professional patent analysis, landscape verification, and prior art search.

## Features

- **Patent Search**: Search patents by keywords, country, date, status, assignee, etc.
- **Smart Interaction**: Includes UI state management tools for "Human-in-the-loop" confirmation flows.
- **Structured Data**: Returns clean, normalized JSON data optimized for LLM consumption.

## Installation

### Using uvx (Recommended)

```bash
uvx sjaicom-google-patents
```

### Using pip

```bash
pip install sjaicom-google-patents
python -m sjaicom_google_patents
```

## Configuration

This service requires the following environment variables:

- `SERPAPI_KEY`: Your SerpApi API key (Get one at https://serpapi.com/)

## Tools

1.  `search_patents`: Main tool for searching patents.
    - Inputs: `keywords`, `country_codes`, `status` (GRANT/APPLICATION), `date_range`, etc.
2.  `update_ui_state`: Tool for managing conversational UI state (for frontend integration).

## License

MIT
wcommon 2.9.1
常用工具类方法集合

# This is my common Python tool classes.
# Main page is https://pypi.org/project/wcommon/ .


### /data/apps/public/conf.ini content like:
```
[mysql]
host=example.com
port=3306
user=username
passwd=password
database=test
charset=utf8mb4
```

### Find the localhost internal network ip:
```python
from wcommon import *
ip = getLocalIp()
hostname = getLocalHostname()
```

### Decode unusual json string:
```python
from wcommon import *
line = """
{
    name:'java',
    system:'linux'
}
"""
result = dejson(line)
# output result
{"name":"java","system":"linux"}
```

```python
# check the reuquirement list
check_requirement(['requests','flask'])
```

### Operate mysql data:
```python
mysql = Mysql(configuraion_file="/data/apps/public/conf.ini", section="mysql")
```
```python
# query
rows = mysql.query("select * from example_table where status = %s order by id desc limit %s",(1,10))
for row in rows:
    print(row)
```

```python
# bulk_insert
mysql.bulk_insert("test.person",["name","age"],[["mahuateng",40],["liyanhong",39]])
```

```python
# bulk_insert
mysql.bulk_insert2("test.person",[{"name":"ma2","age":40},{"name":"liyanhong2","age":39},{"name":"ren"}] )
```


## Feature

### 1.3.8
可以通过check_requirement方法检测依赖包是否已经安装，如果没有安装，则提示安装。

### 1.6.5 
为`Elasticsearch`增加`query`方法

### 1.8.0.0
增加 AppleScriptTool 类。此类只在Mac上有效

### 2.0.0
增加MqServer和MqProducer类，这两个类是对zeromq的包装

### 2.0.5
增加MqConsumer类

### 2.6.1
增加DubboKit类
```python
    client = DubboTool("10.50.5.10", 20880)
    # 获取服务代理
    StatisticsApi = client.new("com.example.bigdata.statistics.api.StatisticsApi")
    # 直接调用方法，就像本地函数一样
    result = StatisticsApi.getExtUrlData({"sceneId": "264420791", "product": "tracker_view"})
    print("调用结果:", result)
```
toon_serializer 1.0.0
A Python library for working with TOON format, a compact and human readable serialization format optimized for LLM contexts.
Requires Python: >=3.10

# toon_serializer: Token Oriented Object Notation for Python

toon_serializer is a high performance Python serializer/deserializer for TOON (Token Oriented Object Notation).

TOON is a human readable data format designed to minimize token usage for LLMs by removing redundant syntax (braces, quotes, repeated keys) while maintaining structure. It excels at compressing list of dictionaries into Tabular Arrays, often reducing payload sizes by 30-50% compared to JSON.

## Features

- 📉 Token Efficient Replaces repetitive JSON keys with compact CSV-like headers.
- 🧠 Adaptive Schema The decoder "learns" column types from the first row to parse massive tables instantly.
- ⚡ Fast Primitives Optimized integer, float, and boolean parsing.
- csv-compatible Handles complex string escaping and quoting automatically.
- Lazy Decoding Iterates over lines lazily, efficient for large datasets.

## Installation

```bash
pip install toon_serializer
```


## Usage

toon_serializery mimics the standard Python json API with loads and dumps.

1. Encoding Data (Serialization)

toon_serializer automatically detects Uniform Lists of Dictionaries and compresses them into a tabular format.

Input

```python
import toon_serializer

data = {
    "model": "gpt-4",
    "parameters": {
        "temperature": 0.7,
        "stream": True
    },
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum physics."},
        {"role": "assistant", "content": "Quantum physics studies..."}
    ]
}

toon_str = toon_serializer.dumps(data)
print(toon_str)
```

Output:

```yaml
model: gpt-4
parameters:
  temperature: 0.7
  stream: true
messages[3]{role,content}:
  system,"You are a helpful assistant."
  user,"Explain quantum physics."
  assistant,"Quantum physics studies..."
```

2. Decoding Data (Deserialization)

The decoder handles primitives, standard lists, and adaptive tabular arrays seamlessly.

```python
import toon_serializer

toon_str = """
version: 1.0
users[2]{id,name,is_active}:
  1,Alice,true
  2,Bob,false
tags[3]: python, rust, go
"""

data = toon_serializer.loads(toon_str)

print(data["users"][0]) 
# {'id': 1, 'name': 'Alice', 'is_active': True}

print(data["tags"])
# ['python', 'rust', 'go']
```

## Performance Notes

- Encoder recursively checks for "uniformity" in lists. If a list contains mixed types, it gracefully falls back to a standard bulleted list.
- Decoder uses a Pushback Iterator to parse line-by-line without loading the entire string into memory.
- Adaptive Parsing when decoding tables, toon_serializer inspects the first row to generate a specialized converter function (e.g., "Column 1 is int, Column 2 is string"), speeding up parsing for the remaining rows.

## Contributing

1. Fork the repository.
2. Create a feature branch.
3. Add tests.
4. Submit a Pull Request.
wiverno 1.0.3
A lightweight WSGI framework for building fast and flexible Python web applications
Requires Python: >=3.12
Keywords: framework, web, wsgi

# Wiverno

**Wiverno** — a lightweight WSGI framework for building fast and flexible Python web applications.

## Installation

Clone the repository and install the package using `pip`:

```bash
pip install wiverno
```

## Minimal example

```python
from wiverno import Wiverno

app = Wiverno()

@app.get("/")
def index(request):
    return "200 OK", "<h1>Hello, World!</h1>"

@app.get("/users/{id:int}")
def get_user(request):
    user_id = request.path_params["id"]
    return "200 OK", f"<h1>User {user_id}</h1>"
```

## Running

Save the example above to `run.py` and start the development server:

```bash
wiverno run dev
```

The application will be available at `http://localhost:8000/`.


## Documentation

Full documentation is available at: **[https://sayrrexe.github.io/Wiverno/](https://sayrrexe.github.io/Wiverno/)**

The documentation includes:

- 📖 **User Guide** — getting started, routing, requests, and project structure
- 🔧 **API Reference** — complete API documentation for all modules
- 👨‍💻 **Developer Guide** — contributing guidelines, testing, and architecture overview

## Quick Links

- 📚 [Documentation](https://sayrrexe.github.io/Wiverno/)
- 🐛 [Issue Tracker](https://github.com/Sayrrexe/Wiverno/issues)
- 💬 [Discussions](https://github.com/Sayrrexe/Wiverno/discussions)
- 📋 [Changelog](https://github.com/Sayrrexe/Wiverno/releases)

## Requirements

- Python 3.12 or higher
- WSGI-compatible server (for production deployment)


## Contributing

Contributions are welcome! Please read the [Contributing Guide](https://sayrrexe.github.io/Wiverno/dev/contributing/) before submitting a pull request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## License

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

## Authors

- **Sayrrexe** — [GitHub](https://github.com/Sayrrexe)