dilipjagannathan / complex-reasoning-with-react-and-langchain

Complex Reasoning with ReAct and LangChain

Complex Reasoning with ReAct using Langchain Agents and Amazon Bedrock

In this workshop, you will learn how to use multiple different techniques and models to build a ReAct based framework. ReAct is an approach to problem solving with large language models based on 2 main premises: Reasoning and Action. With ReAct, you combine reasoning, through chain-of-thought, with the ability to perform actions through a set of tools. This enables the model to (Re)ason through the input request to determine what steps need to be performed, and uses the available tools to perform (ACT)ions as part of a step-by-step resolution.

More details on ReAct can be found in this research paper: ReAct: Synergizing Reasoning and Acting in Language Models and the Google AI Blog

Workshop Environment Setup

Before beginning, you'll need to 1/ open your lab account 2/ setup access to the Amazon Bedrock model's used in this workshop 3/ go into Amazon SageMaker Studio and then clone the github repo that will be used for the remainder of the workshop.

Please follow the detailed steps below to access your workshop AWS account:

1. To access your lab environment, log in to bit.ly link your instructor provided

2. Click Email One-Time Password (OTP) button.

3. Enter your own email account and click the Send passcode button.

4. In your email inbox, check for the email subject "Your one-time passcode email" and copy the passcode. Paste the copied passcode as shown below, then press the Sign in button.

5. Review the Terms and Conditions, scrolling down to click I agree with the Terms and Conditions. Click Join event

6. On the bottom left under AWS account access, select Open AWS console (us-west-2)

Please follow the detailed steps below to setup access to the Amazon Bedrock models that will be used for the workshop:

1. From the AWS console, search for and click on Amazon Bedrock, then click Get started

2. From the left menu, scroll down & select Model access

3. Click Manage model access

4. You'll be using two models for this workshop so first select Anthropic - Claude 3 Sonnet.

5. From the same page, select Llama 2 Chat 13B

6. Click Save changes

Please follow the detailed steps below to access Amazon SageMaker Studio:

1. From the AWS console, search for and click on Amazon SageMaker

2. From the Amazon SageMaker console, select Studio on the left-hand menu

3. Click Open Studio using the pre-populated default user as shown below. Note: Your username may be different than the image.

4. Click View JupyterLab spaces

5. Click Create JupyterLab space

6. Enter a name for your space, then click Create space

7. Click Run space

8. It will take a few minutes to create your space but once it's ready you'll see the Open JupyterLab button. Click Open JupyterLab.

Clone the github repository that will be used for the workshop:

1. From inside your JupyterLab environment, open a terminal environment by clicking Terminal

2. From the terminal, clone the github repo by copying and pasting the command below inside the terminal session

git clone https://github.com/giuseppe-zappia/complex-reasoning-with-react-and-langchain

3. You'll now see the cloned github repository on the left hand pane of your Studio environment. Double-click the folder complex-reasoning-with-react-and-langchain

4. Double-click the notebook called ReAct-bedrock.ipynb. When the select kernel pop-up appears, keep Python 3 (ipykernel) and click Select

5. The rest of the workshop will be performed in your notebook.

HAPPY BUILDING!!