Close httplib2 connections.
create(parent, body=None, x__xgafv=None)
Creates an analysis. The long running operation is done when the analysis has completed.
Deletes an analysis.
Gets an analysis.
list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)
Lists analyses.
Retrieves the next page of results.
close()
Close httplib2 connections.
create(parent, body=None, x__xgafv=None)
Creates an analysis. The long running operation is done when the analysis has completed.
Args:
parent: string, Required. The parent resource of the analysis. (required)
body: object, The request body.
The object takes the form of:
{ # The analysis resource.
"analysisResult": { # The result of an analysis. # Output only. The result of the analysis, which is populated when the analysis finishes.
"callAnalysisMetadata": { # Call-specific metadata created during analysis. # Call-specific metadata created by the analysis.
"annotations": [ # A list of call annotations that apply to this call.
{ # A piece of metadata that applies to a window of a call.
"annotationEndBoundary": { # A point in a conversation that marks the start or the end of an annotation. # The boundary in the conversation where the annotation ends, inclusive.
"transcriptIndex": 42, # The index in the sequence of transcribed pieces of the conversation where the boundary is located. This index starts at zero.
"wordIndex": 42, # The word index of this boundary with respect to the first word in the transcript piece. This index starts at zero.
},
"annotationStartBoundary": { # A point in a conversation that marks the start or the end of an annotation. # The boundary in the conversation where the annotation starts, inclusive.
"transcriptIndex": 42, # The index in the sequence of transcribed pieces of the conversation where the boundary is located. This index starts at zero.
"wordIndex": 42, # The word index of this boundary with respect to the first word in the transcript piece. This index starts at zero.
},
"channelTag": 42, # The channel of the audio where the annotation occurs. For single-channel audio, this field is not populated.
"entityMentionData": { # The data for an entity mention annotation. This represents a mention of an `Entity` in the conversation. # Data specifying an entity mention.
"entityUniqueId": "A String", # The key of this entity in conversation entities. Can be used to retrieve the exact `Entity` this mention is attached to.
"sentiment": { # The data for a sentiment annotation. # Sentiment expressed for this mention of the entity.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"type": "A String", # The type of the entity mention.
},
"holdData": { # The data for a hold annotation. # Data specifying a hold.
},
"intentMatchData": { # 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. # Data specifying an intent match.
"intentUniqueId": "A String", # The id of the matched intent. Can be used to retrieve the corresponding intent information.
},
"interruptionData": { # The data for an interruption annotation. # Data specifying an interruption.
},
"issueMatchData": { # The data for an issue match annotation. # Data specifying an issue match.
"issueAssignment": { # Information about the issue. # Information about the issue's assignment.
"displayName": "A String", # Immutable. Display name of the assigned issue. This field is set at time of analyis and immutable since then.
"issue": "A String", # Resource name of the assigned issue.
"score": 3.14, # Score indicating the likelihood of the issue assignment. currently bounded on [0,1].
},
},
"phraseMatchData": { # The data for a matched phrase matcher. Represents information identifying a phrase matcher for a given match. # Data specifying a phrase match.
"displayName": "A String", # The human-readable name of the phrase matcher.
"phraseMatcher": "A String", # The unique identifier (the resource name) of the phrase matcher.
},
"sentimentData": { # The data for a sentiment annotation. # Data specifying sentiment.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"silenceData": { # The data for a silence annotation. # Data specifying silence.
},
},
],
"entities": { # All the entities in the call.
"a_key": { # 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.
"displayName": "A String", # The representative name for the entity.
"metadata": { # Metadata associated with the entity. For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`) and Knowledge Graph MID (`mid`), if they are available. For the metadata associated with other entity types, see the Type table below.
"a_key": "A String",
},
"salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range. The salience score for an entity provides information about the importance or centrality of that entity to the entire document text. Scores closer to 0 are less salient, while scores closer to 1.0 are highly salient.
"sentiment": { # The data for a sentiment annotation. # The aggregate sentiment expressed for this entity in the conversation.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"type": "A String", # The entity type.
},
},
"intents": { # All the matched intents in the call.
"a_key": { # The data for an intent. Represents a detected intent in the conversation, for example MAKES_PROMISE.
"displayName": "A String", # The human-readable name of the intent.
"id": "A String", # The unique identifier of the intent.
},
},
"issueModelResult": { # Issue Modeling result on a conversation. # Overall conversation-level issue modeling result.
"issueModel": "A String", # Issue model that generates the result. Format: projects/{project}/locations/{location}/issueModels/{issue_model}
"issues": [ # All the matched issues.
{ # Information about the issue.
"displayName": "A String", # Immutable. Display name of the assigned issue. This field is set at time of analyis and immutable since then.
"issue": "A String", # Resource name of the assigned issue.
"score": 3.14, # Score indicating the likelihood of the issue assignment. currently bounded on [0,1].
},
],
},
"phraseMatchers": { # All the matched phrase matchers in the call.
"a_key": { # The data for a matched phrase matcher. Represents information identifying a phrase matcher for a given match.
"displayName": "A String", # The human-readable name of the phrase matcher.
"phraseMatcher": "A String", # The unique identifier (the resource name) of the phrase matcher.
},
},
"qaScorecardResults": [ # Results of scoring QaScorecards.
{ # The results of scoring a single conversation against a QaScorecard. Contains a collection of QaAnswers and aggregate score.
"agentId": "A String", # ID of the agent that handled the conversation.
"conversation": "A String", # The conversation scored by this result.
"createTime": "A String", # Output only. The timestamp that the revision was created.
"name": "A String", # Identifier. The name of the scorecard result. Format: projects/{project}/locations/{location}/qaScorecardResults/{qa_scorecard_result}
"normalizedScore": 3.14, # The normalized score, which is the score divided by the potential score. Any manual edits are included if they exist.
"potentialScore": 3.14, # The maximum potential overall score of the scorecard. Any questions answered using `na_value` are excluded from this calculation.
"qaAnswers": [ # Set of QaAnswers represented in the result.
{ # An answer to a QaQuestion.
"answerSources": [ # List of all individual answers given to the question.
{ # A question may have multiple answers from varying sources, one of which becomes the "main" answer above. AnswerSource represents each individual answer.
"answerValue": { # Message for holding the value of a QaAnswer. QaQuestion.AnswerChoice defines the possible answer values for a question. # The answer value from this source.
"boolValue": True or False, # Boolean value.
"key": "A String", # A short string used as an identifier. Matches the value used in QaQuestion.AnswerChoice.key.
"naValue": True or False, # A value of "Not Applicable (N/A)". Should only ever be `true`.
"normalizedScore": 3.14, # Output only. Normalized score of the questions. Calculated as score / potential_score.
"numValue": 3.14, # Numerical value.
"potentialScore": 3.14, # Output only. The maximum potential score of the question.
"score": 3.14, # Output only. Numerical score of the answer.
"strValue": "A String", # String value.
},
"sourceType": "A String", # What created the answer.
},
],
"answerValue": { # Message for holding the value of a QaAnswer. QaQuestion.AnswerChoice defines the possible answer values for a question. # The main answer value, incorporating any manual edits if they exist.
"boolValue": True or False, # Boolean value.
"key": "A String", # A short string used as an identifier. Matches the value used in QaQuestion.AnswerChoice.key.
"naValue": True or False, # A value of "Not Applicable (N/A)". Should only ever be `true`.
"normalizedScore": 3.14, # Output only. Normalized score of the questions. Calculated as score / potential_score.
"numValue": 3.14, # Numerical value.
"potentialScore": 3.14, # Output only. The maximum potential score of the question.
"score": 3.14, # Output only. Numerical score of the answer.
"strValue": "A String", # String value.
},
"conversation": "A String", # The conversation the answer applies to.
"qaQuestion": "A String", # The QaQuestion answered by this answer.
"questionBody": "A String", # Question text. E.g., "Did the agent greet the customer?"
"tags": [ # User-defined list of arbitrary tags. Matches the value from QaScorecard.ScorecardQuestion.tags. Used for grouping/organization and for weighting the score of each answer.
"A String",
],
},
],
"qaScorecardRevision": "A String", # The QaScorecardRevision scored by this result.
"qaTagResults": [ # Collection of tags and their scores.
{ # Tags and their corresponding results.
"normalizedScore": 3.14, # The normalized score the tag applies to.
"potentialScore": 3.14, # The potential score the tag applies to.
"score": 3.14, # The score the tag applies to.
"tag": "A String", # The tag the score applies to.
},
],
"score": 3.14, # The overall numerical score of the result, incorporating any manual edits if they exist.
"scoreSources": [ # List of all individual score sets.
{ # A scorecard result may have multiple sets of scores from varying sources, one of which becomes the "main" answer above. A ScoreSource represents each individual set of scores.
"normalizedScore": 3.14, # The normalized score, which is the score divided by the potential score.
"potentialScore": 3.14, # The maximum potential overall score of the scorecard. Any questions answered using `na_value` are excluded from this calculation.
"qaTagResults": [ # Collection of tags and their scores.
{ # Tags and their corresponding results.
"normalizedScore": 3.14, # The normalized score the tag applies to.
"potentialScore": 3.14, # The potential score the tag applies to.
"score": 3.14, # The score the tag applies to.
"tag": "A String", # The tag the score applies to.
},
],
"score": 3.14, # The overall numerical score of the result.
"sourceType": "A String", # What created the score.
},
],
},
],
"sentiments": [ # Overall conversation-level sentiment for each channel of the call.
{ # One channel of conversation-level sentiment data.
"channelTag": 42, # The channel of the audio that the data applies to.
"sentimentData": { # The data for a sentiment annotation. # Data specifying sentiment.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
},
],
"silence": { # Conversation-level silence data. # Overall conversation-level silence during the call.
"silenceDuration": "A String", # Amount of time calculated to be in silence.
"silencePercentage": 3.14, # Percentage of the total conversation spent in silence.
},
},
"endTime": "A String", # The time at which the analysis ended.
},
"annotatorSelector": { # Selector of all available annotators and phrase matchers to run. # To select the annotators to run and the phrase matchers to use (if any). If not specified, all annotators will be run.
"issueModels": [ # The issue model to run. If not provided, the most recently deployed topic model will be used. The provided issue model will only be used for inference if the issue model is deployed and if run_issue_model_annotator is set to true. If more than one issue model is provided, only the first provided issue model will be used for inference.
"A String",
],
"phraseMatchers": [ # The list of phrase matchers to run. If not provided, all active phrase matchers will be used. If inactive phrase matchers are provided, they will not be used. Phrase matchers will be run only if run_phrase_matcher_annotator is set to true. Format: projects/{project}/locations/{location}/phraseMatchers/{phrase_matcher}
"A String",
],
"qaConfig": { # Configuration for the QA feature. # Configuration for the QA annotator.
"scorecardList": { # Container for a list of scorecards. # A manual list of scorecards to score.
"qaScorecardRevisions": [ # List of QaScorecardRevisions.
"A String",
],
},
},
"runEntityAnnotator": True or False, # Whether to run the entity annotator.
"runIntentAnnotator": True or False, # Whether to run the intent annotator.
"runInterruptionAnnotator": True or False, # Whether to run the interruption annotator.
"runIssueModelAnnotator": True or False, # Whether to run the issue model annotator. A model should have already been deployed for this to take effect.
"runPhraseMatcherAnnotator": True or False, # Whether to run the active phrase matcher annotator(s).
"runQaAnnotator": True or False, # Whether to run the QA annotator.
"runSentimentAnnotator": True or False, # Whether to run the sentiment annotator.
"runSilenceAnnotator": True or False, # Whether to run the silence annotator.
"runSummarizationAnnotator": True or False, # Whether to run the summarization annotator.
"summarizationConfig": { # Configuration for summarization. # Configuration for the summarization annotator.
"conversationProfile": "A String", # Resource name of the Dialogflow conversation profile. Format: projects/{project}/locations/{location}/conversationProfiles/{conversation_profile}
"summarizationModel": "A String", # Default summarization model to be used.
},
},
"createTime": "A String", # Output only. The time at which the analysis was created, which occurs when the long-running operation completes.
"name": "A String", # Immutable. The resource name of the analysis. Format: projects/{project}/locations/{location}/conversations/{conversation}/analyses/{analysis}
"requestTime": "A String", # Output only. The time at which the analysis was requested.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # 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](https://github.com/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](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}
delete(name, x__xgafv=None)
Deletes an analysis.
Args:
name: string, Required. The name of the analysis to delete. (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)
Gets an analysis.
Args:
name: string, Required. The name of the analysis to get. (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The analysis resource.
"analysisResult": { # The result of an analysis. # Output only. The result of the analysis, which is populated when the analysis finishes.
"callAnalysisMetadata": { # Call-specific metadata created during analysis. # Call-specific metadata created by the analysis.
"annotations": [ # A list of call annotations that apply to this call.
{ # A piece of metadata that applies to a window of a call.
"annotationEndBoundary": { # A point in a conversation that marks the start or the end of an annotation. # The boundary in the conversation where the annotation ends, inclusive.
"transcriptIndex": 42, # The index in the sequence of transcribed pieces of the conversation where the boundary is located. This index starts at zero.
"wordIndex": 42, # The word index of this boundary with respect to the first word in the transcript piece. This index starts at zero.
},
"annotationStartBoundary": { # A point in a conversation that marks the start or the end of an annotation. # The boundary in the conversation where the annotation starts, inclusive.
"transcriptIndex": 42, # The index in the sequence of transcribed pieces of the conversation where the boundary is located. This index starts at zero.
"wordIndex": 42, # The word index of this boundary with respect to the first word in the transcript piece. This index starts at zero.
},
"channelTag": 42, # The channel of the audio where the annotation occurs. For single-channel audio, this field is not populated.
"entityMentionData": { # The data for an entity mention annotation. This represents a mention of an `Entity` in the conversation. # Data specifying an entity mention.
"entityUniqueId": "A String", # The key of this entity in conversation entities. Can be used to retrieve the exact `Entity` this mention is attached to.
"sentiment": { # The data for a sentiment annotation. # Sentiment expressed for this mention of the entity.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"type": "A String", # The type of the entity mention.
},
"holdData": { # The data for a hold annotation. # Data specifying a hold.
},
"intentMatchData": { # 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. # Data specifying an intent match.
"intentUniqueId": "A String", # The id of the matched intent. Can be used to retrieve the corresponding intent information.
},
"interruptionData": { # The data for an interruption annotation. # Data specifying an interruption.
},
"issueMatchData": { # The data for an issue match annotation. # Data specifying an issue match.
"issueAssignment": { # Information about the issue. # Information about the issue's assignment.
"displayName": "A String", # Immutable. Display name of the assigned issue. This field is set at time of analyis and immutable since then.
"issue": "A String", # Resource name of the assigned issue.
"score": 3.14, # Score indicating the likelihood of the issue assignment. currently bounded on [0,1].
},
},
"phraseMatchData": { # The data for a matched phrase matcher. Represents information identifying a phrase matcher for a given match. # Data specifying a phrase match.
"displayName": "A String", # The human-readable name of the phrase matcher.
"phraseMatcher": "A String", # The unique identifier (the resource name) of the phrase matcher.
},
"sentimentData": { # The data for a sentiment annotation. # Data specifying sentiment.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"silenceData": { # The data for a silence annotation. # Data specifying silence.
},
},
],
"entities": { # All the entities in the call.
"a_key": { # 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.
"displayName": "A String", # The representative name for the entity.
"metadata": { # Metadata associated with the entity. For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`) and Knowledge Graph MID (`mid`), if they are available. For the metadata associated with other entity types, see the Type table below.
"a_key": "A String",
},
"salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range. The salience score for an entity provides information about the importance or centrality of that entity to the entire document text. Scores closer to 0 are less salient, while scores closer to 1.0 are highly salient.
"sentiment": { # The data for a sentiment annotation. # The aggregate sentiment expressed for this entity in the conversation.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"type": "A String", # The entity type.
},
},
"intents": { # All the matched intents in the call.
"a_key": { # The data for an intent. Represents a detected intent in the conversation, for example MAKES_PROMISE.
"displayName": "A String", # The human-readable name of the intent.
"id": "A String", # The unique identifier of the intent.
},
},
"issueModelResult": { # Issue Modeling result on a conversation. # Overall conversation-level issue modeling result.
"issueModel": "A String", # Issue model that generates the result. Format: projects/{project}/locations/{location}/issueModels/{issue_model}
"issues": [ # All the matched issues.
{ # Information about the issue.
"displayName": "A String", # Immutable. Display name of the assigned issue. This field is set at time of analyis and immutable since then.
"issue": "A String", # Resource name of the assigned issue.
"score": 3.14, # Score indicating the likelihood of the issue assignment. currently bounded on [0,1].
},
],
},
"phraseMatchers": { # All the matched phrase matchers in the call.
"a_key": { # The data for a matched phrase matcher. Represents information identifying a phrase matcher for a given match.
"displayName": "A String", # The human-readable name of the phrase matcher.
"phraseMatcher": "A String", # The unique identifier (the resource name) of the phrase matcher.
},
},
"qaScorecardResults": [ # Results of scoring QaScorecards.
{ # The results of scoring a single conversation against a QaScorecard. Contains a collection of QaAnswers and aggregate score.
"agentId": "A String", # ID of the agent that handled the conversation.
"conversation": "A String", # The conversation scored by this result.
"createTime": "A String", # Output only. The timestamp that the revision was created.
"name": "A String", # Identifier. The name of the scorecard result. Format: projects/{project}/locations/{location}/qaScorecardResults/{qa_scorecard_result}
"normalizedScore": 3.14, # The normalized score, which is the score divided by the potential score. Any manual edits are included if they exist.
"potentialScore": 3.14, # The maximum potential overall score of the scorecard. Any questions answered using `na_value` are excluded from this calculation.
"qaAnswers": [ # Set of QaAnswers represented in the result.
{ # An answer to a QaQuestion.
"answerSources": [ # List of all individual answers given to the question.
{ # A question may have multiple answers from varying sources, one of which becomes the "main" answer above. AnswerSource represents each individual answer.
"answerValue": { # Message for holding the value of a QaAnswer. QaQuestion.AnswerChoice defines the possible answer values for a question. # The answer value from this source.
"boolValue": True or False, # Boolean value.
"key": "A String", # A short string used as an identifier. Matches the value used in QaQuestion.AnswerChoice.key.
"naValue": True or False, # A value of "Not Applicable (N/A)". Should only ever be `true`.
"normalizedScore": 3.14, # Output only. Normalized score of the questions. Calculated as score / potential_score.
"numValue": 3.14, # Numerical value.
"potentialScore": 3.14, # Output only. The maximum potential score of the question.
"score": 3.14, # Output only. Numerical score of the answer.
"strValue": "A String", # String value.
},
"sourceType": "A String", # What created the answer.
},
],
"answerValue": { # Message for holding the value of a QaAnswer. QaQuestion.AnswerChoice defines the possible answer values for a question. # The main answer value, incorporating any manual edits if they exist.
"boolValue": True or False, # Boolean value.
"key": "A String", # A short string used as an identifier. Matches the value used in QaQuestion.AnswerChoice.key.
"naValue": True or False, # A value of "Not Applicable (N/A)". Should only ever be `true`.
"normalizedScore": 3.14, # Output only. Normalized score of the questions. Calculated as score / potential_score.
"numValue": 3.14, # Numerical value.
"potentialScore": 3.14, # Output only. The maximum potential score of the question.
"score": 3.14, # Output only. Numerical score of the answer.
"strValue": "A String", # String value.
},
"conversation": "A String", # The conversation the answer applies to.
"qaQuestion": "A String", # The QaQuestion answered by this answer.
"questionBody": "A String", # Question text. E.g., "Did the agent greet the customer?"
"tags": [ # User-defined list of arbitrary tags. Matches the value from QaScorecard.ScorecardQuestion.tags. Used for grouping/organization and for weighting the score of each answer.
"A String",
],
},
],
"qaScorecardRevision": "A String", # The QaScorecardRevision scored by this result.
"qaTagResults": [ # Collection of tags and their scores.
{ # Tags and their corresponding results.
"normalizedScore": 3.14, # The normalized score the tag applies to.
"potentialScore": 3.14, # The potential score the tag applies to.
"score": 3.14, # The score the tag applies to.
"tag": "A String", # The tag the score applies to.
},
],
"score": 3.14, # The overall numerical score of the result, incorporating any manual edits if they exist.
"scoreSources": [ # List of all individual score sets.
{ # A scorecard result may have multiple sets of scores from varying sources, one of which becomes the "main" answer above. A ScoreSource represents each individual set of scores.
"normalizedScore": 3.14, # The normalized score, which is the score divided by the potential score.
"potentialScore": 3.14, # The maximum potential overall score of the scorecard. Any questions answered using `na_value` are excluded from this calculation.
"qaTagResults": [ # Collection of tags and their scores.
{ # Tags and their corresponding results.
"normalizedScore": 3.14, # The normalized score the tag applies to.
"potentialScore": 3.14, # The potential score the tag applies to.
"score": 3.14, # The score the tag applies to.
"tag": "A String", # The tag the score applies to.
},
],
"score": 3.14, # The overall numerical score of the result.
"sourceType": "A String", # What created the score.
},
],
},
],
"sentiments": [ # Overall conversation-level sentiment for each channel of the call.
{ # One channel of conversation-level sentiment data.
"channelTag": 42, # The channel of the audio that the data applies to.
"sentimentData": { # The data for a sentiment annotation. # Data specifying sentiment.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
},
],
"silence": { # Conversation-level silence data. # Overall conversation-level silence during the call.
"silenceDuration": "A String", # Amount of time calculated to be in silence.
"silencePercentage": 3.14, # Percentage of the total conversation spent in silence.
},
},
"endTime": "A String", # The time at which the analysis ended.
},
"annotatorSelector": { # Selector of all available annotators and phrase matchers to run. # To select the annotators to run and the phrase matchers to use (if any). If not specified, all annotators will be run.
"issueModels": [ # The issue model to run. If not provided, the most recently deployed topic model will be used. The provided issue model will only be used for inference if the issue model is deployed and if run_issue_model_annotator is set to true. If more than one issue model is provided, only the first provided issue model will be used for inference.
"A String",
],
"phraseMatchers": [ # The list of phrase matchers to run. If not provided, all active phrase matchers will be used. If inactive phrase matchers are provided, they will not be used. Phrase matchers will be run only if run_phrase_matcher_annotator is set to true. Format: projects/{project}/locations/{location}/phraseMatchers/{phrase_matcher}
"A String",
],
"qaConfig": { # Configuration for the QA feature. # Configuration for the QA annotator.
"scorecardList": { # Container for a list of scorecards. # A manual list of scorecards to score.
"qaScorecardRevisions": [ # List of QaScorecardRevisions.
"A String",
],
},
},
"runEntityAnnotator": True or False, # Whether to run the entity annotator.
"runIntentAnnotator": True or False, # Whether to run the intent annotator.
"runInterruptionAnnotator": True or False, # Whether to run the interruption annotator.
"runIssueModelAnnotator": True or False, # Whether to run the issue model annotator. A model should have already been deployed for this to take effect.
"runPhraseMatcherAnnotator": True or False, # Whether to run the active phrase matcher annotator(s).
"runQaAnnotator": True or False, # Whether to run the QA annotator.
"runSentimentAnnotator": True or False, # Whether to run the sentiment annotator.
"runSilenceAnnotator": True or False, # Whether to run the silence annotator.
"runSummarizationAnnotator": True or False, # Whether to run the summarization annotator.
"summarizationConfig": { # Configuration for summarization. # Configuration for the summarization annotator.
"conversationProfile": "A String", # Resource name of the Dialogflow conversation profile. Format: projects/{project}/locations/{location}/conversationProfiles/{conversation_profile}
"summarizationModel": "A String", # Default summarization model to be used.
},
},
"createTime": "A String", # Output only. The time at which the analysis was created, which occurs when the long-running operation completes.
"name": "A String", # Immutable. The resource name of the analysis. Format: projects/{project}/locations/{location}/conversations/{conversation}/analyses/{analysis}
"requestTime": "A String", # Output only. The time at which the analysis was requested.
}
list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)
Lists analyses.
Args:
parent: string, Required. The parent resource of the analyses. (required)
filter: string, A filter to reduce results to a specific subset. Useful for querying conversations with specific properties.
pageSize: integer, The maximum number of analyses to return in the response. If this value is zero, the service will select a default size. A call might return fewer objects than requested. A non-empty `next_page_token` in the response indicates that more data is available.
pageToken: string, The value returned by the last `ListAnalysesResponse`; indicates that this is a continuation of a prior `ListAnalyses` call and the system should return the next page of data.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The response to list analyses.
"analyses": [ # The analyses that match the request.
{ # The analysis resource.
"analysisResult": { # The result of an analysis. # Output only. The result of the analysis, which is populated when the analysis finishes.
"callAnalysisMetadata": { # Call-specific metadata created during analysis. # Call-specific metadata created by the analysis.
"annotations": [ # A list of call annotations that apply to this call.
{ # A piece of metadata that applies to a window of a call.
"annotationEndBoundary": { # A point in a conversation that marks the start or the end of an annotation. # The boundary in the conversation where the annotation ends, inclusive.
"transcriptIndex": 42, # The index in the sequence of transcribed pieces of the conversation where the boundary is located. This index starts at zero.
"wordIndex": 42, # The word index of this boundary with respect to the first word in the transcript piece. This index starts at zero.
},
"annotationStartBoundary": { # A point in a conversation that marks the start or the end of an annotation. # The boundary in the conversation where the annotation starts, inclusive.
"transcriptIndex": 42, # The index in the sequence of transcribed pieces of the conversation where the boundary is located. This index starts at zero.
"wordIndex": 42, # The word index of this boundary with respect to the first word in the transcript piece. This index starts at zero.
},
"channelTag": 42, # The channel of the audio where the annotation occurs. For single-channel audio, this field is not populated.
"entityMentionData": { # The data for an entity mention annotation. This represents a mention of an `Entity` in the conversation. # Data specifying an entity mention.
"entityUniqueId": "A String", # The key of this entity in conversation entities. Can be used to retrieve the exact `Entity` this mention is attached to.
"sentiment": { # The data for a sentiment annotation. # Sentiment expressed for this mention of the entity.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"type": "A String", # The type of the entity mention.
},
"holdData": { # The data for a hold annotation. # Data specifying a hold.
},
"intentMatchData": { # 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. # Data specifying an intent match.
"intentUniqueId": "A String", # The id of the matched intent. Can be used to retrieve the corresponding intent information.
},
"interruptionData": { # The data for an interruption annotation. # Data specifying an interruption.
},
"issueMatchData": { # The data for an issue match annotation. # Data specifying an issue match.
"issueAssignment": { # Information about the issue. # Information about the issue's assignment.
"displayName": "A String", # Immutable. Display name of the assigned issue. This field is set at time of analyis and immutable since then.
"issue": "A String", # Resource name of the assigned issue.
"score": 3.14, # Score indicating the likelihood of the issue assignment. currently bounded on [0,1].
},
},
"phraseMatchData": { # The data for a matched phrase matcher. Represents information identifying a phrase matcher for a given match. # Data specifying a phrase match.
"displayName": "A String", # The human-readable name of the phrase matcher.
"phraseMatcher": "A String", # The unique identifier (the resource name) of the phrase matcher.
},
"sentimentData": { # The data for a sentiment annotation. # Data specifying sentiment.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"silenceData": { # The data for a silence annotation. # Data specifying silence.
},
},
],
"entities": { # All the entities in the call.
"a_key": { # 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.
"displayName": "A String", # The representative name for the entity.
"metadata": { # Metadata associated with the entity. For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`) and Knowledge Graph MID (`mid`), if they are available. For the metadata associated with other entity types, see the Type table below.
"a_key": "A String",
},
"salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range. The salience score for an entity provides information about the importance or centrality of that entity to the entire document text. Scores closer to 0 are less salient, while scores closer to 1.0 are highly salient.
"sentiment": { # The data for a sentiment annotation. # The aggregate sentiment expressed for this entity in the conversation.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
"type": "A String", # The entity type.
},
},
"intents": { # All the matched intents in the call.
"a_key": { # The data for an intent. Represents a detected intent in the conversation, for example MAKES_PROMISE.
"displayName": "A String", # The human-readable name of the intent.
"id": "A String", # The unique identifier of the intent.
},
},
"issueModelResult": { # Issue Modeling result on a conversation. # Overall conversation-level issue modeling result.
"issueModel": "A String", # Issue model that generates the result. Format: projects/{project}/locations/{location}/issueModels/{issue_model}
"issues": [ # All the matched issues.
{ # Information about the issue.
"displayName": "A String", # Immutable. Display name of the assigned issue. This field is set at time of analyis and immutable since then.
"issue": "A String", # Resource name of the assigned issue.
"score": 3.14, # Score indicating the likelihood of the issue assignment. currently bounded on [0,1].
},
],
},
"phraseMatchers": { # All the matched phrase matchers in the call.
"a_key": { # The data for a matched phrase matcher. Represents information identifying a phrase matcher for a given match.
"displayName": "A String", # The human-readable name of the phrase matcher.
"phraseMatcher": "A String", # The unique identifier (the resource name) of the phrase matcher.
},
},
"qaScorecardResults": [ # Results of scoring QaScorecards.
{ # The results of scoring a single conversation against a QaScorecard. Contains a collection of QaAnswers and aggregate score.
"agentId": "A String", # ID of the agent that handled the conversation.
"conversation": "A String", # The conversation scored by this result.
"createTime": "A String", # Output only. The timestamp that the revision was created.
"name": "A String", # Identifier. The name of the scorecard result. Format: projects/{project}/locations/{location}/qaScorecardResults/{qa_scorecard_result}
"normalizedScore": 3.14, # The normalized score, which is the score divided by the potential score. Any manual edits are included if they exist.
"potentialScore": 3.14, # The maximum potential overall score of the scorecard. Any questions answered using `na_value` are excluded from this calculation.
"qaAnswers": [ # Set of QaAnswers represented in the result.
{ # An answer to a QaQuestion.
"answerSources": [ # List of all individual answers given to the question.
{ # A question may have multiple answers from varying sources, one of which becomes the "main" answer above. AnswerSource represents each individual answer.
"answerValue": { # Message for holding the value of a QaAnswer. QaQuestion.AnswerChoice defines the possible answer values for a question. # The answer value from this source.
"boolValue": True or False, # Boolean value.
"key": "A String", # A short string used as an identifier. Matches the value used in QaQuestion.AnswerChoice.key.
"naValue": True or False, # A value of "Not Applicable (N/A)". Should only ever be `true`.
"normalizedScore": 3.14, # Output only. Normalized score of the questions. Calculated as score / potential_score.
"numValue": 3.14, # Numerical value.
"potentialScore": 3.14, # Output only. The maximum potential score of the question.
"score": 3.14, # Output only. Numerical score of the answer.
"strValue": "A String", # String value.
},
"sourceType": "A String", # What created the answer.
},
],
"answerValue": { # Message for holding the value of a QaAnswer. QaQuestion.AnswerChoice defines the possible answer values for a question. # The main answer value, incorporating any manual edits if they exist.
"boolValue": True or False, # Boolean value.
"key": "A String", # A short string used as an identifier. Matches the value used in QaQuestion.AnswerChoice.key.
"naValue": True or False, # A value of "Not Applicable (N/A)". Should only ever be `true`.
"normalizedScore": 3.14, # Output only. Normalized score of the questions. Calculated as score / potential_score.
"numValue": 3.14, # Numerical value.
"potentialScore": 3.14, # Output only. The maximum potential score of the question.
"score": 3.14, # Output only. Numerical score of the answer.
"strValue": "A String", # String value.
},
"conversation": "A String", # The conversation the answer applies to.
"qaQuestion": "A String", # The QaQuestion answered by this answer.
"questionBody": "A String", # Question text. E.g., "Did the agent greet the customer?"
"tags": [ # User-defined list of arbitrary tags. Matches the value from QaScorecard.ScorecardQuestion.tags. Used for grouping/organization and for weighting the score of each answer.
"A String",
],
},
],
"qaScorecardRevision": "A String", # The QaScorecardRevision scored by this result.
"qaTagResults": [ # Collection of tags and their scores.
{ # Tags and their corresponding results.
"normalizedScore": 3.14, # The normalized score the tag applies to.
"potentialScore": 3.14, # The potential score the tag applies to.
"score": 3.14, # The score the tag applies to.
"tag": "A String", # The tag the score applies to.
},
],
"score": 3.14, # The overall numerical score of the result, incorporating any manual edits if they exist.
"scoreSources": [ # List of all individual score sets.
{ # A scorecard result may have multiple sets of scores from varying sources, one of which becomes the "main" answer above. A ScoreSource represents each individual set of scores.
"normalizedScore": 3.14, # The normalized score, which is the score divided by the potential score.
"potentialScore": 3.14, # The maximum potential overall score of the scorecard. Any questions answered using `na_value` are excluded from this calculation.
"qaTagResults": [ # Collection of tags and their scores.
{ # Tags and their corresponding results.
"normalizedScore": 3.14, # The normalized score the tag applies to.
"potentialScore": 3.14, # The potential score the tag applies to.
"score": 3.14, # The score the tag applies to.
"tag": "A String", # The tag the score applies to.
},
],
"score": 3.14, # The overall numerical score of the result.
"sourceType": "A String", # What created the score.
},
],
},
],
"sentiments": [ # Overall conversation-level sentiment for each channel of the call.
{ # One channel of conversation-level sentiment data.
"channelTag": 42, # The channel of the audio that the data applies to.
"sentimentData": { # The data for a sentiment annotation. # Data specifying sentiment.
"magnitude": 3.14, # A non-negative number from 0 to infinity which represents the abolute magnitude of sentiment regardless of score.
"score": 3.14, # The sentiment score between -1.0 (negative) and 1.0 (positive).
},
},
],
"silence": { # Conversation-level silence data. # Overall conversation-level silence during the call.
"silenceDuration": "A String", # Amount of time calculated to be in silence.
"silencePercentage": 3.14, # Percentage of the total conversation spent in silence.
},
},
"endTime": "A String", # The time at which the analysis ended.
},
"annotatorSelector": { # Selector of all available annotators and phrase matchers to run. # To select the annotators to run and the phrase matchers to use (if any). If not specified, all annotators will be run.
"issueModels": [ # The issue model to run. If not provided, the most recently deployed topic model will be used. The provided issue model will only be used for inference if the issue model is deployed and if run_issue_model_annotator is set to true. If more than one issue model is provided, only the first provided issue model will be used for inference.
"A String",
],
"phraseMatchers": [ # The list of phrase matchers to run. If not provided, all active phrase matchers will be used. If inactive phrase matchers are provided, they will not be used. Phrase matchers will be run only if run_phrase_matcher_annotator is set to true. Format: projects/{project}/locations/{location}/phraseMatchers/{phrase_matcher}
"A String",
],
"qaConfig": { # Configuration for the QA feature. # Configuration for the QA annotator.
"scorecardList": { # Container for a list of scorecards. # A manual list of scorecards to score.
"qaScorecardRevisions": [ # List of QaScorecardRevisions.
"A String",
],
},
},
"runEntityAnnotator": True or False, # Whether to run the entity annotator.
"runIntentAnnotator": True or False, # Whether to run the intent annotator.
"runInterruptionAnnotator": True or False, # Whether to run the interruption annotator.
"runIssueModelAnnotator": True or False, # Whether to run the issue model annotator. A model should have already been deployed for this to take effect.
"runPhraseMatcherAnnotator": True or False, # Whether to run the active phrase matcher annotator(s).
"runQaAnnotator": True or False, # Whether to run the QA annotator.
"runSentimentAnnotator": True or False, # Whether to run the sentiment annotator.
"runSilenceAnnotator": True or False, # Whether to run the silence annotator.
"runSummarizationAnnotator": True or False, # Whether to run the summarization annotator.
"summarizationConfig": { # Configuration for summarization. # Configuration for the summarization annotator.
"conversationProfile": "A String", # Resource name of the Dialogflow conversation profile. Format: projects/{project}/locations/{location}/conversationProfiles/{conversation_profile}
"summarizationModel": "A String", # Default summarization model to be used.
},
},
"createTime": "A String", # Output only. The time at which the analysis was created, which occurs when the long-running operation completes.
"name": "A String", # Immutable. The resource name of the analysis. Format: projects/{project}/locations/{location}/conversations/{conversation}/analyses/{analysis}
"requestTime": "A String", # Output only. The time at which the analysis was requested.
},
],
"nextPageToken": "A String", # A token, which can be sent as `page_token` to retrieve the next page. If this field is omitted, there are no subsequent pages.
}
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.