Integrate with AWS Bedrock
In this guide we show how to integrate a Digital Human with AWS Bedrock. With AWS Bedrock the Digital Human can get access to a range of LLM's and AI agents through a unified API.
In order to integrate with AWS Bedrock we will create a Conversational Plugin that will generate responses to user messages using an AWS Bedrock LLM. In this guide we are focusing on the AWS Bedrock integration, to learn more about how to create a plugin and setup the digital human refer to Create a custom plugin.
To implement this plugin we are going to use python and Langchain.
Requirements
In your python environment install the following dependencies
pip install fastapi
pip install "uvicorn[standard]"
pip install langchain
pip install boto3
pip install python-dotenvNote that langchain is rapidly evolving library and some details of the implementation in this guide might change in newer versions. The versions used in this guide are langchain==0.1.11 langchain-core==0.1.29 langchain-community==0.0.25
Credentials
To access AWS Bedrock you will need credentials to access your AWS account with permissions to run the desired model. In this guide we are going to use the "amazon.titan-text-express-v1" model. Refer to the following AWS Bedrock model access docs to see how to activate the model access.
Once you have the permissions you should get the access keys for programmatic access to AWS services. Let's create a .env file at the root folder of the project and copy the keys there
.env
Model Prediction
As a first step, let's create a python file to test from command line that we are able to access the model predictions.
Create a main.py with the following content.
.env