Udemy Advance RAG Vector to Graph RAG Neo4j Adaptive AutoGen RAG

Files Included :
1 -Introduction to course (73.36 MB)
1 -RAG Code (521.25 KB)
1 -Use Cases and Conclusion (46.97 MB)
1 -Generative AI without RAG Why RAG (24.61 MB)
2 -What is RAG RAG Process (25.69 MB)
1 -What is NLP (16.58 MB)
2 -POS , NER , Chunking, BoW, TF-IDF and Embedding (65.17 MB)
3 -Tokenization, Stemming and Lemmatization (22.43 MB)
4 -Evaluation of NLP (30.57 MB)
5 -Transformer Model (82.72 MB)
1 -Setup VS code , Python, Neo4j, Streamlit, PIP packages (149.97 MB)
2 -Create simple streamlit chatbot (43.07 MB)
1 -What is vector RAG (23.96 MB)
2 -Develop vector RAG with Groq API and Langchain (116.9 MB)
2 -RAG Code (521.25 KB)
1 -What is Graph RAG (37.44 MB)
2 -Implement Graph RAG chatbot to build and show graph with Neo4j (162.35 MB)
3 -Implement hybrid search with Graph RAG and Neo4j (218.4 MB)
3 -RAG Code (521.25 KB)
4 -Vector RAG vs Graph RAG (21.04 MB)
1 -Understand adaptive or self-reflective flow (10.63 MB)
2 -Implement Self-reflective RAG chatbot with Langgraph (293.17 MB)
2 -RAG Code (521.25 KB)
1 -Flow of Ranking RAG and LangChain Python coding (148.61 MB)
1 -Autogen RAG Agentic RAG (85.48 MB)]
Screenshot
[Image: nc5pnlF5_o.jpg]

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