This Jupyter Notebook guides you through building a Retrieval Augmented Generation (RAG) pipeline using Chroma for vector storage and Ollama for embeddings + LLM generation.

In recent years, Retrieval Augmented Generation (RAG) systems have made significant progress in extending the capabilities of Large Language Models (LLM) through external retrieval. However, these ...

Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising solution to address key challenges faced by GenAI, such as hallucination, outdated or non-removable parametric knowledge, and ...

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