⚡ Vectorize embedding formatting and encoding

[chottokun]

💡 What: Vectorized embedding post-processing (_format_embeddings_batch) in src/app/services/embedding.py using NumPy/PyTorch array operations for Matryoshka dimension reduction (arr[:, :dimensions]), L2 re-normalization (np.linalg.norm), and binary serialization (tobytes()).

🎯 Why: Previously, vectors returned from vectors.tolist() were iterated individually in Python loops to apply formatting, slicing, L2 normalization, and Base64 packing via struct.pack. Batch matrix operations eliminate Python loop overhead and scalar conversion costs.

📊 Measured Improvement:

• Float format: Reduced formatting time for 100 vectors (4096->1024 dims) from 42.07 ms to 4.88 ms (~8.6x speedup).
• Base64 format: Reduced encoding time from 72.84 ms to 3.55 ms (~20.5x speedup).

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PR created automatically by Jules for task 13211692168893116465 started by @chottokun

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