Collection

Key concepts

Within each Project, Collections act as containers for related data. If you're familiar with databases, a Collection works like a Table in SQL. If you think in terms of file systems, it's similar to a Folder in your operating system.

Every file or record you upload into a Collection becomes an Asset—a single unit of data ready for processing, analysis, and knowledge extraction.

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Collection Modes

Each Collection operates in one of two modes, depending on how you want to use the data:

Mode	Purpose	Knowledge Graph
Table	Structured data storage with custom columns	Not supported
Agent	Structured storage + builds shareable knowledge	Supported

Table Mode

Data is stored directly in a SQL database. You define as many columns as needed to capture the specific information you care about. Best for structured records where you need fast queries but don't require AI-driven knowledge sharing.

Agent Mode

Includes everything Table mode offers, plus one critical addition: the Collection builds a private knowledge graph. This knowledge can be shared with other Collections, allowing AI agents to connect insights across your organization.

Use Agent mode when your data contains domain expertise that other teams or processes should access.

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Columns

Columns define what information you want to capture from each Asset in your Collection. When raw data enters DATALOG, columns determine how that data gets structured and what insights get extracted.

There are 7 column types divided into two categories:

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Primitive Types (Extracted from Raw Data)

These columns capture information directly from your uploaded files and records.

Type	Description	Example
Date	Timestamps, deadlines, effective dates	Invoice date, contract expiry
Number	Quantities, amounts, measurements	Total amount, quantity ordered
Text	Names, descriptions, categories	Vendor name, product description
Table	Nested tabular data within a document	Line items in an invoice
JSON	Structured data objects	API responses, configuration data

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Advanced Types (System-Enhanced)

These columns go beyond extraction—they add intelligence and automation to your data.

Type	Description	Example
Static	Fixed values set manually by users or via API	Status flags, manual classifications, department codes
Agent	Delegates another AI Agent to review each Asset and populate the final result to the column.	Risk assessment, sentiment analysis, compliance check

Agent columns are powerful. Instead of manually reviewing thousands of documents, you assign a specialized Agent to analyze each Asset and fill in the column automatically. For example, an Agent could read every contract and populate a "Risk Level" column with High, Medium, or Low based on your custom criteria.

Agent Knowledge (RAG)

Assume your organization has a very high frequency update based on Product and Merchant policy.