Dieser Blogpost ist auch auf Deutsch verfügbar
- Part 1: Retrieval-Augmented Generation – Bridging General and Domain-Specific Knowledge*
- Part 2 (this article)
Building a RAG system starts with preparing and formatting selected data in a way that makes it accessible to the Large Language Model (LLM). The quality of the system’s responses heavily depends on how well we handle the document ingestion process. Let’s explore how to prepare documents effectively, tackle common challenges, and understand why “chunking” plays such a vital role.
What is Document Ingestion?
Document ingestion involves collecting, preparing, and storing documents for use in a retrieval system. These can range from PDFs and web pages to database entries, technical documentation, research reports, and FAQs. The goal is to transform these diverse information sources into a structured, searchable format that the retrieval system can quickly and accurately search through.
The Critical Role of Document Ingestion
The success of a RAG system largely hinges on how well we prepare and structure the underlying documents. Poor preparation can lead to missing crucial information or make it hard for the retrieval system to find relevant content. That’s why we need to carefully plan our document ingestion strategy based on the types and structures of our available documents.
Example: When trying to answer questions precisely, we might split a book differently depending on how its information is organized—page by page for compact content, or chapter by chapter when information spans multiple pages.
One key challenge is dealing with different types of documents. A scientific article has paragraphs, headings, and citations, while technical documentation might contain tables, code snippets, and step-by-step instructions. Using a one-size-fits-all approach won’t work here. This is where “chunking” comes into play.
Chunking: Finding the Right Balance
Chunking breaks documents into smaller, coherent sections. These chunks become the basic units that the retrieval system searches through. The key is finding the sweet spot in chunk size that provides enough context without including unnecessary information.
- Why chunking matters: The accuracy of your system’s answers depends on getting relevant, precise chunks for each query. Too large, and you’ll include irrelevant information that muddles the answer. Too small, and you’ll lose important context.
- Matching document structure: Different document types need different chunking approaches. For scientific articles, chunks might follow natural paragraph or section breaks. For technical docs, they might align with individual instructions or function descriptions. There’s no universal solution—you need to tailor your approach to each document type and use case.
- Dynamic approaches: Sometimes it makes sense to vary chunk size based on context and task. You might want to automatically classify documents and adjust your chunking strategy accordingly. This flexible approach can improve retrieval accuracy by better preserving context. For instance, your pipeline might handle PDFs and HTML files differently, preparing chunks in ways that work best for each format.
Smart Structure: Beyond Plain Text
Good document ingestion isn’t just about breaking up text. Not all parts of a document carry equal weight. Headings, bullet points, tables, and highlighted sections often signal important content. That’s why we need to capture and preserve metadata and structural information during ingestion.
- Metadata matters: Details like document title, author, creation date, original page numbers, and keywords help the retrieval system identify the most relevant chunks for each query.
- Smart indexing: Beyond basic chunking, we need to index our content effectively. Each chunk gets a unique identifier plus relevant keywords and context markers. This helps the retrieval system perform quick, precise searches—for example, by referencing URIs or page numbers.
Common Challenges and Best Practices
- Diverse data sources: Handling different document formats is tricky. PDFs, HTML pages, CSV files, and database entries each need their own approach. Your ingestion process should use appropriate tools to convert everything into a consistent, searchable format or leverage databases that can handle various formats effectively.
- Quality checks: Since chunk quality directly impacts answer quality, you need regular verification. Set up automated validation processes to ensure consistent, reliable document ingestion.
- Ongoing maintenance: Document ingestion isn’t a set-it-and-forget-it process. As information changes and new documents arrive, you need to keep monitoring and adjusting. Automated update and monitoring systems help keep your knowledge base current and relevant.
Final Thoughts: Building a Strong Foundation
Document ingestion might not be the most exciting part of building a RAG system, but it’s crucial for success. By carefully structuring and preparing documents based on their unique characteristics, you create the foundation for efficient, accurate retrieval. Chunking is particularly important—it determines how much context your system has to work with and how precisely it can pull information.
When done right, document ingestion enables your RAG system to tap into a rich, diverse knowledge base and deliver accurate, contextual answers. It’s worth investing time and expertise in getting this foundational piece right.
This article is an excerpt from our free primer on Retrieval-Augmented Generation (in German). A quick introduction for software architects and developers.