Schemas

Every LLM call in DocETL has an output schema specifying the structure and types of its output. DocETL enforces schemas via structured outputs or the tool API (see How We Enforce Schemas).

Schema Simplicity

We've observed that the more complex the output schema is, the worse the quality of the output tends to be. Keep your schemas as simple as possible for better results.

Defining Schemas

Schemas are defined in the output section of an operator. They support various data types:

Type	Aliases	Description
string	str, text, varchar	For text data
integer	int	For whole numbers
number	float, decimal	For decimal numbers
boolean	bool	For true/false values
enum	-	For a set of possible values
list	-	For arrays or sequences of items (must specify element type)
Objects	-	Using notation {field: type}

Filter Operation Schemas

Filter operation schemas must have a boolean output field, which determines whether each item is kept.

Examples

Simple Schema

YAMLPython

output:
  schema:
    summary: string
    sentiment: string
    include_item: boolean # For filter operations

output={
    "schema": {
        "summary": "string",
        "sentiment": "string",
        "include_item": "boolean",  # For filter operations
    }
}

Complex Schema

YAMLPython

output:
  schema:
    insights: "list[{insight: string, confidence: number}]"
    metadata: "{timestamp: string, source: string}"

output={
    "schema": {
        "insights": "list[{insight: string, confidence: number}]",
        "metadata": "{timestamp: string, source: string}",
    }
}

Lists and Objects

Lists in schemas must specify their element type:

• list[string]: A list of strings
• list[int]: A list of integers
• list[{name: string, age: integer}]: A list of objects

Objects are defined using curly braces and must have typed fields:

• {name: string, age: integer, is_active: boolean}

Complex List Example

YAMLPython

output:
  schema:
    users: "list[{name: string, age: integer, hobbies: list[string]}]"

Make sure that you put the type in quotation marks, if it references an object type (i.e., has curly braces)! Otherwise the yaml won't compile!

output={
    "schema": {
        "users": "list[{name: string, age: integer, hobbies: list[string]}]"
    }
}

Enum Types

Enum values are validated against the declared set of possible values:

YAMLPython

output:
  schema:
    sentiment: "enum[positive, negative, neutral]"

output={"schema": {"sentiment": "enum[positive, negative, neutral]"}}

Lists of enums also work:

YAMLPython

output:
  schema:
    possible_sentiments: "list[enum[positive, negative, neutral]]"

output={"schema": {"possible_sentiments": "list[enum[positive, negative, neutral]]"}}

How We Enforce Schemas

DocETL supports two output modes that determine how the LLM generates structured outputs:

Tools Mode (Default)

Uses the OpenAI tools/function calling API to enforce schema structure.

YAMLPython

output:
  schema:
    summary: string
    sentiment: string
  mode: "tools"  # Optional - this is the default

output={
    "schema": {
        "summary": "string",
        "sentiment": "string",
    },
    "mode": "tools",  # Optional - this is the default
}

Structured Output Mode

Uses LiteLLM's structured output feature with JSON schema validation. This mode can provide more reliable schema adherence for complex outputs.

YAMLPython

output:
  schema:
    insights: "list[{insight: string, confidence: number}]"
  mode: "structured_output"

output={
    "schema": {
        "insights": "list[{insight: string, confidence: number}]",
    },
    "mode": "structured_output",
}

When to Use Structured Output Mode

Consider using structured_output mode when:

• You have complex nested schemas with lists and objects
• You need more consistent schema adherence
• You're experiencing schema validation issues with tools mode

Mode Configuration

Set mode in the output section of any operation:

YAMLPython

operations:
  - name: analyze_text
    type: map
    prompt: "Analyze the following text..."
    output:
      schema:
        topics: "list[{topic: string, relevance: number}]"
      mode: "structured_output"  # or "tools"
    model: gpt-4o-mini

pipeline = pipeline.map(
    name="analyze_text",
    prompt="Analyze the following text...",
    output={
        "schema": {"topics": "list[{topic: string, relevance: number}]"},
        "mode": "structured_output",  # or "tools"
    },
    model="gpt-4o-mini",
)

Best Practices

1. Keep output fields simple and use string types whenever possible.
2. Only use structured fields (like lists and objects) when necessary for downstream analysis or reduce operations.
3. If you need to reference structured fields in downstream operations, consider breaking complex structures into multiple simpler operations.

Breaking Down Complex Schemas

Instead of:

YAMLPython

output:
  schema:
    summary: string
    key_points: "list[{point: string, sentiment: string}]"

output={
    "schema": {
        "summary": "string",
        "key_points": "list[{point: string, sentiment: string}]",
    }
}

Consider:

YAMLPython

output:
  schema:
    summary: string
    key_points: "string"

output={
    "schema": {
        "summary": "string",
        "key_points": "string",
    }
}

Where in the prompt you can say something like: In your key points, please include the sentiment of each point.

The only reason to use the complex schema is if you need to do an operation at the point level, like resolve them and reduce on them.