chunkin 0.1.1

Document chunking and indexing library for vector stores

Chunkin

A Python library for document chunking and indexing into vector stores, built on LangChain.

Built on LangChain

Chunkin leverages LangChain for:

• Document Loaders: Load PDF, DOCX, TXT, MD, CSV, XLSX, PPT formats
• Text Splitters: 6 chunking strategies including semantic chunking
• Vector Stores: 50+ vector store integrations (FAISS, Chroma, Pinecone, etc.)

Learn more about LangChain's document processing capabilities.

Modules

Module	Description
chunkin	Document chunking using LangChain text splitters
chunkin_indexer	Index chunks to 50+ vector stores via LangChain integrations
chunkin_processor	Unified end-to-end processing

Quick Start

from chunkin_processor import DocProcessor
from langchain_openai import OpenAIEmbeddings

processor = DocProcessor(
    embeddings=OpenAIEmbeddings(),
    vector_store_type="faiss",
    chunk_size=500,
)

processor.process_file("document.pdf")
results = processor.search("your query", k=3)

Installation

# Core only
pip install chunkin

# With OpenAI + FAISS (recommended)
pip install chunkin[core]

# With semantic chunking
pip install chunkin[semantic]

# Local vector stores (Chroma, Milvus, LanceDB, etc.)
pip install chunkin[local]

# Specific cloud providers
pip install chunkin[aws]     # Amazon AWS
pip install chunkin[azure]   # Microsoft Azure
pip install chunkin[gcp]     # Google Cloud

# All vector stores
pip install chunkin[all]

Documentation

• Overview
• Installation
• Usage Guide
• API Reference
• Chunking Strategies
• Vector Stores
• Doc Processor

Supported Formats

Chunkin uses LangChain document loaders:

Format	Extensions
PDF	.pdf
Word	.docx, .doc
Text	.txt
Markdown	.md
CSV	.csv
Excel	.xlsx, .xls
PowerPoint	.pptx, .ppt

Supported Vector Stores

Built on LangChain vector store integrations:

Local (No External Service)

FAISS, Chroma, Milvus, LanceDB, LambdaDB, Deep Lake, Annoy

Amazon AWS

OpenSearch, Valkey, DocumentDB

Microsoft Azure

Azure AI Search, Azure Cosmos DB, Azure Cosmos DB NoSQL

Google Cloud

Databricks Vector Search, Vertex AI Vector Search, BigQuery, AlloyDB

Other

Qdrant, Weaviate, Pinecone, MongoDB Atlas, PGVector, Astra DB, Elasticsearch, Oracle, Neo4j, SingleStore, Supabase, MyScale, Zilliz, Marqo, Vectara, Meilisearch, Typesense, and more...

See docs/indexer.md for full list.

Supported Chunking Strategies

Uses LangChain text splitters:

Strategy	LangChain Class	Description
recursive	RecursiveCharacterTextSplitter	Recursively splits by paragraphs, sentences, words
character	CharacterTextSplitter	Simple character-based splitting
markdown	MarkdownTextSplitter	Markdown-aware splitting
markdown_headers	MarkdownHeaderTextSplitter	Split by markdown headers
html_headers	HTMLHeaderTextSplitter	Split by HTML header tags
semantic	SemanticChunker	Embedding-based semantic splitting

See docs/strategies.md for details.

Project Structure

chunkin/
├── chunkin/                 # Document chunking module
│   └── chunker.py          # DocumentChunker class
├── chunkin_indexer/         # Vector store indexing module
│   └── indexer.py          # DocIndexer class
├── chunkin_processor/       # Unified module
│   └── doc_processor.py    # DocProcessor class
├── docs/                    # MkDocs documentation
├── pyproject.toml           # Package configuration
└── README.md

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

# Build package
python -m build

# Serve docs locally
cd docs && pip install -r requirements.txt && mkdocs serve

LangChain Resources

• LangChain Documentation
• Text Splitters
• Vector Stores
• Document Loaders

License

MIT License