Add enterprise docs structure and include pending core maintenance updates.

docs/features/vector-search.md

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+# 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.