ChatSubmit

ChatSubmit[chat,prompt]

submits prompt to be appended with its follow-ups to the ChatObject chat asynchronously.

更多信息

  • ChatSubmit is used to continue the conversation in a ChatObject asynchronously.
  • ChatSubmit requires external service authentication, billing and internet connectivity.
  • Possible values for prompt include:
  • "text"static text
    LLMPrompt["name"]a repository prompt
    StringTemplate[…]templated text
    TemplateObject[…]template for creating a prompt
    Image[…]an image
    {prompt1,…}a list of prompts
  • Prompt created with TemplateObject can contain text and images. Not every LLM supports image input.
  • ChatSubmit returns a TaskObject[…].
  • The following options can be specified:
  • AuthenticationInheritedexplicit user ID and API key
    HandlerFunctionshow to handle generated events
    HandlerFunctionsKeysAutomaticparameters to supply to handler functions
    LLMEvaluatorInheritedLLM configuration to use
  • During the asynchronous execution of ChatSubmit, various events can be generated.
  • Events triggered by the LLM:
  • "ContentChunkReceived"incremental message content received
    "StoppingReasonReceived"stopping reason for the generation received
    "MetadataReceived"other metadata received
    "ToolRequestReceived"LLMToolRequest[…] received
    "UsageInformationReceived"incremental usage information received
  • Events triggered by local processing:
  • "ChatObjectGenerated"the final ChatObject[…] generated
    "ToolResponseGenerated"an LLMToolResponse[…] generated
  • Events triggered by the task framework:
  • "FailureOccurred"failure generated during the computation
    "TaskFinished"task completely finished
    "TaskRemoved"task being removed
    "TaskStarted"task started
    "TaskStatusChanged"task status changed
  • HandlerFunctionsf uses f for all the events.
  • With the specification HandlerFunctions-><|…,"eventi"->fi,…|>, fi[assoc] is evaluated whenever eventi is generated. The elements of assoc have keys specified by the setting for HandlerFunctionsKeys.
  • Possible keys specified by HandlerFunctionsKeys include:
  • "ChatObject"modified ChatObject[…]
    "ContentChunk"a message part
    "EventName"the name of the event being handled
    "Failure"failure object generated if task failed
    "Model"model used to generate the message
    "Role"role of the message author
    "StoppingReason"why the generation has stopped
    "Task"the task object generated by ChatSubmit
    "TaskStatus"the status of the task
    "TaskUUID"unique task identifier
    "Timestamp"timestamp of the message
    "ToolRequest"received LLMToolRequest[…]
    "ToolResponse"last generated LLMToolResponse[…]
    "UsageIncrement"token usage update
    {key1,…}a list of keys
    Allall the keys
    Automatickeys lexically present in HandlerFunctions
  • Values that have not yet been received are given as Missing["NotAvailable"].
  • If LLMEvaluator is set to Inherited, the LLM configuration specified in chat is used.
  • 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 or LLMPromptGenerator objects
    "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|>fully specified model
  • Prompts specified in "Prompts" are prepended to the messages in chat with role set as "System".
  • Multiple prompts are separated by the "PromptDelimiter" property.
  • The generated text is sampled from a distribution. Details of the sampling can be specified using the following properties of the 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)
  • The Automatic value of these parameters uses the default for the specified "Model".
  • Possible values for "ToolMethod" include:
  • "Service"rely on the tool mechanism of service
    "Textual"use prompt-based tool calling
  • Possible values for Authentication are:
  • Automaticchoose the authentication scheme automatically
    Inheritedinherit settings from chat
    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 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].
  • When using Authenticationassoc, assoc can contain the following keys:
  • "ID"user identity
    "APIKey"API key used to authenticate
  • ChatSubmit uses machine learning. Its methods, training sets and biases included therein may change and yield varied results in different versions of the Wolfram Language.

范例

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基本范例  (2)

Create a new chat:

Add a message to the conversation and submit it for a response, assigning the result to res:

Inspect the generated conversation:

Retrieve the generated response chunks from a multimodal model as they are received:

Show all the generation steps:

Scope  (2)

Start a new conversation asynchronously:

Inspect the task current status:

Inspect the generated conversation:

Continue an existing conversation asynchronously:

Inspect the generated conversation:

Options  (14)

Authentication  (4)

Provide an authentication key for the API:

Look for the key in the system keychain:

Specify the name of the key:

Look for the key in the system environment:

Authenticate via a service object:

HandlerFunctions  (2)

Print all generated event names in the messages window:

Capture the service-generated failure for an non-existing model:

HandlerFunctionsKeys  (3)

List explicitly the handler function keys to be passed to the handler functions:

Set HandlerFunctionsKeys values to be inferred lexically from the slots present in the handler functions (default):

Include all available handler function keys in the handler function argument:

LLMEvaluator  (5)

Specify the service used to generate the answer:

Specify both the service and the model:

By default, the text generation continues until a termination token is generated:

Limit the amount of generated samples (tokens):

Specify that the sampling should be performed at zero temperature:

Specify a high temperature to get more variation in the generation:

Compare results:

Specify the maximum cumulative probability before cutting off the distribution:

Specify a prompt to be automatically added to the conversation:

Applications  (1)

Tool Calling  (1)

Define a tool that can be called by the LLM:

Instantiate a chat object with the tool:

Ask a question that can get a precise answer using the tool:

Inspect the generated conversation:

Wolfram Research (2025)，ChatSubmit，Wolfram 语言函数，https://reference.wolfram.com/language/ref/ChatSubmit.html.

文本

Wolfram Research (2025)，ChatSubmit，Wolfram 语言函数，https://reference.wolfram.com/language/ref/ChatSubmit.html.

CMS

Wolfram 语言. 2025. "ChatSubmit." Wolfram 语言与系统参考资料中心. Wolfram Research. https://reference.wolfram.com/language/ref/ChatSubmit.html.

APA

Wolfram 语言. (2025). ChatSubmit. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/ChatSubmit.html 年

BibTeX

@misc{reference.wolfram_2025_chatsubmit, author="Wolfram Research", title="{ChatSubmit}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/ChatSubmit.html}", note=[Accessed: 15-January-2025 ]}

BibLaTeX

@online{reference.wolfram_2025_chatsubmit, organization={Wolfram Research}, title={ChatSubmit}, year={2025}, url={https://reference.wolfram.com/language/ref/ChatSubmit.html}, note=[Accessed: 15-January-2025 ]}