Use Amazon SageMaker Unified Studio to construct advanced AI workflows utilizing Amazon Bedrock Flows

Organizations face the problem to handle information, a number of synthetic intelligence and machine studying (AI/ML) instruments, and workflows throughout totally different environments, impacting productiveness and governance. A unified growth setting consolidates information processing, mannequin growth, and AI software deployment right into a single system. This integration streamlines workflows, enhances collaboration, and accelerates AI resolution growth from idea to manufacturing.

The subsequent technology of Amazon SageMaker is the middle on your information, analytics, and AI. SageMaker brings collectively AWS AI/ML and analytics capabilities and delivers an built-in expertise for analytics and AI with unified entry to information. Amazon SageMaker Unified Studio is a single information and AI growth setting the place you’ll find and entry your information and act on it utilizing AWS analytics and AI/ML companies, for SQL analytics, information processing, mannequin growth, and generative AI software growth.

With SageMaker Unified Studio, you possibly can effectively construct generative AI functions in a trusted and safe setting utilizing Amazon Bedrock. You’ll be able to select from a number of high-performing basis fashions (FMs) and superior customization and tooling akin to Amazon Bedrock Information Bases, Amazon Bedrock Guardrails, Amazon Bedrock Brokers, and Amazon Bedrock Flows. You’ll be able to quickly tailor and deploy generative AI functions, and share with the built-in catalog for discovery.

On this publish, we exhibit how you should use SageMaker Unified Studio to create advanced AI workflows utilizing Amazon Bedrock Flows.

Resolution overview

Contemplate FinAssist Corp, a number one monetary establishment creating a generative AI-powered agent help software. The answer affords the next key options:

• Criticism reference system – An AI-powered system offering fast entry to historic grievance information, enabling customer support representatives to effectively deal with buyer follow-ups, help inner audits, and assist in coaching new employees.
• Clever data base – A complete information supply of resolved complaints that rapidly retrieves related grievance particulars, decision actions, and consequence summaries.
• Streamlined workflow administration – Enhanced consistency in buyer communications by means of standardized entry to previous case data, supporting compliance checks and course of enchancment initiatives.
• Versatile question functionality – An easy interface supporting numerous question situations, from buyer inquiries about previous resolutions to inner evaluations of grievance dealing with procedures.

Let’s discover how SageMaker Unified Studio and Amazon Bedrock Flows, built-in with Amazon Bedrock Information Bases and Amazon Bedrock Brokers, handle these challenges by creating an AI-powered grievance reference system. The next diagram illustrates the answer structure.

The answer makes use of the next key elements:

• SageMaker Unified Studio – Supplies the event setting
• Stream app – Orchestrates the workflow, together with:
  • Information base queries
  • Immediate-based classification
  • Conditional routing
  • Agent-based response technology

The workflow processes person queries by means of the next steps:

1. A person submits a complaint-related query.
2. The data base offers related grievance data.
3. The immediate classifies if the question is about decision timing.
4. Based mostly on the classification utilizing the situation, the applying takes the next motion:
   1. Routes the question to an AI agent for particular decision responses.
   2. Returns normal grievance data.
5. The applying generates an acceptable response for the person.

Stipulations

For this instance, you want the next:

• Entry to SageMaker Unified Studio. (You will want the SageMaker Unified Studio portal URL out of your administrator). You’ll be able to authenticate utilizing both:
• The IAM person or IAM Identification Heart person will need to have acceptable permissions for:
  • SageMaker Unified Studio.
  • Amazon Bedrock (together with Amazon Bedrock Flows, Amazon Bedrock Brokers, Amazon Bedrock Immediate Administration, and Amazon Bedrock Information Bases).
  • For extra data, seek advice from Identification-based coverage examples.
• Entry to Amazon Bedrock FMs (make certain these are enabled on your account), for instance:Anthropic’s Claude 3 Haiku (for the agent).
• Configure entry to your Amazon Bedrock serverless fashions for Amazon Bedrock in SageMaker Unified Studio tasks.
• Amazon Titan Embedding (for the data base).
• Pattern grievance information ready in CSV format for creating the data base.

Put together your information

Now we have created a pattern dataset to make use of for Amazon Bedrock Information Bases. This dataset has data of complaints obtained by customer support representatives and backbone data.The next is an instance from the pattern dataset:

complaint_id,product,sub_product,challenge,sub_issue,complaint_summary,action_taken,next_steps,financial_institution,state,submitted_via,resolution_type,timely_response
FIN-2024-001,04/26/24,"Mortgage","Standard mortgage","Fee challenge","Escrow dispute","Buyer disputes mortgage cost improve after current escrow evaluation","Reviewed escrow evaluation, defined property tax improve impression, offered detailed cost breakdown","1. Ship written rationalization of escrow evaluation 2. Schedule annual escrow evaluation 3. Present cost help choices","Monetary Establishment-1","TX","Internet","Closed with rationalization","Sure"
FIN-2024-002,04/26/24,"Cash switch","Wire switch","Processing delay","Worldwide switch","Wire switch of $10,000 delayed, buyer involved about worldwide cost deadline","Positioned wire switch in system, expedited processing, waived wire charge","1. Verify receipt with receiving financial institution 2. Replace buyer on supply 3. Doc course of enchancment wants","Monetary Establishment-2","FL","Cellphone","Closed with financial reduction","No"

Create a challenge

In SageMaker Unified Studio, customers can use tasks to collaborate on numerous enterprise use instances. Inside tasks, you possibly can handle information property within the SageMaker Unified Studio catalog, carry out information evaluation, arrange workflows, develop ML fashions, construct generative AI functions, and extra.

To create a challenge, full the next steps:

1. Open the SageMaker Unified Studio touchdown web page utilizing the URL out of your admin.
2. Select Create challenge.
3. Enter a challenge identify and non-compulsory description.
4. For Mission profile, select Generative AI software growth.
5. Select Proceed.

6. Full your challenge configuration, then select Create challenge.

Create a immediate

Let’s create a reusable immediate to seize the directions for FMs, which we’ll use later whereas creating the movement software. For extra data, see Reuse and share Amazon Bedrock prompts.

1. In SageMaker Unified Studio, on the Construct menu, select Immediate underneath Machine Studying & Generative AI.

2. Present a reputation for the immediate.
3. Select the suitable FM (for this instance, we select Claude 3 Haiku).
4. For Immediate message, we enter the next:

You're a grievance evaluation classifier. You'll obtain grievance information from a data base. Analyze the {{enter}} and reply with a single letter:
T: If the enter accommodates details about grievance decision timing, response time, or processing timeline (whether or not well timed or delayed)
F: For all different varieties of grievance data
Return solely 'T' or 'F' primarily based on whether or not the data base response is about decision timing. Don't add any further textual content or rationalization - reply with simply the only letter 'T' or 'F'.

5. Select Save.

6. Select Create model.

Create a chat agent

Let’s create a chat agent to deal with particular decision responses. Full the next steps:

1. In SageMaker Unified Studio, on the Construct menu, select Chat agent underneath Machine Studying & Generative AI.
2. Present a reputation for the immediate.
3. Select the suitable FM (for this instance, we select Claude 3 Haiku).
4. For Enter a system immediate, we enter the next:

You're a Monetary Complaints Assistant AI. You'll obtain grievance data from a data base and questions on decision timing.
When responding to decision timing queries:
1. Use the offered grievance data to verify if it was resolved inside timeline
2. For well timed resolutions, present:
   - Affirmation of well timed completion
   - Particular actions taken (from the offered grievance information)
   - Subsequent steps that have been accomplished
2. For delayed resolutions, present:
   - Acknowledgment of delay
   - Commonplace compensation bundle:
     • $75 service credit score
     • Precedence Standing improve for six months
     • Service charges waived for present billing cycle
   - Actions taken (from the offered grievance information)
   - Contact data for follow-up: Precedence Line: ************** 
At all times reference the precise grievance particulars offered in your enter when discussing actions taken and backbone course of.

5. Select Save.

6. After the agent is saved, select Deploy.
7. For Alias identify, enter demoAlias.
8. Select Deploy.

Create a movement

Now that we’ve got our immediate and agent prepared, let’s create a movement that may orchestrate the grievance dealing with course of:

1. In SageMaker Unified Studio, on the Construct menu, select Stream underneath Machine Studying & Generative AI.

2. Create a brand new movement referred to as demo-flow.

Add a data base to your movement software

Full the next steps so as to add a data base node to the movement:

1. Within the navigation pane, on the Nodes tab, select Information Base.
2. On the Configure tab, present the next data:
   1. For Node identify, enter a reputation (for instance, complaints_kb).
   2. Select Create new Information Base.
3. Within the Create Information Base pane, enter the next data:
   1. For Title, enter a reputation (for instance, complaints).
   2. For Description, enter an outline (for instance, person complaints data).
   3. For Add information sources, choose Native file and add the complaints.txt file.
   4. For Embeddings mannequin, select Titan Textual content Embeddings V2.
   5. For Vector retailer, select OpenSearch Serverless.
   6. Select Create.

4. After you create the data base, select it within the movement.
5. Within the particulars identify, present the next data:
6. For Response technology mannequin, select Claude 3 Haiku.
7. Join the output of the movement enter node with the enter of the data base node.
8. Join the output of the data base node with the enter of the movement output node.

9. Select Save.

Add a immediate to your movement software

Now let’s add the immediate you created earlier to the movement:

1. On the Nodes tab within the Stream app builder pane, add a immediate node.
2. On the Configure tab for the immediate node, present the next data:
3. For Node identify, enter a reputation (for instance, demo_prompt).
4. For Immediate, select financeAssistantPrompt.
5. For Model, select 1.
6. Join the output of the data base node with the enter of the immediate node.
   
7. Select Save.

Add a situation to your movement software

The situation node determines how the movement handles various kinds of queries. It evaluates whether or not a question is about decision timing or normal grievance data, enabling the movement to route the question appropriately. When a question is about decision timing, it will likely be directed to the chat agent for specialised dealing with; in any other case, it’s going to obtain a direct response from the data base. Full the next steps so as to add a situation:

1. On the Nodes tab within the Stream app builder pane, add a situation node.
2. On the Configure tab for the situation node, present the next data:
   1. For Node identify, enter a reputation (for instance, demo_condition).
   2. Below Circumstances, for Situation, enter conditionInput == "T".
   3. Join the output of the immediate node with the enter of the situation node.
      
3. Select Save.

Add a chat agent to your movement software

Now let’s add the chat agent you created earlier to the movement:

1. On the Nodes tab within the Stream app builder pane, add the agent node.
2. On the Configure tab for the agent node, present the next data:
   1. For Node identify, enter a reputation (for instance, demo_agent).
   2. For Chat agent, select DemoAgent.
   3. For Alias, select demoAlias.
3. Create the next node connections:
   1. Join the enter of the situation node (demo_condition) to the output of the immediate node (demo_prompt).
   2. Join the output of the situation node:
      1. Set If situation is true to the agent node (demo_agent).
      2. Set If situation is fake to the prevailing movement output node (FlowOutputNode).
   3. Join the output of the data base node (complaints_kb) to the enter of the next:
      1. The agent node (demo_agent).
      2. The movement output node (FlowOutputNode).
   4. Join the output of the agent node (demo_agent) to a brand new movement output node named FlowOutputNode_2.
      
4. Select Save.

Check the movement software

Now that the movement software is prepared, let’s check it. On the proper facet of the web page, select the broaden icon to open the Check pane.

Within the Enter immediate textual content field, we are able to ask a number of questions associated to the dataset created earlier. The next screenshots present some examples.

Clear up

To wash up your sources, delete the movement, agent, immediate, data base, and related OpenSearch Serverless sources.

Conclusion

On this publish, we demonstrated the right way to construct an AI-powered grievance reference system utilizing a movement software in SageMaker Unified Studio. Through the use of the built-in capabilities of SageMaker Unified Studio with Amazon Bedrock options like Amazon Bedrock Information Bases, Amazon Bedrock Brokers, and Amazon Bedrock Flows, you possibly can quickly develop and deploy subtle AI functions with out in depth coding.

As you construct AI workflows utilizing SageMaker Unified Studio, bear in mind to stick to the AWS Shared Duty Mannequin for safety. Implement SageMaker Unified Studio safety finest practices, together with correct IAM configurations and information encryption. You too can seek advice from Safe a generative AI assistant with OWASP High 10 mitigation for particulars on the right way to assess the safety posture of a generative AI assistant utilizing OWASP TOP 10 mitigations for frequent threats. Following these tips helps set up sturdy AI functions that keep information integrity and system safety.

To be taught extra, seek advice from Amazon Bedrock in SageMaker Unified Studio and be part of discussions and share your experiences in AWS Generative AI Group.

We stay up for seeing the revolutionary options you’ll create with these highly effective new options.

In regards to the authors

Sumeet Tripathi is an Enterprise Assist Lead (TAM) at AWS in North Carolina. He has over 17 years of expertise in expertise throughout numerous roles. He’s enthusiastic about serving to clients to cut back operational challenges and friction. His focus space is AI/ML and Vitality & Utilities Phase. Exterior work, He enjoys touring with household, watching cricket and flicks.

Vishal Naik is a Sr. Options Architect at Amazon Internet Providers (AWS). He’s a builder who enjoys serving to clients accomplish their enterprise wants and clear up advanced challenges with AWS options and finest practices. His core space of focus contains Generative AI and Machine Studying. In his spare time, Vishal loves making brief movies on time journey and alternate universe themes.