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06_OpenSource_examples

README.md

Open-Source Module: Amazon Bedrock Integration with LangChain and Agentic Frameworks

Welcome to the Open-Source Module, where we demonstrate how to use Amazon Bedrock with popular open-source libraries and frameworks. This repository includes a series of Jupyter notebooks that range from high-level LangChain use cases to advanced agentic frameworks with retrieval-augmented generation (RAG) and agent evaluation. Additionally, we provide an example using CrewAI for building a React-style agents, contrasting with the Agentic workflow approach in LangGraph.

Table of Contents

Overview

Amazon Bedrock is a fully managed service that lets you easily build and scale generative AI applications with foundation models (FMs) from top AI providers, all accessible via a single API. In this module, we:

Illustrate high-level examples of text generation, code translation, and summarization using Bedrock with LangChain.
Dive into advanced agent-based setups using RAG (retrieval-augmented generation), multi-agent frameworks, agent evaluation, and React-based agents (via CrewAI) as an alternative to LangGraph’s workflow approach.

Prerequisites

AWS Account & Credentials
You’ll need access to Amazon Bedrock. Ensure your AWS CLI or environment variables are properly configured to allow these notebooks to authenticate.

Python 3.12+
Install and use a version of Python that is 3.12 or higher.

JupyterLab or Jupyter Notebook
You can run the .ipynb notebooks locally or in a managed environment (e.g., Amazon SageMaker AI).

Installation

Clone the Repository

git clone https://github.com/aws-samples/amazon-bedrock-workshop.git
cd amazon-bedrock-workshop/06_OpenSource_examples
pip install -r requirements.txt

Notebook Descriptions

These notebooks are each independent and can be executed in any order.

High-Level Use Cases

These notebooks are located in the text-generation-with-langchain/ directory and demonstrate core ways to use Amazon Bedrock with LangChain:

  • 00_simple_chatbot.ipynb: Create a multi-lingal greeter chatbot using langchain with conversation retention and prompt templating.
  • 01_zero_shot_generation.ipynb: Zero-shot text generation with foundation models on Bedrock, wrapped in LangChain’s prompt interface.
  • 02_code_interpret_w_langchain.ipynb: Code interpretation and generation, showing how to integrate Bedrock models with LangChain abstractions for developer workflows.
  • 03_code_translate_w_langchain.ipynb: Demonstrates code translation from one programming language to another, powered by Bedrock + LangChain’s flexible prompt layering.
  • 04_long_text_summarization_using_LCEL_chains_on_Langchain.ipynb: Explores how to summarize lengthy documents or text using LCEL (Language Chain Execution Logic) in LangChain, backed by Bedrock models.

Agentic Frameworks, Evaluations and RAG

Beyond the high-level examples, you’ll find a set of notebooks that delve into agent frameworks, retrieval-augmented generation, and agent evaluation (using Ragas). Some of these also highlight the difference between a React agent approach (using CrewAI) and a workflow agentic approach (using LangGraph).

  • advance-langragph-multi-agent-setup.ipynb: Shows how to set up a multi-agent environment leveraging LangGraph workflows and Amazon Bedrock.
  • find-relevant-information-using-RAG.ipynb: Demonstrates retrieval-augmented generation (RAG) with Bedrock for more accurate and context-rich responses.
  • intermediate-langragph-agent-setup-w-tools.ipynb: Focuses on an intermediate agentic workflow in LangGraph, integrating additional tools to handle complex tasks.
  • ragas-agent-evaluation.ipynb: Explains evaluating agent performance using Ragas, focusing on retrieval and generation quality with Bedrock.
  • simple-crewai-agent-setup.ipynb: Illustrates a minimal React agent approach using CrewAI, contrasting with LangGraph workflows.
  • simple-langragph-agent-setup.ipynb: Provides a starting point for building a single-agent flow with LangGraph, leveraging Bedrock behind the scenes.

AWS Configuration Make sure your environment is set up with AWS credentials that can call Amazon Bedrock. If necessary, export environment variables or configure the AWS CLI. Run & Explore Execute the cells in each notebook sequentially. You can modify prompts, model parameters, agent setups, or workflows to see how results change.

For more information on the fine-grained action and resource permissions in Bedrock, check out the Bedrock Developer Guide.