Feat:Optimize face embedding storage in SQLite using raw float32 BLOB with backward compatibility

[Surajshivam-123]

Describe the feature

Issue

Currently, faces.py stores face embeddings as JSON strings (TEXT), whereas image_embeddings.py and video_frames.py already store embeddings as raw float32 BLOBs .

Storing embeddings as JSON strings introduces unnecessary overhead in storage size, CPU deserialization cycles, and numeric formatting precision.

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Impact

1. Storage Reduction: 512-dim float32 vectors take 2 KB as binary BLOB vs 8–10 KB as JSON text which will be crucial in case of large data.
2. Fast Deserialization: np.frombuffer() eliminates the CPU overhead of json.loads() when querying thousands of faces for clustering and search.

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Backward Compatibility

• Startup Data Migration: In db_create_faces_table(), migrate existing typeof(embeddings) = 'text' rows to BLOB in-place.
• Dual-mode Deserializer: Add a fallback helper supporting both bytes (np.frombuffer) and legacy str (json.loads).

Proposed Changes

• faces.py: Update insertion and reader queries to write/read BLOB, plus add one-time migration logic.
• test_faces_db.py: Add tests for BLOB storage and legacy JSON backward compatibility.

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@maintainer I would like to solve this.

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[github-actions]

👋 Thanks for opening this issue! The maintainers will review it shortly. Till then, do not open any PR. This is a strict rule we like to follow around here :)

[RounakKumarAgarwal]

Hi ,I would like to work on this issue , could you assign it to me?

[Surajshivam-123]

Hi @rohan-pandeyy can I start to work in this issue?

[RounakKumarAgarwal]

@Surajshivam-123 I'm already working on it

[Surajshivam-123]

@RounakKumarAgarwal See contributor guidelines.

[RounakKumarAgarwal]

My bad didn't check this is your issue @Surajshivam-123