Gen AI based Chatbot for Standard Operations

Type: Design Idea

This blog provides a simple design idea for a Gen AI based Chat Bot for the operations support in a small organization. 

1. Components Overview:

• Amazon S3: Stores documents related to SoPs, processes, known issues, and standards.

• Amazon Lex: Frontend chatbot for user interaction.

• AWS Lambda: Backend handler that interacts with S3 and SageMaker, retrieves and processes the requested data.

• Amazon SageMaker: Provides generative AI capabilities to summarize or enhance the document content fetched from S3.

• Amazon API Gateway : Exposes the Lambda functions as REST APIs.

• IAM Roles: Provides appropriate permissions for S3, Lambda, and SageMaker interaction.

2. Design Workflow:

Step 1: User Interaction with Lex

• The user asks a question in Amazon Lex (e.g., "What is the process for X?" or "Give me details about the known issues in Y").

  
  

Step 2: Triggering Lambda from Lex

• Lex sends the user's input as an event to a pre-configured AWS Lambda function.

  
  

Step 3: Lambda Processing

• The Lambda function:

  • Extracts keywords or context from the user input.

  • Based on the extracted information, it searches and retrieves relevant documents from the S3 bucket.

  • (Optional) If the documents are in a format that needs processing (e.g., PDFs or Word files), the Lambda function can preprocess and extract text content.

    
    

Step 4: Sending Data to SageMaker

• The Lambda function sends the extracted document text or a query to an Amazon SageMaker model for further AI-based processing.

  • SageMaker could be used to:

    • Summarize the document.

    • Generate a detailed response.

    • Provide specific information based on user queries.

      
      

Step 5: Generative AI Response from SageMaker

• SageMaker processes the input and generates a response based on the contents of the document (e.g., answering a user query, summarizing a section of a document, or providing suggestions).

  
  

Step 6: Returning the Response to Lex

• The Lambda function receives the response from SageMaker and sends it back to Amazon Lex.

• Lex provides the generated response as a reply to the user.

  
  

3. Key Design Considerations:

1. Document Structure in S3: Organize your S3 bucket with clear prefixes (folders) for SoPs, standards, known issues, and processes. It will help Lambda functions find relevant files faster.

   
   

2. Lambda Function:

   • Handle document parsing, text extraction, and error management (e.g., if a document is not found).

   • Should be stateless but capable of managing sessions (if needed) to maintain context during multi-step conversations.

     
     

3. SageMaker Model:

   • Choose or fine-tune a large language model (LLM) to understand domain-specific jargon, context, and the structure of your documents.

   • You may consider models that are pre-trained for document retrieval, summarization, and question-answering tasks (e.g., GPT-3-based models).

     
     

4. Latency and Caching:

   • Since generative AI models can have processing time, consider optimizing latency by caching frequent responses or indexing document content for faster retrieval.

     
     

5. IAM Roles:

   • Ensure proper IAM roles are configured so Lambda has permission to read from S3 and interact with SageMaker.

   • Lex also needs permission to trigger the Lambda function.

     
     

6. Security:

   • Encrypt the documents in S3.

   • Use API Gateway (optional) to secure Lambda with API keys or OAuth for external access.

     
     

4. Example Flow Diagram:

User Query (via Lex)

      ↓

Amazon Lex

      ↓

Amazon Lambda (Triggers on user intent)

      ↓

Fetch documents from S3 based on query (Lambda)

      ↓

Send extracted content to SageMaker for AI processing

      ↓

Receive AI-generated response from SageMaker (Lambda)

      ↓

Send the response back to Lex

      ↓

User receives an answer in the Lex chatbot

This design enables an interactive, AI-powered chatbot that fetches relevant information from your stored documents and uses generative AI to respond intelligently to user queries.