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build-on-aws / bedrock-agents-streamlit

Creating Amazon Bedrock agents with Streamlit Framework
https://www.youtube.com/watch?v=kzzlchi0DzU
MIT No Attribution
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ai-agent amazon-bedrock claude streamlit

readme

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

Table of Contents

  1. Introduction
  2. Prerequisites
  3. Diagram
  4. Configuration and Setup
  5. Step 5: Setup Knowledge Base with Bedrock Agent
  6. Step 6: Create an Alias
  7. Step 7: Testing the Setup
  8. Step 8: Setup and Run Streamlit App on EC2 (Optional)
  9. Cleanup
  10. Security
  11. License

Overview

In this project, we will set up an Amazon Bedrock agent with an action group that dynamically creates an investment company portfolio based on specific parameters. The agent also has Q&A capabilities for Federal Open Market Committee (FOMC) reports, leveraging a Streamlit framework for the user interface. Additionally, this exercise includes a method for sending emails, although the email functionality will not be fully configured.

For those who prefer an Infrastructure-as-Code (IaC) solution, we provide an AWS CloudFormation template that will deploy most of the necessary resources, including S3 buckets, an action group, and a Lambda function. You will still need to manually create the knowledge base with the already provided resources, but the steps are relatively straightforward. If you would like to deploy this workflow via AWS CloudFormation, please refer to the workshop guide here.

Alternatively, this README will walk you through the step-by-step process to set up the Amazon Bedrock agent manually using the AWS Console.

Prerequisites

Diagram

Diagram

Configuration and Setup

Step 1: Creating S3 Buckets

Bucket create 1

Bucket create 2

bucket domain data

Step 2: Knowledge Base Setup in Bedrock Agent

Model access

Access granted

create_kb_btn

KB details

KB setup

Vector Store Config

Review and Create KB

kb complete

Step 3: Lambda Function Configuration

Create Function

Create Function2

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

Lambda deploy

Permissions config

Lambda resource policy create

Lambda resource policy

Step 4: Setup Bedrock Agent and Action Group

You are an investment analyst. Your job is to assist in investment analysis, create research summaries, generate profitable company portfolios, and facilitate communication through emails. Here is how I want you to think step by step:

1. Portfolio Creation:
    Analyze the user's request to extract key information such as the desired number of companies and industry. 
    Based on the criteria from the request, create a portfolio of companies. Use the template provided to format the portfolio.

2. Company Research and Document Summarization:
    For each company in the portfolio, conduct detailed research to gather relevant financial and operational data.
    When a document, like the FOMC report, is mentioned, retrieve the document and provide a concise summary.

3. Email Communication:
    Using the email template provided, format an email that includes the newly created company portfolio and any summaries of important documents.
    Utilize the provided tools to send an email upon request, That includes a summary of provided responses and portfolios created.
{
  "openapi": "3.0.1",
  "info": {
    "title": "PortfolioCreatorAssistant API",
    "description": "API for creating a company portfolio, search company data, and send summarized emails",
    "version": "1.0.0"
  },
  "paths": {
    "/companyResearch": {
      "post": {
        "description": "Get financial data for a company by name",
        "parameters": [
          {
            "name": "name",
            "in": "query",
            "description": "Name of the company to research",
            "required": true,
            "schema": {
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "description": "Successful response with company data",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/CompanyData"
                }
              }
            }
          }
        }
      }
    },
    "/createPortfolio": {
      "post": {
        "description": "Create a company portfolio of top profit earners by specifying number of companies and industry",
        "parameters": [
          {
            "name": "numCompanies",
            "in": "query",
            "description": "Number of companies to include in the portfolio",
            "required": true,
            "schema": {
              "type": "integer",
              "format": "int32"
            }
          },
          {
            "name": "industry",
            "in": "query",
            "description": "Industry sector for the portfolio companies",
            "required": true,
            "schema": {
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "description": "Successful response with generated portfolio",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/Portfolio"
                }
              }
            }
          }
        }
      }
    },
    "/sendEmail": {
      "post": {
        "description": "Send an email with FOMC search summary and created portfolio",
        "parameters": [
          {
            "name": "emailAddress",
            "in": "query",
            "description": "Recipient's email address",
            "required": true,
            "schema": {
              "type": "string",
              "format": "email"
            }
          },
          {
            "name": "fomcSummary",
            "in": "query",
            "description": "Summary of FOMC search results",
            "required": true,
            "schema": {
              "type": "string"
            }
          },
          {
            "name": "portfolio",
            "in": "query",
            "description": "Details of the created stock portfolio",
            "required": true,
            "schema": {
              "$ref": "#/components/schemas/Portfolio"
            }
          }
        ],
        "responses": {
          "200": {
            "description": "Email sent successfully",
            "content": {
              "text/plain": {
                "schema": {
                  "type": "string",
                  "description": "Confirmation message"
                }
              }
            }
          }
        }
      }
    }
  },
  "components": {
    "schemas": {
      "CompanyData": {
        "type": "object",
        "description": "Financial data for a single company",
        "properties": {
          "name": {
            "type": "string",
            "description": "Company name"
          },
          "expenses": {
            "type": "string",
            "description": "Annual expenses"
          },
          "revenue": {
            "type": "number",
            "description": "Annual revenue"
          },
          "profit": {
            "type": "number",
            "description": "Annual profit"
          }
        }
      },
      "Portfolio": {
        "type": "object",
        "description": "Stock portfolio with specified number of companies",
        "properties": {
          "companies": {
            "type": "array",
            "items": {
              "$ref": "#/components/schemas/CompanyData"
            },
            "description": "List of companies in the portfolio"
          }
        }
      }
    }
  }
}

ag creation

advance_prompt_btn

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 an email formatted. 

<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>

advance_prompt_setup

advance_prompt_overridden

Step 5: Setup Knowledge Base with Bedrock Agent

Knowledge base add2

Step 6: Create an alias

Create alias

Step 7: Testing the Setup

Testing the Knowledge Base

KB sync

Select model test

KB prompt

Testing the Bedrock Agent

Agent test

Step 8: Setup and Run Streamlit App on EC2 (Optional)

  1. Obtain CF template to launch the streamlit app: Download the Cloudformation template from here. This template will be used to deploy an EC2 instance that has the Streamlit code to run the UI. Please note, the CIDR range and VPC used in the CloudFormation template may need to be modified if the defined subnet and VPC are not applicable.

  2. Deploy template via Cloudformation:

    • In your mangement console, search, then go to the CloudFormation service.
    • Create a stack with new resources (standard)

    Create stack

    • Prepare template: Choose existing template -> Specify template: Upload a template file -> upload the template donaloaded from the previous step.

    Create stack config

    • Next, Provide a stack name like ec2-streamlit. Keep the instance type on the default of t3.small, then go to Next.

    Stack details

    • On the Configure stack options screen, leave every setting as default, then go to Next.

    • Scroll down to the capabilities section, and acknowledge the warning message before submitting.

    • Once the stack is complete, go to the next step.

Stack complete

  1. Edit the app to update agent IDs:

    • Navigate to the EC2 instance management console. Under instances, you should see EC2-Streamlit-App. Select the checkbox next to it, then connect to it via EC2 Instance Connect.

    ec2 connect clip

    • If you see a message that says EC2 Instance Connect service IP addresses are not authorized, then you will need to re-deploy the template and select the correct CIDR range for the EC2 based on the region you are in. This will allow you to cannect to the EC2 instance via SSH. By default, it is the allowed CIDR range for us-west-2 region. However, if you are in the us-east-1 region for example, the CIDR range will need to be 18.206.107.24/29 when deploying the AWS Cloudformation template. Additional CIDR ranges for each region can be found here.

    ec2 ssh error

    • Next, use the following command to edit the invoke_agent.py file:

      sudo vi app/streamlit_app/invoke_agent.py

    • Press i to go into edit mode. Then, update the AGENT ID and Agent ALIAS ID values.

    file_edit

    • After, hit Esc, then save the file changes with the following command:

      :wq!

    • Now, start the streamlit app by running the following command:

      streamlit run app/streamlit_app/app.py

    • You should see an external URL. Copy & paste the URL into a web browser to start the streamlit application.

External IP

Running App

Trace events

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 Builder tools tab.
    • Select, then delete the created knowledge base.

  5. Clean Up EC2 Environment:

    • Navigate to the EC2 management console.
    • Select the EC2 instance you created, then delete.

Security

See CONTRIBUTING for more information.

License

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