ADR-0005: Qdrant as the primary vector store¶
- Status: Accepted
- Date: 2026-01-01
- Deciders: Data Architect, Knowledge Platform Lead
- Tags: knowledge, vector, persistence
Context¶
The Knowledge Platform performs semantic search over blueprints, docs, patterns, and code to assemble context packages for agents. It needs a performant, self-hostable vector database with strong filtering (for tenant and classification scoping) and predictable cost at scale.
Decision¶
Use Qdrant as the primary vector store for embeddings and semantic memory. Azure AI Search is supported as an alternative where a managed service is preferred. Vector data is rebuildable by re-embedding source artifacts.
Consequences¶
Positive¶
- High-performance similarity search with metadata filtering for
tenantId/classification. - Self-hostable and cost-predictable; snapshot backups.
Negative / trade-offs¶
- Operational ownership of Qdrant (deployment, scaling, backups).
Neutral¶
- Vector store sits behind the Knowledge Platform's services; consumers never query it directly.
Alternatives considered¶
| Alternative | Why not chosen |
|---|---|
| Azure AI Search (only) | Retained as an alternative; primary chosen for cost/control and filtering. |
| pgvector | Adequate for small scale; less specialized for high-volume ANN workloads. |