semvec/pypi-packages huggingface dataset:
lib-shop-api 1.2.1
Library-api for web-shop

# lib_shop_api

`lib_shop_api` это библиотека Python предоставляет api для получения информации о товарах с сайта

## Установка из PyPI

```bash
pip install lib_shop_api
```

## Использование репозитория GitHub

```bash
git clone https://github.com/YuranIgnatenko/lib_shop_api.git
cd lib_shop_api
pip install .
```

## Пример использования

```python
# импорт парсера
from lib_shop_api.parser import Parser
# имрорт категорий товаров для поиска
from lib_shop_api.constants import K_NOTEBOOK, K_MONITOR, K_DVD

# создание парсера
parser = Parser()

# получение количества всех страниц
count_pages = parser.get_count_pages(K_NOTEBOOK)

# получение количества всего товара из категории
count_products = parser.get_count_products(K_MONITOR)

# получение списка товаров в виде объектов класса models.Product
number_page = 1
products = parser.get_products(K_DVD, number_page)

# получить поля продукта
product = products[0]

print(str(product))
print(product.title)
print(product.image)
print(product.price)
print(product.catalog)
print(product.description)

# получиени из итератора
#с указанием максимально возможного кол-ва
#продуктов на выходе
max_wait_out_product = 5
for product in parser.get_products_iterator(K_DVD, max_wait_out_product)
	print(product.title)

# Поддерживаемые категории:
K_NOTEBOOK
K_MONITOR
K_SYS_BLOCK
K_MFU
K_MONO_BLOCK
K_SMARTPHONE
K_TABLET
K_SMART_WATCH
K_WATCH
K_VR
K_FITNES
K_E_BOOK
K_ACTION_CAMERA
K_FOR_GARDEN
K_ELECTROTOOL
K_SENSORSTOOL
K_STRONG_TECH
K_MACHINE
K_AUDIO_TECH
K_DVD
K_OTH_FOR_PHOTO_VIDEO
K_HOME_KINOTEATR
K_MICROPHONE
K_FOR_TV
K_TV
K_HI_FI_TECH
K_CAMERA
K_PROECTOR
K_CAMERA_TOOLS
K_VIDEOCAMERA
K_SALES
K_OTHERS




```

> using sources from site:
> `https://skypka.com/`
mcp-http-streamable-ysx-test 0.1.1
输入OpenWeather-API-KEY，获取天气信息。
Requires Python: >=3.10

## 一、基于HTTP流式传输的MCP服务器开发流程
- 创建项目文件：
```bash
# cd /root/autodl-tmp/MCP
uv init mcp-weather-http
cd mcp-weather-http
# 创建虚拟环境
uv venv
# 激活虚拟环境
source .venv/bin/activate
uv add mcp httpx
```

## 代码解释
如果你传的是 --port 3000，访问路径就是 http://localhost:3000/mcp

## 修改配置文件pyproject.toml：
- 参考:
```bash
[build-system]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "mcp-weather-http"
version = "1.1.0"
description = "输入OpenWeather-API-KEY，获取天气信息。"
readme = "README.md"
requires-python = ">=3.9"
dependencies = [
    "httpx>=0.28.1",
    "mcp>=1.8.0",
]

[project.scripts]
mcp-weather-http = "mcp_weather_http:main"

[tool.setuptools]
package-dir = {"" = "src"}

[tool.setuptools.packages.find]
where = ["src"]
```

## 二、HTTP流式传输MCP服务器开启与测试
- 在创建完server.py后，我们可以开启服务并进行测试。需要注意的是，我们需要先开启流式HTTP MCP服务器，然后再开启Inspector：
### 开启流式HTTP MCP服务器
```bash
# 回到项目主目录
# cd /root/autodl-tmp/MCP/mcp-weather-http

uv run ./src/mcp_weather_http/server.py --port 3001 --api-key YOUR_KEY
```
### 开启Inspector
```bash
# 回到项目主目录
# cd /root/autodl-tmp/MCP/mcp-weather-http
# source .venv/bin/activate

npx -y @modelcontextprotocol/inspector
```
访问: MCP Inspector is up and running at http://127.0.0.1:6274
进入inspector界面:
- Transport Type选择“Streamable HTTP”
- URL设置 http://localhost:3001/mcp
- connect连接成功

##  三、流式HTTP MCP服务器异地调用
- 接下来即可在异地环境（也可以是本地）通过HTTP方式调用MCP服务了。
- 这里以本地安装的Cherry Studio为例，进行调用演示。
- 此处需要保持远程流式HTTP MCP服务器处于开启状态，然后配置即可。

## 四、流式HTTP MCP服务器发布流程
- 测试完成后，即可上线发布。这里仍然考虑发布到pypi平台，并使用cherry studio进行本地调用测试。

### 打包上传：
```bash
# 回到项目主目录
# cd xx/MCP/mcp-http-streamable

uv pip install build twine
python -m build
python -m twine upload dist/*

# View at: https://pypi.org/project/mcp-http-streamable-ysx-test/0.1.0/
```
pyROTMOD 1.0.2
Development Status :: 4 - Beta
Requires Python: >=3.6

# pyROTMOD
pyROTMOD emulates the GIPSY modules ROTMASS and ROTMOD in a almost independent manner. The fitting and modelling are developed compeletely independent from the GIPSY code with the notable exception of the implementation of the cassertano profiles for arbitrary disks.


Installation
----

pyROTMOD is a fully python based code hence installation should simply entail

pip install pyROTMOD


Easy use
----
The easiest way to use pyROTMOD is to use a yaml configuration file. An example with all the defaults can be obtained by typing pyROTMOD print_examples=True, this will then produce a yaml file called pyROTMOD-default.yml
After adapting this file to your specific needs pyROTMOD can be ran by.

pyROTMOD configuration_file=my_conf.yaml

Where my_conf.yaml should be replaced by the name of your configuartion file.
langchain-gel 1.0.1
Gel integration package for the LangChain framework
Requires Python: >=3.9

# langchain-gel

This package enables LangChain to interact with Gel as a vectorstore.
See LangChain's [documentation](https://python.langchain.com/docs/concepts/vectorstores/) to learn more about how to take advantage of that.

> Note: check out Gel's [AI extension](https://docs.geldata.com/reference/ai) to learn how to automate embedding management away while taking advantage of poweful schema and EdgeQL query language.

## Usage 

1. Install Gel's Python binding and this package

```bash
pip install gel langchain-gel
```

2. Initialize the project

Locally:

```bash
gel project init
```

In the [cloud](https://docs.geldata.com/cloud):

```bash
gel project init --server-instance <org-name>/<instance-name>

```

3. Add necessary components to the schema. Gel uses explicit schema and migrations, which gives you more control and preserves data integrity. `langchain-gel` expects the following schema: 

```gel
using extension pgvector;
                                    
module default {
    scalar type EmbeddingVector extending ext::pgvector::vector<1536>;

    type Record {
        required collection: str;
        text: str;
        embedding: EmbeddingVector;
        external_id: str {
            constraint exclusive;
        };
        metadata: json;

        index ext::pgvector::hnsw_cosine(m := 16, ef_construction := 128)
            on (.embedding)
    } 
}
```
Copy-paste this to `dbschema/default.gel` and run a migration:

```bash
gel migration create \
&& gel migrate
```

4. Use `GelVectorStore` as usual. It's a drop-in replacement for any other vectorstore in the LangChain ecosystem.

```python
from langchain_gel import GelVectorStore

vectorstore = GelVectorStore()
```

## Next steps

When you are ready to migrate to Gel's native vector handling, check out [Gel's documentation](docs.geldata.com) to find instructions.