🚀 RAG COURSE

A basic introduction to Retrieval-Augmented Generation (RAG) with LangChain and Ollama, featuring a companion PDF and two hands-on notebooks.

📜 Table of Contents

| Section | Description | | --- | --- | | 1. 🔎 Retrieval-Augmented Generation (RAG) | Definition and overview
Why it goes beyond a stand-alone LLM | | 2. 💡 Concept | Core meaning of RAG
Key benefits and features | | 3. ⚙️ How to Build? | Guardrails
Caching
Monitoring
Evaluation
LLM Security | | 4. 🏗 RAG System Design (Overview) | Input/Output orchestrator
Retriever
Data load & splitting
Data conversion
Storage component
LLM setup
Data indexing
Prompt management | | 5. 👀 Retriever: Indexing Pipeline | Data load & splitting
Data conversion
Storage (e.g., FAISS) | | 6. 🧩 Generation Pipeline | Retriever: Query analysis & information retrieval

Prompt Management: Contextual, few-shot, controlled, chain of thought

LLM: Model configuration & generation flow | | 7. 🔧 Hands-On |

• Practical notebooks demonstrating LangChain, Ollama, and FAISS for RAG.
• Introduction to LangChain and Ollama
  • 0_Intro_LangChain_Ollama.ipynb
  • Overview of LangChain & Ollama integration
  • Explanation of ChatOllama & ChatPromptTemplate
• Fundamentals of RAG with Ollama
  • 1_RAG_LLM_Basics.ipynb
  • Introduction to RAG
  • How LangChain, FAISS & LangGraph combine for document retrieval

Note: These two notebooks are the primary hands-on exercises for a basic RAG workflow.

|

🔗 References

[1] LangChain: Retrieval. n.d. Available at https://python.langchain.com/docs/concepts/retrieval/ [2] Ollama: Library. n.d. Available at https://ollama.com/library [3] Wolfe, Cameron R. The Basics of AI-Powered Vector Search. n.d. Available at https://cameronrwolfe.substack.com/p/the-basics-of-ai-powered-vector-search

⚖ License

This project is licensed under the MIT License.