muntasirhsn/Introduction-to-Retrieval-Augmented-Generation

This is an introduction to Retrieval-Augmented Generation (RAG) for beginners . It uses Llama 2 LLM, FAISS vector store, and LangChain as the orchestrator to perform generative question answering (QA) to a knowledge base/data source.

17
Stars
5
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
Jupyter Notebook

Why it ranks

Momentum is cooling relative to the rest of the index.

RAG · vector database

🛡️ Enterprise Readiness & Commercial Safety

Organic Growth
95%
High Confidence
Growth pattern looks organic.
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.
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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