NirDiamant/Controllable-RAG-Agent resource

Health v2 · maintenance only

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Adoption not part of the score

1621 stars · 268 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A tutorial repository demonstrating an advanced, controllable RAG agent for complex question answering, built with a deterministic graph-based algorithm using LangGraph and LangChain. It is delivered as Jupyter notebooks that show how to answer non-trivial questions from your own data beyond simple semantic similarity retrieval.

Use cases

• build a RAG agent that answers complex questions from my own documents
• learn how to use langgraph to control an autonomous RAG pipeline
• implement graph-based routing for retrieval-augmented generation
• go beyond simple semantic similarity retrieval for question answering
• study an advanced RAG agent implementation in python
• make my RAG system more controllable and deterministic

When to choose

• you want to learn advanced, controllable RAG patterns through runnable notebooks
• your questions require multi-step reasoning that naive similarity retrieval fails at
• you use the LangChain/LangGraph/OpenAI ecosystem and want a reference implementation

When to avoid

• you need a production-ready, maintained RAG library rather than educational notebooks
• you want a framework-agnostic or non-OpenAI solution out of the box
• you need a simple plug-and-play RAG service with an API

Facets

learning-resource · maturity active

rag agent-framework llm-inference nlp large-language-models python langgraph langchain advanced-rag tutorial notebook controllable-agent graph-based-retrieval retrieval-augmented-generation ai-agents natural-language-processing

Member repositories

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markdown · JSON · MCP: product_card(name="NirDiamant/Controllable-RAG-Agent")