What types of data sources can be used in RAG?

RAG can use many kinds of data sources as long as the information can be processed and made searchable.

Common data sources include:

• Documents — PDF, DOCX, TXT, Markdown, and similar files.
• Web content — websites, documentation pages, help centers, and knowledge portals.
• Databases — relational databases, document databases, and other structured data stores.
• Spreadsheets and tabular data — CSV, Excel, and exported reports.
• Internal knowledge bases — company policies, SOPs, manuals, wikis, and support documentation.
• APIs — product catalogs, ticketing systems, CRM data, inventory systems, or other application data.
• Messages and conversations — support tickets, chat history, or approved communication archives.
• Multimodal content — images, scanned documents, charts, diagrams, or other visual information after appropriate extraction or representation.

Structured vs unstructured sources

RAG systems commonly work with both:

• Unstructured data, such as paragraphs in PDFs or web pages.
• Structured data, such as rows in a database or spreadsheet.
• Semi-structured data, such as HTML, JSON, or documents containing headings and tables.

The ingestion approach may differ for each type.

For example, a PDF may need parsing and chunking, while a database may be queried directly or converted into searchable records.

The important requirement

The source itself does not have to be a vector database.

What matters is that the system can transform or access the source in a way that allows it to retrieve the information relevant to a user's query.

PDFs        ─┐
Web pages    │
Databases    ├→ Process / index → Retrieve relevant information
Spreadsheets │
APIs        ─┘

Different sources may also require different handling for permissions, updates, metadata, and parsing quality.

In short, RAG can use documents, web pages, databases, spreadsheets, APIs, internal knowledge systems, and other searchable information sources as external knowledge.