Put tenant and document-visibility checks in the SQL query that retrieves vectors—and consider PostgreSQL row-level security (RLS) as an additional database-enforced boundary. Filtering after fetching nearest neighbors is too late: unauthorized rows may already have reached application code, and removing them can leave fewer results than requested.
Why permissions belong in the vector query
A vector search finds rows by distance, but distance does not determine who may read them. Apply authorization as part of the database query so the search is constrained to rows the caller is allowed to see. For example:
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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 claim that a particular authorization function is safe. Define the tenant and document-access rules for your schema, and verify that every relevant query path uses them. SQL privileges still apply; a query predicate is not a substitute for a deliberate role and permission model.
What a filtered approximate search does—and does not do
pgvector supports ordinary SQL WHERE filters on nearest-neighbor queries. But with approximate indexes, such as HNSW or IVFFlat, pgvector applies filtering after the index scan. As its official README puts it: “With approximate indexes, filtering is applied after the index is scanned.”
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That distinction matters for search behavior, not just query syntax. The index scan may find nearby candidates that do not match the filter; filtering those candidates can leave fewer authorized rows than the requested limit and can affect recall. A predicate in the query does not mean the approximate index traverses only authorized rows.
The README illustrates the effect with a 10% filter and a default hnsw.ef_search of 40: the expectation is four matching rows on average. That is an illustration, not a guarantee or a benchmark for your data. Measure results using your own tenant sizes and filter distributions.
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Use RLS as a database-enforced row boundary
PostgreSQL row-level security can make row visibility a database policy rather than relying only on each query author to remember a predicate. RLS supplements ordinary SQL privileges. When RLS is enabled, applicable policies govern normal row access; if no policy applies, PostgreSQL uses default deny. See the PostgreSQL 18 row-security documentation.
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RLS and an explicit tenant predicate serve related but distinct purposes: RLS establishes a database-enforced visibility boundary, while query filters also express the intended search scope and can shape search behavior. If you use both, make sure their rules agree and that your application executes queries under the role you intend.
Check which role actually runs the query
RLS is not an absolute barrier against every database role. Superusers and roles with BYPASSRLS bypass it. Table owners normally bypass RLS too, unless the table uses FORCE ROW LEVEL SECURITY. Review the execution role, table ownership, superuser status, and BYPASSRLS attribute before treating a policy as an effective boundary. PostgreSQL documents these exceptions in its row-security overview.
Account for policy and view behavior
PostgreSQL generally evaluates policy conditions before conditions supplied by the query, with an exception for leakproof functions. Views normally use the view owner’s rights and policies unless configured as security invoker. If a query goes through a view or uses security-definer code, examine those boundaries as part of the access design. The details are covered in the official CREATE POLICY documentation.
Choose a filtered-search strategy based on your data
Security policy and approximate-search recall are separate concerns. Once permissions are enforced in the database, choose indexes and search settings that return enough authorized matches for your workload.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchIndex filter columns or use partial indexes
pgvector documents indexing filter columns as one option. If a filter has only a few repeated values, a partial index for those values may be useful. Partial indexes are not a universal fit: the number and distribution of filter values affect whether this design is practical.
Consider partitioning or separate tables for tenant isolation
For many filter values, pgvector suggests partitioning. For tenant isolation specifically, its documentation warns that vectors in a shared approximate index can affect another tenant’s recall and speed, and recommends list partitioning or separate tables. There is no single layout that is best for every deployment; compare tenant count, data distribution, measured recall and latency, storage, and operational complexity.
Use iterative scans when filtering leaves too few matches
Iterative index scans can continue searching for enough filtered results, subject to configured stopping limits. pgvector says iterative scans were introduced in version 0.8.0. Strict ordering preserves distance order; relaxed ordering can improve recall while allowing results to arrive slightly out of order. If exact ordering is required after a relaxed scan, reorder the returned results as appropriate for your query.
Check the pgvector version installed in your environment before relying on a setting or syntax. Project metadata reports version 0.8.6 and a PostgreSQL 13.0 runtime prerequisite, but deployed versions can differ. Likewise, check the PostgreSQL major version in production: the policy behavior linked here is documented for PostgreSQL 18.
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Test authorization independently from approximate-search quality. A filtered query can enforce the right visibility boundary and still return too few neighbors; adjusting a scan setting cannot correct a faulty permission rule.
Quick Recap
- Authorization: Verify tenant and document access for each relevant query path, including views or security-definer functions. Confirm the execution role does not bypass RLS unintentionally.
- Recall and result counts: Test representative tenant and filter distributions, including cases where matching rows are sparse. Check whether the query returns enough authorized results.
- Performance: Measure latency, candidate counts, and execution plans alongside recall. Compare approximate settings and possible index or partition layouts on representative data rather than assuming a general performance figure.
- Version compatibility: Check the deployed PostgreSQL and pgvector versions before adopting documented settings or syntax.
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