shahidazam2020-oss/A-Systematic-Literature-Review-of-Retrieval-Augmented-Generation-RAG-Models

A systematic literature review of Retrieval Augmented Generation (RAG) models, covering architectures, retrieval techniques, real world applications, performance improvements, emerging challenges, and future research directions across healthcare, education, finance, and enterprise AI.

135
Stars
1
Forks
0.0
Radar Score
Declining
Momentum

Scoring Breakdown

Impact0.0
Velocity0.0
Health0.0

Growth Metrics

7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
—

Why it ranks

Momentum is cooling relative to the rest of the index.

RAG · rag framework

🛡️ Enterprise Readiness & Commercial Safety

Organic Growth
51%
Mixed Signals
License
Unlicensed / Custom
Review license terms carefully before commercial adoption.
Health Grade
C
Based on maintenance, issue response, and release cadence.
Radar Score
0.0
Composite of Impact, Velocity, and Health.
⚠️ Growth Anomaly Detected:
  • Issue engagement: 0 open issues for 135 stars (0.0%).
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.

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