Close httplib2 connections.
create(parent, body=None, generatorId=None, x__xgafv=None)
Creates a generator.
Deletes a generator.
Retrieves a generator.
list(parent, pageSize=None, pageToken=None, x__xgafv=None)
Lists generators.
Retrieves the next page of results.
patch(name, body=None, updateMask=None, x__xgafv=None)
Updates a generator.
close()
Close httplib2 connections.
create(parent, body=None, generatorId=None, x__xgafv=None)
Creates a generator.
Args:
parent: string, Required. The project/location to create generator for. Format: `projects//locations/` (required)
body: object, The request body.
The object takes the form of:
{ # LLM generator.
"createTime": "A String", # Output only. Creation time of this generator.
"description": "A String", # Optional. Human readable description of the generator.
"inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
"maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
"temperature": 3.14, # 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": 42, # 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": 3.14, # 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.
},
"name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
"summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
"fewShotExamples": [ # Optional. List of few shot examples.
{ # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response. NEXT_ID: 11
"conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
"messageEntries": [ # Optional. List of message transcripts in the conversation.
{ # Represents a message entry of a conversation.
"createTime": "A String", # Optional. Create time of the message entry.
"languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
"role": "A String", # Optional. Participant role of the message.
"text": "A String", # Optional. Transcript content of the message.
},
],
},
"extraInfo": { # 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">
"a_key": "A String",
},
"output": { # Suggestion generated using a Generator. # Required. Example output of the model.
"summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
"summarySections": [ # Required. All the parts of generated summary.
{ # A component of the generated summary.
"section": "A String", # Required. Name of the section.
"summary": "A String", # Required. Summary text for the section.
},
],
},
},
"summarizationSectionList": { # List of summarization sections. # Summarization sections.
"summarizationSections": [ # Optional. Summarization sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
},
},
],
"outputLanguageCode": "A 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.
"summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
"version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
},
"triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
"updateTime": "A String", # Output only. Update time of this generator.
}
generatorId: string, Optional. The ID to use for the generator, which will become the final component of the generator's resource name. The generator ID must be compliant with the regression formula `a-zA-Z*` with the characters length in range of [3,64]. If the field is not provided, an Id will be auto-generated. If the field is provided, the caller is responsible for 1. the uniqueness of the ID, otherwise the request will be rejected. 2. the consistency for whether to use custom ID or not under a project to better ensure uniqueness.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # LLM generator.
"createTime": "A String", # Output only. Creation time of this generator.
"description": "A String", # Optional. Human readable description of the generator.
"inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
"maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
"temperature": 3.14, # 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": 42, # 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": 3.14, # 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.
},
"name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
"summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
"fewShotExamples": [ # Optional. List of few shot examples.
{ # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response. NEXT_ID: 11
"conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
"messageEntries": [ # Optional. List of message transcripts in the conversation.
{ # Represents a message entry of a conversation.
"createTime": "A String", # Optional. Create time of the message entry.
"languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
"role": "A String", # Optional. Participant role of the message.
"text": "A String", # Optional. Transcript content of the message.
},
],
},
"extraInfo": { # 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">
"a_key": "A String",
},
"output": { # Suggestion generated using a Generator. # Required. Example output of the model.
"summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
"summarySections": [ # Required. All the parts of generated summary.
{ # A component of the generated summary.
"section": "A String", # Required. Name of the section.
"summary": "A String", # Required. Summary text for the section.
},
],
},
},
"summarizationSectionList": { # List of summarization sections. # Summarization sections.
"summarizationSections": [ # Optional. Summarization sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
},
},
],
"outputLanguageCode": "A 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.
"summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
"version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
},
"triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
"updateTime": "A String", # Output only. Update time of this generator.
}
delete(name, x__xgafv=None)
Deletes a generator.
Args:
name: string, Required. The generator resource name to delete. Format: `projects//locations//generators/` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
}
get(name, x__xgafv=None)
Retrieves a generator.
Args:
name: string, Required. The generator resource name to retrieve. Format: `projects//locations/`/generators/` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # LLM generator.
"createTime": "A String", # Output only. Creation time of this generator.
"description": "A String", # Optional. Human readable description of the generator.
"inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
"maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
"temperature": 3.14, # 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": 42, # 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": 3.14, # 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.
},
"name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
"summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
"fewShotExamples": [ # Optional. List of few shot examples.
{ # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response. NEXT_ID: 11
"conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
"messageEntries": [ # Optional. List of message transcripts in the conversation.
{ # Represents a message entry of a conversation.
"createTime": "A String", # Optional. Create time of the message entry.
"languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
"role": "A String", # Optional. Participant role of the message.
"text": "A String", # Optional. Transcript content of the message.
},
],
},
"extraInfo": { # 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">
"a_key": "A String",
},
"output": { # Suggestion generated using a Generator. # Required. Example output of the model.
"summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
"summarySections": [ # Required. All the parts of generated summary.
{ # A component of the generated summary.
"section": "A String", # Required. Name of the section.
"summary": "A String", # Required. Summary text for the section.
},
],
},
},
"summarizationSectionList": { # List of summarization sections. # Summarization sections.
"summarizationSections": [ # Optional. Summarization sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
},
},
],
"outputLanguageCode": "A 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.
"summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
"version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
},
"triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
"updateTime": "A String", # Output only. Update time of this generator.
}
list(parent, pageSize=None, pageToken=None, x__xgafv=None)
Lists generators.
Args:
parent: string, Required. The project/location to list generators for. Format: `projects//locations/` (required)
pageSize: integer, Optional. Maximum number of conversation models to return in a single page. Default to 10.
pageToken: string, Optional. The next_page_token value returned from a previous list request.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Response of ListGenerators.
"generators": [ # List of generators retrieved.
{ # LLM generator.
"createTime": "A String", # Output only. Creation time of this generator.
"description": "A String", # Optional. Human readable description of the generator.
"inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
"maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
"temperature": 3.14, # 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": 42, # 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": 3.14, # 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.
},
"name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
"summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
"fewShotExamples": [ # Optional. List of few shot examples.
{ # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response. NEXT_ID: 11
"conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
"messageEntries": [ # Optional. List of message transcripts in the conversation.
{ # Represents a message entry of a conversation.
"createTime": "A String", # Optional. Create time of the message entry.
"languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
"role": "A String", # Optional. Participant role of the message.
"text": "A String", # Optional. Transcript content of the message.
},
],
},
"extraInfo": { # 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">
"a_key": "A String",
},
"output": { # Suggestion generated using a Generator. # Required. Example output of the model.
"summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
"summarySections": [ # Required. All the parts of generated summary.
{ # A component of the generated summary.
"section": "A String", # Required. Name of the section.
"summary": "A String", # Required. Summary text for the section.
},
],
},
},
"summarizationSectionList": { # List of summarization sections. # Summarization sections.
"summarizationSections": [ # Optional. Summarization sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
},
},
],
"outputLanguageCode": "A 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.
"summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
"version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
},
"triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
"updateTime": "A String", # Output only. Update time of this generator.
},
],
"nextPageToken": "A String", # Token to retrieve the next page of results, or empty if there are no more results in the list.
}
list_next()
Retrieves the next page of results.
Args:
previous_request: The request for the previous page. (required)
previous_response: The response from the request for the previous page. (required)
Returns:
A request object that you can call 'execute()' on to request the next
page. Returns None if there are no more items in the collection.
patch(name, body=None, updateMask=None, x__xgafv=None)
Updates a generator.
Args:
name: string, Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/` (required)
body: object, The request body.
The object takes the form of:
{ # LLM generator.
"createTime": "A String", # Output only. Creation time of this generator.
"description": "A String", # Optional. Human readable description of the generator.
"inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
"maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
"temperature": 3.14, # 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": 42, # 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": 3.14, # 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.
},
"name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
"summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
"fewShotExamples": [ # Optional. List of few shot examples.
{ # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response. NEXT_ID: 11
"conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
"messageEntries": [ # Optional. List of message transcripts in the conversation.
{ # Represents a message entry of a conversation.
"createTime": "A String", # Optional. Create time of the message entry.
"languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
"role": "A String", # Optional. Participant role of the message.
"text": "A String", # Optional. Transcript content of the message.
},
],
},
"extraInfo": { # 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">
"a_key": "A String",
},
"output": { # Suggestion generated using a Generator. # Required. Example output of the model.
"summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
"summarySections": [ # Required. All the parts of generated summary.
{ # A component of the generated summary.
"section": "A String", # Required. Name of the section.
"summary": "A String", # Required. Summary text for the section.
},
],
},
},
"summarizationSectionList": { # List of summarization sections. # Summarization sections.
"summarizationSections": [ # Optional. Summarization sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
},
},
],
"outputLanguageCode": "A 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.
"summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
"version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
},
"triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
"updateTime": "A String", # Output only. Update time of this generator.
}
updateMask: string, Optional. The list of fields to update.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # LLM generator.
"createTime": "A String", # Output only. Creation time of this generator.
"description": "A String", # Optional. Human readable description of the generator.
"inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
"maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
"temperature": 3.14, # 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": 42, # 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": 3.14, # 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.
},
"name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
"summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
"fewShotExamples": [ # Optional. List of few shot examples.
{ # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response. NEXT_ID: 11
"conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
"messageEntries": [ # Optional. List of message transcripts in the conversation.
{ # Represents a message entry of a conversation.
"createTime": "A String", # Optional. Create time of the message entry.
"languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
"role": "A String", # Optional. Participant role of the message.
"text": "A String", # Optional. Transcript content of the message.
},
],
},
"extraInfo": { # 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">
"a_key": "A String",
},
"output": { # Suggestion generated using a Generator. # Required. Example output of the model.
"summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
"summarySections": [ # Required. All the parts of generated summary.
{ # A component of the generated summary.
"section": "A String", # Required. Name of the section.
"summary": "A String", # Required. Summary text for the section.
},
],
},
},
"summarizationSectionList": { # List of summarization sections. # Summarization sections.
"summarizationSections": [ # Optional. Summarization sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
},
},
],
"outputLanguageCode": "A 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.
"summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
{ # Represents the section of summarization.
"definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
"key": "A String", # Optional. Name of the section, for example, "situation".
"type": "A String", # Optional. Type of the summarization section.
},
],
"version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
},
"triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
"updateTime": "A String", # Output only. Update time of this generator.
}