LlamaExtract Core Concepts

LlamaExtract is designed to be a flexible and scalable extraction platform. At the core of the platform are the following concepts:

• Extraction Agents: Reusable extractors configured with a specific schema and extraction settings.
• Data Schema: Structured definition for the data you want to extract in JSON/Pydantic format. See detailed explanation below.
• Extraction Target: Defines the scope of extraction and how your schema is applied to documents. See detailed explanation below.
• Extraction Jobs: Asynchronous extraction tasks that involve running an extraction agent on a set of files.
• Extraction Runs: The results of an extraction job including the extracted data and other metadata.

Data Schema

The Data Schema defines the structure of the data you want to extract from your documents. It is a JSON Schema that specifies the fields, types, and descriptions for the information you need.

While the schema is fundamentally a JSON Schema (supporting a subset of the full JSON Schema specification), our Python SDK allows you to use Pydantic models for a more Pythonic experience with type validation and IDE support.

Learn more:

• Schema Design and Restrictions - Best practices, patterns, and limitations for creating effective schemas

Extraction Target

The Extraction Target determines how your schema is applied to the document and what granularity of results you receive. This is an important configuration option as it fundamentally changes how data is extracted.

	PER_DOC (Default)	PER_PAGE	PER_TABLE_ROW
When to Use	Default mode for extracting data from the full document based on your JSON schema	Each page independently contains information about a different entity (e.g., each page contains financial information about a different portfolio company)	Document contains an ordered list of entities (in tables, bulleted/numbered lists, or separated by headers) and you want to extract the same information for each entity
How It Works	Schema is applied to the entire document as a single unit	Schema is applied independently to each page of the document	Schema is applied to each identified entity in the document. LlamaExtract automatically detects formatting patterns that distinguish entities (table rows, list items, section headers, etc.)
Returns	A single JSON object matching your schema	An array of JSON objects, one per page, each matching your schema	An array of JSON objects, one per entity/row, each matching your schema
Example Use Cases	Extracting summary information from a contract, annual report, or research paper	Multi-page forms where each page represents a different entity, or a document with one record per page	Invoice line items (each row is a product/service) Employee lists or directories Purchase orders with multiple items Any document with repeating structured entities
Important Notes	-	Your schema should describe a single entity/page, not a list. Don't use extracted_result: list[template], instead provide the template directly that will be applied at the page level	Your schema should describe a single entity, not a list. Don't use extracted_result: list[template], instead provide the template directly that will be applied at the entity level The document must have some formatting or structure that distinguishes the different entities (table formatting, bullets, numbering, headers, etc.) Entities should appear in an ordered manner in the document