> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rovax.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Tables — Overview

> Understand the data model for tables (datagrids) in Timely.ai: core concepts, when to use tables versus Knowledge Bases, and typical use cases.

Tables — referred to internally as **datagrids** — are structured data stores that AI agents can query and populate during conversations. Unlike a Knowledge Base, which stores free text for semantic search, a table stores data in columns and rows with defined types, like a lightweight database directly accessible by the agent.

## Core Concepts

| Concept                    | Description                                                                                                                                                                                                                  |
| -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Table (Datagrid)**       | A data structure with named and typed columns, organized into rows. Each table belongs to a workspace and can be connected to one or more agents.                                                                            |
| **Column**                 | Defines a field in the table. Each column has a name (up to 50 characters), description (3 to 250 characters), data type (`String`, `Number`, `Boolean`), and optional settings such as semantic search and required field.  |
| **Row**                    | A record in the table. Each row contains values for each defined column. Rows can be inserted manually through the interface, imported via CSV, or created by the agent during conversations.                                |
| **Datagrid Tool**          | A capability made available to the agent for interacting with the table. The available actions are: `semantic_search`, `similarity_search`, `insert_row`, and `update_row`. Each action is activated individually per table. |
| **Column Semantic Search** | When enabled on a `String` column, values are vectorized using the `text-embedding-3-small` model (1,536 dimensions) and indexed for similarity search — the agent finds rows by meaning, not just exact match.              |

## Why Tables Matter

1. **Real-time data without external integration**: the agent accesses tables directly during a conversation. No webhooks, APIs, or integrations need to be configured for the agent to query a product catalog or register a lead.
2. **Separation of structured and unstructured knowledge**: Knowledge Bases are ideal for free text — manuals, policies, FAQs. Tables are ideal for data with a defined structure — catalogs, schedules, inventories, records. Using each resource for what it was designed for improves agent accuracy.
3. **Writing during the conversation**: unlike Knowledge Bases (read-only for the agent), tables allow the agent to insert and update data in real time — the agent collects information from the user and records it in a structured way in the table, without human intervention.
4. **Updates independent of the prompt**: data changes in the table without needing to edit the agent prompt. A price updated in the table is immediately available for the agent on the next query.

Typical use cases where tables outperform other approaches:

* Product catalog with price, availability, and description
* Professional or event schedule with time slots and vacancies
* Lead records collected during conversations
* Inventory with real-time quantity tracking