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Multi Tenancy

Multi-tenancy is essential for SaaS applications, allowing a single instance to serve multiple users or groups while ensuring data privacy and security. This blog outlines the design and implementation a simple multi-tenant vector search system with

Designing the Database Schema

The success of user-based isolation in multi-tenant systems hinges on a well-structured database schema:

Users and Documents

Each user is identified by a unique user_id, which directly links them to their documents for private access. The documents table contains each user's documents, each with a distinct id, title, and content. Additional metadata can be included in the documents table to improve search functionality. The user_id is linked to the documents instead of chunk embeddings to replicate real-world scenarios more accurately.

CREATE TABLE documents (
    user_id INTEGER NOT NULL,
    title TEXT,
    content TEXT,
    -- Additional metadata


Embeddings store vectorized representations of chunks of documents, and use document_id to reference its document.

    document_id INTEGER REFERENCES documents(id),
    embedding vector(512) NOT NULL

Entity-Relation Diagram

Implementing Multi-Tenancy

Create Index

Create vector index on chunks.embedding to accelerate query process. Here we use dot product as the similarity measure.

CREATE INDEX idx_chunks_embedding ON chunks USING vectors (embedding vector_dot_ops);

Run the query with user_id

SELECT AS chunk_id, AS document_id, documents.title
FROM chunks
INNER JOIN documents ON chunks.document_id =
WHERE documents.user_id = 'alice' /* Replace with user_id */
ORDER BY chunks.embedding <-> '[3,2,1]' /* Replace with query embedding */ LIMIT 5;

In this query:

  • The SELECT statement now includes AS chunk_id, AS document_id, and documents.title to return the chunk ID, the document ID, and the document title, respectively.
  • The INNER JOIN ensures that only chunks associated with documents owned by the specified user ('alice') are considered.
  • The ORDER BY clause calculates the distance between the chunk's embedding and the provided vector [3,2,1], sorting the results by similarity. The closest or most similar embeddings are returned first.
  • Replace [3,2,1] with the query embedding and 'specific_user_id' with the actual ID of the user for whom you are performing the search.

Performance Tip

The vector search will scan through additional points and evaluate the conditions individually. Consequently, if there is a large number of user IDs or the filter criteria are challenging to meet, the query speed may be impacted. To enhance performance, you may want to explore utilizing PostgreSQL's partition table function.