ToolFunction

Types for defining tools, functions, and their execution configurations.

Overview

Tool types define how LLMs can call external functions, including schemas, parameters, HTTP configurations, and retry logic.

ToolFunction

Tool function definition with schema and optional HTTP request configuration.

from adaline_api.models.tool_function import ToolFunction

Fields

str

required

Must be "function".

ToolFunctionDefinition

required

The function definition containing the schema. See ToolFunctionDefinition.

FunctionRequestHttp | None

Optional HTTP request configuration for executing the function via REST API. See FunctionRequestHttp.

Example

from adaline_api.models.tool_function import ToolFunction
from adaline_api.models.tool_function_definition import ToolFunctionDefinition
from adaline_api.models.function_schema import FunctionSchema

tool = ToolFunction(
    type="function",
    definition=ToolFunctionDefinition(
        var_schema=FunctionSchema(
            name="get_weather",
            description="Get current weather",
            parameters={
                "type": "object",
                "properties": {
                    "city": {"type": "string"}
                },
                "required": ["city"]
            }
        )
    )
)

ToolFunctionDefinition

See the dedicated ToolFunctionDefinition page for full documentation. Wrapper for a function schema within a tool definition.

from adaline_api.models.tool_function_definition import ToolFunctionDefinition

Fields

FunctionSchema

required

The function schema. Aliased as schema in JSON — use var_schema in Python to avoid collision with the Python reserved word.

Example

from adaline_api.models.tool_function_definition import ToolFunctionDefinition
from adaline_api.models.function_schema import FunctionSchema

definition = ToolFunctionDefinition(
    var_schema=FunctionSchema(
        name="search_database",
        description="Search internal database",
        parameters={
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"},
                "limit": {"type": "number", "default": 10}
            },
            "required": ["query"]
        }
    )
)

FunctionSchema

See the dedicated FunctionSchema page for full documentation. Function/tool schema definition for LLM function calling.

from adaline_api.models.function_schema import FunctionSchema

Fields

str

required

Function name. Must match ^[a-zA-Z0-9_]{1,64}$ (alphanumeric and underscores, max 64 chars).

str

required

Description of what the function does. Max 4096 characters.

dict[str, Any]

required

JSON Schema object describing the function parameters.

bool | None

Whether to enforce strict schema validation. When True, the LLM must conform exactly to the parameter schema.

Example

from adaline_api.models.function_schema import FunctionSchema

weather_function = FunctionSchema(
    name="get_weather",
    description="Get current weather for a city",
    parameters={
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "City name"
            },
            "units": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "default": "celsius",
                "description": "Temperature units"
            }
        },
        "required": ["city"]
    },
    strict=True
)

JSON:

{
  "name": "get_weather",
  "description": "Get weather for a city",
  "parameters": {
    "type": "object",
    "properties": {
      "city": { "type": "string" }
    },
    "required": ["city"]
  }
}

FunctionRequestHttp

HTTP request configuration for executing functions via REST API.

from adaline_api.models.function_request_http import FunctionRequestHttp

Fields

str

required

Must be "http".

str

required

HTTP method. One of: "get", "post".

str

required

The request URL. Supports {{variable}} template syntax.

dict[str, str] | None

Optional HTTP headers.

dict[str, str] | None

Optional query parameters.

dict[str, Any] | None

Optional request body.

str | None

Optional proxy URL.

dict[str, str] | None

Optional proxy headers.

FunctionRequestRetry | None

Optional retry configuration. See FunctionRequestRetry.

Example

from adaline_api.models.function_request_http import FunctionRequestHttp
from adaline_api.models.function_request_retry import FunctionRequestRetry

http_request = FunctionRequestHttp(
    type="http",
    method="post",
    url="https://api.weather.com/v1/current",
    headers={
        "Authorization": "Bearer {{API_KEY}}",
        "Content-Type": "application/json"
    },
    query={"units": "metric"},
    body={"location": "{{city}}"},
    retry=FunctionRequestRetry(
        max_attempts=3,
        initial_delay=1000,
        exponential_factor=2
    )
)

FunctionRequestRetry

Retry configuration with exponential backoff.

from adaline_api.models.function_request_retry import FunctionRequestRetry

Fields

int

required

Maximum number of retry attempts. Minimum: 1.

int

required

Initial delay in milliseconds. Minimum: 1.

int

required

Multiplier for each subsequent retry. Minimum: 1.

Example

from adaline_api.models.function_request_retry import FunctionRequestRetry

retry = FunctionRequestRetry(
    max_attempts=3,
    initial_delay=1000,       # Start with 1 second
    exponential_factor=2      # Double each retry: 1s, 2s, 4s
)

Complete Example

from adaline.main import Adaline
from adaline_api.models.tool_function import ToolFunction
from adaline_api.models.tool_function_definition import ToolFunctionDefinition
from adaline_api.models.function_schema import FunctionSchema
from adaline_api.models.function_request_http import FunctionRequestHttp
from adaline_api.models.function_request_retry import FunctionRequestRetry

# Define retry configuration
retry = FunctionRequestRetry(
    max_attempts=3,
    initial_delay=1000,
    exponential_factor=2
)

# Define HTTP request
request = FunctionRequestHttp(
    type="http",
    method="post",
    url="https://api.weather.com/current",
    headers={
        "Authorization": "Bearer sk_abc123",
        "Content-Type": "application/json"
    },
    body={"city": "{{city}}", "units": "{{units}}"},
    retry=retry
)

# Define function schema
schema = FunctionSchema(
    name="get_weather",
    description="Get current weather for a city",
    parameters={
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "City name"
            },
            "units": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "default": "celsius"
            }
        },
        "required": ["city"]
    }
)

# Complete tool definition
tool = ToolFunction(
    type="function",
    definition=ToolFunctionDefinition(var_schema=schema),
    request=request
)

Using Tools from a Deployment

adaline = Adaline()

deployment = await adaline.get_latest_deployment(
    prompt_id="prompt_with_tools",
    deployment_environment_id="environment_abc123"
)

print(f"Tools available: {len(deployment.prompt.tools)}")
for t in deployment.prompt.tools:
    fn = t.definition.var_schema
    print(f"  - {fn.name}: {fn.description}")

Get started

API

SDKs

Proxy

ToolFunction

ToolFunction

Overview

ToolFunction

Fields

Example

ToolFunctionDefinition

Fields

Example

FunctionSchema

Fields

Example

FunctionRequestHttp

Fields

Example

FunctionRequestRetry

Fields

Example

Complete Example

Using Tools from a Deployment

​ToolFunction

​Overview

​ToolFunction

​Fields

​Example

​ToolFunctionDefinition

​Fields

​Example

​FunctionSchema

​Fields

​Example

​FunctionRequestHttp

​Fields

​Example

​FunctionRequestRetry

​Fields

​Example

​Complete Example

​Using Tools from a Deployment

ToolFunction

Overview

ToolFunction

Fields

Example

ToolFunctionDefinition

Fields

Example

FunctionSchema

Fields

Example

FunctionRequestHttp

Fields

Example

FunctionRequestRetry

Fields

Example

Complete Example

Using Tools from a Deployment