What happens during RAG ingestion?

During RAG ingestion, raw source data is transformed into searchable knowledge that can later be retrieved when a user asks a question.

The exact steps vary by system, but a typical ingestion flow looks like this:

1. Load the source data

The system reads content from sources such as PDFs, DOCX files, web pages, databases, or other repositories.

2. Parse the content

Useful information is extracted from the source, such as text, headings, tables, page numbers, and metadata.

3. Clean and normalize

Noise such as repeated headers, broken formatting, or unnecessary characters may be removed while preserving meaningful structure.

4. Split the content into chunks

Large documents are divided into smaller units so the retrieval system can return only the sections relevant to a query.

5. Add or preserve metadata

Each chunk may retain information such as:

• Document name
• Page number
• Section heading
• Source URL
• Document version
• Other filtering attributes

6. Create searchable representations

Depending on the retrieval approach, the system may generate embeddings, build keyword-search fields, or prepare both.

7. Store the processed data

The chunks, metadata, and searchable representations are stored in a vector database, search engine, database, or another index.

Raw source
   ↓
Parse
   ↓
Clean / normalize
   ↓
Chunk
   ↓
Attach metadata
   ↓
Create searchable representation
   ↓
Index / store

After ingestion is complete, the knowledge is ready for the retrieval pipeline to search at query time.

Not every ingestion pipeline uses exactly the same steps. For example, scanned documents may require OCR, while tables, charts, and images may require specialized processing.

For the definition and role of this process, see “What is an ingestion pipeline in RAG?”.

In short, RAG ingestion converts raw source content into structured, chunked, metadata-rich, searchable data that can be retrieved later.