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.
- An active AWS Account.
- Familiarity with AWS services like Amazon Bedrock, S3, Lambda, and Cloud9.
- Please make sure that you are in the us-west-2 region. If another region is required, you will need to update the region variable
theRegionin theInvokeAgent.pyfile 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
curlcommands 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.
- 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 3 Haiku. 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.)
- When the knowledge base is complete, you will see a green message at the top similar to the following:
- 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.
- Select AWS service, then use the following settings to configure the resource based policy:
- Service -
Other - Statement ID -
allow-bedrock-agent - Principal -
bedrock.amazonaws.com - Source ARN -
arn:aws:bedrock:us-west-2:{account-id}:agent/*- (Please note, AWS recommends least privilage so only an allowed agent can invoke this Lambda function. A * at the end of the ARN grants any agent in the account access to invoke this Lambda. Ideally, we would not use this in a production environment.) - Action -
lambda:InvokeFunction
- Once your configurations look similar to the above screenshot, select Save at the bottom.
-
Navigate to the Bedrock console. Go to the toggle on the left, and under Orchestration select Agents. Provide an agent name, like PortfolioCreator then create the agent.
-
The agent description is optional, and we will use the default new service role. For the model, select Anthropic: Claude 3 Haiku. Next, provide the following instruction for the agent:
Role: You are an investment analyst responsible for creating portfolios, researching companies, summarizing documents, and formatting emails.
Objective: Assist in investment analysis by generating company portfolios, providing research summaries, and facilitating communication through formatted emails.
1. Portfolio Creation:
Understand the Query: Analyze the user's request to extract key information such as the desired number of companies and industry.
Generate Portfolio: 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:
Research Companies: For each company in the portfolio, conduct detailed research to gather relevant financial and operational data.
Summarize Documents: When a document, like the FOMC report, is mentioned, retrieve the document and provide a concise summary.
3. Email Communication:
Format Email: Using the email template provided, format an email that includes the newly created company portfolio and any summaries of important documents.
Send Email: Utilize the provided tools to send an email upon request, That includes a summary of provided responses and and portfolios created.
-
After, scroll to the top and Save
-
The instructions for the Generative AI Investment Analyst Tool outlines a comprehensive framework designed to assist in investment analysis. This tool is tasked with creating tailored portfolios of companies based on specific industry criteria, conducting thorough research on these companies, and summarizing relevant financial documents. Additionally, the tool formats and sends professional emails containing the portfolios and document summaries. The process involves continuous adaptation to user feedback and maintaining a contextual understanding of ongoing requests to ensure accurate and efficient responses.
-
Next, we will add an action group. Scroll down to
Action groupsthen select Add. -
Call the action group
PortfolioCreator-actions. We will set theAction group typeto Define with API schemas.Action group invocationsshould be set to select an existing Lambda function. For the Lambda function, selectPortfolioCreator-actions. -
For the
Action group Schema, we will choose Define via in-line schema editor. Replace the default schema in the In-line OpenAPI schema editor with the schema provided below. You can also retrieve the schema from the repo here. After, select Add.(This API schema is needed so that the bedrock agent knows the format structure and parameters needed for the action group to interact with the Lambda function.)
{
"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": {
"$ref": "#/components/schemas/Portfolio"
}
}
],
"responses": {
"200": {
"description": "Email sent successfully",
"content": {
"text/plain": {
"schema": {
"type": "string",
"description": "Confirmation message"
}
}
}
}
}
}
}
},
"components": {
"schemas": {
"CompanyData": {
"type": "object",
"description": "Financial data for a single company",
"properties": {
"name": {
"type": "string",
"description": "Company name"
},
"ticker": {
"type": "string",
"description": "Stock ticker symbol"
},
"revenue": {
"type": "number",
"description": "Annual revenue"
},
"profit": {
"type": "number",
"description": "Annual profit"
}
}
},
"Portfolio": {
"type": "object",
"description": "Stock portfolio with specified number of companies",
"properties": {
"companies": {
"type": "array",
"items": {
"$ref": "#/components/schemas/CompanyData"
},
"description": "List of companies in the portfolio"
}
}
}
}
}
}
-
This API schema defines three primary endpoints,
/companyResearch,/createPortfolio, and/sendEmaildetailing how to interact with the API, the required parameters, and the expected responses. -
Now, we need to provide the Bedrock agent a prompt that are examples of a formatted response for an investment company portfolio, and email. In the creation of an agent, it's initially configured with four foundational prompt templates for pre-processing, orchestration, knowledge base response generation, and post-processing. These prompts guide how the agent interacts with the foundation model across various steps of the process. These templates are crucial for processing user inputs, orchestrating the flow between the foundation model, action groups, and knowledge bases, as well as formatting the responses sent to users. By customizing these templates and incorporating advanced prompts or few-shot examples, you can significantly improve the agent's precision and performance in handling specific tasks. More information on advanced prompting for an agent can be found here. Additionally, there is an option to use a custom parser Lambda function for more granular formatting.
-
Next, scroll 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 22-23, 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 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>- The results should look similar to the following:
-
Scroll to the bottom and select the Save and exit button.
-
Now, check to confirm that the Orchestration in the Advance prompt section is Overridden.
-
While on the Bedrock agent console, scroll down to Knowledge base and select Add. 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 your input, then select Add.
-
Scroll to the top and select Prepare so that the changes made are updated. Then select Save and exit.
- Create an alias (new version), and choose a name of your liking. After it's done, make sure to copy your Alias ID and Agent ID. You will need this in step 8.
- 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 3 Haiku 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?
- 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 2023Tell me the participants view on economic conditions and economic outlookProvide any important information I should know about inflation, or rising pricesTell 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 test@example.com that includes the company portfolio and FOMC summary(The logic for this method is not implemented to send emails)
- Navigate in the Cloud9 management console. Then, select Create Environment
- 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)
- 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!
-
Obtain the Streamlit App ZIP File: Download the zip file of the project here.
-
Upload to Cloud9:
- In your Cloud9 environment, upload the ZIP file.
- Before going to the next step, make sure that the project finished uploading.
-
Unzip the File:
-
Use the following command to extract the contents:
unzip bedrock-agents-streamlit-main.zip
-
-
Navigate to streamlit_app Folder:
- Change to the directory containing the Streamlit app. Use this command
cd ~/environment/bedrock-agents-streamlit-main/streamlit_app
- Change to the directory containing the Streamlit app. Use this command
-
Update Configuration:
- Open the
InvokeAgent.pyfile. - Update the
agentIdandagentAliasIdvariables with the appropriate values, then save it.
- Open the
-
Install Streamlit (if not already installed):
-
Run the following command to install all of the dependencies needed:
pip install streamlit boto3 pandas
-
-
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.
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:
- 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.
- Remove Lambda Function:
- Go to the Lambda console.
- Select the "PortfolioCreator-actions" function.
- Click 'Delete' and confirm the action.
- Delete Bedrock Agent:
- In the Bedrock console, navigate to 'Agents'.
- Select the created agent, then choose 'Delete'.
- 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.
- Clean Up Cloud9 Environment:
- Navigate to the Cloud9 management console.
- Select the Cloud9 environment you created, then delete.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.
