Building a Policy Analysis Chatbot: A Detailed Guide into Amazon AWS Lex and AWS Bedrock and Lambda

The world of AI is buzzing with chatbots, and they are transforming the way we interact with data and services. Imagine having a chatbot that can analyze policies and provide insights based on complex documents like budget reports or the Hunger Index. Today, we will explore how to build a Policy Analysis Chatbot using Amazon Lex and Bedrock and Lambda. This blog breaks down the process into easy-to-follow steps to help you get started.

Step 1: Setting the Stage — Resources We Need

Before we embark on this journey, let’s prepare our toolkit. Here’s what we’ll need:

• An AWS Account with Admin Privileges: This serves as our playground. Ensure you have an active AWS account and are logged in as an IAM user with admin privileges. Avoid using the root account, as it might limit certain setup steps, especially related to the Bedrock knowledge base.
• Access to Two Powerful Models:
• Anthropic Claude 2: This model powers the chatbot’s conversational abilities, allowing it to handle questions and conversations intelligently.
• Amazon Titan Text Embeddings: This model enables the chatbot to process and understand text by converting it into numerical representations (vectors) for efficient searching and reasoning.
• You might need to request access to these models via the AWS console. The approval process is quick for commonly used models.
• Policy Documents for Analysis: Gather relevant documents for the chatbot to analyze, such as the Indian Budget PDF 2024, the Hunger Index Report 2024, or any other policy-related materials. These documents will form the knowledge base for the chatbot.

Step 2: Creating a Home for Our Documents — The S3 Bucket

Amazon S3 provides secure storage for your policy documents.

1. Navigate to the S3 section in the AWS console.
2. Click “Create bucket” and give it a unique name, such as policy-docs-bucket.
3. Upload your documents (e.g., budget PDFs, Hunger Index PDFs) to this bucket. These files will now be ready for analysis.

Step 3: Building the Brains — The Bedrock Knowledge Base

Give the chatbot intelligence through Amazon Bedrock.

1. In the AWS console, go to Bedrock and click “Create knowledge base.”
2. Name your knowledge base (e.g., PolicyAnalysisKB).
3. Allow Bedrock to create a new service role with the necessary permissions.
4. Select S3 as the data source and specify your S3 bucket.
5. Choose “Titan embeddings G1 text” as the embeddings model.
6. Use “Amazon OpenSearch Serverless Vector Store” for efficient vector-based searches.
7. Click “Create knowledge base.” This process may take a few minutes.

Step 4: Syncing for Searchability

Sync the knowledge base with your uploaded documents.

1. In the Bedrock console, go to “Data Sources.”
2. Select your S3 data source and click “Sync.” This ensures all documents are processed and made searchable.

Step 5: The Chatbot’s Personality — Amazon Lex

Now, let’s create the chatbot’s interface using Amazon Lex.

1. Open the Amazon Lex console and click “Create bot.”
2. Select the blank bot option for greater control and later enhancements.
3. Name your bot (e.g., PolicyAnalysisBot) and add a brief description.
4. Create a “Welcome Intent” to handle initial interactions. Add utterances like “Hi” and “Help” and specify the bot’s response, e.g., “Welcome! I can help analyze policies like budgets and hunger indices.”
5. Save the intent. Lex includes a default “Fallback Intent” for unrecognized inputs.
6. Add a “Q&A Intent” for interacting with the knowledge base:

• Choose “Anthropic Claude V2” as the model.
• Provide the ID of the Bedrock knowledge base.
• Define utterances like “What are the tax slabs in the 2024 budget?” or “What does the Hunger Index say about India’s ranking?”

7. Save and build your bot to compile the configurations.

8. Test your bot in the Lex console using the test window.

Step 6: Enhancing Functionality with AWS Lambda

AWS Lambda adds custom logic to your Lex bot, enabling it to execute dynamic actions or respond with additional context.

Setting Up AWS Lambda:

1. Go to the AWS Lambda console and click “Create function.”
2. Choose “Author from scratch.”

• Function name: PolicyAnalysisFunction
• Runtime: Python 3.9 (or your preferred runtime)

3. Write a Lambda function to handle policy-specific queries. For example, the function can parse specific documents or fetch additional data:

import json

def lambda_handler(event, context):
    user_query = event['inputTranscript']  # User's input
    if "budget" in user_query.lower():
        response = "The 2024 budget includes tax slabs ranging from 5% to 30%."
    elif "hunger index" in user_query.lower():
        response = "India ranks 101st in the Hunger Index 2024."
    else:
        response = "Sorry, I don't have the information you're looking for."
    
    return {
        "sessionState": {
            "dialogAction": {"type": "Close"},
            "intent": {"name": event['sessionState']['intent']['name']},
            "state": "Fulfilled"
        },
        "messages": [{"contentType": "PlainText", "content": response}]
    }

4. Deploy the function and copy its ARN (Amazon Resource Name).

Linking Lambda with Lex:

1. In the Lex console, open the intent where you want to use Lambda.
2. Under “Fulfillment,” choose AWS Lambda function and paste the function’s ARN.
3. Save and rebuild the bot.

Step 7: Putting the Chatbot to the Test

1. Use the Lex console test window to interact with the bot:

• Start with greetings like “Hi” to trigger the “Welcome Intent.”
• Ask specific questions like “What are the key allocations in the 2024 budget?” or “How does the Hunger Index rank India?”

2. Test Lambda-enhanced functionality by asking dynamic queries the Lambda function is programmed to handle.

Step 8: Cleaning Up — Deleting Resources

Once you’re done, clean up to avoid incurring unnecessary costs:

• Bedrock Knowledge Base: Delete it from the Bedrock console.
• OpenSearch Serverless Collection: Remove it from the OpenSearch service.
• S3 Bucket: Empty and delete the bucket.
• Lex Bot and Lambda Function: Delete both from their respective consoles.

Beyond the Basics

To enhance usability, integrate your chatbot with a web UI or messaging platform. AWS provides easy integration options for applications like Slack or websites. Explore GitHub repositories for sample web UI setups tailored to policy bots.

Conclusion

Congratulations! You’ve built a Policy Analysis Chatbot capable of leveraging Lex, Bedrock, and Lambda for powerful, insightful responses to policy-related queries. With this foundation, you can expand your bot’s capabilities to include advanced features like real-time data updates, multi-lingual support, or deeper integrations. Happy bot building!