SplitterMR

Description

SplitterMR is a library for chunking data into convenient text blocks compatible with your LLM applications.

Important

New version v0.5.0

• Add embedding models to encode the text into distributed vectorized representations. See documentation here.
• Add support for chunking files based on semantic similarity between sentences. See documentation here.

Features

Different input formats

SplitterMR can read data from multiples sources and files. To read the files, it uses the Reader components, which inherits from a Base abstract class, BaseReader. This object allows you to read the files as a properly formatted string, or convert the files into another format (such as markdown or json).

Currently, there are supported three readers: VanillaReader, and MarkItDownReader and DoclingReader. These are the differences between each Reader component:

Reader	Unstructured files & PDFs	MS Office suite files	Tabular data	Files with hierarchical schema	Image files	Markdown conversion
VanillaReader	txt, md, pdf	xlsx, docx, pptx	csv, tsv, parquet	json, yaml, html, xml	jpg, png, webp, gif	Yes
MarkItDownReader	txt, md, pdf	docx, xlsx, pptx	csv, tsv	json, html, xml	jpg, png, pneg	Yes
DoclingReader	txt, md, pdf	docx, xlsx, pptx	–	html, xhtml	png, jpeg, tiff, bmp, webp	Yes

Several splitting methods

SplitterMR allows you to split files in many different ways depending on your needs. The available splitting methods are described in the following table:

Splitting Technique	Description
Character Splitter	Splits text into chunks based on a specified number of characters. Supports overlapping by character count or percentage. Parameters: chunk_size (max chars per chunk), chunk_overlap (overlapping chars: int or %). Compatible with: Text.
Word Splitter	Splits text into chunks based on a specified number of words. Supports overlapping by word count or percentage. Parameters: chunk_size (max words per chunk), chunk_overlap (overlapping words: int or %). Compatible with: Text.
Sentence Splitter	Splits text into chunks by a specified number of sentences. Allows overlap defined by a number or percentage of words from the end of the previous chunk. Customizable sentence separators (e.g., ., !, ?). Parameters: chunk_size (max sentences per chunk), chunk_overlap (overlapping words: int or %), sentence_separators (list of characters). Compatible with: Text.
Paragraph Splitter	Splits text into chunks based on a specified number of paragraphs. Allows overlapping by word count or percentage, and customizable line breaks. Parameters: chunk_size (max paragraphs per chunk), chunk_overlap (overlapping words: int or %), line_break (delimiter(s) for paragraphs). Compatible with: Text.
Recursive Splitter	Recursively splits text based on a hierarchy of separators (e.g., paragraph, sentence, word, character) until chunks reach a target size. Tries to preserve semantic units as long as possible. Parameters: chunk_size (max chars per chunk), chunk_overlap (overlapping chars), separators (list of characters to split on, e.g., ["\n\n", "\n", " ", ""]). Compatible with: Text.
Token Splitter	Splits text into chunks based on the number of tokens, using various tokenization models (e.g., tiktoken, spaCy, NLTK). Useful for ensuring chunks are compatible with LLM context limits. Parameters: chunk_size (max tokens per chunk), model_name (tokenizer/model, e.g., "tiktoken/cl100k_base", "spacy/en_core_web_sm", "nltk/punkt"), language (for NLTK). Compatible with: Text.
Paged Splitter	Splits text by pages for documents that have page structure. Each chunk contains a specified number of pages, with optional word overlap. Parameters: num_pages (pages per chunk), chunk_overlap (overlapping words). Compatible with: Word, PDF, Excel, PowerPoint.
Row/Column Splitter	For tabular formats, splits data by a set number of rows or columns per chunk, with possible overlap. Row-based and column-based splitting are mutually exclusive. Parameters: num_rows, num_cols (rows/columns per chunk), overlap (overlapping rows or columns). Compatible with: Tabular formats (csv, tsv, parquet, flat json).
JSON Splitter	Recursively splits JSON documents into smaller sub-structures that preserve the original JSON schema. Parameters: max_chunk_size (max chars per chunk), min_chunk_size (min chars per chunk). Compatible with: JSON.
Semantic Splitter	Splits text into chunks based on semantic similarity, using an embedding model and a max tokens parameter. Useful for meaningful semantic groupings. Parameters: embedding_model (model for embeddings), max_tokens (max tokens per chunk). Compatible with: Text.
HTML Tag Splitter	Splits HTML content based on a specified tag, or automatically detects the most frequent and shallowest tag if not specified. Each chunk is a complete HTML fragment for that tag. Parameters: chunk_size (max chars per chunk), tag (HTML tag to split on, optional). Compatible with: HTML.
Header Splitter	Splits Markdown or HTML documents into chunks using header levels (e.g., #, ##, or <h1>, <h2>). Uses configurable headers for chunking. Parameters: headers_to_split_on (list of headers and semantic names), chunk_size (unused, for compatibility). Compatible with: Markdown, HTML.
Code Splitter	Splits source code files into programmatically meaningful chunks (functions, classes, methods, etc.), aware of the syntax of the specified programming language (e.g., Python, Java, Kotlin). Uses language-aware logic to avoid splitting inside code blocks. Parameters: chunk_size (max chars per chunk), language (programming language as string, e.g., "python", "java"). Compatible with: Source code files (Python, Java, Kotlin, C++, JavaScript, Go, etc.).

Architecture

SplitterMR is designed around a modular pipeline that processes files from raw data all the way to chunked, LLM-ready text. There are three main components: Readers, Models and Splitters.

• Readers
  • The BaseReader components read a file and optionally converts to other formats to subsequently conduct a splitting strategy.
  • Supported readers (e.g., VanillaReader, MarkItDownReader, DoclingReader) produce a ReaderOutput dictionary containing:
    • Text content (in markdown, text, json or another format).
    • Document metadata.
    • Conversion method.
• Models:
  • The BaseModel component is used to read non-text content using a Visual Language Model (VLM).
  • Supported models are AzureOpenAI and OpenAI, but more models will be available soon.
  • All the models have a extract_text method which returns the LLM response based on a prompt, the client and the model parameters.
• Splitters
  • The BaseSplitter components take the ReaderOutput text content and divide that text into meaningful chunks for LLM or other downstream use.
  • Splitter classes (e.g., CharacterSplitter, SentenceSplitter, RecursiveSplitter, etc.) allow flexible chunking strategies with optional overlap and rich configuration.

How to install

Package is published on PyPi. To install it, you can use the official Python package manager:

pip install splitter-mr

Alternatively, you can install it using other Python package management tools such as uv, Conda or Poetry:

uv add splitter-mr

Note

Python 3.11 or greater is required to use this library.

How to use

Read files

Firstly, you need to instantiate an object from a BaseReader class, for example, VanillaReader.

from splitter_mr.reader import VanillaReader

reader = VanillaReader()

To read any file, provide the file path within the read() method. If you use DoclingReader or MarkItDownReader, your files will be automatically parsed to markdown text format. The result of this reader will be a ReaderOutput object, a dictionary with the following shape:

reader_output = reader.read('https://raw.githubusercontent.com/andreshere00/Splitter_MR/refs/heads/main/data/lorem_ipsum.txt')
print(reader_output)

text='Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum sit amet ultricies orci. Nullam et tellus dui.', 
document_name='lorem_ipsum.txt',
document_path='https://raw.githubusercontent.com/andreshere00/Splitter_MR/refs/heads/main/data/lorem_ipsum.txt', 
document_id='732b9530-3e41-4a1a-a4ea-1d9d6fe815d3', 
conversion_method='txt', 
reader_method='vanilla', 
ocr_method=None, 
page_placeholder=None,
metadata={}

Note

Note that you can read from an URL, a variable and from a file_path. See Developer guide.

Split text

To split the text, first import the class that implements your desired splitting strategy (e.g., by characters, recursively, by headers, etc.). Then, create an instance of this class and call its split method, which is defined in the BaseSplitter class.

For example, we will split by characters with a maximum chunk size of 50, with an overlap between chunks:

from splitter_mr.splitter import CharacterSplitter

char_splitter = CharacterSplitter(chunk_size=50, chunk_overlap = 10)
splitter_output = char_splitter.split(reader_output)
print(splitter_output)

chunks=['Lorem ipsum dolor sit amet, consectetur adipiscing', 'adipiscing elit. Vestibulum sit amet ultricies orc', 'ricies orci. Nullam et tellus dui.'], 
chunk_id=['db454a9b-32aa-4fdc-9aab-8770cae99882', 'e67b427c-4bb0-4f28-96c2-7785f070d1c1', '6206a89d-efd1-4586-8889-95590a14645b'], 
document_name='lorem_ipsum.txt', 
document_path='https://raw.githubusercontent.com/andreshere00/Splitter_MR/refs/heads/main/data/lorem_ipsum.txt', 
document_id='732b9530-3e41-4a1a-a4ea-1d9d6fe815d3', 
conversion_method='txt', 
reader_method='vanilla', 
ocr_method=None, 
split_method='character_splitter', 
split_params={'chunk_size': 50, 'chunk_overlap': 10}, 
metadata={}

The returned object is a SplitterOutput dataclass, which provides all the information you need to further process your data. You can easily add custom metadata, and you have access to details such as the document name, path, and type. Each chunk is uniquely identified by an UUID, allowing for easy traceability throughout your LLM workflow.

Compatibility with vision tools for image processing and annotations

Pass a VLM model to any Reader via the model parameter:

from splitter_mr.reader import VanillaReader
from splitter_mr.model.models import AzureOpenAIVisionModel

model = AzureOpenAIVisionModel()
reader = VanillaReader(model=model)
output = reader.read(file_path="data/lorem_ipsum.pdf")
print(output.text)

These VLMs can be used for captioning, annotation or text extraction. In fact, you can use these models to process the files as you want using the prompt parameter in the read method for every class which inherits from BaseReader.

Note

To see more details, consult documentation here.

Updates

Next features

• Add embedding model support.
  • Add HuggingFace embeddings model support.
  • Add OpenAI embeddings model support.
  • Add Gemini embeddings model support.
  • Add Claude Anthropic embeddings model support.
  • Add Grok VLMs model support.
• Add asynchronous methods for Splitters and Readers.
• Add batch methods to process several documents at once.
• Add support to read formulas.
• Modularize library into several sub-libraries.
• Add classic OCR models: easyocr and pytesseract.
• Add new models:
  • Add HuggingFace VLMs model support.
  • Add Gemini VLMs model support.
  • Add Claude Anthropic VLMs model support.
  • Add Grok VLMs model support.
• Add support to generate output in markdown, json, yaml formats.

Previously implemented

• Implement a method to split by embedding similarity: SemanticSplitter.
• Add new supported formats to be analyzed with OpenAI and AzureOpenAI models.
• Add support to read images using VanillaReader.
• Add support to read xlsx, docx and pptx files using VanillaReader.
• Add support to read images using VanillaReader.
• Implement a method to split a document by pages (PagedSplitter).
• Add support to read PDF as scanned pages.
• Add support to change image placeholders.
• Add support to change page placeholders.
• Add Pydantic models to define Reader and Splitter outputs.

Contact

If you want to collaborate, please contact me through the following media:

• My mail.
• My LinkedIn
• PyPI package