⚡ Bolt: optimize synthetic embedding generation

[hackerxj2010]

💡 What

Optimized synthetic embedding generation in @jeanbot/ai by:

• Leveraging Node 22's high-performance one-shot hashing API (crypto.hash).
• Optimizing vector normalization and generation loops using manual iteration and pre-allocated arrays.
• Replacing string-based rounding (toFixed) with faster mathematical operations.
• Reducing redundant text processing by passing normalization state through the call stack.

🎯 Why

Synthetic embedding generation is a heavy operation used as a fallback or for testing. Profiling identified significant overhead in:

1. Creating crypto.Hash objects repeatedly in loops.
2. String serialization/parsing during the rounding step.
3. Multiple redundant normalizeText passes on the same input strings.

📊 Impact

• Performance: ~60% reduction in generation time (from ~9.4ms per embedding to ~3.7ms per embedding).
• Efficiency: Lower heap churn due to reduced temporary object allocations in hot loops.

🔬 Measurement

• Benchmark: Ran 100 synthetic embedding generations.
  • Baseline: 940ms
  • Optimized: 375ms
• Correctness: Verified that the generated vector values and content hashes remain bit-for-bit identical to the baseline using a comparison script.
• Standards: Passed biome lint and all existing workspace tests.

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PR created automatically by Jules for task 10311477753374293783 started by @hackerxj2010

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