{"repo":"aws-samples/build-an-agentic-llm-assistant","free":true,"listed":false,"github":"https://github.com/aws-samples/build-an-agentic-llm-assistant","clone":"git clone https://github.com/aws-samples/build-an-agentic-llm-assistant.git","description":"Labs for the \"Build an agentic LLM assistant on AWS\" workshop. A step by step agentic llm assistant development workshop using serverless three-tier architecture.","language":"Jupyter Notebook","stars":81,"topics":["amazon-bedrock","aws-lambda","claude","llm-agent","question-answering","serverless","text-to-sql"],"license":"MIT-0","category":"ai-agents","readme_excerpt":"Build an Agentic LLM assistant on AWS This hands-on workshop, aimed at developers and solution builders, trains you on how to build a real-life serverless LLM application using foundation models (FMs) through Amazon Bedrock and advanced design patterns such as: Reason and Act (ReAct) Agent, text-to-SQL, and Retrieval Augemented Generation (RAG). It complements the Amazon Bedrock Workshop by helping you transition from practicing standalone design patterns in notebooks to building an end-to-end llm serverless application. Within the labs of this workshop, you'll explore some of the most common and advanced LLM applications design patterns used by customers to improve business operations with Generative AI. Namely, these labs together help you build step by step a complex Agentic LLM assistant capable of answering retrieval and analytical questions on your internal knowledge bases. Lab 1: Explore IaC with AWS CDK to streamline building LLM applications on AWS Lab 2: Build a basic serverless LLM assistant with AWS Lambda and Amazon Bedrock Lab 3: Refactor the LLM assistant in AWS Lambda into a custom LLM agent with basic tools Lab 4: Extend the LLM agent with semantic retrieval from internal knowledge bases Lab 5: Extend the LLM agent with the ability to query a SQL database Throughout these labs, you will be using and extending the CDK stack of the Serverless LLM Assistant available under the folder serverless llm assistant . Prerequisites 1. Create an AWS Cloud9 environment to","default_branch":null,"files":null,"tree":[],"storefront":"/r/aws-samples","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/aws-samples/build-an-agentic-llm-assistant/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}