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AI Agent 指数 第 26(共 51) ↓ 下载分享海报

56 综合分
可观测采用度当前有效 · 2026-11-10
27
动量当前有效 · 2026-11-09
82
关注度当前有效 · 2026-11-09
76
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方法论 v2.0 · 快照 2026-11-10 · 超过 2 天视为过期

项目介绍

Reasoning-based RAG  ◦  No Vector DB, No Chunking  ◦  Context-Aware Retrieval  ◦  Reads Like a Human

Are you frustrated with vector database retrieval accuracy for long professional documents? Traditional vector-based RAG relies on semantic similarity rather than true relevance. But similarity ≠ relevance — what we truly need in retrieval is relevance, and that requires reasoning. When working with professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search often falls short — missing what's relevant but not similar, and returning what's similar yet not relevant.

Inspired by AlphaGo, we propose…

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