61 lines
1.8 KiB
Markdown
61 lines
1.8 KiB
Markdown
# Vector Search
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## Purpose And Business Outcome
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Enable similarity search for embedding-driven workloads directly in embedded storage.
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## Scope And Non-Goals
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Scope:
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- Vector index configuration
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- Approximate nearest-neighbor query execution
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Non-goals:
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- External model training
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- Cross-database vector federation
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## User And System Workflows
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1. Consumer registers vector index for embedding field.
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2. Documents persist embeddings in collection payloads.
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3. Query issues vector search request with `k` nearest neighbors.
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4. Engine returns ranked matches.
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## Interfaces And APIs
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- Vector index configuration via model builder
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- Query extensions under `VectorSearchExtensions`
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- Index implementation in `VectorSearchIndex`
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## Permissions And Data Handling
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- Embeddings may contain sensitive semantic information.
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- Apply host-level access restrictions and retention controls.
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## Dependencies And Failure Modes
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Dependencies:
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- Correct embedding dimensionality
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- Index parameter tuning for workload
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Failure modes:
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- Dimension mismatch between data and query vectors
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- Poor recall due to incorrect index configuration
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## Monitoring, Alerts, And Troubleshooting
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- Validate vector query quality during release smoke checks.
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- Use [`../runbook.md`](../runbook.md) for incident handling.
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- Follow [`../security.md`](../security.md) for embedding-data handling controls.
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- Use [`../troubleshooting.md`](../troubleshooting.md#query-and-index-issues) for vector query remediation.
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## Rollout And Change Considerations
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- Treat vector index parameter changes as performance-sensitive releases.
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- Document compatibility impact for existing persisted indexes.
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## Validation Guidance
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- Run vector search tests in `tests/CBDD.Tests/VectorSearchTests.cs`.
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- Add benchmark runs for large-vector workloads before release.
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