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ML Integration for Search

Use machine learning to improve retrieval quality after your baseline keyword search is stable. Start with embedding-based recall, then add ranking signals only when you can measure precision and latency impact.

Overview​

This guide explains how to integrate embeddings into search pipelines, how to roll out model changes safely, and how to link the implementation with existing observability and API guidelines.

Retrieval Flow​

The first stage maximizes recall by semantic similarity. The second stage applies deterministic business constraints such as availability, recency, and policy rules before results are returned.

Minimal Integration Pattern​

type SearchRequest = { query: string; userId: string };

export async function semanticSearch(req: SearchRequest) {
  const queryVector = await embeddingClient.embed(req.query);
  const candidates = await vectorStore.search(queryVector, { topK: 200 });
  return ranker.rank(candidates, { userId: req.userId }).slice(0, 20);
}

Keep this layer thin. Push model orchestration, feature generation, and fallback behavior into dedicated services so the API contract stays stable.

Rollout and Safety​

  • Use canary rollout for model version changes.
  • Log model version, feature set version, and ranking score breakdown.
  • Define fallback behavior when embedding service latency exceeds threshold.
  • Validate quality with offline metrics and online A/B outcomes before broad rollout.