LLMExampleFunction

This functionality requires LLM access »

LLMExampleFunction[{in₁out₁,in₂out₂,…}]

creates an LLMFunction from few-shot examples.

LLMExampleFunction[{in₁,in₂,…}{out₁,out₂,…}]

generates the same result.

LLMExampleFunction[{header,training}]

prefaces the prompt with header.

includes the interpreter form to apply to the response.

Details and Options

An LLMExampleFunction can be used to generate text using a large language model (LLM) with a prompt dynamically generated from a list of examples.
LLMExampleFunction requires external service authentication, billing and internet connectivity.
LLMExampleFunction returns an LLMFunction.
LLMExampleFunction supports all options of LLMFunction:

InsertionFunction	TextString	function or format to apply before inserting expressions
CombinerFunction	StringJoin	function to apply to combine pieces within a prompt
Authentication	Automatic	explicit user ID and API key
LLMEvaluator	$LLMEvaluator	LLM configuration to use

LLMEvaluator can be set to an LLMConfiguration object or an association with any of the following keys:

	"MaxTokens"	maximum amount of tokens to generate
	"Model"	base model
	"PromptDelimiter"	string to insert between prompts
	"Prompts"	initial prompts
	"StopTokens"	tokens on which to stop generation
	"Temperature"	sampling temperature
	"ToolMethod"	method to use for tool calling
	"Tools"	list of LLMTool objects to make available
	"TopProbabilities"	sampling classes cutoff
	"TotalProbabilityCutoff"	sampling probability cutoff (nucleus sampling)

Valid forms of "Model" include:
name named model

{service,name} named model from service

<|"Service"service,"Name"name,"Task"task|> fully specified model
The generated text is sampled from a distribution. Details of the sampling can be specified using the following properties of LLMEvaluator:

"Temperature"t	Automatic	sample using a positive temperature t
"TopProbabilities"k	Automatic	sample only among the k highest-probability classes
"TotalProbabilityCutoff"p	Automatic	sample among the most probable choices with an accumulated probability of at least p (nucleus sampling)

	Automatic	choose the authentication scheme automatically
	Environment	check for a key in the environment variables
	SystemCredential	check for a key in the system keychain
	ServiceObject[…]	inherit the authentication from a service object
	assoc	provide an explicit key and user ID

With AuthenticationAutomatic, the function checks the variable ToUpperCase[service]<>"_API_KEY" in Environment and SystemCredential; otherwise, it uses ServiceConnect[service].
LLMExampleFunction uses machine learning. Its methods, training sets and biases included therein may change and yield varied results in different versions of the Wolfram Language.

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