AI: Graph RAG (Microsoft)

Intro to GraphRAG - YouTube by Microsoft "Reactor" (learning)

a technique that enhances document analysis and question-and-answer performance by leveraging large language models (LLMs) to create knowledge graphs. We'll explore how GraphRAG builds upon Retrieval-Augmented Generation (RAG) by using knowledge graphs instead of vector similarity for retrieval. You'll learn how to set up the environment, prepare data, and implement GraphRAG using LangChain, with practical code examples. Additionally, we'll explore some advanced features and customization options available in LangChain to optimize and tailor GraphRAG to your specific needs.

GitHub - microsoft/RAG_Hack: Hack Together: RAG Hack | Register, Learn, Hack

Event Series | Microsoft Reactor 

RAGHack 2024

AI: LangChain vs LlamaIndex

 LangChain vs LlamaIndex: Choose the Best Framework for Your AI Applications

LlamaIndex is a powerful tool for data indexing and retrieval, designed to enhance information accessibility. It streamlines the process of efficiently indexing data, making it easier to locate and retrieve relevant information. By focusing on effective data retrieval, LlamaIndex ensures that users can access the information they need quickly and accurately. LlamaIndex is particularly adept at indexing and storing data into embeddings, which significantly improves the relevance and precision of data retrieval.

LangChain, on the other hand, is a versatile framework designed to empower developers to create a wide range of language model-powered applications. The modular architecture of LangChain enables developers to efficiently design customized solutions for various use cases. It provides interfaces for prompt management, interaction with language models, and chain management. It also includes memory management to remember previous interactions. LangChain excels at chatbot applications, generating text, answering queries, and language translations.