What information is stored with vectors in a RAG database?

A vector in a RAG database is rarely stored by itself.

To make retrieval useful, the system usually stores the embedding together with the original content or a reference to it, plus metadata that helps with filtering, traceability, updates, and access control.

1. The embedding vector

This is the numerical representation used for similarity search.

Example:

[0.12, -0.44, 0.78, ...]

The vector is what the retrieval engine compares with the user's query embedding.

2. The text chunk or content reference

The database often stores either:

• The actual chunk text

or

• A pointer to where the content is stored

Example:

"Enterprise customers can request a refund within 45 days."

After retrieval, the application needs this content to send useful evidence to the LLM.

3. Document and chunk identifiers

Useful identifiers include:

• document_id
• chunk_id
• parent_chunk_id
• source_id

These help the system trace each retrieved vector back to the original document and support updates or deletion.

4. Document structure

For larger documents, it is useful to preserve structure such as:

• Page number
• Section
• Heading
• Chapter
• Paragraph position
• Figure or table number

This helps reconstruct meaningful context after retrieval.

For example, if a small chunk is retrieved, the system may use its parent section to provide more complete context to the LLM.

5. Source information

Useful source metadata may include:

• File name
• Document URL
• Repository path
• Website page
• Database record ID

This is important for citations and source tracking.

6. Metadata used for filtering

Metadata lets the retriever narrow the search before or after vector similarity.

Examples:

• Product
• Department
• Region
• Language
• Document type
• Customer
• Tenant
• Version
• Effective date
• Status

Example:

User asks about Product A.

Instead of searching the entire vector index, the system can search only records where:

product = "Product A"

This can improve both relevance and efficiency.

7. Permission information

Enterprise RAG systems often need access-related metadata.

Examples:

• tenant_id
• department_id
• access_level
• allowed_group
• owner_id

This helps ensure retrieval is scoped to content the user is allowed to access.

However, metadata filtering should complement proper authorization controls rather than being the only security boundary.

8. Version and freshness information

Useful fields include:

• document_version
• updated_at
• effective_from
• effective_to
• embedding_model
• embedding_model_version

These help prevent stale documents or incompatible embeddings from being retrieved.

Example record

A simplified vector record may contain:

chunk_id: chunk_1042 document_id: policy_27 text: "Enterprise customers can request a refund within 45 days." embedding: [0.12, -0.44, ...] section: "Refund Policy" page: 8 product: "Enterprise Plan" version: 3 updated_at: 2026-09-15 embedding_model: model_v2

The vector answers:

"What content is semantically similar?"

The metadata answers:

"Where did it come from, is it allowed, is it current, and how should it be used?"

Interview takeaway

A RAG vector database usually stores more than embeddings. Each vector should be connected to the original chunk or content reference, document and chunk IDs, source and structural metadata, filtering fields, permissions, version information, and embedding-model metadata. The embedding enables similarity search, while the surrounding metadata makes retrieval accurate, traceable, secure, and maintainable.