vector-search

Multi-tenant vector search with Qdrant is achieved by enforcing workspaceId filters during vector ingestion and similarity search, ensuring tenant-scoped retrieval without cross-tenant data exposure across multiple workspaces.

To generate and ingest embeddings for similarity search, you upload and upsert 1536-dimension OpenAI embedding vectors into a named Qdrant collection, applying workspace scoping and utilizing built-in retry strategies for robust error handling.

Yes, you can use 1536-dimension OpenAI embeddings with Qdrant for tenant-isolated document retrieval. The workflow enforces workspaceId filters to provide robust, tenant-scoped access control during similarity search.

The best way to filter similarity search results by workspace is to apply workspaceId filters during your Qdrant vector queries. This enforces tenant isolation and ensures high-relevance, workspace-based access control.

Yes, Qdrant vector search supports robust collection management and retry strategies. It provides collection management to handle named collections and retry strategies to ensure reliable vector ingestion and similarity search operations.