### Install tnkeeh

Source: https://github.com/arbml/tnkeeh/blob/master/README.md

Use pip to install the library.

```bash
pip install tnkeeh
```

--------------------------------

### Remove Repeated Characters

Source: https://context7.com/arbml/tnkeeh/llms.txt

Reduces sequences of repeated characters to a single instance. For example, 'هههههههه' becomes 'هه'.

```python
import tnkeeh as tn

# Remove repeated characters
text = "هههههههه"
clean = tn._remove_repeated_chars(text)
```

--------------------------------

### Initialize Tnkeeh and Clean Dataset

Source: https://github.com/arbml/tnkeeh/blob/master/Demo.ipynb

Import the library and load a dataset, then initialize the cleaner and process the text column.

```python
import tnkeeh as tn 
from datasets import load_dataset
```

```python
dataset = load_dataset('metrec')
```

```python
dataset
```

```python
cleander = tn.Tnkeeh(remove_diacritics = True)
cleaned_dataset = cleander.clean_hf_dataset(dataset, 'text')
```

```python
print(cleaned_dataset['train'][0]['text'])
print(dataset['train'][0]['text'])
```

--------------------------------

### Initialize and use the Tnkeeh class for reusable pipelines

Source: https://context7.com/arbml/tnkeeh/llms.txt

The Tnkeeh class allows for configuring a persistent cleaning pipeline that can be applied to various data formats including HuggingFace datasets, raw strings, and pandas DataFrames.

```python
import tnkeeh as tn
from datasets import load_dataset

# Initialize cleaner with desired preprocessing options
cleaner = tn.Tnkeeh(
    remove_diacritics=True,      # Remove Arabic diacritical marks
    remove_special_chars=True,    # Remove special characters
    remove_tatweel=True,          # Remove tatweel character (ـ)
    normalize=True,               # Normalize digit encodings
    remove_english=False,         # Keep English characters
    remove_repeated_chars=True,   # Remove repeated characters (ههههه -> هه)
    remove_html_elements=True,    # Strip HTML tags
    remove_links=True,            # Remove URLs
    remove_twitter_meta=True,     # Remove @mentions and #hashtags
    remove_long_words=False,      # Keep words longer than 15 chars
    excluded_chars=['+', '-']     # Characters to preserve during special char removal
)

# Clean a HuggingFace dataset
dataset = load_dataset('metrec')
cleaned_dataset = cleaner.clean_hf_dataset(dataset, 'text')
# Output: Dataset with 'text' field cleaned according to configured options

# Clean raw text directly
raw_text = "وَأفْجَـعُ مَن فَقَدْنَا"
cleaned_text = cleaner.clean_raw_text(raw_text)
# Output: "وأفجـع من فقدنا"

# Clean a pandas DataFrame
import pandas as pd
df = pd.DataFrame({'content': ['السلام عليكم', 'كيًف حالكم']})
cleaned_df = cleaner.clean_data_frame(df, 'content')
# Output: DataFrame with cleaned 'content' column
```

--------------------------------

### Clean HuggingFace Datasets

Source: https://github.com/arbml/tnkeeh/blob/master/README.md

Clean a HuggingFace dataset using the Tnkeeh class.

```python
import tnkeeh as tn 
from datasets import load_dataset

dataset = load_dataset('metrec')

cleaner = tn.Tnkeeh(remove_diacritics = True)
cleaned_dataset = cleaner.clean_hf_dataset(dataset, 'text')
```

--------------------------------

### Tnkeeh Class - Reusable Text Cleaner

Source: https://context7.com/arbml/tnkeeh/llms.txt

The Tnkeeh class allows for the creation of reusable preprocessing pipelines. It can be initialized with various options to clean HuggingFace datasets, text files, raw text strings, and pandas DataFrames.

```APIDOC
## Tnkeeh Class - Reusable Text Cleaner

### Description
The `Tnkeeh` class provides a reusable preprocessing pipeline that can be configured once and applied to multiple datasets. It supports cleaning HuggingFace datasets, text files, raw text strings, and pandas DataFrames with consistent preprocessing options.

### Initialization
```python
import tnkeeh as tn

cleaner = tn.Tnkeeh(
    remove_diacritics=True,      # Remove Arabic diacritical marks
    remove_special_chars=True,    # Remove special characters
    remove_tatweel=True,          # Remove tatweel character (ـ)
    normalize=True,               # Normalize digit encodings
    remove_english=False,         # Keep English characters
    remove_repeated_chars=True,   # Remove repeated characters (ههههه -> هه)
    remove_html_elements=True,    # Strip HTML tags
    remove_links=True,            # Remove URLs
    remove_twitter_meta=True,     # Remove @mentions and #hashtags
    remove_long_words=False,      # Keep words longer than 15 chars
    excluded_chars=['+', '-']     # Characters to preserve during special char removal
)
```

### Methods
#### `clean_hf_dataset(dataset, text_column, **kwargs)`
Cleans a HuggingFace dataset.

- **dataset** (DatasetDict) - The HuggingFace dataset to clean.
- **text_column** (str) - The name of the column containing text to clean.
- **kwargs** - Additional arguments passed to the `Tnkeeh` constructor.

```python
from datasets import load_dataset

dataset = load_dataset('metrec')
cleaned_dataset = cleaner.clean_hf_dataset(dataset, 'text')
```

#### `clean_raw_text(text)`
Cleans a raw text string.

- **text** (str) - The raw text string to clean.

```python
raw_text = "وَأفْجَـعُ مَن فَقَدْنَا"
cleaned_text = cleaner.clean_raw_text(raw_text)
# Output: "وأفجـع من فقدنا"
```

#### `clean_data_frame(df, text_column, **kwargs)`
Cleans a pandas DataFrame.

- **df** (pd.DataFrame) - The DataFrame to clean.
- **text_column** (str) - The name of the column containing text to clean.
- **kwargs** - Additional arguments passed to the `Tnkeeh` constructor.

```python
import pandas as pd

df = pd.DataFrame({'content': ['السلام عليكم', 'كيًف حالكم']})
cleaned_df = cleaner.clean_data_frame(df, 'content')
```
```

--------------------------------

### Split Raw Text Data

Source: https://context7.com/arbml/tnkeeh/llms.txt

Splits a raw text file into training and testing sets based on a specified ratio. It creates 'train.txt' and 'test.txt' files in a 'data' directory.

```python
import tnkeeh as tn

# Split raw text data (80% train, 20% test)
tn.split_raw_data('corpus.txt', split_ratio=0.8)
```

--------------------------------

### Data Splitting API

Source: https://github.com/arbml/tnkeeh/blob/master/README.md

Utilities for splitting raw, classification, or parallel data into training and testing sets.

```APIDOC
## POST /split_raw_data

### Description
Splits raw data into training and testing sets.

### Parameters
#### Request Body
- **data_path** (string) - Required - Path to the raw data file.
- **split_ratio** (float) - Required - Ratio for splitting (e.g., 0.8).

## POST /split_classification_data

### Description
Splits data and labels into training and testing sets.

### Parameters
#### Request Body
- **data_path** (string) - Required - Path to the data file.
- **lbls_path** (string) - Required - Path to the labels file.
- **split_ratio** (float) - Required - Ratio for splitting.

## POST /split_parallel_data

### Description
Splits input and target data, commonly used for translation tasks.

### Parameters
#### Request Body
- **ar_data_path** (string) - Required - Path to Arabic data.
- **en_data_path** (string) - Required - Path to English data.
```

--------------------------------

### Clean Data

Source: https://github.com/arbml/tnkeeh/blob/master/README.md

Clean a text file by specifying input and output paths.

```python
import tnkeeh as tn
tn.clean_data(file_path = 'data.txt', save_path = 'cleaned_data.txt',)
```

--------------------------------

### Citation

Source: https://github.com/arbml/tnkeeh/blob/master/README.md

BibTeX citation for the library.

```bibtex
@misc{tnkeeh2020,
  author = {Zaid Alyafeai and Maged Saeed},
  title = {tkseem: A Preprocessing Library for Arabic.},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ARBML/tnkeeh}}
}
```

--------------------------------

### Data Splitting Functions

Source: https://context7.com/arbml/tnkeeh/llms.txt

Functions for splitting raw, classification, or parallel datasets into training and testing sets.

```APIDOC
## Data Splitting Functions

### Description
Provides utilities to split datasets into training and testing sets based on specific data types (raw, classification, or parallel).

### Methods
- **split_raw_data(file_path, split_ratio)**: Splits plain text files.
- **split_classification_data(text_path, label_path, split_ratio)**: Splits labeled data.
- **split_parallel_data(source_path, target_path, split_ratio)**: Splits translation pairs.

### Parameters
- **split_ratio** (float) - Required - The ratio for the training set (e.g., 0.8 for 80%).
```

--------------------------------

### Low-Level Text Processing

Source: https://context7.com/arbml/tnkeeh/llms.txt

Individual functions for fine-grained control over Arabic text preprocessing.

```APIDOC
## Low-Level Text Processing

### Description
Exposes individual functions for specific text cleaning tasks such as removing diacritics, HTML, Twitter metadata, and extra spaces.

### Available Functions
- **_remove_diacritics(text)**: Removes Arabic tashkeel.
- **_remove_special_chars(text, excluded_chars)**: Removes special characters while optionally preserving specific ones.
- **_remove_html_elements(text)**: Strips HTML tags.
- **_remove_twitter_meta(text)**: Removes mentions, hashtags, and links.
- **_remove_repeated_chars(text)**: Reduces repeated characters.
- **_remove_extra_spaces(text)**: Normalizes whitespace.
- **_remove_links(text)**: Removes URLs.
```

--------------------------------

### Split Data

Source: https://github.com/arbml/tnkeeh/blob/master/README.md

Split raw, classification, or parallel data into training and testing sets.

```python
import tnkeeh as tn
tn.split_raw_data(data_path, split_ratio = 0.8)
```

```python
import tnkeeh as tn
tn.split_classification_data(data_path, lbls_path, split_ratio = 0.8)
```

```python
tn.split_parallel_data('ar_data.txt','en_data.txt')
```

--------------------------------

### clean_hf_dataset - HuggingFace Dataset Cleaning

Source: https://context7.com/arbml/tnkeeh/llms.txt

The `clean_hf_dataset` function applies preprocessing to HuggingFace datasets, cleaning a specified text field across all splits using the dataset's map function.

```APIDOC
## clean_hf_dataset - HuggingFace Dataset Cleaning

### Description
The `clean_hf_dataset` function applies preprocessing to HuggingFace datasets, cleaning a specified text field across all splits (train, test, validation) using the dataset's map function for efficient processing.

### Parameters
#### Path Parameters
- **dataset** (DatasetDict) - Required - The HuggingFace dataset to clean.
- **text_column** (str) - Required - The name of the column containing the text to be cleaned.

#### Query Parameters
- **remove_diacritics** (bool) - Optional - Remove Arabic diacritical marks.
- **remove_special_chars** (bool) - Optional - Remove special characters.
- **normalize** (bool) - Optional - Normalize digit encodings.
- **remove_tatweel** (bool) - Optional - Remove tatweel character (ـ).
- **remove_english** (bool) - Optional - Keep or remove English characters.
- **remove_repeated_chars** (bool) - Optional - Remove repeated characters.
- **remove_html_elements** (bool) - Optional - Strip HTML tags.
- **remove_links** (bool) - Optional - Remove URLs.
- **remove_twitter_meta** (bool) - Optional - Remove @mentions and #hashtags.
- **remove_long_words** (bool) - Optional - Remove words longer than 15 characters.
- **excluded_chars** (list[str]) - Optional - Characters to preserve during special char removal.

### Request Example
```python
import tnkeeh as tn
from datasets import load_dataset

# Load an Arabic dataset
dataset = load_dataset('metrec')

# Clean the 'text' field with specified options
cleaned_dataset = tn.clean_hf_dataset(
    dataset,
    'text',
    remove_diacritics=True,
    remove_special_chars=True,
    normalize=True
)

# Compare original vs cleaned
print(dataset['train'][0]['text'])
# Output: "يا ضيفَ طيفٍ ما هَداهُ لمَضجَعي # إلا لهيبٌ في الحشى يتوقّدُ"

print(cleaned_dataset['train'][0]['text'])
# Output: "يا ضيف طيف ما هداه لمضجعي # إلا لهيب في الحشى يتوقد"
```

### Response
Returns a `DatasetDict` with the specified text column cleaned across all splits.
```

--------------------------------

### Clean HuggingFace Dataset with Translation

Source: https://context7.com/arbml/tnkeeh/llms.txt

Cleans a HuggingFace dataset by processing a specified text column. It can remove diacritics and translate the text to English using Helsinki-NLP models.

```python
translated_dataset = tn.clean_hf_dataset(
    dataset,
    'text',
    remove_diacritics=True,
    translate='en'  # Translate Arabic to English
)
```

--------------------------------

### Split Parallel Data for Translation

Source: https://context7.com/arbml/tnkeeh/llms.txt

Splits parallel text data (e.g., source and target language sentences) into training and testing sets. This is useful for machine translation tasks.

```python
import tnkeeh as tn

# Split parallel data for translation tasks
tn.split_parallel_data(
    'ar_data.txt',    # Arabic source sentences
    'en_data.txt',    # English target sentences
    split_ratio=0.8
)
```

--------------------------------

### Read Data

Source: https://github.com/arbml/tnkeeh/blob/master/README.md

Read split data based on the specified mode.

```python
import tnkeeh as tn
train_data, test_data = tn.read_data(mode = 0)
```

--------------------------------

### Split Classification Data

Source: https://context7.com/arbml/tnkeeh/llms.txt

Splits text data and their corresponding labels into training and testing sets. It generates separate files for training/testing data and labels.

```python
import tnkeeh as tn

# Split classification data with labels
tn.split_classification_data(
    'texts.txt',      # One text per line
    'labels.txt',     # Corresponding labels
    split_ratio=0.8
)
```

--------------------------------

### Read Split Data

Source: https://context7.com/arbml/tnkeeh/llms.txt

Reads data that has been previously split using the tnkeeh splitting functions. Supports different modes for raw, classification, and parallel data.

```python
import tnkeeh as tn

# Read split data back
train_data, test_data = tn.read_data(mode=0)  # Raw data
train_data, test_data, train_lbls, test_lbls = tn.read_data(mode=1)  # Classification
train_inp, train_tar, test_inp, test_tar = tn.read_data(mode=2)  # Parallel
```

--------------------------------

### Clean HuggingFace datasets using clean_hf_dataset

Source: https://context7.com/arbml/tnkeeh/llms.txt

The clean_hf_dataset function applies cleaning operations to a specific text field across all splits of a HuggingFace DatasetDict.

```python
import tnkeeh as tn
from datasets import load_dataset

# Load an Arabic dataset
dataset = load_dataset('metrec')
# DatasetDict with train (47124 rows) and test (8316 rows) splits

# Clean the 'text' field with diacritics removal
cleaned_dataset = tn.clean_hf_dataset(
    dataset,
    'text',
    remove_diacritics=True,
    remove_special_chars=True,
    normalize=True
)

# Compare original vs cleaned
print(dataset['train'][0]['text'])
# Output: "يا ضيفَ طيفٍ ما هَداهُ لمَضجَعي # إلا لهيبٌ في الحشى يتوقّدُ"

print(cleaned_dataset['train'][0]['text'])
# Output: "يا ضيف طيف ما هداه لمضجعي # إلا لهيب في الحشى يتوقد"
```

--------------------------------

### Clean text files using clean_data

Source: https://context7.com/arbml/tnkeeh/llms.txt

The clean_data function processes text files directly, supporting chunked processing for large files to optimize memory usage.

```python
import tnkeeh as tn

# Basic file cleaning with diacritics removal
tn.clean_data(
    file_path='arabic_corpus.txt',
    save_path='cleaned_corpus.txt',
    remove_diacritics=True,
    remove_special_chars=True,
    normalize=True
)
# Output: Cleaned text saved to 'cleaned_corpus.txt'

# Process large files in chunks
tn.clean_data(
    file_path='large_corpus.txt',
    save_path='cleaned_large.txt',
    remove_diacritics=True,
    remove_html_elements=True,
    remove_links=True,
    by_chunk=True,
    chunk_size=100000  # Process 100k lines at a time
)
# Output: Multiple files: cleaned_large_0.txt, cleaned_large_1.txt, etc.

# Full preprocessing pipeline for social media data
tn.clean_data(
    file_path='tweets.txt',
    save_path='cleaned_tweets.txt',
    remove_diacritics=True,
    remove_special_chars=True,
    remove_twitter_meta=True,    # Remove @mentions and #hashtags
    remove_links=True,
    remove_repeated_chars=True,  # Handle expressions like "هههههه"
    remove_long_words=True       # Remove words > 15 characters
)
```

--------------------------------

### clean_data_frame

Source: https://context7.com/arbml/tnkeeh/llms.txt

Applies Arabic text preprocessing to a specified column in a pandas DataFrame.

```APIDOC
## clean_data_frame

### Description
Applies Arabic text preprocessing to a specified column in a pandas DataFrame, returning the modified DataFrame with cleaned text.

### Parameters
#### Request Body
- **df** (DataFrame) - Required - The pandas DataFrame containing the text column.
- **column_name** (str) - Required - The name of the column to clean.
- **remove_diacritics** (bool) - Optional - Whether to remove Arabic diacritics.
- **remove_special_chars** (bool) - Optional - Whether to remove special characters.

### Request Example
{
  "df": "pandas_dataframe_object",
  "column_name": "text",
  "remove_diacritics": true,
  "remove_special_chars": true
}
```

--------------------------------

### clean_data - File-Based Text Cleaning

Source: https://context7.com/arbml/tnkeeh/llms.txt

The `clean_data` function processes text files directly, applying various cleaning operations and saving results to a new file. It supports chunked processing for large files.

```APIDOC
## clean_data - File-Based Text Cleaning

### Description
The `clean_data` function processes text files directly, applying various cleaning operations and saving results to a new file. It supports chunked processing for large files to manage memory efficiently.

### Parameters
#### Path Parameters
- **file_path** (str) - Required - Path to the input text file.
- **save_path** (str) - Required - Path to save the cleaned text file.

#### Query Parameters
- **remove_diacritics** (bool) - Optional - Remove Arabic diacritical marks.
- **remove_special_chars** (bool) - Optional - Remove special characters.
- **normalize** (bool) - Optional - Normalize digit encodings.
- **remove_html_elements** (bool) - Optional - Strip HTML tags.
- **remove_links** (bool) - Optional - Remove URLs.
- **remove_twitter_meta** (bool) - Optional - Remove @mentions and #hashtags.
- **remove_repeated_chars** (bool) - Optional - Remove repeated characters.
- **remove_long_words** (bool) - Optional - Remove words longer than 15 characters.
- **by_chunk** (bool) - Optional - Process the file in chunks (for large files).
- **chunk_size** (int) - Optional - Number of lines to process per chunk when `by_chunk` is True.

### Request Example
```python
import tnkeeh as tn

# Basic file cleaning
tn.clean_data(
    file_path='arabic_corpus.txt',
    save_path='cleaned_corpus.txt',
    remove_diacritics=True,
    remove_special_chars=True,
    normalize=True
)

# Process large files in chunks
tn.clean_data(
    file_path='large_corpus.txt',
    save_path='cleaned_large.txt',
    remove_diacritics=True,
    remove_html_elements=True,
    remove_links=True,
    by_chunk=True,
    chunk_size=100000
)

# Full preprocessing pipeline for social media data
tn.clean_data(
    file_path='tweets.txt',
    save_path='cleaned_tweets.txt',
    remove_diacritics=True,
    remove_special_chars=True,
    remove_twitter_meta=True,
    remove_links=True,
    remove_repeated_chars=True,
    remove_long_words=True
)
```

### Response
Cleaned text is saved to the specified `save_path`. If `by_chunk` is True, multiple files named like `cleaned_large_0.txt`, `cleaned_large_1.txt`, etc., will be created.
```

--------------------------------

### Remove URLs

Source: https://context7.com/arbml/tnkeeh/llms.txt

Removes all URLs (http, https) from a given text string.

```python
import tnkeeh as tn

# Remove URLs/links
text = "زوروا موقعنا http://example.com للمزيد"
clean = tn._remove_links(text)
```

--------------------------------

### Data Cleaning API

Source: https://github.com/arbml/tnkeeh/blob/master/README.md

Cleans Arabic text files by applying various filters such as diacritic removal, special character removal, and normalization.

```APIDOC
## POST /clean_data

### Description
Cleans a text file based on specified cleaning parameters and saves the output to a new file.

### Method
POST

### Parameters
#### Request Body
- **file_path** (string) - Required - Path to the input text file.
- **save_path** (string) - Required - Path to save the cleaned text file.
- **segment** (boolean) - Optional - Uses farasa for segmentation.
- **remove_diacritics** (boolean) - Optional - Removes all diacritics.
- **remove_special_chars** (boolean) - Optional - Removes all special characters.
- **remove_english** (boolean) - Optional - Removes English alphabets and digits.
- **normalize** (boolean) - Optional - Matches digits with different encodings.
- **remove_tatweel** (boolean) - Optional - Removes the tatweel character.
- **remove_repeated_chars** (boolean) - Optional - Removes characters appearing three times in sequence.
- **remove_html_elements** (boolean) - Optional - Removes HTML elements.
- **remove_links** (boolean) - Optional - Removes URLs.
- **remove_twitter_meta** (boolean) - Optional - Removes Twitter mentions, links, and hashtags.
- **remove_long_words** (boolean) - Optional - Removes words longer than 15 characters.
- **by_chunk** (boolean) - Optional - Reads files by chunks.
- **chunk_size** (integer) - Optional - Size of chunks if by_chunk is enabled.
```

--------------------------------

### Remove HTML Elements

Source: https://context7.com/arbml/tnkeeh/llms.txt

Strips all HTML tags and their content from a given text string.

```python
import tnkeeh as tn

# Remove HTML elements
text = '<doc id="7" url="https://ar.wikipedia.org" title="ماء">'
clean = tn._remove_html_elements(text)
```

--------------------------------

### Remove Arabic Diacritics

Source: https://context7.com/arbml/tnkeeh/llms.txt

Removes diacritical marks (tashkeel) from Arabic text.

```python
import tnkeeh as tn

# Remove Arabic diacritics (tashkeel)
text = "وَأفْجَـعُ مَن فَقَدْنَا مَن وَّجَدْنَا"
clean = tn._remove_diacritics(text)
```

--------------------------------

### Remove Extra Spaces

Source: https://context7.com/arbml/tnkeeh/llms.txt

Replaces multiple consecutive whitespace characters with a single space.

```python
import tnkeeh as tn

# Remove extra spaces
text = "اهلا     كيف حالك"
clean = tn._remove_extra_spaces(text)
```

--------------------------------

### Remove Special Characters with Exclusions

Source: https://context7.com/arbml/tnkeeh/llms.txt

Removes special characters from text while allowing specific characters to be preserved. Useful for keeping mathematical operators or date separators.

```python
import tnkeeh as tn

# Remove special chars with exclusions
text = "3 + 3 = 6 and 9/8/1770"
clean = tn._remove_special_chars(text, excluded_chars=['+', '=', '/'])
```

--------------------------------

### Remove Special Characters

Source: https://context7.com/arbml/tnkeeh/llms.txt

Removes special characters from text, preserving Arabic letters, English letters, and digits. By default, it removes most punctuation and symbols.

```python
import tnkeeh as tn

# Remove special characters (preserving Arabic, English, digits)
text = "كيف حالكم ، يا أشقاء!"
clean = tn._remove_special_chars(text)
```

--------------------------------

### Clean Pandas DataFrame with Arabic Text

Source: https://context7.com/arbml/tnkeeh/llms.txt

Applies Arabic text preprocessing to a specified column in a pandas DataFrame. This function can remove diacritics and special characters.

```python
import tnkeeh as tn
import pandas as pd

# Create DataFrame with Arabic text
df = pd.DataFrame({
    'id': [1, 2, 3],
    'text': [
        'السلام عليكم',
        'كيًف حالكم يا أصدقاء؟',
        'هذا نصٌّ عربيٌّ مُشَكَّل'
    ]
})

# Clean the text column
cleaned_df = tn.clean_data_frame(
    df,
    'text',
    remove_diacritics=True,
    remove_special_chars=True
)

print(cleaned_df)
```

--------------------------------

### Remove Twitter Metadata

Source: https://context7.com/arbml/tnkeeh/llms.txt

Removes common Twitter metadata elements such as mentions (@username), hashtags (#topic), and URLs from a text string.

```python
import tnkeeh as tn

# Remove Twitter metadata (mentions, hashtags, links)
text = "@arthurlacoste check this link: https://t.co/abc ! so #nsfw"
clean = tn._remove_twitter_meta(text)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.