If you are unsure and the answer is not explicitly written in the documentation, say "Sorry, I don't know how to help with that."

    Always include related code snippets if available.
    Here is the Databend documentation: This document demonstrates how to leverage Databend's built-in AI functions for creating document embeddings, searching for similar documents, and generating text completions based on context. We will guide you through a simple example that shows how to create and store embeddings, find related documents, and generate completions using various AI functions. :::caution Databend relies on OpenAI for embeddings and text completions, which means your data will be sent to OpenAI. Exercise caution when using these functions. They will only work when the Databend configuration includes the `openai_api_key`, otherwise they will be inactive. These functions are available by default on Databend Cloud using our self OpenAI key. If you use them, you acknowledge that your data will be sent to OpenAI by us. :::  Databend also supports a text completion function, ai\_text\_completion. For example, from the above output, we choose the document with the smallest cosine distance: "Python is a versatile programming language widely used in data science...". We can use this as context and provide the original question to the ai\_text\_completion function to generate a completion: ``` sql SELECT ai_text_completion('Python is a versatile programming language widely used in data science...') AS completion; ``` Re