Defines a "function" that can be provided to an LLM for the LLM to optionally execute and pass argument data to.

A function is defined using a schema.

• name - The name of the function given to the LLM.
• description - A description of the function provided to the LLM. This should describe what the function is used for or what it returns. This information is used by the LLM to decide which function to call and for what purpose.
• parameters - A list of Function.FunctionParam structs that are converted to a JSONSchema format. (Use in place of parameters_schema)
• parameters_schema - A JSONSchema structure that describes the required data structure format for how arguments are passed to the function. (Use if greater control or unsupported features are needed.)
• function - An Elixir function to execute when an LLM requests to execute the function. The function should return {:ok, "text for LLM"}, {:ok, "text for LLM", processed_content}, {:error, "and text explanation of the error"} or just plain "text response", which is returned to the LLM.
• async - Boolean value that flags if this can function can be executed asynchronously, potentially concurrently with other calls to the same function. Defaults to true.

When passing arguments from an LLM to a function, they go through a single map argument. This allows for multiple keys or named parameters.

Example

This example defines a function that an LLM can execute for performing basic math calculations. NOTE: This is a partial implementation of the LangChain.Tools.Calculator.

Function.new(%{
  name: "calculator",
  description: "Perform basic math calculations",
  parameters_schema: %{
    type: "object",
    properties: %{
      expression: %{type: "string", description: "A simple mathematical expression."}
    },
    required: ["expression"]
  },
  function:
    fn(%{"expression" => expr} = _args, _context) ->
      {:ok, "42?"}
    end)
})copy

The function attribute is an Elixir function that can be executed when the function is "called" by the LLM.

The args argument is the JSON data passed by the LLM after being parsed to a map.

The context argument is passed through as the context on a LangChain.Chains.LLMChain. This is whatever context data is needed for the function to do it's work.

Context examples could be data like user_id, account_id, account struct, billing level, etc.

Function Parameters

The parameters field is a list of LangChain.FunctionParam structs. This is a convenience for defining the parameters to the function. If it does not work for more complex use-cases, then use the parameters_schema to declare it as needed.

The parameters_schema is an Elixir map that follows a JSONSchema structure. It is used to define the required data structure format for receiving data to the function from the LLM.

NOTE: Only use parameters or parameters_schema, not both.

Expanded Parameter Examples

Function with no arguments:

alias LangChain.Function

Function.new!(%{name: "get_current_user_info"})copy

Function that takes a simple required argument:

alias LangChain.FunctionParam

Function.new!(%{name: "set_user_name", parameters: [
  FunctionParam.new!(%{name: "user_name", type: :string, required: true})
]})copy

Function that takes an array of strings:

Function.new!(%{name: "set_tags", parameters: [
  FunctionParam.new!(%{name: "tags", type: :array, item_type: "string"})
]})copy

Function that takes two arguments and one is an object/map:

Function.new!(%{name: "update_preferences", parameters: [
  FunctionParam.new!(%{name: "unique_code", type: :string, required: true})
  FunctionParam.new!(%{name: "data", type: :object, object_properties: [
    FunctionParam.new!(%{name: "auto_complete_email", type: :boolean}),
    FunctionParam.new!(%{name: "items_per_page", type: :integer}),
  ]})
]})copy

The LangChain.FunctionParam is nestable allowing for arrays of object and objects with nested objects.

Example that also stores the Elixir result

Sometimes we want to process a ToolCall from the LLM and keep the processed Elixir data for ourselves. This is particularly useful when using an LLM to perform structured data extraction. Our Elixir function may even process that data into a newly created Ecto Schema database entry. The result of the ToolCall that goes back to the LLM must be in a string form. That typically means returning a JSON string of the result data.

To make it easier to process the data, return a string response to the LLM, but keep the original Elixir data as well, our Elixir function can return a 3-tuple result.

Function.new!(%{name: "create_invoice",
  parameters: [
    FunctionParam.new!(%{name: "vendor_name", type: :string, required: true})
    FunctionParam.new!(%{name: "total_amount", type: :string, required: true})
  ],
  function: &execute_create_invoice/2
})

# ...

def execute_create_invoice(args, %{account_id: account_id} = _context) do
  case MyApp.Invoices.create_invoice(account_id, args) do
    {:ok, invoice} ->
      {:ok, "SUCCESS", invoice}

    {:error, changeset} ->
      {:error, "ERROR: " <> LangChain.Utils.changeset_error_to_string(changeset)}
  end
endcopy

In this example, the LangChain.Function is tied to the MyApp.Invoices.create_invoice/2 function in our application.

The Elixir function returns a 3-tuple result. The "SUCCESS" is returned to the LLM. In our scenario, we don't care to return a JSON version of the invoice. The important part is we return the actual %MyApp.Invoices.Invoice{} struct in the tuple. This is stored on the LangChain.ToolResult's processed_content field.

This is really helpful when all we want is the final, fully processed Elixir result. This pairs well with the LLMChain.run(chain, mode: :until_success). This is when we want the LLM to perform some data extraction and it should be re-run until it succeeds and we have our final, processed result in the ToolResult.

Note: The LLM may issue one or more ToolCalls in a single assistant message. Each Elixir function's ToolResult may contain a processed_content.