AI Table
Overview
This function leverages an AI model (compatible with OpenAI’s API structure and supporting JSON output format) to generate structured data in the form of a table (a 2D list). It takes a prompt describing the desired table content and can optionally use header information and source data to guide the generation process.
Usage
Instructs an AI model to generate a table based on a prompt, optional header, and optional source data, returning the result as a 2D list.
=AI_TABLE(prompt, [header], [source], [temperature], [model], [max_tokens])Arguments:
| Argument | Type | Description | Default |
|---|---|---|---|
prompt | string | The instruction describing the table the AI should create. | |
header | 2D list | Optional: A single row list defining the exact column headers for the table. If this is not specified, the model will generate its own headers. | None |
source | 2D list | Optional: Source data provided to the AI to use as a basis for generating the table content. This is useful for getting the model to summarize information in a table. | None |
temperature | float | Optional: Controls the randomness/creativity of the response (0.0 to 2.0). Lower values are more deterministic. | 0.0 |
model | string | Optional: The specific AI model ID to use (must support JSON mode, e.g., ‘mistral-small-2501’). | mistral-small-2501 |
max_tokens | int | Optional: Maximum number of tokens for the generated table content. | 1500 |
Returns:
| Return Value | Type | Description |
|---|---|---|
| Table Data | 2D list | A list of lists representing the generated table. The first row typically contains headers (unless provided via header argument). Returns [[\"Error: ...\"]] on failure. |
Examples
1. Basic Table Generation
Generate a simple table listing smartphone features.
=AI_TABLE("Create a table listing the features of different smartphones including brand, model, camera quality, battery life.")Sample Output:
| Brand | Model | Camera Quality | Battery Life |
|---|---|---|---|
| Apple | iPhone 15 | Excellent | Good |
| Samsung | Galaxy S24 | Excellent | Very Good |
| Pixel 8 | Very Good | Good | |
| OnePlus | 12 | Good | Excellent |
This prompt generates a table with columns for brand, model, camera quality, and battery life for various smartphones.
2. Using a Specific Header
Generate a table of tourist destinations using a predefined header.
Sample Header Data (Range A1:D1):
| Country | Popular Attractions | Best Time to Visit | Average Cost |
|---|
=AI_TABLE("Generate a table of top 5 tourist destinations.", A1:D1)Sample Output:
| Country | Popular Attractions | Best Time to Visit | Average Cost |
|---|---|---|---|
| France | Eiffel Tower, Louvre | Spring, Fall | $150/day |
| Japan | Mt. Fuji, Temples | Spring, Fall | $120/day |
| Italy | Colosseum, Canals | Spring, Summer | $140/day |
| USA | Grand Canyon, NYC | Spring, Fall | $160/day |
| Thailand | Beaches, Temples | Winter | $80/day |
This uses the header data provided in range A1:D1 to structure the output.
3. Using Source Data
Generate a table summarizing product sales based on provided source data.
Sample Input Data (Range A1:C8):
| Product | Category | Sales Amount |
|---|---|---|
| Laptop | Tech | 1200 |
| Mouse | Tech | 25 |
| Keyboard | Tech | 75 |
| T-Shirt | Apparel | 20 |
| Jeans | Apparel | 50 |
| Laptop | Tech | 1350 |
| Hoodie | Apparel | 45 |
=AI_TABLE("Summarize the sales data by product category.", , A1:C8)Sample Output:
| Category | Total Sales | Number of Items |
|---|---|---|
| Tech | 2650 | 4 |
| Apparel | 115 | 3 |
Assuming A1:C8 contains raw sales data, this generates a summary table based on that input.