Should I use vector search for tenant-aware document retrieval?

Yes—vector search can be a good fit for tenant-aware document retrieval, but only if you enforce tenant isolation correctly.

When it’s a good choice

Use vector search if you need:

• Semantic search across documents
• Natural language queries like “find contracts about renewal penalties”
• Retrieval over large, unstructured text corpora
• Ranking results by meaning, not just keywords

Tenant-aware requirements

If documents belong to multiple tenants, you should treat tenant identity as a hard filter, not a soft preference.

Recommended pattern

1. Store each chunk/document with metadata like:

   • tenant_id
   • document_id
   • acl / permissions
   • source_type
   • created_at

2. At query time:

   • first determine the caller’s allowed tenant_ids
   • apply a metadata filter to restrict search to those tenants
   • then run vector similarity search within that filtered set

This avoids cross-tenant leakage.

Important warning

Do not rely on embeddings alone to separate tenants. Similarity search can surface cross-tenant matches if the filter is missing or misconfigured.

Best practice

For most multi-tenant systems:

• Use vector search for retrieval
• Use strict metadata filtering for tenant isolation
• Optionally combine with keyword search or re-ranking

When not to use it

Vector search may be overkill if:

• your retrieval is mostly exact lookup
• documents are small and highly structured
• you only need simple filtering by IDs or tags

Practical architecture

A common setup is:

• Postgres + pgvector for moderate scale
• Dedicated vector DB if scale/latency demands it
• Metadata filters enforced in the retrieval layer
• Row-level security or separate indexes/collections per tenant if you need stronger isolation

If you want, I can also sketch a safe multi-tenant retrieval architecture or compare single shared index vs per-tenant indexes.