Overview

A Table is a structured dataset made of rows and columns, where each column can run AI, code, or web-based transformations at scale.

Each Table can have its own structure, transformations, and data, allowing you to build complete workflows or break complex processes into multiple connected tables.

Tables are edited and operated through a spreadsheet-like interface, making it easy to configure workflows, review results, and iterate.

Use Tables to:

Extract structured data from documents (invoices, contracts, emails...)
Categorize and tag content automatically
Generate text, summaries, or analyses
Transform data with Python code
Enrich data with web search

Table structure

Tables are made of 3 core elements:

Columns

Columns define the structure of your data. Each column has:

A name identifying the data it holds
A type defining how values are stored and displayed (Text, Number, File, Date, etc.)
An optional generation logic used to determine how values are generated (User input, AI, Code, Web Search)

Rows

Rows represent individual records in your AI Table. Each row contains all the relevant data for a single item — for example, one invoice, one email, or one product.

Cells

Cells are the individual values at the intersection of a column and a row. A cell can contain user-provided input or display the output of a transformation.

Column types

Column types define how data is stored and displayed. Choose the type that matches your data.

Type	Description	Example use
Text	Free-form text content	Extracted descriptions, AI-generated summaries
Number	Numeric values	Amounts, quantities, scores
File	PDF documents	Source documents for extraction
Single Select	One option from a predefined list	Category, status
Multiple Select	Multiple options from a predefined list	Tags, labels
JSON	Structured data in JSON format	Complex nested extractions
Collection	Extracts multiple rows from a single document	Line items, list of entities
Match	Links data to an existing dataset	Supplier matching, product lookup

Each column type has dedicated documentation covering configuration and best practices. See Columns for details.

Select the best column type for your workflow

Generating Table values: the power of Tools

AI Tables define what data exists and how it's structured. To generate values, you can either do it manually or attach specific transformation reasoning to columns.

This reasoning is handled by Tools, which run when you generate values. You can configure a tool for any column (except File columns).

At a high level:

Tools operate at the column level
They run row by row
They can read input from other columns
They write results into the column they're attached to

Phacet supports different tools depending on your needs:

AI Tool: Uses AI to generate values based on a prompt. The AI can read other columns as context.
Python Tool: Executes custom Python code to transform data. Use this for deterministic logic, calculations, or data manipulation.
Web Search Tool: Use AI in combination with external web search to enrich results.

Each tool has its own configuration, behavior, and best practices. See Tools for details.

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You can chain tools across columns: for example, extract text with AI, then process it with Python, then enrich it with Web Search.

Key concepts

Concept	Description
References	Use `@column_name` to pass data between columns	→
Generate & Run	Trigger tools at cell, row, or column level	→
Import & Export	Get data in and out of your tables	→
Sessions	Organize rows into batches for recurring workflows	→