LlamaIndex is a comprehensive Python framework designed for building Large Language Model (LLM) applications, with a primary focus on Retrieval-Augmented Generation (RAG) and agentic workflows. It functions as a modular library rather than a monolithic application, providing abstractions for data ingestion, indexing, retrieval, and query orchestration. The architecture is split between a core package (`llama-index-core`) containing base interfaces and logic, and a vast ecosystem of integration packages (`llama-index-integrations`) that connect to external LLM providers, vector databases, and data sources. The codebase demonstrates a strong plugin-based design, allowing developers to swap components like embeddings, retrievers, and LLMs without rewriting the core orchestration logic.
This repository is the source code for the Hugging Face Agents Course, a structured educational curriculum teaching the fundamentals and practical implementation of AI agents. The course is organized into units covering agent theory (ReAct, tool calling, observations), framework-specific tutorials (smolagents, LlamaIndex, LangGraph), agentic RAG, observability and evaluation, and a final project based on the GAIA benchmark. The content is delivered as MDX files with embedded code examples, quizzes, and links to interactive notebooks, and is translated into multiple languages (English, Spanish, French, Chinese, Korean, Russian, Vietnamese). While primarily an educational resource, the repository also contains reusable scripts for automated translation of course content and for pushing quiz data to the Hugging Face Hub, as well as CI/CD workflows for building documentation. The codebase is not a production application or a data pipeline, but rather a hybrid of educational content and supporting tooling for course maintenance.
This repository is a comprehensive, educational collection of 42+ Jupyter notebooks and runnable scripts demonstrating a wide array of Retrieval-Augmented Generation (RAG) techniques. It is primarily a data_research and educational resource, not a deployable application or library. The codebase serves as a practical cookbook for engineers and researchers, covering the full RAG stack from basic chunking and retrieval (e.g., simple RAG, BM25) to advanced methods like GraphRAG, Self-RAG, HyDE, RAPTOR, and agentic RAG. It also includes a dedicated evaluation framework with notebooks and reusable scripts for measuring faithfulness, relevance, and correctness using tools like DeepEval and custom LLM judges. The architecture is modular, with a shared helper_functions.py providing common utilities for PDF loading, embedding, and retrieval, and an evaluation/ module offering reusable evaluation pipelines. The runnable scripts in all_rag_techniques_runnable_scripts/ provide cleaner, class-based implementations of the techniques that are more suitable for integration into other projects than the notebooks themselves. While the code is well-structured for an educational project and demonstrates high craftsmanship in its reusable components, it is not designed for production deployment. Reproducibility is limited by the lack of a pinned dependency file and reliance on external API keys. The repository is actively maintained and highly influential (29k+ stars), making it a valuable reference for anyone building or evaluating RAG systems.
This repository accompanies a Korean-language book on practical LLM application development, containing hands-on code examples organized by chapter. It demonstrates the full LLM lifecycle including foundational concepts (transformers), model optimization (quantization, PEFT), application building (RAG, agents, multimodal systems), and deployment techniques. The primary value lies in its educational notebooks that show how to implement LLM-powered solutions for tasks like Text2SQL, retrieval-augmented generation, and AI agents using frameworks such as LlamaIndex, AutoGen, and vLLM. While designed for Google Colab execution, the code illustrates reusable patterns for LLM engineering.