SplitterMR

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

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 four readers: VanillaReader, MarkItDownReader, DoclingReader, and TextractReader. These are the differences between each Reader component:

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:

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, TextractReader) 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 include OpenAI, AzureOpenAI, Grok, HuggingFace, Gemini, Anthropic, and OpenRouter.
  • All the models have a analyze_content 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, RecursiveCharacterSplitter, etc.) allow flexible chunking strategies with optional overlap and rich configuration.
• Embedders
  • The BaseEmbedder components are used to encode the text into embeddings. These embeddings are used to split text by semantic similarity.
  • Supported models include OpenAI, AzureOpenAI, HuggingFace, Gemini, Anthropic (Voyage), and OpenRouter.
  • All the models have a encode_text method which returns the embeddings based on a text, the client and the model parameters.

How to install

Package is published on PyPi.

By default, only the core dependencies are installed. If you need additional features (e.g., MarkItDown, Docling, Textract, multimodal processing), you can install the corresponding extras.

Core install

Installs the basic text splitting and file parsing features (lightweight, fast install):

pip install splitter-mr

Optional extras

Multiple extras

You can combine extras by separating them with commas:

pip install "splitter-mr[markitdown,docling,textract]"

Using other package managers

You can also install it with uv, conda or poetry:

uv add splitter-mr

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. If you use TextractReader, visual files are OCR'd with AWS Textract after PNG normalization, while text-native formats (md, json, yaml, txt) are delegated to VanillaReader. 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={}

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 OpenRouterVisionModel

model = OpenRouterVisionModel()
reader = VanillaReader(model=model)
output = reader.read(file_path="data/sample_pdf.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.

Complete workflows

For end-to-end examples covering fixed-size splitting, semantic chunking, multimodal PDF reading, RAG ingestion, and the MCP/REST server, see SKILL.md.

MCP and REST server

Install the optional extra and start the FastAPI app. Swagger documents the typed REST wrappers; MCP tools share the same pipeline at /mcp.

pip install "splitter-mr[mcp]"
splitter-mr-mcp

• Swagger UI: http://127.0.0.1:8000/docs
• ReDoc: http://127.0.0.1:8000/redoc
• OpenAPI: http://127.0.0.1:8000/openapi.json
• Health: http://127.0.0.1:8000/health
• MCP (Streamable HTTP): http://127.0.0.1:8000/mcp

import httpx

payload = {
    "file_path": "Lorem ipsum dolor sit amet.",
    "reader": {"reader": "VanillaReader"},
    "kwargs": {"document_name": "lorem.txt"},
    "splitter": {"splitter": "CharacterSplitter", "chunk_size": 50, "chunk_overlap": 10},
}
response = httpx.post("http://127.0.0.1:8000/api/v1/read-and-split", json=payload)
print(response.json()["chunks"])

import asyncio
from fastmcp import Client

async def main() -> None:
    async with Client("http://127.0.0.1:8000/mcp") as client:
        result = await client.call_tool(
            "read_and_split",
            {
                "request": {
                    "file_path": "Lorem ipsum dolor sit amet.",
                    "reader": {"reader": "VanillaReader"},
                    "kwargs": {"document_name": "lorem.txt"},
                    "splitter": {"splitter": "RecursiveCharacterSplitter"},
                }
            },
        )
        print(result)

asyncio.run(main())

POST /api/v1/read mirrors BaseReader.read(file_path, model=None, **kwargs). Pass a vision model as the top-level model object (requires splitter-mr[multimodal]); omit api_key to use provider environment variables.

POST /api/v1/split accepts a full ReaderOutput, a splitter discriminator, optional extra constructor arguments in kwargs, and an optional embedding object. kwargs is how you pass splitter parameters other than chunk_size and chunk_overlap (for example patterns, separators, include_delimiters, headers_to_split_on, language). SemanticSplitter requires embedding (OpenAI, Azure OpenAI, OpenRouter, Gemini, Hugging Face, or Anthropic/Voyage).

POST /api/v1/read-and-split uses the same read fields (file_path, reader, model, kwargs) plus splitter, optional embedding, and splitter_kwargs.

File paths are server-local and disabled until SPLITTER_MR_ALLOWED_ROOT is set. URL sources require SPLITTER_MR_ALLOW_URLS=true. The server is stateless and does not persist chunks. This version has no authentication: deploy it only on a private network or behind an authenticated reverse proxy. Inline API keys may be captured by proxies, MCP clients, or traces.

poe docs serves MkDocs on port 8001 so it does not collide with splitter-mr-mcp on port 8000.

Docker:

docker build -f Dockerfile.server -t splitter-mr-mcp .
docker run --rm -p 8000:8000 splitter-mr-mcp

See the server API reference for environment variables, tool contracts, and security defaults.

Updates

Next features

• Add support to read Latex files using VanillaReader.
• Persist chunked documents and answer queries over them via MCP.
• Add a new BaseVisionModel class to support generic API-provided models.
• Add asynchronous methods for Splitters and Readers.
• Add batch methods to process several documents at once.
• Add support to read formulas.
• Add classic OCR models: easyocr and pytesseract.
• Add support to generate output in markdown for all data types in VanillaReader.
• Add methods to support Markdown, JSON and XML data types when returning output.

Previously implemented (^v1.0.0)

• Provide an MCP server (and FastAPI REST wrappers) for read, split, and read-and-split.
• Add examples on how to implement SplitterMR in RAGs, MCPs and Agentic RAGs.
• Add a method to read PDFs using Textract.
• Add OpenRouter support for vision and embedding models.
• Add custom Error and Warning handling for better logging and traceability.
• Add KeywordSplitter support.

Previously implemented (up to v1.0.0)

• Add embedding model support.
  • Add OpenAI embeddings model support.
  • Add OpenRouter embeddings model support.
  • Add HuggingFace embeddings model support.
  • Add Gemini embeddings model support.
  • Add Claude Anthropic embeddings model support.
  • Add OpenRouter embeddings model support.
• Add Vision models:
  • Add OpenAI vision model support.
  • Add OpenRouter embeddings model support.
  • Add Grok VLMs model support.
  • Add HuggingFace VLMs model support.
  • Add Gemini VLMs model support.
  • Add Claude Anthropic VLMs model support.
  • Add OpenRouter VLMs model support.
• Modularize library into several sub-libraries.
• 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