Client Application Integration (Optional)

Target

You will turn Python code into a professional Web Chatbot GUI (Graphical User Interface) that is user-friendly (similar to the ChatGPT interface) in just a few minutes.

We use:

  • Backend: Python.
  • Frontend: Streamlit.
  • AI Model: Claude 3.5 Sonnet.

Implementation Steps

Part I: Configure AWS Credentials

Step 1: Install AWS CLI

Open Terminal on your computer.

# macOS
brew install awscli

# Linux
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

Step 2: Configure credentials

aws configure

Enter the information when prompted:

  • AWS Access Key ID: YOUR_ACCESS_KEY
  • AWS Secret Access Key: YOUR_SECRET_KEY
  • Default region name: us-east-1
  • Default output format: json

Step 3: Verify configuration

# Check credentials
aws sts get-caller-identity

# Check Bedrock connection
aws bedrock-agent-runtime list-knowledge-bases --region ap-southeast-1

Security notes:

  • DO NOT commit credentials to Git
  • DO NOT share credentials with others
  • Use IAM roles when possible
  • Rotate credentials periodically

Required permissions:

IAM User needs the following permissions:

  • bedrock:InvokeModel
  • bedrock:RetrieveAndGenerate
  • bedrock:Retrieve
  • s3:GetObject (for Knowledge Base)

Troubleshooting:

Error “Unable to locate credentials”:

  • Check if ~/.aws/credentials file exists
  • Check file format is correct
  • Try running aws configure again

Error “AccessDeniedException”:

  • Check IAM permissions
  • Ensure region is correct (ap-southeast-1)
  • Check Knowledge Base ID is correct

Error “ExpiredToken”:

  • Credentials have expired
  • Need to create new credentials from AWS Console

Part II: Clone Project from pre-made GitHub

Step 1: Access the following GitHub link

Please download and open the folder above using Visual Studio Code:

https://github.com/DazielNguyen/chatbot_with_bedrock.git

Step 2: Install libraries and Python environment

  1. Install environment:

    • MacOS: python3 -m venv .venv
    • Win: python -m venv .venv

  2. Activate environment:

    • MacOS: source .venv/bin/activate
    • Win: .venv\Scripts\activate

  3. Install libraries:

    • MacOS/ Win: pip install -r requirements.txt

Step 3: Get the ID of the created Knowledge Base

  1. Access Amazon Bedrock -> Knowledge Base -> knowledge-base-demo

Take_ID_KB

  1. Update “KB_ID=“YOUR_KNOWLEDGE_BASE_ID””

Take_ID_KB

Step 4: Run Streamlit - Chatbot UI and Experience

  1. Run Terminal:
streamlit run start.py
  1. When the command finishes running, the following page will appear:

Take_ID_KB

  1. Try asking some questions you uploaded to the Knowledge Base earlier.

Take_ID_KB

  1. The Chatbot has returned results based on the data file you provided, with citations of your data sources.

Conclusion

Congratulations on successfully building a Web Chatbot built with Amazon Bedrock