semvec/pypi-packages huggingface dataset:
pam-python 0.1.33
Pam Python Library
Requires Python: >=3.8

# pam-python

**pam-python** is a framework designed to simplify the creation of data plugins for PAM Real CDP. These data plugins allow you to extend the capabilities of PAM CDP to process data in a fully customizable way. For example, you can create RFM segments, run analytics such as finding top spenders, and more.

## Features

- Streamlined creation of data plugins for PAM Real CDP.
- Scaffolding for project and service structures.
- Built-in templates for rapid development.
- Command-line utility for managing projects and services.
- Easy-to-run testing for individual services.

## Getting Started

### Prerequisites

- Python 3.6 or later
- pip for package management

## Installation

### 1. Create a new folder for your project

```bash
mkdir my_data_plugin
cd my_data_plugin
```

### 2. Set up a Python virtual environment

```bash
python3 -m venv venv
source venv/bin/activate
```

### 3. Install pam-python

```bash
pip install pam-python
```

> Once installed, the `pam` command-line utility will be available.

## Usage

### 1. Initialize a New Project

To initialize a new data plugin project, run:

```bash
pam init
```

> This command will create the necessary files and structure for your project.

### 2. Add a New Service

Within your project, you can create multiple services. To add a new service, use:

```bash
pam new service <service_name>
```

> This will create a folder named `<service_name>` containing the following:
> Basic template files to help you start developing your service.

### 3. Test a Service

To test a specific service, use:

```bash
pam test <service_name>
```

> This command will automatically run all test files (\*.test.py) associated with the specified service.

---

## Example Workflow

### 1. Initialize the project

```bash
pam init
```

### 2. Create a new service

```bash
pam new service RFMAnalysis
```

### 3.Customize the service by editing the generated templates in RFMAnalysis

### 4. Test the service

```bash
pam test RFMAnalysis
```
tapdb-mcp-agent 0.0.6
A MCP Agent for TapDB
Requires Python: >=3.10

# TapDB MCP Agent

## 概览
TapDB 的 MCP Agent，用于列出项目并执行只读 SQL 查询。

## 能力

### Tools
提供 2 个核心工具：

- `list_projects`
  - 列出当前可访问的项目
  - 输入：无
  - 返回：项目列表（包含 `id`、`name`、`appid` 等信息）

- `list_project_tables`
  - 查询指定项目的可访问数据表清单
  - 输入：
    - `project_id` (string): 项目的数字 ID（来自 `list_projects`）
  - 返回：返回结果是一个对象列表，对象包含'table_name','description'等信息


- `execute_project_query`
  - 对指定项目执行只读 SQL
  - 输入：
    - `project_id` (string): 项目的数字 ID（来自 `list_projects`）
    - `sql` (string): 只读 SQL（仅 SELECT/DESC 等）
    - `result_format` (string,optional): 选择'csv'或者'dict',默认是csv
    - `save_file_path` (string,optional): 本地保存文件路径，结果写入该文件，并返回一个空list
  - 返回：result_format=csv时返回一个list[list[str,Any]]，=dict时返回list[dict[str, Any]]

## 从源码构建

```bash
cd /path/to/tapdb-mcp-agent
uv pip install .
uv build
```

## 与 Cline 一起使用

```bash
# 添加到 cline_mcp_settings.json
"mcpServers": {
  "tapdb-mcp-agent": {
    "command": "uv",
    "args": [
      "run",
      "--with",
      "tapdb-mcp-agent",
      "tapdb-mcp-agent"
    ],
    "env": {
      "TAPDB_MCP_KEY": "{由TapDB分配给您的Access Key}",
      "TAPDB_MCP_ENDPOINT": "{cn=国内环境，sg=海外环境。默认是国内环境}"
    }
  }
}
```
jurispacy-tokenizer 1.2.1
Flair tokenizer adapted to French court decisions using spacy tokenization
Requires Python: >=3.10
Keywords: nlp, spacy, flair, tokenizer, legal

# JuriSpacyTokenizer

## Description

Tokenizer(s) used in our NLP projects. Built using [Flair](https://github.com/flairNLP/flair) and [spaCy](https://github.com/explosion/spaCy/)

## Installation

```bash
pip install jurispacy-tokenizer
python -m spacy download fr_core_news_sm-3.8.0
```

## Usage

### Tokenize strings

You can use this library to tokenize a string into a list of strings representing tokens:

```python
from jurispacy_tokenizer import JuriSpacyTokenizer

tokenizer = JuriSpacyTokenizer()
text = "M.Paul et Jean-Pierre sont heureux."

tokens = tokenizer.tokenize(text)

for token in tokens:
    print(token)
```

This should ouptut:

```
M.
Paul
et
Jean-Pierre
sont
heureux
.
```

## Tokenize longer text into sentences

You can also parse longer text to create Flair Sentence objects:

```python
from jurispacy_tokenizer import JuriSpacyTokenizer

tokenizer = JuriSpacyTokenizer()

text = """Bonjour tout le monde! Je m'appelle Amaury.

Je travaille avec Paul."""

sentences = tokenizer.get_tokenized_sentences(text)

for s in sentences:
    print(s)

```

This should output:

```
Sentence[5]: "Bonjour tout le monde!"
Sentence[5]: "Je m'appelle Amaury."
Sentence[5]: "Je travaille avec Paul."
```