Chat-GP3P0: A Conversational AI Model

In this project, I aim to architect a functional AI-powered large language model (LLM) on a web application that pulls data from a custom knowledge base and utilizes retrieval augmented generation to answer relevant questions. My chatbot, Chat-GP3P0, is primed with extensive knowledge about Star Wars: A New Hope, and is happy to give thoughtful insight to users who are curious about the movie, its significance, cultural impact, and more!

Defining the Requirements

Functional Requirements:

Provide intelligent, thoughtful responses to all questions about Star Wars: A New Hope
Integrate the chatbot with a comprehensive knowledge base with detailed information

Non-functional Requirements:

Ensure that the model maintains an average response time of under 3 seconds for user queries
Uptime Availability of 99.95%

Workflow and Draft Architecture

Order of Operations:

Choose an appropriate Foundational Model in Amazon Bedrock
Store reference data into a bucket using Amazon S3
Create a knowledge base in Amazon Bedrock, add the S3 bucket to the base, and connect the Foundational Model to use the knowledge base
Configure the AI chatbot agent using Amazon Lex and add intents
Test and monitor performance!

Selecting a Foundational Model

Claude by Anthropic is perfect for my project because of its advanced language understanding and generation skills. With a serverless deployment type and a maximum token count of 100,000, this model will easily be able to handle application (pull?) requests

2. Creating an S3 bucket

Created a general-purpose bucket with encryption enabled. Uploaded full script of Star Wars: A New Hope into the bucket as the first data deposit

3. Integrating a knowledge base

Next, I went back to bedrock to create a knowledge base that my FM could pull from to answer questions. I chose S3 as the data source and pointed the knowledge base to the bucket I created in step 2. I opted for the default embedding model and vector store for this portion

4. Configuring a chatbot with Amazon Lex

The next step was to configure my chatbot. I named the bot, created an IAM role to allow Lex the basic permissions necessary to carry out the task, and added a basic intent that welcomed new users. Additionally, I added an AWS built-in generative AI intent to help to parse the knowledge base and generate thoughtful responses to user questions

Chatbot naming convention & IAM role assignment

Here, I configured two intents. WelcomeIntent is a hard-coded greeting to fit the project theme, and QnABotIntent is a proprietary AWS GenAI intent tool used to parse the knowledge base to help generate responses

5. Testing for effectiveness

Now for the fun bit! Now that the chatbot had been developed, primed with a knowledge base, and connected to a web application, I ran various queries to gauge the response accuracy and overall effectiveness

As I added more information to the S3 bucket to give the FM context, the responses became more insightful

Recap

In this project, I built a intelligent and fun AI chatbot designed to provide insight into one of the greatest movies ever made!

Requirements Evaluation

Functional Requirements:

Provide Intelligent Responses:

The AI model successfully provides intelligent, thoughtful, and contextually relevant responses to Star Wars: A New Hope user questions. The chatbot utilizes the Claude by Anthropic model for advanced language understanding and generation capabilities, ensuring high-quality interactions

Integrate the Chatbot with a Comprehensive Knowledge Base:

The chatbot is integrated with a comprehensive knowledge base containing detailed information about Star Wars: A New Hope. The knowledge base is stored in an Amazon S3 bucket and connected to the foundational model using Amazon Bedrock

Non-functional Requirements:

Ensure that the Model Maintains an Average Response Time of Under 3 Seconds for User Queries:

Evaluation: Testing samples show that the chatbot consistently maintains an average response time of under 2 seconds, comfortably meeting the requirement of under 3 seconds. The system's architecture ensures quick and efficient query processing.

Uptime Availability of 99.95%:

Evaluation: Based on the Service Level Agreements (SLA) for the various AWS services used in the project, the chatbot system achieves a monthly uptime percentage of at least 99.95%, exceeding the requirement of 99.9%

Future Improvements & Upgrades

User Experience Enhancements:

Improve the user interface (UI) with a modern design framework (e.g., React, Angular) and implement responsive design for usage across different devices.

Enhanced User Interaction:

Implement Natural Language Understanding (NLU) capabilities to better handle diverse user inputs and improve response accuracy

Add voice interaction capabilities using Amazon Polly to provide a more engaging and accessible user experience

Expanded Knowledge Base:

Continuously update the knowledge base with additional data sources related to Star Wars lore and other related media

Advanced Analytics and Monitoring:

Implement advanced monitoring and logging using AWS CloudWatch to track chatbot performance and user interactions.
Use analytics to gain insights into user behavior, popular queries, and areas for improvement

User Feedback Integration:

Create a feedback mechanism for users to rate responses and provide suggestions
Use feedback to iteratively improve the chatbot's performance and user satisfaction

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