aiagent.club
中文
GitHubPyPI

PageIndex

#23 of 50 in the AI Agent Index ↓ Download poster

56 Score
Observed adoptionCurrent · 2026-10-30
24
MomentumCurrent · 2026-10-29
88
AttentionCurrent · 2026-10-29
76
Signal confidence Based on how many independent score dimensions currently have data.
High3/3 · 2 source types

Methodology v2.0 · snapshot 2026-10-30 · stale after 2 days

About

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…

Across sources

118.0k Downloads / month
  • Downloads / month 118.0k
  • Downloads / week 62.5k
  • Downloads / day 8.6k
39.9k Stars
  • Stars 39.9k
  • Forks 3.4k
  • Commits 558
  • Releases 28