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Put tenant and document-access conditions in the SQL that performs the pgvector search, and consider PostgreSQL row-level security (RLS) as an additional database-enforced boundary. Do not fetch unrestricted nearest neighbors and check permissions afterward. One important distinction: SQL and RLS govern which rows a query may return, but with approximate indexes pgvector applies filters after the index scan. That can reduce the number of authorized results and affect recall.
How to include permissions in a pgvector search
Add tenant and authorization predicates to the same query that orders by vector distance and applies the result limit. For example:
SELECT id, content
FROM documents
WHERE tenant_id = $1
AND can_read_document(id, $2)
ORDER BY embedding <=> $3
LIMIT 10;This is an illustrative pattern, not a tested query or a guarantee that a particular authorization function is safe. Define access rules for your schema and verify every path that can read the data. A SQL predicate makes the intended filter explicit in the search query; RLS can enforce row visibility at the database policy layer as well.
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What RLS adds—and which roles can bypass it
RLS supplements ordinary SQL privileges. When RLS is enabled, applicable policies govern normal row access; if a table has no policy, PostgreSQL uses default deny for policy-governed access. Grants still matter: a policy does not replace the need to grant appropriate table privileges.
Review the identity that actually executes each search. PostgreSQL superusers and roles with the BYPASSRLS attribute bypass row security. Table owners normally bypass it too, unless the table has FORCE ROW LEVEL SECURITY enabled. A policy is only an effective boundary for query paths that run under roles subject to it.
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PostgreSQL generally evaluates policy conditions before conditions supplied by the query, with an exception for leakproof functions. Views normally access underlying tables with the view owner’s rights and policies; a view configured as security invoker behaves differently. Check these boundaries when searches pass through views or privileged functions, and consult the documentation for your deployed PostgreSQL major version.
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Why approximate pgvector searches can return too few matches
pgvector supports ordinary SQL WHERE filters with nearest-neighbor queries. However, its official README cautions: “With approximate indexes, filtering is applied after the index is scanned.” In other words, a tenant predicate does not make an HNSW or IVFFlat approximate index scan only that tenant’s vectors. Candidates are found by the approximate scan and then filtered, so the query may return fewer rows than its limit and may have lower recall among authorized rows.
The README illustrates the effect with a 10% filter and the default hnsw.ef_search value of 40: four matches are the expected average in that example. It is an illustration, not a benchmark, guarantee, or prediction for a different dataset. Tenant distribution, index settings, and query conditions matter; measure your own workloads.
Options for filtered approximate search
- Index the filter column. A conventional index on a tenant or other frequently filtered column can help PostgreSQL handle filtering.
- Use a partial index for a few repeated values. This can suit a small number of distinct filter values, but is not a universal layout.
- Partition when there are many filter values. pgvector documents partitioning as an option for many distinct values.
- Enable iterative scans where appropriate. Starting with pgvector 0.8.0, iterative index scans can continue searching for enough filtered matches or until configured limits are reached. Limits mean a scan may still stop before producing the requested count.
Iterative scans support strict ordering, which preserves distance ordering, and relaxed ordering, which can improve recall while allowing results to be slightly out of order. If exact distance order is required after a relaxed scan, reorder the returned candidates as appropriate for your query.
Choose tenant isolation based on measured behavior
For multi-tenant data, pgvector warns that vectors in a shared approximate index can affect another tenant’s recall and speed. It recommends list partitioning or separate tables for tenant isolation. There is no universal best layout: compare measured recall, latency, storage, operational complexity, and the number and distribution of tenants.
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|---|---|---|
| Shared table and approximate index | When a shared layout is operationally suitable and filters return enough authorized candidates. | Recall and latency by tenant; filtered result counts; query plans. |
| Partial index | When filtering uses a few repeated values. | Index coverage, storage, and behavior as values or data grow. |
| List partitioning | When isolating tenants or many filter values is useful. | Tenant count, partition management, storage, recall, and latency. |
| Separate tables | When per-tenant separation is worth the added operational work. | Management overhead alongside measured search behavior and storage. |
The table describes design considerations, not performance results. Test with representative tenant sizes and access patterns rather than assuming a partitioning or indexing choice will improve every workload.
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- Confirm the deployed versions. Check the PostgreSQL major version and installed pgvector extension version, then verify supported settings and syntax against those versions. The project metadata in the reviewed source reports pgvector 0.8.6 and a PostgreSQL 13.0 runtime prerequisite; iterative scans are documented from pgvector 0.8.0.
- Audit the execution role. Check its grants, ownership, superuser and
BYPASSRLSattributes, and whether table owners are subject toFORCE ROW LEVEL SECURITY. - Trace all read paths. Verify SQL predicates and applicable RLS policies for direct queries, views, and privileged functions. Check view security-invoker configuration where relevant.
- Test authorization separately from recall. Attempt cross-tenant and unauthorized-document reads under the actual application role, then measure authorized result counts and recall for representative queries.
- Inspect plans and tune limits. Compare execution plans, latency, and result counts. For approximate filtered searches, test iterative-scan settings and their stopping limits; compare strict and relaxed ordering if both are acceptable options.
- Reassess layout with realistic distributions. Compare a shared index, partial indexes where applicable, partitioning, or separate tables against the same representative workload.
PostgreSQL’s cited row-security and policy documentation is for PostgreSQL 18; behavior and available syntax should be checked against the major version you run. pgvector settings and features can also change, so confirm them in the installed extension’s documentation.
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