CBDD/docs/features/vector-search.md

Vector Search

Purpose And Business Outcome

Enable similarity search for embedding-driven workloads directly in embedded storage.

Scope And Non-Goals

Scope:

• Vector index configuration
• Approximate nearest-neighbor query execution

Non-goals:

• External model training
• Cross-database vector federation

User And System Workflows

1. Consumer registers vector index for embedding field.
2. Documents persist embeddings in collection payloads.
3. Query issues vector search request with k nearest neighbors.
4. Engine returns ranked matches.

Interfaces And APIs

• Vector index configuration via model builder
• Query extensions under VectorSearchExtensions
• Index implementation in VectorSearchIndex

Permissions And Data Handling

• Embeddings may contain sensitive semantic information.
• Apply host-level access restrictions and retention controls.

Dependencies And Failure Modes

Dependencies:

• Correct embedding dimensionality
• Index parameter tuning for workload

Failure modes:

• Dimension mismatch between data and query vectors
• Poor recall due to incorrect index configuration

Monitoring, Alerts, And Troubleshooting

• Validate vector query quality during release smoke checks.
• Use ../runbook.md for incident handling.
• Follow ../security.md for embedding-data handling controls.
• Use ../troubleshooting.md for vector query remediation.

Rollout And Change Considerations

• Treat vector index parameter changes as performance-sensitive releases.
• Document compatibility impact for existing persisted indexes.

Validation Guidance

• Run vector search tests in tests/CBDD.Tests/VectorSearchTests.cs.
• Add benchmark runs for large-vector workloads before release.