Summary
Highlights
The video outlines the chatbot's architecture, which includes classifying customer input (refund, product query, complaint), applying guardrail checks, generating JSON-formatted responses from a Large Language Model (LLM), and then a final guardrail check before presenting the response to the customer. It begins by demonstrating how to install OpenAI and check Python and OpenAI versions, then sets up a basic chatbot with a system prompt defining its role as a polite e-commerce customer support assistant.
The next step involves adding intent classification to the chatbot. A function is created to classify user intent into 'refund request', 'product query', or 'complaint'. The video tests this function with various user inputs, demonstrating its accuracy in categorizing phrases like 'I want to return my product' (refund), 'how much is the radio' (product query), and 'the product came back broken' (complaint). The classified intent then dictates a pre-defined bot response.
The tutorial then enhances the chatbot to provide responses in a structured JSON format. The system prompt is updated to explicitly request JSON output, specifying fields like 'intent', 'message', and 'action required'. This ensures machine-readable and consistent responses. The video demonstrates how to parse this JSON string into a Python dictionary, allowing easy access to the structured information.
A crucial security feature, guardrails, is added to filter out inappropriate content. An 'input guardrail' checks user input against a list of blocked words (e.g., 'hack', 'illegal', 'fraud'). If a blocked word is detected, the bot refuses to assist. An 'output guardrail' is also implemented to check the LLM's response for similar inappropriate terms, preventing the bot from providing harmful information. This makes the chatbot safer and more reliable.
The final section focuses on deploying the chatbot as a web application using Streamlit. This involves setting up the Streamlit environment, securely handling API keys using an environment file, and integrating all previously developed functionalities (intent classification, JSON responses, and guardrails) into a user-friendly graphical interface. The video walks through creating the `app.py` file and running the Streamlit application, showcasing the interactive chatbot and its real-time responses and guardrail activations.