function

Functions in BAML define the contract between your application and AI models, providing type-safe interfaces for AI operations.

Overview

A BAML function consists of:

• Input parameters with explicit types
• A return type specification
• An LLM client
• A prompt (as a block string)

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function FunctionName(param: Type) -> ReturnType {
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    client ModelName
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    prompt #"
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        Template content
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    "#
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}

Function Declaration

Syntax

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function name(parameters) -> return_type {
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    client llm_specification
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    prompt block_string_specification
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}

Parameters

• name: The function identifier (must start with a capital letter!)
• parameters: One or more typed parameters (e.g., text: string, data: CustomType)
• return_type: The type that the function guarantees to return (e.g., string | MyType)
• llm_specification: The LLM to use (e.g., "openai/gpt-4o-mini", GPT4Turbo, Claude2)
• block_string_specification: The prompt template using Jinja syntax

Type System

Functions leverage BAML’s strong type system, supporting:

Built-in Types

• string: Text data
• int: Integer numbers
• float: Decimal numbers
• bool: True/false values
• array: Denoted with [] suffix (e.g., string[])
• map: Key-value pairs (e.g., map<string, int>)
• literal: Specific values (e.g., "red" | "green" | "blue")
• See all

Custom Types

Custom types can be defined using class declarations:

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class CustomType {
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    field1 string
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    field2 int
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    nested NestedType
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}
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function ProcessCustomType(data: CustomType) -> ResultType {
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    // ...
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}

Prompt Templates

Jinja Syntax

BAML uses Jinja templating for dynamic prompt generation:

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prompt #"
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    Input data: {{ input_data }}
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    {% if condition %}
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        Conditional content
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    {% endif %}
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    {{ ctx.output_format }}
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"#

Special Variables

• ctx.output_format: Automatically generates format instructions based on return type
• ctx.client: Selected client and model name
• _.role: Define the role of the message chunk

Error Handling

Functions automatically handle common AI model errors and provide type validation:

• JSON parsing errors are automatically corrected
• Type mismatches are detected and reported
• Network and rate limit errors are propagated to the caller

Usage Examples

Basic Function

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function ExtractEmail(text: string) -> string {
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    client GPT4Turbo
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    prompt #"
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        Extract the email address from the following text:
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        {{ text }}
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        {{ ctx.output_format }}
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    "#
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}

Complex Types

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class Person {
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    name string
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    age int
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    contacts Contact[]
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}
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class Contact {
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    type "email" | "phone"
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    value string
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}
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function ParsePerson(data: string) -> Person {
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    client "openai/gpt-4o"
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    prompt #"
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        {{ ctx.output_format }}
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        {{ _.role('user') }}
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        {{ data }}
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    "#
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}

baml_client Integration

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from baml_client import b
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from baml_client.types import Person
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async def process() -> Person:
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    result = b.ParsePerson("John Doe, 30 years old...")
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    print(result.name)  # Type-safe access
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    return result