Performance 5 3 License and Copyright 7 4 Installation 9 5 The Basics 15 6 Tutorial 35 7 PyMuPDF, LLM & RAG 45 8 Resources 49 9 Opening Files 51 10 Text 53 11 Images 73 12 Annotations 89 13 Drawing and 24.2. 44 Chapter 6. Tutorial CHAPTER SEVEN PYMUPDF, LLM & RAG Integrating into your Large Language Model (LLM) framework and overall RAG (Retrieval-Augmented Generation) solution provides the fastest PyMuPDFReader loader = PyMuPDFReader() documents = loader.load(file_path="example.pdf") See Building RAG from Scratch for more. 7.3 Preparing Data for Chunking Chunking (or splitting) data is essential

working on a retrieval augmented generation system, you should also capture the specific aspects of the RAG system that impact what content was inserted into the prompt, including the query, chunk settings

(LLMs), vector search can be used in various �→ scenarios such as Retrieval-Augmented Generation (RAG), semantic �→ search, and recommendation systems. DB Operations and Observability models (LLMs), vector search can be used in various scenarios such as Retrieval-Augmented Generation (RAG), semantic search, and recommendation systems. Starting from v8.4.0, TiDB supports vector data types Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is an architecture designed to optimize the out- put of Large Language Models (LLMs). By using vector search, RAG applications can store

(LLMs), vector search can be used in various �→ scenarios such as Retrieval-Augmented Generation (RAG), semantic �→ search, and recommendation systems. DB Operations and Observability models (LLMs), vector search can be used in various scenarios such as Retrieval-Augmented Generation (RAG), semantic search, and recommendation systems. Starting from v8.4.0, TiDB supports vector data types Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is an architecture designed to optimize the out- put of Large Language Models (LLMs). By using vector search, RAG applications can store