# Welcome to Open Parse
**Easily chunk complex documents the same way a human would.**
Chunking documents is a challenging task that underpins any RAG system. High quality results are critical to a sucessful AI application, yet most open-source libraries are limited in their ability to handle complex documents.
Open Parse is designed to fill this gap by providing a flexible, easy-to-use library capable of visually discerning document layouts and chunking them effectively.
## Features
- ๐ Visually-Driven: Open-Parse visually analyzes documents for superior LLM input, going beyond naive text splitting.
- โ๏ธ Markdown Support: Basic markdown support for parsing headings, bold and italics.
- ๐ High-Precision Table Support: Extract tables into clean Markdown formats with accuracy that surpasses traditional tools.
- ๐ ๏ธ Extensible: Easily implement your own post-processing steps.
- ๐กIntuitive: Great editor support. Completion everywhere. Less time debugging.
## Quick Start
## Basic Example
```python
import openparse
basic_doc_path = "./sample-docs/mobile-home-manual.pdf"
parser = openparse.DocumentParser()
parsed_basic_doc = parser.parse(basic_doc_path)
for node in parsed_basic_doc.nodes:
print(node)
```
**๐ Try the sample notebook** here
## Semantic Processing Example
Chunking documents is fundamentally about grouping similar semantic nodes together. By embedding the text of each node, we can then cluster them together based on their similarity.
```python
from openparse import processing, DocumentParser
semantic_pipeline = processing.SemanticIngestionPipeline(
openai_api_key=OPEN_AI_KEY,
model="text-embedding-3-large",
min_tokens=64,
max_tokens=1024,
)
parser = DocumentParser(
processing_pipeline=semantic_pipeline,
)
parsed_content = parser.parse(basic_doc_path)
```
**๐ Sample notebook** here
## Cookbooks
[Other Cookbooks](https://github.com/Filimoa/open-parse/tree/main/src/cookbooks)
## Sponsors
Does your use case need something special? Reach [out](https://www.linkedin.com/in/sergey-osu/).