Setup Amazon Bedrock agent, knowledge base, and action group with Streamlit

Introduction

This guide details the setup process for an Amazon Bedrock agent on AWS, which will include setting up S3 buckets, a knowledge base, an action group, and a Lambda function. We will use the Streamlit framework for the user interface. The agent is designed to dynamically create an investment company portfolio based on specific parameters, and has Q&A capability to FOMC(Federal Open Market Committee) reports. This exercise will also include a sending email method, but will not be fully configured.

Prerequisites

• An active AWS Account.
• Familiarity with AWS services like Amazon Bedrock, S3, Lambda, and Cloud9.

Diagram

Configuration and Setup

Step 1: Creating S3 Buckets

• Please make sure that you are in the us-west-2 region. If another region is required, you will need to update the region in the InvokeAgent.py file on line 22 of the code.
• Domain Data Bucket: Create an S3 bucket to store the domain data. For example, call the S3 bucket knowledgebase-bedrock-agent-alias. We will use the default settings.

• Next, we will download the domain data from here. On your computer, open terminal or command prompt, and run the following curl commands to download the data:

• For Mac

    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230201.pdf --output ~/Documents/fomcminutes20230201.pdf
  
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230322.pdf --output ~/Documents/fomcminutes20230322.pdf
  
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230614.pdf --output ~/Documents/fomcminutes20230614.pdf
  
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230726.pdf --output ~/Documents/fomcminutes20230726.pdf
  
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230920.pdf --output ~/Documents/fomcminutes20230920.pdf
  
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20231101.pdf --output ~/Documents/fomcminutes20231101.pdf
  

• For Windows

    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230201.pdf --output %USERPROFILE%\Documents\fomcminutes20230201.pdf
    
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230322.pdf --output %USERPROFILE%\Documents\fomcminutes20230322.pdf
    
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230614.pdf --output %USERPROFILE%\Documents\fomcminutes20230614.pdf
    
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230726.pdf --output %USERPROFILE%\Documents\fomcminutes20230726.pdf
    
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20230920.pdf --output %USERPROFILE%\Documents\fomcminutes20230920.pdf
    
    curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/s3Docs/fomcminutes20231101.pdf --output %USERPROFILE%\Documents\fomcminutes20231101.pdf

• Also, you have the option to download the .pdf files from here. These files will download to your Documents folder. Upload these files to S3 bucket knowledgebase-bedrock-agent-{alias}. These files are the Federal Open Market Committee documents describing monetary policy decisions made at the Federal Reserved board meetings. The documents include discussions of economic conditions, policy directives to the Federal Reserve Bank of New York for open market operations, and votes on the federal funds rate. More information can be found here. Once uploaded, please select one of the documents to open and review the content.

• Artifacts Bucket: Create another S3 bucket to store artifacts. For example, call it artifacts-bedrock-agent-creator-{alias}. Then, download the API schema file from here by running the following curl command in the terminal or command prompt:

• For Mac

  curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/ActionSchema.json --output ~/Documents/ActionSchema.json
  

• For Windows

  curl https://raw.githubusercontent.com/build-on-aws/bedrock-agents-streamlit/main/ActionSchema.json --output %USERPROFILE%\Documents\ActionSchema.json
  

• You can also download this .json file from here. Upload this file from the Documents folder to S3 bucket artifacts-bedrock-agent-creator-{alias}. The provided API schema is an OpenAPI specification for the "PortfolioCreator API," which outlines the structure and capabilities of a service designed for company portfolio creation, company financial research, and sending an email. This API Schema is a rich description of each action, so agents know when to use it, and how to call it and use results. This schema defines three primary endpoints, /companyResearch, /createPortfolio, and /sendEmail detailing how to interact with the API, the required parameters, and the expected responses. Once uploaded, please select and open the .json document to review the content.

Step 2: Knowledge Base Setup in Bedrock Agent

• Before we setup the knowledge base, we will need to grant access to the models that will be needed for our Bedrock agent. Navigate to the Amazon Bedrock console, then on the left of the screen, scroll down and select Model access. On the right, select the orange Manage model access button.

• Select the checkbox for the base model columns Amazon: Titan Embeddings G1 - Text and Anthropic: Claude Instant. This will provide you access to the required models. After, scroll down to the bottom right and select Request model access.

• After, verify that the Access status of the Models are green with Access granted.

• Now, we will create a knowledge base by selecting Knowledge base on the left, then selecting the orange button Create knowledge base.

• You can use the default name, or enter in your own. Then, select Next at the bottom right of the screen.

• Sync S3 bucket knowledgebase-bedrock-agent-alias to this knowledge base.

• For the embedding model, choose Titan Embeddings G1 - Text v1.2. Leave the other options as default, and scroll down to select Next.

• On the next screen, review your work, then select Create knowledge base (Creating the knowledge base may take a few minutes. Please wait for it to finish before going to the next step.)

Step 3: Lambda Function Configuration

• Create a Lambda function (Python 3.12) for the Bedrock agent's action group. We will call this Lambda function PortfolioCreator-actions.

• Copy the python code provided below, or from the file here into your Lambda function.

import json

def lambda_handler(event, context):
    print(event)
  
    # Mock data for demonstration purposes
    company_data = [
        #Technology Industry
        {"companyId": 1, "companyName": "TechStashNova Inc.", "industrySector": "Technology", "revenue": 10000, "expenses": 3000, "profit": 7000, "employees": 10},
        {"companyId": 2, "companyName": "QuantumPirateLeap Technologies", "industrySector": "Technology", "revenue": 20000, "expenses": 4000, "profit": 16000, "employees": 10},
        {"companyId": 3, "companyName": "CyberCipherSecure IT", "industrySector": "Technology", "revenue": 30000, "expenses": 5000, "profit": 25000, "employees": 10},
        {"companyId": 4, "companyName": "DigitalMyricalDreams Gaming", "industrySector": "Technology", "revenue": 40000, "expenses": 6000, "profit": 34000, "employees": 10},
        {"companyId": 5, "companyName": "NanoMedNoLand Pharmaceuticals", "industrySector": "Technology", "revenue": 50000, "expenses": 7000, "profit": 43000, "employees": 10},
        {"companyId": 6, "companyName": "RoboSuperBombTech Industries", "industrySector": "Technology", "revenue": 60000, "expenses": 8000, "profit": 52000, "employees": 12},
        {"companyId": 7, "companyName": "FuturePastNet Solutions", "industrySector": "Technology",  "revenue": 60000, "expenses": 9000, "profit": 51000, "employees": 10},
        {"companyId": 8, "companyName": "InnovativeCreativeAI Corp", "industrySector": "Technology", "revenue": 65000, "expenses": 10000, "profit": 55000, "employees": 15},
        {"companyId": 9, "companyName": "EcoLeekoTech Energy", "industrySector": "Technology", "revenue": 70000, "expenses": 11000, "profit": 59000, "employees": 10},
        {"companyId": 10, "companyName": "TechyWealthHealth Systems", "industrySector": "Technology", "revenue": 80000, "expenses": 12000, "profit": 68000, "employees": 10},
    
        #Real Estate Industry
        {"companyId": 11, "companyName": "LuxuryToNiceLiving Real Estate", "industrySector": "Real Estate", "revenue": 90000, "expenses": 13000, "profit": 77000, "employees": 10},
        {"companyId": 12, "companyName": "UrbanTurbanDevelopers Inc.", "industrySector": "Real Estate", "revenue": 100000, "expenses": 14000, "profit": 86000, "employees": 10},
        {"companyId": 13, "companyName": "SkyLowHigh Towers", "industrySector": "Real Estate", "revenue": 110000, "expenses": 15000, "profit": 95000, "employees": 18},
        {"companyId": 14, "companyName": "GreenBrownSpace Properties", "industrySector": "Real Estate", "revenue": 120000, "expenses": 16000, "profit": 104000, "employees": 10},
        {"companyId": 15, "companyName": "ModernFutureHomes Ltd.", "industrySector": "Real Estate", "revenue": 130000, "expenses": 17000, "profit": 113000, "employees": 10},
        {"companyId": 16, "companyName": "CityCountycape Estates", "industrySector": "Real Estate", "revenue": 140000, "expenses": 18000, "profit": 122000, "employees": 10},
        {"companyId": 17, "companyName": "CoastalFocalRealty Group", "industrySector": "Real Estate", "revenue": 150000, "expenses": 19000, "profit": 131000, "employees": 10},
        {"companyId": 18, "companyName": "InnovativeModernLiving Spaces", "industrySector": "Real Estate", "revenue": 160000, "expenses": 20000, "profit": 140000, "employees": 10},
        {"companyId": 19, "companyName": "GlobalRegional Properties Alliance", "industrySector": "Real Estate", "revenue": 170000, "expenses": 21000, "profit": 149000, "employees": 11},
        {"companyId": 20, "companyName": "NextGenPast Residences", "industrySector": "Real Estate", "revenue": 180000, "expenses": 22000, "profit": 158000, "employees": 260}
    ]
    
  
    def get_named_parameter(event, name):
        return next(item for item in event['parameters'] if item['name'] == name)['value']
    
 
    def companyResearch(event):
        companyName = get_named_parameter(event, 'name').lower()
        print("NAME PRINTED: ", companyName)
        
        for company_info in company_data:
            if company_info["companyName"].lower() == companyName:
                return company_info
        return None
    
    def createPortfolio(event, company_data):
        numCompanies = int(get_named_parameter(event, 'numCompanies'))
        industry = get_named_parameter(event, 'industry').lower()

        industry_filtered_companies = [company for company in company_data
                                       if company['industrySector'].lower() == industry]

        sorted_companies = sorted(industry_filtered_companies, key=lambda x: x['profit'], reverse=True)

        top_companies = sorted_companies[:numCompanies]
        return top_companies

 
    def sendEmail(event, company_data):
        emailAddress = get_named_parameter(event, 'emailAddress')
        fomcSummary = get_named_parameter(event, 'fomcSummary')
    
        # Retrieve the portfolio data as a string
        portfolioDataString = get_named_parameter(event, 'portfolio')
    

        # Prepare the email content
        email_subject = "Portfolio Creation Summary and FOMC Search Results"
        #email_body = f"FOMC Search Summary:\n{fomcSummary}\n\nPortfolio Details:\n{json.dumps(portfolioData, indent=4)}"
    
        # Email sending code here (commented out for now)
    
        return "Email sent successfully to {}".format(emailAddress)   
      
      
    result = ''
    response_code = 200
    action_group = event['actionGroup']
    api_path = event['apiPath']
    
    print("api_path: ", api_path )
    
    if api_path == '/companyResearch':
        result = companyResearch(event)
    elif api_path == '/createPortfolio':
        result = createPortfolio(event, company_data)
    elif api_path == '/sendEmail':
        result = sendEmail(event, company_data)
    else:
        response_code = 404
        result = f"Unrecognized api path: {action_group}::{api_path}"
        
    response_body = {
        'application/json': {
            'body': result
        }
    }
        
    action_response = {
        'actionGroup': event['actionGroup'],
        'apiPath': event['apiPath'],
        'httpMethod': event['httpMethod'],
        'httpStatusCode': response_code,
        'responseBody': response_body
    }

    api_response = {'messageVersion': '1.0', 'response': action_response}
    return api_response

• Then, select Deploy in the tab section of the Lambda console. Review the code provided before moving to the next step. You will see that we are using mock data to represent various companies in the technology and real estate insdustry, along with functions that we will call later in this workshop.

• Next, apply a resource policy to the Lambda to grant Bedrock agent access. To do this, we will switch the top tab from code to configuration and the side tab to Permissions. Then, scroll to the Resource-based policy statements section and click the Add permissions button.

• Here is an example of the resource policy. (At this part of the setup, we will not have a Bedrock agent Source ARN. So, enter in arn:aws:bedrock:us-west-2:{accoundID}:agent/BedrockAgentID for now. We will include the ARN once it’s generated in step 6 after creating the Bedrock Agent alias):

Step 4: Setup Bedrock Agent and Action Group

• Navigate to the Bedrock console, go to the toggle on the left, and under Orchestration select Agents, then select Create Agent.

• On the next screen, provide an agent name, like “PortfolioCreator”. Leave the other options as default, then select Next.

• Select the Anthropic: Claude V1.2 model. Now, we need to add instructions by creating a prompt that defines the rules of operation for the agent. In the prompt below, we provide specific direction on how the model should use tools to answer questions. Copy, then paste the details below into the agent instructions.

You are an investment analyst who creates portfolios of companies based on the number of companies, and industry in the {question}. An example of a portfolio looks like this template {portfolio_example}. You also research companies, and summarize documents. When requested, you format emails like this template {email_format}, then use the provided tools to send an email that has the company portfolio created, and summary of the FOMC report searched. 

• When creating the agent, select Lambda function PortfolioCreator-actions. Next, select the schema file ActionSchema.json from the s3 bucket artifacts-bedrock-agent-creator-alias. Then, select Next.

Step 5: Setup Knowledge Base with Bedrock Agent

• When integrating the KB with the agent, you will need to provide basic instructions on how to handle the knowledge base. For example, use the following:

  Use this knowledge base when a user asks about data, such as economic trends, company financial statements, or the outcomes of the Federal Open Market Committee meetings.
  

Review, then select the Create Agent button.

• Now, we need to provide the Bedrock agent an example of a formatted company portfolio, and a formatted email. On the Agent Overview screen, scroll down and select Working draft.

• Go down to Advanced prompts and select Edit.

• Select the Orchestration tab. Toggle on the radio button Override orchestration template defaults. Make sure Activate orchestration template is enabled as well.

• In the Prompt template editor, scroll down to line seven right below the closing tag </auxiliary_instructions>. Make two line spaces, then copy/paste in the following portfolio example and email format:

Here is an example of a company portfolio.  

<portfolio_example>

Here is a portfolio of the top 3 real estate companies:

  1. NextGenPast Residences with revenue of $180,000, expenses of $22,000 and profit of $158,000 employing 260 people. 
  
  2. GlobalRegional Properties Alliance with revenue of $170,000, expenses of $21,000 and profit of $149,000 employing 11 people.
  
  3. InnovativeModernLiving Spaces with revenue of $160,000, expenses of $20,000 and profit of $140,000 employing 10 people.

</portfolio_example>

Here is an example of a formatted email. Double check that the FOMC report is a summary of the knowledge base responses.

<email_format>

Company Portfolio:

  1. NextGenPast Residences with revenue of $180,000, expenses of $22,000 and profit of $158,000 employing 260 people. 
  
  2. GlobalRegional Properties Alliance with revenue of $170,000, expenses of $21,000 and profit of $149,000 employing 11 people.
  
  3. InnovativeModernLiving Spaces with revenue of $160,000, expenses of $20,000 and profit of $140,000 employing 10 people.  


FOMC Report:

  Participants noted that recent indicators pointed to modest growth in spending and production. Nonetheless, job gains had been robust in recent months, and the unemployment rate remained low. Inflation had eased somewhat but remained elevated.
   
  Participants recognized that Russia’s war against Ukraine was causing tremendous human and economic hardship and was contributing to elevated global uncertainty. Against this background, participants continued to be highly attentive to inflation risks.
</email_format>

• The results should look similar to the following:

• When your done, scroll to the bottom and select Save and exit.

• Now, within your working draft, check to confirm that the Orchestration in the Advance prompt section is Overridden

Step 6: Create an alias

-Create an alias (new version), and choose a name of your liking. Make sure to copy and save your Agent ID and Agent Alias ID. You will need these in step 9.

• Next, navigate to the Agent Overview settings for the agent created by selecting Agents under the Orchestration dropdown menu on the left of the screen, then select the agent. Copy the Agent ARN, then add this ARN to the resource policy of Lambda function PortfolioCreator-actions previously created in step 3.

Step 7: Testing the Setup

Testing the Knowledge Base

• While in the Bedrock console, select Knowledge base under the Orchestration tab, then the KB you created. Scroll down to the Data source section, and make sure to select the Sync button.

• You will see a user interface on the right where you will need to select a model. Choose the Anthropic Claude V1.2 model, then select Apply.

• You should now have the ability to enter prompts in the user interface provided.

• Test Prompts:

  Give me a summary of financial market developments and open market operations in January 2023.
  

  Can you provide information about inflation or rising prices?
  

  What can you tell me about the Staff Review of the Economic & Financial Situation?
  

Testing the Bedrock Agent

• While in the Bedrock console, select Agents under the Orchestration tab, then the agent you created. You should be able to enter prompts in the user interface provided to test your knowledge base and action groups from the agent.

• Example prompts for Knowledge base:

  Give me a summary of financial market developments and open market operations in January 2023
  

  Tell me the participants view on economic conditions and economic outlook
  

  Provide any important information I should know about inflation, or rising prices
  

  Tell me about the Staff Review of the Economic & financial Situation
  

• Example prompts for Action groups:

  Create a portfolio with 3 companies in the real estate industry

  Create portfolio of 3 companies that are in the technology industry

  Create a new investment portfolio of companies

  Do company research on TechStashNova Inc.

• Example prompt for KB & AG

  Send an email to [email protected] that includes the company portfolio and FOMC summary
  

  (The logic for this method is not implemented to send emails)

Step 8: Setting Up Cloud9 Environment (IDE)

1. Navigate in the Cloud9 management console. Then, select Create Environment

2. Here, you will enter the following values in each field
   • Name: Bedrock-Environment (Enter any name)
   • Instance type: t3.small
   • Platform: Ubuntu Server 22.04 LTS
   • Timeout: 1 hour

• Once complete, select the Create button at the bottom of the screen. The environment will take a couple of minutes to spin up. If you get an error spinning up Cloud9 due to lack of resources, you can also choose t2.micro for the instance type and try again. (The Cloud9 environment has Python 3.10.12 version at the time of this publication)

3. Navigate back to the Cloud9 Environment, then select open next to the Cloud9 you just created. Now, you are ready to setup the Streamlit app!

Step 9: Setting Up and Running the Streamlit App

1. Obtain the Streamlit App ZIP File: Download the zip file of the project here.

2. Upload to Cloud9:

   • In your Cloud9 environment, upload the ZIP file.

• Before going to the next step, make sure that the project finished uploading.

3. Unzip the File:

   • Use the following command to extract the contents:

     unzip bedrock-agents-streamlit-main.zip
     

4. Navigate to Streamlit_App Folder:

   • Change to the directory containing the Streamlit app. Use this command

     cd ~/environment/bedrock-agents-streamlit-main/Streamlit_App
     

5. Update Configuration:

   • Open the InvokeAgent.py file.
   • Update the agentId and agentAliasId variables with the appropriate values, then save it.

6. Install Streamlit (if not already installed):

   • Run the following command to install all of the dependencies needed:

     pip install streamlit boto3 pandas
     

7. Run the Streamlit App:

   • Execute the command:

     streamlit run app.py --server.address=0.0.0.0 --server.port=8080
     

   • Streamlit will start the app, and you can view it by selecting Preview within the Cloud9 IDE at the top, then Preview Running Application.

     

   • Once the app is running, please test some of the sample prompts provided. (On 1st try, if you receive an error, try again.)

• Optionally, you can review the trace events in the left toggle of the screen. This data will include the Preprocessing, Orchestration, and PostProcessing traces.

Cleanup

After completing the setup and testing of the Bedrock Agent and Streamlit app, follow these steps to clean up your AWS environment and avoid unnecessary charges:

1. Delete S3 Buckets:

• Navigate to the S3 console.
• Select the buckets "knowledgebase-bedrock-agent-alias" and "artifacts-bedrock-agent-creator-alias". Make sure that both of these buckets are empty by deleting the files.
• Choose 'Delete' and confirm by entering the bucket name.

2. Remove Lambda Function:

• Go to the Lambda console.
• Select the "PortfolioCreator-actions" function.
• Click 'Delete' and confirm the action.

3. Delete Bedrock Agent:

• In the Bedrock console, navigate to 'Agents'.
• Select the created agent, then choose 'Delete'.

4. Deregister Knowledge Base in Bedrock:

• Access the Bedrock console, then navigate to “Knowledge base” under the Orchestration tab.
• Select, then delete the created knowledge base.

5. Clean Up Cloud9 Environment:

• Navigate to the Cloud9 management console.
• Select the Cloud9 environment you created, then delete.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.