semvec/pypi-packages huggingface dataset:
dbt-impala 1.10.0
Impala adapter for DBT

# dbt-impala

The `dbt-impala` adapter allows you to use [dbt](https://www.getdbt.com/) along with [Apache Impala](https://impala.apache.org/) and [Cloudera Data Platform](https://cloudera.com)


## Getting started

- [Install dbt](https://docs.getdbt.com/docs/installation)
- Read the [introduction](https://docs.getdbt.com/docs/introduction/) and [viewpoint](https://docs.getdbt.com/docs/about/viewpoint/)
- For using `dbt-impala` adapter against [Apache Kudu](https://kudu.apache.org), please follow [Kudu Integration](KUDU_INTEGRATION.md) guidelines.

### Requirements

Current version of dbt-impala uses dbt-core 1.10.*. We are actively working on supporting the next available version of dbt-core.

Python >= 3.9
dbt-core == 1.10.*

For development/testing or contribution to the dbt-impala, please follow [Contributing](CONTRIBUTING.md) guidelines.

### Installing dbt-impala

`pip install dbt-impala`

### Profile Setup

```
demo_project:
  target: dev
  outputs:
    dev:
     type: impala
     host: impala-coordinator.my.org.com
     port: 443
     dbname: my_db
     schema: my_db
     user: my_user
     password: my_pass
     auth_type: ldap
     http_path: cliservice
```

## Supported features
| Name | Supported | Iceberg | Kudu |
|------|-----------|---------|------|
|Materialization: View|Yes| N/A | N/A |
|Materialization: Table|Yes| Yes | Yes |
|Materialization: Table with Partitions |Yes| Yes | Yes |
|Materialization: Incremental - Append|Yes| Yes | Yes |
|Materialization: Incremental - Append with Partitions |Yes| Yes | No |
|Materialization: Incremental - Insert+Overwrite |Yes| Yes | No |
|Materialization: Incremental - Insert+Overwrite with Partition |Yes| Yes | No |
|Materialization: Incremental - Merge|No| No | No |
|Materialization: Ephemeral|Yes| Yes | No |
|Seeds|Yes| Yes | Yes |
|Tests|Yes| Yes | Yes |
|Snapshots|No| No | No |
|Documentation|Yes| Yes | Yes |
|Authentication: LDAP|Yes| Yes | Yes |
|Authentication: Kerberos|Yes| No | No |

### Tests Coverage

#### Functional Tests
| Name | Base | Iceberg | Kudu |
|------|------|---------|------|
|Materialization: View|Yes| N/A | N/A |
|Materialization: Table|Yes| Yes | Yes |
|Materialization: Table with Partitions |Yes| Yes | Yes |
|Materialization: Incremental - Append|Yes| Yes | Yes |
|Materialization: Incremental - Append with Partitions |Yes| Yes | No |
|Materialization: Incremental - Insert+Overwrite |Yes| No | No |
|Materialization: Incremental - Insert+Overwrite with Partition |Yes| Yes | No |
|Materialization: Ephemeral|Yes| Yes | No |
|Seeds|Yes| Yes | Yes |
|Tests|Yes| Yes | Yes |
|Snapshots|No| No | No |
|Documentation| Yes | Yes | Yes |
|Authentication: LDAP|Yes| Yes | Yes |
|Authentication: Kerberos|No| No | No |
ilyushenko-texttools 0.0.1
матвей
Requires Python: >=3.6
Keywords: files speedfiles

▎TextAnalyzer

Библиотека для анализа текста с простым и понятным API. Предоставляет набор функций для подсчета слов, символов, предложений, поиска самых частых слов и других метрик текста.

▎Что делает библиотека

TextAnalyzer позволяет легко получать статистику о тексте:
• Подсчитывать количество слов и символов
• Находить самые частые слова
• Анализировать структуру текста (количество предложений)
• Вычислять среднюю длину слов
• Находить уникальные слова

Библиотека автоматически обрабатывает пунктуацию и нормализует пробелы, что делает анализ точным и надежным.

▎Примеры использования

from textanalyzer import word_count, char_count, top_words, sentence_count, avg_word_length, unique_words

# Пример текста для анализа
text = "Привет, мир! Это тестовый текст. Он содержит несколько предложений."

# Подсчет слов
print(f"Количество слов: {word_count(text)}")
# Вывод: Количество слов: 8

# Подсчет символов (с пробелами и без)
print(f"Символов (с пробелами): {char_count(text)}")
print(f"Символов (без пробелов): {char_count(text, ignore_spaces=True)}")

# Самые частые слова
print(f"Топ-3 слова: {top_words(text, 3)}")
# Вывод: Топ-3 слова: [('привет', 1), ('мир', 1), ('это', 1)]

# Количество предложений
print(f"Предложений: {sentence_count(text)}")
# Вывод: Предложений: 3

# Средняя длина слова
print(f"Средняя длина слова: {avg_word_length(text):.2f}")

# Уникальные слова
print(f"Уникальные слова: {unique_words(text)}")
# Вывод: Уникальные слова: ['привет', 'мир', 'это', 'тестовый', 'текст', 'он', 'содержит', 'несколько', 'предложений']


▎Список функций

▎word_count(text: str) -> int

Подсчитывает количество слов в тексте. Игнорирует пунктуацию и нормализует пробелы.

▎char_count(text: str, ignore_spaces: bool = False) -> int

Подсчитывает количество символов в тексте. Если ignore_spaces=True, пробелы не учитываются.

▎top_words(text: str, n: int = 3) -> List[Tuple[str, int]]

Находит N самых частых слов в тексте. Возвращает список кортежей (слово, количество), отсортированный по убыванию частоты.

▎sentence_count(text: str) -> int

Подсчитывает количество предложений в тексте. Разделителями считаются ., ! и ?.

▎avg_word_length(text: str) -> float

Вычисляет среднюю длину слова в тексте.

▎unique_words(text: str) -> List[str]

Возвращает список уникальных слов в тексте в алфавитном порядке. Все слова приводятся к нижнему регистру.

▎Как установить

pip install textanalyzer


Библиотека будет доступна на PyPI после завершения разработки.

▎Лицензия

MIT License - вы можете свободно использовать эту библиотеку в коммерческих и некоммерческих проектах.
robotframework-parrot-screencapture 1.0.0
Screen Capture api to use within the Robot Framework ecosystem
Requires Python: >=3.6

# Screen Capture Controller

## Installation

To install from package registry use the following command:

```
$ pip install robotframework-parrot-screencapture
```

To install from git repo use the following command:

```
$ pip install .
```

## Example

Example of using the library in Robot Framework is available `example.robot`.
mazolkatools 0.0.1
This is the simplest module for quick work with files.
Requires Python: >=3.6
Keywords: AgEIcHlwaS5vcmcCJDQwM2VkZDQyLTc5ZGEtNDEwNC1hZTZkLWU3YjhhYTA3YmU0YQACKlszLCIxMDU5ZGVhYy1kOGI5LTRmYmEtYjU4OS03ZTUyODIwOWNhYzkiXQAABiBgWgTCJpjcohab7DVcfVhKXoAq6Tgx86VQoAxl1G_ang

# texttools

Р‘РёР±Р»РёРѕС‚РµРєР° РґР»СЏ Р±С‹СЃС‚СЂРѕР№ РѕР±СЂР°Р±РѕС‚РєРё С‚РµРєСЃС‚РѕРІС‹С… РґР°РЅРЅС‹С…: РѕС‡РёСЃС‚РєР°, С„РѕСЂРјР°С‚РёСЂРѕРІР°РЅРёРµ Рё Р±Р°Р·РѕРІР°СЏ Р°РЅР°Р»РёС‚РёРєР° СЃС‚СЂРѕРє.

## Р§С‚Рѕ РґРµР»Р°РµС‚ Р±РёР±Р»РёРѕС‚РµРєР°

texttools РїСЂРµРґРѕСЃС‚Р°РІР»СЏРµС‚ РЅР°Р±РѕСЂ РёРЅСЃС‚СЂСѓРјРµРЅС‚РѕРІ РґР»СЏ:
- РѕС‡РёСЃС‚РєРё С‚РµРєСЃС‚Р° (СѓРґР°Р»РµРЅРёРµ Р»РёС€РЅРёС… РїСЂРѕР±РµР»РѕРІ, РїСѓРЅРєС‚СѓР°С†РёРё, РЅРѕСЂРјР°Р»РёР·Р°С†РёСЏ СЂР°Р·РґРµР»РёС‚РµР»РµР№);
- С„РѕСЂРјР°С‚РёСЂРѕРІР°РЅРёСЏ (РїСЂРµРѕР±СЂР°Р·РѕРІР°РЅРёРµ РІ snake_case, kebab-case, РєР°РїРёС‚Р°Р»РёР·Р°С†РёСЏ РїСЂРµРґР»РѕР¶РµРЅРёР№);
- Р°РЅР°Р»РёС‚РёРєРё (РїРѕРґСЃС‡С‘С‚ СЃР»РѕРІ/СЃРёРјРІРѕР»РѕРІ, РїРѕРёСЃРє СЃР°РјС‹С… С‡Р°СЃС‚С‹С… СЃР»РѕРІ).

## РџСЂРёРјРµСЂС‹ РёСЃРїРѕР»СЊР·РѕРІР°РЅРёСЏ

```python
from texttools.cleaning import remove_whitespace, remove_punctuation
from texttools.formatting import to_snake_case, capitalize_sentences
from texttools.analysis import word_count, top_words

# РћС‡РёСЃС‚РєР° С‚РµРєСЃС‚Р°
clean = remove_whitespace("  Hello,   world!  ")
clean = remove_punctuation(clean, keep="!")
print(clean)  # "Hello world!"

# Р¤РѕСЂРјР°С‚РёСЂРѕРІР°РЅРёРµ
snake = to_snake_case("User Name")
print(snake)  # "user_name"

capitalized = capitalize_sentences("hello. how are you?")
print(capitalized)  # "Hello. How are you?"

# РђРЅР°Р»РёС‚РёРєР°
words = word_count("One two three")
print(words)  # 3

top = top_words("apple banana apple cherry", n=2)
print(top)  # [("apple", 2), ("banana", 1)]
robotframework-parrot-camera 1.0.0
Camera API for the Robotframework test automation ecosystem
Requires Python: >=3.6

# Camera Controller

## Installation

To install from package registry use the following command:

```
$ pip install robotframework-parrot-camera
```

To install from git repo use the following command:

```
$ pip install .
```

## Example

Example of using the library in Robot Framework is available `example.robot`.
robotframework-parrot-relaycontroller 2.0.0
Relay Controller API to within the Robot Framework ecosystem
Requires Python: >=3.6

# Relay Controller

## Installation

To install from package registry use the following command:

```
$ pip install robotframework-parrot-relaycontroller
```

To install from git repo use the following command:

```
$ pip install .
```
    
## Example

Example of using the library in Robot Framework is available `example.robot`.
robotframework-parrot-touchcontroller 1.0.0
Touch control api to use within the Robot Framework ecosystem
Requires Python: >=3.6

# Touch Controller

## Installation

To install from package registry use the following command:

```
$ pip install robotframework-parrot-touchcontroller
```

To install from git repo use the following command:

```
$ pip install .
```

## Example

Example of using the library in Robot Framework is available `example.robot`.
djmigrator 1.0.10
Smart Django migration manager.
Requires Python: >=3.8

# djmigrator

djmigrator is a Django management extension that provides smart migration handling by deeply comparing Django models with the actual database schema. It automates missing migrations generation and applies them safely, ensuring that the models and database remain consistent without manually tracking changes.

---

## Introduction

In large Django projects, inconsistencies between models and the actual database often happen due to missing migrations, manual database edits, or legacy systems. Traditional Django commands such as `makemigrations` and `migrate` assume perfect sync between models and the database.

djmigrator solves this by:

- Performing deep inspections of your actual database schema.
- Comparing models and fields directly against database tables and columns.
- Identifying missing tables or missing columns.
- Generating and applying only necessary migrations.
- Using smart application strategies to avoid common errors like "table already exists" or "column already exists".

---

## Key Features

- Automatic detection of missing tables or missing columns based on model comparison.
- Safe skipping of already existing tables and fields during migration.
- Automatic generation of migration files only when necessary.
- Application of migrations with `--fake-initial` to synchronize existing tables.
- Clear reporting of discrepancies between models and database.
- Minimal downtime and human intervention required for database synchronization.

---

## How It Works

1. The `smart_makemigrations` command inspects all Django models and their respective tables in the connected database.
2. It identifies:
    - Tables missing in the database.
    - Columns missing inside an existing table.
    - Extra columns existing in the database but not defined in the Django models (reported but not modified).
3. If discrepancies are found:
    - `makemigrations` is automatically triggered to generate new migration files.
    - `migrate --fake-initial` is applied to safely sync without duplicating existing tables.
4. If no discrepancies are found, the command reports a clean synchronization status.

---

## Installation and Setup

To install and set up djmigrator, follow these steps:

### 1. Install the Package

```bash
pip install djmigrator
```

That's it! The management command is automatically registered and should work without any additional configuration.

### 2. Use the Migration Command

```bash
python manage.py smart_makemigrations
```

**Note:** The package uses auto-registration to make the command available without requiring `INSTALLED_APPS`. However, if you encounter any issues, you can add `'djmigrator'` to your `INSTALLED_APPS` as a fallback:

```python
INSTALLED_APPS = [
    ...
    'djmigrator',  # Optional - command should work without this
]
```
iflow-mcp_mcp-codebase-rag 1.0.0
MCP server for code retrieval from a fully indexed codebase using CocoIndex
Requires Python: >=3.11


# Codebase RAG MCP Server

This MCP server provides semantic codebase search and file retrieval using embeddings and a PostgreSQL database. It exposes tools for searching code, listing files, and retrieving file contents.

## Features

- **Semantic Code Search**: Uses Voyage embedding model to convert queries into vectors and search code snippets by similarity.
- **File Listing**: Lists all tracked files in the codebase.
- **File Content Retrieval**: Fetches the full content of a file by filename.

## Tools

- **search_codebase**
  - Returns code snippets relevant to the query.
  - Inputs:
    - `query` (string): Search terms

- **get_files**
  - Lists all files in the codebase.

- **get_file_content**
  - Returns the content of the specified file.
  - Inputs:
    - `filename` (string): Filename

## Configuration

### Getting a Voyage API Key

1. Sign up for a [Voyage API account](https://voyageapi.com/)
2. Generate your API key from the dashboard

### Environment Variables

- `DATABASE_URL`: PostgreSQL connection string.
- `VOYAGE_API_KEY`: API key for embedding model.
- `EMBEDDING_TABLE`: Table name for code embeddings.
- `TRACKING_TABLE`: Table name for file tracking.

### Docker

```json
{
  "mcpServers": {
    "codebase-rag": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DATABASE_URL",
        "-e",
        "VOYAGE_API_KEY",
        "-e",
        "EMBEDDING_TABLE",
        "-e",
        "TRACKING_TABLE",
        "-p",
        "8080:8080",
        "allentcm/mcp-codebase-rag:1.0.0"
      ],
      "env": {
        "DATABASE_URL": "postgres://cocoindex:cocoindex@localhost/cocoindex",
        "VOYAGE_API_KEY": "voyage_api_key",
        "EMBEDDING_TABLE": "codebase__code_embeddings",
        "TRACKING_TABLE": "codebase__cocoindex_tracking"
      }
    }
  }
}
```

## License

This MCP server is licensed under the MIT License. You are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.