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WHERE tenant_id = $tenant is useful query logic, but it is not an independent authorization boundary. If application code can omit or change the condition, a database-side control is needed to constrain normal access to other tenants’ rows. PostgreSQL row-level security (RLS) can provide that layer when privileges, policies, ownership, and bypass roles are configured deliberately. With pgvector, authorization is only part of the design: approximate-search layout also affects tenant isolation and retrieval results.
Why is a tenant filter not enough?
A query such as SELECT * FROM rag_chunks WHERE tenant_id = $tenant returns rows matching the condition the application supplied. It does not, by itself, make the database reject a query that omits or changes that condition. The predicate remains valuable for expressing the intended query and may help query planning, but if it is the only tenant control, a query-construction mistake can expose rows across tenants.
PostgreSQL RLS moves row authorization into the database. Its documentation says that when RLS is enabled, normal access to select or modify rows must be permitted by an applicable row-security policy; if no policy applies, access is denied by default. RLS supplements rather than replaces ordinary SQL privileges: a role needs the required table or column grant as well as a policy permitting the row operation. PostgreSQL row-security documentation
How should RLS policies handle reads and writes?
Policies need to express both which existing rows a role may access and which tenant identity is acceptable on rows it creates or changes. PostgreSQL separates these checks:
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USINGdetermines which existing rows are visible or eligible for an operation.WITH CHECKvalidates rows being inserted or updated.
For example, this illustrates the shape of a policy for a table with a UUID tenant identifier:
ALTER TABLE rag_chunks ENABLE ROW LEVEL SECURITY;
CREATE POLICY tenant_chunks ON rag_chunks
USING (tenant_id = current_setting('app.tenant_id')::uuid)
WITH CHECK (tenant_id = current_setting('app.tenant_id')::uuid);This is illustrative SQL, not a complete deployment recipe. A session setting does not establish a trustworthy tenant identity merely because the policy reads it. The application must pass authenticated tenant context to the database through a controlled path, and the design must account for how transactions and pooled connections are reused. PostgreSQL documents that where applicable, a policy without an explicit WITH CHECK may use its USING expression as the check expression; writing both clauses makes the intended read and write rules easier to inspect. Confirm command-specific behavior against the PostgreSQL version in use. PostgreSQL row-security documentation
Which roles can bypass row security?
RLS is not a defense against every database role or configuration. PostgreSQL superusers and roles with the BYPASSRLS attribute bypass row security. Table owners normally bypass it as well; ALTER TABLE ... FORCE ROW LEVEL SECURITY subjects the owner to policies. Review the role used by application connections and avoid privileged bypass roles unless there is a specifically justified design. PostgreSQL row-security documentation
Also verify that RLS is enabled on every protected table and that policies cover the commands the application uses. A policy on a chunks table does not automatically secure related tables that hold tenant data.
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How does pgvector filtering affect approximate search?
pgvector uses exact nearest-neighbor search by default; it provides perfect recall. HNSW and IVFFlat indexes support approximate nearest-neighbor search, trading some recall for speed. With approximate indexes, filtering happens after the index scan. If a tenant filter matches only a small fraction of the rows encountered, a query can return fewer matches than its requested LIMIT. pgvector README
The pgvector README describes iterative scans, partial indexing for a small number of filter values, and partitioning when filtering on many distinct values as possible ways to address filtering behavior. Which approach works depends on tenant count, query patterns, and the recall the application needs; validate it with representative workload tests. The README is the project’s rolling master documentation, so check feature details against the extension version actually deployed.
Which tenant-aware vector layout should you consider?
Authorization and retrieval layout solve different problems. RLS governs which rows normal database access may read or modify, subject to its role and policy conditions. The physical organization of vectors affects approximate-search behavior. pgvector specifically warns that vectors in a shared approximate index can affect recall and speed for other tenants, and recommends list partitioning or separate tables for tenant isolation. pgvector README
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute| Search or storage choice | Authorization consideration | Retrieval and tenant-isolation consideration |
|---|---|---|
| Exact search | Use database privileges and RLS policies to authorize row access; exact search is not itself an authorization mechanism. | pgvector says exact nearest-neighbor search is the default and has perfect recall. |
| Shared approximate index with filtering | Keep database policy enforcement in place; filtering in the query does not replace it. | Post-scan filtering can return fewer rows than requested. The README warns that tenants sharing an approximate index can affect one another’s recall and speed. |
| List partitioning | Ensure policies and privileges protect each relevant table and its rows. | pgvector recommends partitioning as an option for many distinct filter values and recommends list partitioning for multi-tenant approximate-index isolation. |
| Separate tables | Review grants, policies, ownership, and application access for every table. | pgvector recommends separate tables as another multi-tenant approximate-index isolation option; workload-specific latency, resource use, and operating costs are not stated in the README. |
The documentation establishes the recall and isolation considerations above, not a universal winner on latency, memory, index-build expense, migration effort, or maintenance. Measure those factors with the tenant cardinality and workload you expect rather than assuming partitioning or table separation will always be cheaper or faster.
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What should you verify before relying on the design?
- Inspect grants and role attributes for every role used by the application, including whether any has
BYPASSRLS. - Confirm RLS is enabled on each protected table, and decide whether owner access should also be subject to policies.
- Review policy coverage for reads, inserts, and updates; check both
USINGandWITH CHECKbehavior where relevant. - Trace how authenticated tenant identity reaches the database, then test transaction boundaries and connection-pool reuse in the actual application stack.
- Test authorization with attempts to read and write across tenant boundaries, including queries that omit the application’s tenant predicate.
- Test retrieval separately: measure whether searches return the needed number of tenant-matching results at the required recall, using representative tenant sizes and filters.
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