This Python application integrates a question-answering system using the Llama Index with Google Docs as the data source. It leverages OpenAI's GPT models for generating responses and ChainLit for creating interactive chat applications.

The application defines a function load_context() that either loads a pre-existing index from storage or creates a new one by reading documents from Google Docs. It uses the LLMPredictor with the ChatOpenAI model for predictions and a PromptHelper for managing the context of the interactions.

Upon starting a chat session, the application initializes the query engine and sets it in the user session. When a user sends a message, the application uses the query engine to generate a response based on the content of the Google Docs and streams the response back to the user.

1. Set your OpenAI API key in your environment variables.
2. Define the Google Docs IDs you want to index in the load_context() function.
3. Run the application to start interacting with the chatbot.

• load_context(): Loads the index from storage or creates a new one if storage is not found. It initializes necessary components like LLMPredictor, PromptHelper, and ServiceContext.
• start(): An asynchronous function that is triggered when a chat starts. It loads the context and sets up the query engine.
• main(message: cl.Message): The main asynchronous function that handles incoming messages and uses the query engine to generate and send responses.