LLMExampleFunction

This functionality requires an external account »

LLMExampleFunction[{in1out1,in2out2,}]

creates an LLMFunction from few-shot examples.

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

generates the same result.

LLMExampleFunction[{header,training}]

prefaces the prompt with header.

LLMExampleFunction[prompting,form]

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:
  • InsertionFunctionTextStringfunction or format to apply before inserting expressions
    CombinerFunctionStringJoinfunction to apply to combine pieces within a prompt
    AuthenticationAutomaticexplicit user ID and API key
    LLMEvaluator$LLMEvaluatorLLM 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:
  • namenamed 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"tAutomaticsample using a positive temperature t
    "TopProbabilities"kAutomaticsample only among the k highest-probability classes
    "TotalProbabilityCutoff"pAutomaticsample among the most probable choices with an accumulated probability of at least p (nucleus sampling)
  • Possible values for Authentication are:
  • Automaticchoose the authentication scheme automatically
    Environmentcheck for a key in the environment variables
    SystemCredentialcheck for a key in the system keychain
    ServiceObject[]inherit the authentication from a service object
    assocprovide an explicit key and user ID
  • With AuthenticationAutomatic, 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.

Examples

open allclose all

Basic Examples  (2)

Create an LLMFunction from a small training set:

Evaluate the function on an input:

Clarify the task and process the output string using an interpreter type:

Evaluate the function on an input:

Scope  (3)

Specify examples as a list of rules:

Evaluate the function on an input:

Prepend a prompt to clarify the task:

Evaluate the function on an input:

Specify an interpreter type to process the output:

Evaluate the function on an input:

Possible Issues  (1)

Some language models are more verbose than others:

They may also provide answers in an arbitrary programming language:

Wolfram Research (2023), LLMExampleFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/LLMExampleFunction.html.

Text

Wolfram Research (2023), LLMExampleFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/LLMExampleFunction.html.

CMS

Wolfram Language. 2023. "LLMExampleFunction." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/LLMExampleFunction.html.

APA

Wolfram Language. (2023). LLMExampleFunction. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/LLMExampleFunction.html

BibTeX

@misc{reference.wolfram_2024_llmexamplefunction, author="Wolfram Research", title="{LLMExampleFunction}", year="2023", howpublished="\url{https://reference.wolfram.com/language/ref/LLMExampleFunction.html}", note=[Accessed: 08-December-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_llmexamplefunction, organization={Wolfram Research}, title={LLMExampleFunction}, year={2023}, url={https://reference.wolfram.com/language/ref/LLMExampleFunction.html}, note=[Accessed: 08-December-2024 ]}