超越向量搜尋：為代理式AI系統建構自適應檢索路由器
本文探討如何為代理式AI系統建構自適應檢索路由器，超越單純的向量搜尋，透過依據查詢特徵智慧選擇關鍵字、向量或混合搜尋方法，並從回饋中學習，以處理多步驟檢索並提升任務完成度。
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Beyond Vector Search: Building an Adaptive Retrieval Router for Agentic AI Systems
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A hands-on guide to making retrieval a learnable decision layer—with code, architecture, and production trade-offs.
## TL;DR
Vector search works great for “one query, one answer” workflows. But agentic AI systems retrieve multiple times across a plan — and a small miss early becomes a compounding error that derails the entire task.
This post shows how to build an adaptive retrieval router that:
Chooses between keyword, vector, and hybrid retrieval per query
Uses query features (IDs, rare tokens, length) to make the decision
Learns from evaluation feedback to improve over time
Logs everything for debugging and analysis
🔗 View on GitHub
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Written by Abi
"Follow me for latest AI updates .My opinions are my own #disclaimer"
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