In this lesson, you will learn how to isolate vector embeddings belonging to different users within a single shared database instance.
The Challenge of Shared Vector Spaces
When storing vector embeddings in a shared database, the default behavior of an Approximate Nearest Neighbor (ANN) search is to query the entire index. If User A performs a search, the database might return results that actually belong to User B. In a multi-tenant architecture, this is both a security risk and a retrieval quality issue, as cross-tenant matches are usually irrelevant.
Implementing Logical Isolation
To achieve multi-tenancy within a single instance, we treat the tenant_id as a first-class citizen alongside the vector data. There are two primary architectural approaches to enforcing this:
1. Metadata Filtering (Post-Filtering)
The most common implementation involves attaching a tenant_id to the metadata of every stored vector. During the search process, the query includes a metadata filter. The vector database first identifies the nearest neighbors in the high-dimensional space and then prunes any results that do not match the requested tenant_id.
-- Conceptual example of a filtered vector search
SELECT id, content, distance
FROM vector_table
WHERE tenant_id = 'user_123'
ORDER BY embedding <-> '[0.12, 0.45, ...]'
LIMIT 10;
2. Namespace/Collection Sharding
Some modern vector stores allow for "Namespaces" or "Collections" defined by tenant ID. By placing each user's data into their own partition, the indexing process is physically or logically siloed. When a query is initiated, the engine only traverses the graph or tree structure assigned to that specific namespace. This is often more performant than post-filtering, as the search algorithm never even considers the vast majority of vectors belonging to other tenants.
Trade-offs
While metadata filtering is easy to implement, it can become expensive if a single tenant has a very small amount of data scattered across a massive global index. In contrast, sharding or namespaces provide better isolation and speed but require more complex application-side logic to ensure that every query is routed to the correct partition.
Key Takeaway: To ensure secure and relevant multi-tenancy, you must either enforce metadata-based filtering at query time or partition your vector indices into tenant-specific namespaces to restrict the search scope.