semvec/pypi-packages huggingface dataset:
mianshiya-search-mcp 0.1.0
面试鸭题目搜索 MCP 服务器
Requires Python: >=3.10
Keywords: interview, mcp, mianshiya, 面试题, 面试鸭

# 面试鸭题目搜索 MCP 服务器

基于 MCP 协议的面试鸭题目搜索服务器，使用 Python 实现。

## 工具

### question_search

根据搜索词搜索面试鸭面试题目。

- **输入**: `search_text` - 搜索关键词
- **输出**: 面试鸭搜索结果的题目链接列表（最多5条）

## 安装

```bash
uv sync
```

## 在 Cursor 中使用

在 Cursor 的 MCP 配置文件中添加：

```json
{
  "mcpServers": {
    "mianshiya-search": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/mianshiya-search-mcp",
        "run",
        "mianshiya-search-mcp"
      ]
    }
  }
}
```

请将 `/path/to/mianshiya-search-mcp` 替换为实际的项目路径。
dav-tools 0.4.22
Various utilies to aid in program development.
Requires Python: >=3.10

# dav_tools
Personal Python library to aid in software development

## Installation
```bash
$ pip install dav-tools
```

## Usage on python programs
`dav-tools` contains a variety of diffent submodules, each with its own set of functionalities.

Importing is done in the form of
```python
from dav_tools import <submodule>
```
since it usually doesn't make sense to import the whole library.

## Usage on non-python programs
Some modules can also be executed in other scripts, using:
```bash
python -m dav_tools.modulename [options]
```

Only basic functionalities are available.
Error management requires the original program to exit if a subcommand exits with an error code.
docling-demo 0.1.1
Add your description here
Requires Python: >=3.10

Step 1: PDFAnalyzer (detect doc type)
        ↓
Step 2: ChunkerHandler (split text)
        ↓
Step 3: EmbeddingHandler (add vectors)
        ↓
Step 4: SearchHandler (upload)
        ↓
Step 5: Pipeline (connect all)
        ↓
Step 6: Test end-to-end
yaka-llm 1.0.1
Faster and Smaller alternative to Lanchain
Requires Python: >=3.12

# YakaLLM 
A smaller, faster and cooler version of LangChain. the repo without `.git` is only 74k. Its like preact for react, but nothing is backward compatible.


## how to install
pip:
```
pip install yaka-llm
# or 
pip3 install yaka-llm
```
uv:
```
uv add yaka-llm
# or 
uv pip install yaka-llm
```

## Usage 
```python
from yaka_llm import GeminiModel
from yaka_llm.core import UserPrompt, ModelPrompt
import os

gm = GeminiModel("gemini-2.5-flash", os.getenv("LLM_API_KEY"))

gm.call(["Hello there"], prompt="What's up?")

# or

history = [UserPrompt("What are the three largest cities in Spain?"),
           ModelPrompt("The three largest cities in Spain are Madrid, Barcelona, and Valencia.")]
print(gm.call(history, prompt="What is the most famous landmark in the second one?"))
```