#python #knowledge_graph #large_language_model #logical_reasoning #multi_hop_question_answering #trustfulness

KAG (Knowledge Augmented Generation) is a powerful tool that helps computers understand and reason with complex information better. It uses large language models and a special engine to build logical reasoning and question-answering systems, especially in professional domains like medicine or finance. KAG improves upon older methods by reducing errors and noise, and it can handle multiple steps of reasoning and fact-checking.

The benefit to the user is that KAG provides more accurate and reliable answers to complex questions, integrating both structured and unstructured data. This makes it very useful for professionals who need precise information and logical reasoning in their work.

https://github.com/OpenSPG/KAG

#jupyter_notebook #amazon_bedrock #amazon_titan #bedrock #embeddings #generative_ai #knowledge_base #langchain #rag

This repository provides pre-built examples to help you get started with Amazon Bedrock, a service for working with generative AI. You can learn the basics of Bedrock, how to craft effective prompts, implement AI agents, import custom models, and more. It also includes guides on responsible AI use, productionizing workloads, and improving model observability. To use these examples, ensure you have the necessary AWS IAM permissions and follow the detailed instructions in each folder. This resource helps you quickly and effectively use Amazon Bedrock for various AI tasks, making it easier to integrate generative AI into your projects.

https://github.com/aws-samples/amazon-bedrock-samples