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    REST Resource: projects.generators

    Resource: Generator

    LLM generator.

    JSON representation 
     { 
 "name" 
 : 
 string 
 , 
 "description" 
 : 
 string 
 , 
 "inferenceParameter" 
 : 
 { 
 object (  InferenceParameter 
 
) 
 } 
 , 
 "triggerEvent" 
 : 
 enum (  TriggerEvent 
 
) 
 , 
 "createTime" 
 : 
 string 
 , 
 "updateTime" 
 : 
 string 
 , 
 "tools" 
 : 
 [ 
 string 
 ] 
 , 
 // Union field context 
can be only one of the following: 
 "freeFormContext" 
 : 
 { 
 object (  FreeFormContext 
 
) 
 } 
 , 
 "summarizationContext" 
 : 
 { 
 object (  SummarizationContext 
 
) 
 } 
 // End of list of possible types for union field context 
. 
 // Union field foundation_model 
can be only one of the following: 
 "publishedModel" 
 : 
 string 
 // End of list of possible types for union field foundation_model 
. 
 }
    Fields
    name

    string

    Output only. Identifier. The resource name of the generator. Format: projects/<Project ID>/locations/<Location ID>/generators/<Generator ID>

    description

    string

    Optional. Human readable description of the generator.

    inferenceParameter

    object ( InferenceParameter )

    Optional. Inference parameters for this generator.

    triggerEvent

    enum ( TriggerEvent )

    Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.

    createTime

    string ( Timestamp format)

    Output only. Creation time of this generator.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    updateTime

    string ( Timestamp format)

    Output only. Update time of this generator.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    tools[]

    string

    Optional. Resource names of the tools that the generator can choose from. Format: projects/<Project ID>/locations/<Location ID>/tools/<tool ID> .

    Union field context . Required. Input context of the generator. context can be only one of the following:
    freeFormContext

    object ( FreeFormContext )

    Input of free from generator to LLM.

    summarizationContext

    object ( SummarizationContext )

    Input of Summarization feature.

    Union field foundation_model . The foundation model to use for generating suggestions. If a foundation model isn't specified here, a model specifically tuned for the feature type (and version when applicable) will be used. foundation_model can be only one of the following:
    publishedModel

    string

    Optional. The published Large Language Model name. * To use the latest model version, specify the model name without version number. Example: text-bison * To use a stable model version, specify the version number as well. Example: text-bison@002 .

    FreeFormContext

    Free form generator context that customer can configure.

    JSON representation 
     { 
 "text" 
 : 
 string 
 }
    Fields
    text

    string

    Optional. Free form text input to LLM.

    SummarizationContext

    Summarization context that customer can configure.

    JSON representation 
     { 
 "summarizationSections" 
 : 
 [ 
 { 
 object (  SummarizationSection 
 
) 
 } 
 ] 
 , 
 "fewShotExamples" 
 : 
 [ 
 { 
 object (  FewShotExample 
 
) 
 } 
 ] 
 , 
 "version" 
 : 
 string 
 , 
 "outputLanguageCode" 
 : 
 string 
 }
    Fields
    summarizationSections[]

    object ( SummarizationSection )

    Optional. List of sections. Note it contains both predefined section sand customer defined sections.
    fewShotExamples[]

    object ( FewShotExample )

    Optional. List of few shot examples.
    version

    string

    Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
    outputLanguageCode

    string

    Optional. The target language of the generated summary. The language code for conversation will be used if this field is empty. Supported 2.0 and later versions.

    SummarizationSection

    Represents the section of summarization.

    JSON representation 
     { 
 "key" 
 : 
 string 
 , 
 "definition" 
 : 
 string 
 , 
 "type" 
 : 
 enum (  Type 
 
) 
 }
    Fields
    key

    string

    Optional. Name of the section, for example, "situation".
    definition

    string

    Optional. Definition of the section, for example, "what the customer needs help with or has question about."
    type

    enum ( Type )

    Optional. Type of the summarization section.

    Type

    Type enum of the summarization sections.

    Enums
    TYPE_UNSPECIFIED Undefined section type, does not return anything.
    SITUATION What the customer needs help with or has question about. Section name: "situation".
    ACTION What the agent does to help the customer. Section name: "action".
    RESOLUTION Result of the customer service. A single word describing the result of the conversation. Section name: "resolution".
    REASON_FOR_CANCELLATION Reason for cancellation if the customer requests for a cancellation. "N/A" otherwise. Section name: "reason_for_cancellation".
    CUSTOMER_SATISFACTION "Unsatisfied" or "Satisfied" depending on the customer's feelings at the end of the conversation. Section name: "customer_satisfaction".
    ENTITIES Key entities extracted from the conversation, such as ticket number, order number, dollar amount, etc. Section names are prefixed by "entities/".
    CUSTOMER_DEFINED Customer defined sections.
    SITUATION_CONCISE Concise version of the situation section. This type is only available if type SITUATION is not selected.
    ACTION_CONCISE Concise version of the action section. This type is only available if type ACTION is not selected.

    FewShotExample

    Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response.

    JSON representation 
     { 
 "conversationContext" 
 : 
 { 
 object (  ConversationContext 
 
) 
 } 
 , 
 "extraInfo" 
 : 
 { 
 string 
 : 
 string 
 , 
 ... 
 } 
 , 
 "output" 
 : 
 { 
 object (  GeneratorSuggestion 
 
) 
 } 
 , 
 // Union field instruction_list 
can be only one of the following: 
 "summarizationSectionList" 
 : 
 { 
 object (  SummarizationSectionList 
 
) 
 } 
 // End of list of possible types for union field instruction_list 
. 
 }
    Fields
    conversationContext

    object ( ConversationContext )

    Optional. Conversation transcripts.

    extraInfo

    map (key: string, value: string)

    Optional. Key is the placeholder field name in input, value is the value of the placeholder. E.g. instruction contains "@price", and ingested data has <"price", "10">

    An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" } .

    output

    object ( GeneratorSuggestion )

    Required. Example output of the model.

    Union field instruction_list . Instruction list of this few_shot example. instruction_list can be only one of the following:
    summarizationSectionList

    object ( SummarizationSectionList )

    Summarization sections.

    ConversationContext

    Context of the conversation, including transcripts.

    JSON representation 
     { 
 "messageEntries" 
 : 
 [ 
 { 
 object (  MessageEntry 
 
) 
 } 
 ] 
 }
    Fields
    messageEntries[]

    object ( MessageEntry )

    Optional. List of message transcripts in the conversation.

    MessageEntry

    Represents a message entry of a conversation.

    JSON representation 
     { 
 "role" 
 : 
 enum (  Role 
 
) 
 , 
 "text" 
 : 
 string 
 , 
 "languageCode" 
 : 
 string 
 , 
 "createTime" 
 : 
 string 
 }
    Fields
    role

    enum ( Role )

    Optional. Participant role of the message.
    text

    string

    Optional. Transcript content of the message.
    languageCode

    string

    Optional. The language of the text. See Language Support for a list of the currently supported language codes.
    createTime

    string ( Timestamp format)

    Optional. Create time of the message entry.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    Role

    Enumeration of the roles a participant can play in a conversation.

    Enums
    ROLE_UNSPECIFIED Participant role not set.
    HUMAN_AGENT Participant is a human agent.
    AUTOMATED_AGENT Participant is an automated agent, such as a Dialogflow agent.
    END_USER Participant is an end user that has called or chatted with Dialogflow services.

    SummarizationSectionList

    List of summarization sections.

    JSON representation 
     { 
 "summarizationSections" 
 : 
 [ 
 { 
 object (  SummarizationSection 
 
) 
 } 
 ] 
 }
    Fields
    summarizationSections[]

    object ( SummarizationSection )

    Optional. Summarization sections.

    InferenceParameter

    The parameters of inference.

    JSON representation 
     { 
 "maxOutputTokens" 
 : 
 integer 
 , 
 "temperature" 
 : 
 number 
 , 
 "topK" 
 : 
 integer 
 , 
 "topP" 
 : 
 number 
 }
    Fields
    maxOutputTokens

    integer

    Optional. Maximum number of the output tokens for the generator.
    temperature

    number

    Optional. Controls the randomness of LLM predictions. Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.
    topK

    integer

    Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.
    topP

    number

    Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.

    TriggerEvent

    The event that triggers the generator and LLM execution.

    Enums
    TRIGGER_EVENT_UNSPECIFIED Default value for TriggerEvent.
    END_OF_UTTERANCE Triggers when each chat message or voice utterance ends.
    MANUAL_CALL Triggers on the conversation manually by API calls, such as Conversations.GenerateStatelessSuggestion and Conversations.GenerateSuggestions.
    CUSTOMER_MESSAGE Triggers after each customer message only.
    AGENT_MESSAGE Triggers after each agent message only.

    Methods

    create

    		Creates a generator.

    list

    		Lists generators.
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