# 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`](../runbook.md) for incident handling.
- Follow [`../security.md`](../security.md) for embedding-data handling controls.
- Use [`../troubleshooting.md`](../troubleshooting.md#query-and-index-issues) 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.