---
title: "Document Processing Pipeline"
description: "How to process and chunk documents for RAG systems"
keywords: ["document processing", "chunking", "text splitting", "RAG"]
category: "processing"
tags: ["documents", "chunking", "pipeline"]
---

# Document Processing Pipeline

Processing documents for RAG (Retrieval-Augmented Generation) systems requires careful chunking and metadata extraction.

## Chunking Strategies

### Fixed-Size Chunking
Split documents into fixed-size chunks with optional overlap. Simple but may break semantic units.

**Pros:**
- Simple to implement
- Predictable chunk sizes
- Works with any content

**Cons:**
- May split sentences or paragraphs
- Loses document structure

### Semantic Chunking
Split at natural boundaries like paragraphs, sections, or sentences.

**Pros:**
- Preserves semantic units
- Better context preservation
- Respects document structure

**Cons:**
- Variable chunk sizes
- More complex implementation

## Metadata Extraction

Extract metadata from documents to improve search relevance:
- Title and description
- Keywords and tags
- Author and date
- Category and topic

## Best Practices

1. Choose chunk size based on your embedding model's context window
2. Use overlap to preserve context across chunks
3. Extract and preserve document structure
4. Include metadata for filtering and ranking
