View Source API Reference google_api_contact_center_insights v0.9.0
Modules
API client metadata for GoogleApi.ContactCenterInsights.V1.
API calls for all endpoints tagged Projects
.
Handle Tesla connections for GoogleApi.ContactCenterInsights.V1.
The analysis resource.
The result of an analysis.
Call-specific metadata created during analysis.
A point in a conversation that marks the start or the end of an annotation.
Selector of all available annotators and phrase matchers to run.
Configuration for summarization.
The feedback that the customer has about a certain answer in the conversation.
Agent Assist Article Suggestion data.
The metadata for a bulk analyze conversations operation.
The request to analyze conversations in bulk.
The response for a bulk analyze conversations operation.
The metadata for a bulk delete conversations operation.
The request to delete conversations in bulk.
The response for a bulk delete conversations operation.
Response of querying an issue model's statistics.
The response for calculating conversation statistics.
A time series representing conversations over time.
A single interval in a time series.
A piece of metadata that applies to a window of a call.
The conversation resource.
Call-specific metadata.
The conversation source, which is a combination of transcript and audio.
One channel of conversation-level sentiment data.
Conversation-level silence data.
The call participant speaking for a given utterance.
Conversation metadata related to quality management.
Information about an agent involved in the conversation.
Conversation summarization suggestion data.
A message representing the transcript of a conversation.
A segment of a full transcript.
Metadata from Dialogflow relating to the current transcript segment.
Word-level info for words in a transcript.
Metadata for a create analysis operation.
Metadata for creating an issue model.
The request to create an issue model.
Metadata for deleting an issue model.
The request to delete an issue model.
Metadata for deploying an issue model.
The request to deploy an issue model.
The response to deploy an issue model.
The data for a Dialogflow intent. Represents a detected intent in the conversation, e.g. MAKES_PROMISE.
Dialogflow interaction data.
A Dialogflow source of conversation data.
A customer-managed encryption key specification that can be applied to all created resources (e.g. Conversation).
The data for an entity annotation. Represents a phrase in the conversation that is a known entity, such as a person, an organization, or location.
The data for an entity mention annotation. This represents a mention of an Entity
in the conversation.
Exact match configuration.
Metadata for an export insights operation.
The request to export insights.
A BigQuery Table Reference.
Response for an export insights operation.
Metadata used for export issue model.
Request to export an issue model.
Google Cloud Storage Object URI to save the issue model to.
Response from export issue model
Agent Assist frequently-asked-question answer data.
A Cloud Storage source of conversation data.
The data for a hold annotation.
Metadata used for import issue model.
Request to import an issue model.
Google Cloud Storage Object URI to get the issue model file from.
Response from import issue model
The metadata for an IngestConversations operation.
Statistics for IngestConversations operation.
The request to ingest conversations.
Configuration that applies to all conversations.
Configuration for Cloud Storage bucket sources.
Configuration for processing transcript objects.
The response to an IngestConversations operation.
Metadata for initializing a location-level encryption specification.
The request to initialize a location-level encryption specification.
The response to initialize a location-level encryption specification.
The data for an intent. Represents a detected intent in the conversation, for example MAKES_PROMISE.
The data for an intent match. Represents an intent match for a text segment in the conversation. A text segment can be part of a sentence, a complete sentence, or an utterance with multiple sentences.
The data for an interruption annotation.
The issue resource.
Information about the issue.
The data for an issue match annotation.
The issue model resource.
Configs for the input data used to create the issue model.
Aggregated statistics about an issue model.
Aggregated statistics about an issue.
Issue Modeling result on a conversation.
The response to list analyses.
The response of listing conversations.
The response of listing issue models.
The response of listing issues.
The response of listing phrase matchers.
The response of listing views.
The data for a matched phrase matcher. Represents information identifying a phrase matcher for a given match.
The data for a phrase match rule.
Configuration information of a phrase match rule.
A message representing a rule in the phrase matcher.
The phrase matcher resource.
DLP resources used for redaction while ingesting conversations. DLP settings are applied to conversations ingested from the UploadConversation
and IngestConversations
endpoints, including conversation coming from CCAI Platform. They are not applied to conversations ingested from the CreateConversation
endpoint or the Dialogflow / Agent Assist runtime integrations. When using Dialogflow / Agent Assist runtime integrations, redaction should be performed in Dialogflow / Agent Assist.
An annotation that was generated during the customer and agent interaction.
Explicit input used for generating the answer
The data for a sentiment annotation.
The CCAI Insights project wide settings. Use these settings to configure the behavior of Insights. View these settings with getsettings
and change the settings with updateSettings
.
Default configuration when creating Analyses in Insights.
The data for a silence annotation.
Agent Assist Smart Compose suggestion data.
Agent Assist Smart Reply data.
Speech-to-Text configuration. Speech-to-Text settings are applied to conversations ingested from the UploadConversation
and IngestConversations
endpoints, including conversation coming from CCAI Platform. They are not applied to conversations ingested from the CreateConversation
endpoint.
Metadata for undeploying an issue model.
The request to undeploy an issue model.
The response to undeploy an issue model.
The metadata for an UploadConversation
operation.
Request to upload a conversation.
The analysis resource.
The result of an analysis.
Call-specific metadata created during analysis.
A point in a conversation that marks the start or the end of an annotation.
Selector of all available annotators and phrase matchers to run.
Configuration for summarization.
The feedback that the customer has about a certain answer in the conversation.
Agent Assist Article Suggestion data.
The metadata for a bulk analyze conversations operation.
The request to analyze conversations in bulk.
The response for a bulk analyze conversations operation.
The metadata for a bulk delete conversations operation.
The request to delete conversations in bulk.
The response for a bulk delete conversations operation.
A piece of metadata that applies to a window of a call.
The conversation resource.
Call-specific metadata.
The conversation source, which is a combination of transcript and audio.
One channel of conversation-level sentiment data.
Conversation-level silence data.
The call participant speaking for a given utterance.
Conversation metadata related to quality management.
Information about an agent involved in the conversation.
Conversation summarization suggestion data.
A message representing the transcript of a conversation.
A segment of a full transcript.
Metadata from Dialogflow relating to the current transcript segment.
Word-level info for words in a transcript.
Metadata for a create analysis operation.
Metadata for creating an issue model.
The request to create an issue model.
Metadata for deleting an issue model.
The request to delete an issue model.
Metadata for deploying an issue model.
The request to deploy an issue model.
The response to deploy an issue model.
The data for a Dialogflow intent. Represents a detected intent in the conversation, e.g. MAKES_PROMISE.
Dialogflow interaction data.
A Dialogflow source of conversation data.
A customer-managed encryption key specification that can be applied to all created resources (e.g. Conversation).
The data for an entity annotation. Represents a phrase in the conversation that is a known entity, such as a person, an organization, or location.
The data for an entity mention annotation. This represents a mention of an Entity
in the conversation.
Metadata for an export insights operation.
The request to export insights.
A BigQuery Table Reference.
Response for an export insights operation.
Metadata used for export issue model.
Request to export an issue model.
Google Cloud Storage Object URI to save the issue model to.
Response from export issue model
Agent Assist frequently-asked-question answer data.
A Cloud Storage source of conversation data.
The data for a hold annotation.
Metadata used for import issue model.
Request to import an issue model.
Google Cloud Storage Object URI to get the issue model file from.
Response from import issue model
The metadata for an IngestConversations operation.
Statistics for IngestConversations operation.
The request to ingest conversations.
Configuration that applies to all conversations.
Configuration for Cloud Storage bucket sources.
Configuration for processing transcript objects.
The response to an IngestConversations operation.
Metadata for initializing a location-level encryption specification.
The request to initialize a location-level encryption specification.
The response to initialize a location-level encryption specification.
The data for an intent. Represents a detected intent in the conversation, for example MAKES_PROMISE.
The data for an intent match. Represents an intent match for a text segment in the conversation. A text segment can be part of a sentence, a complete sentence, or an utterance with multiple sentences.
The data for an interruption annotation.
Information about the issue.
The data for an issue match annotation.
The issue model resource.
Configs for the input data used to create the issue model.
Aggregated statistics about an issue model.
Aggregated statistics about an issue.
Issue Modeling result on a conversation.
The data for a matched phrase matcher. Represents information identifying a phrase matcher for a given match.
DLP resources used for redaction while ingesting conversations. DLP settings are applied to conversations ingested from the UploadConversation
and IngestConversations
endpoints, including conversation coming from CCAI Platform. They are not applied to conversations ingested from the CreateConversation
endpoint or the Dialogflow / Agent Assist runtime integrations. When using Dialogflow / Agent Assist runtime integrations, redaction should be performed in Dialogflow / Agent Assist.
An annotation that was generated during the customer and agent interaction.
Explicit input used for generating the answer
The data for a sentiment annotation.
The data for a silence annotation.
Agent Assist Smart Compose suggestion data.
Agent Assist Smart Reply data.
Speech-to-Text configuration. Speech-to-Text settings are applied to conversations ingested from the UploadConversation
and IngestConversations
endpoints, including conversation coming from CCAI Platform. They are not applied to conversations ingested from the CreateConversation
endpoint.
Metadata for undeploying an issue model.
The request to undeploy an issue model.
The response to undeploy an issue model.
The metadata for an UploadConversation
operation.
Request to upload a conversation.
The response message for Operations.ListOperations.
This resource represents a long-running operation that is the result of a network API call.
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); }
The Status
type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status
message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide.