What is Data Parsing

What is data parsing in RAG context

Data parsing / Loading data in RAG systems means converting different types of files and documents into plain text that can be processed by the LLM. LLM can only process text, so we need to convert other types of data into text.

Full RAG Pipeline from How does RAG work?

This process involves:

1. Reading the original file format (like PDF, Word, CSV, or HTML)
2. Extracting the text content
3. Cleaning up the text by removing unnecessary formatting
4. Preparing the text for the next steps in the RAG pipeline

Why is data parsing important?

Parsing Data is the first step in every RAG systems. If your data is not parsed properly, no matter how good your embeddings and vector database are, you won't get good results.

Good parsing ensures that your RAG system can understand and use the information correctly.

Common Types of Data Sources

• Text Documents (PDF, TXT, DOC)
• Web Content (HTML, XML)
• Structured Data (JSON, CSV)
• Code and Technical Documentation
• Databases

Each type of data source requires different parsing methods to extract the text properly. I will be showing you examples of how to parse each type of data source.

Next Steps

Learn practical examples of parsing data for different types of files and documents:

• Parsing Text Documents
• Parsing Webpages
• Parsing PDFs
• Parsing CSVs