Filters, Joins, Transactions, and Referential Integrity

pgvector becomes especially useful when semantic ranking must obey relational rules. PostgreSQL can join chunks to documents, tenants, products, and permissions; filter state and dates; and atomically update source truth with derived retrieval records. Filters narrow candidates, but only trusted server policy and database privileges make those predicates an authorization control.

What will you be able to do?

Combine similarity order with relational predicates.
Avoid duplicate rows from permission joins.
Use EXISTS for membership checks.
Update business and retrieval state atomically.
Separate optional relevance filters from mandatory authorization.

How do filters and joins work together?

sql

SELECT c.id, c.content, c.embedding <=> $3::vector AS distance
FROM app.chunks AS c
JOIN app.documents AS d ON d.id = c.document_id
WHERE d.tenant_id = $1
  AND d.status = 'published'
  AND d.language_code = $2
  AND EXISTS (
    SELECT 1
    FROM app.document_principals AS p
    WHERE p.document_id = d.id
      AND p.principal_id = ANY($4::uuid[])
  )
ORDER BY c.embedding <=> $3::vector
LIMIT $5;

EXISTS avoids duplicating a chunk when a user has multiple matching groups. The application constructs $1 and $4 from authenticated server context. A client may request a language, but may never choose an unauthorized tenant or principal set.

When should a transaction include embeddings?

Generating an embedding is an external and potentially slow operation; do not hold a database transaction open during the network call. Claim a job in a short transaction, embed outside it, then commit the vector only if the source checksum and expected job version still match.

sql

UPDATE app.embedding_jobs
SET status = 'ready', finished_at = now()
WHERE id = $1 AND status = 'processing';

-- In the same short transaction, insert the embedding with its model and checksum.

Optimistic version checks prevent stale workers from overwriting newer content.

How do you verify it?

Create two tenants, two principals, and overlapping document groups. For every retrieval query, run a matrix of authorized and unauthorized identities. Assert exact IDs, not only row counts. Kill a worker between claim and completion; prove the lease or retry policy recovers without a duplicate embedding.

What breaks in production?

A user-supplied tenant_id becomes the only boundary.
Permission joins duplicate results and distort ranking.
A transaction stays open during an embedding API call.
Retried jobs write embeddings for stale source text.
Deleted parents leave orphaned chunks because referential integrity was omitted.

AI pair-work prompt

Threat-model this filtered vector query and embedding transaction [paste]. Mark every input as server-derived, user-selectable, or database-owned. Find duplicate-row risks, stale-write races, missing constraints, and authorization bypass paths. Return adversarial fixtures and transaction timelines.

Verification contract: Integration tests must attempt cross-tenant access through every API path, cache, reranker, and citation resolver—not only the primary SQL query.

Check your understanding

Why is a filter not automatically an authorization control?
When is EXISTS safer than joining a many-to-many permission table?
Sketch the three short stages of an asynchronous embedding job.

Official references