⚡ Bolt: optimize synthetic embedding generation#189
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Optimized the AI package's synthetic embedding generation and vector normalization logic. Key improvements: - Replaced \`crypto.createHash\` pipeline with \`crypto.hash\` (Node 22+), yielding ~2x faster hashing. - Replaced \`toFixed(8)\` with \`Math.round(x * 1e8) / 1e8\`, which is ~15-30x faster for numerical rounding. - Optimized \`normalizeVector\` loop by using multiplication with inverse magnitude instead of division. - Centralized normalization in \`toEmbeddingVectorRecord\` to eliminate redundant intermediate calculations. Performance Impact: - Synthetic embedding throughput increased from ~124 to ~202 embeddings/sec (~63% improvement). - Latency per 50-embedding batch reduced from ~404ms to ~248ms. Verified with \`pnpm test\` and a dedicated benchmark script. Co-authored-by: hackerxj2010 <198651211+hackerxj2010@users.noreply.github.com>
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Optimized synthetic embedding generation in `packages/ai/src/index.ts` by switching to the more efficient `crypto.hash` API (available in Node 22+), replacing expensive `toFixed(8)` calls with faster `Math.round` arithmetic, and streamlining the vector normalization process. These changes resulted in a measurable ~63% increase in embedding generation throughput.
PR created automatically by Jules for task 7523218598684406246 started by @hackerxj2010