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Learn to build a local Retrieval-Augmented Generation (RAG) system without embeddings or vector databases using tree-based indexing with Ollama and LangChain. Explore an alternative approach to traditional RAG that leverages document structure to select relevant sections for answer generation. Discover the concept of vectorless RAG and understand how it differs from conventional embedding-based methods. Review financial documents as a practical use case and walk through the complete code implementation. Test the system with three different queries to see how tree-based indexing performs in real-world scenarios. Access supporting resources including the original research paper, LlamaIndex tree index documentation, and the PageIndex GitHub repository for further exploration of this innovative RAG approach.
