Central instance to access all Dialogflow related resource activities
A Dialogflow agent is a virtual agent that handles conversations with your end-users. It is a natural language understanding module that understands the nuances of human language. Dialogflow translates end-user text or audio during a conversation to structured data that your apps and services can understand. You design and build a Dialogflow agent to handle the types of conversations required for your system. For more information about agents, see the
Agent guide.
Detail feedback of Agent Assist result.
Feedback for knowledge search.
Feedback for conversation summarization.
Represents a record of a human agent assist answer.
The request message for Participants.AnalyzeContent.
The response message for Participants.AnalyzeContent.
Represents a part of a message possibly annotated with an entity. The part can be an entity or purely a part of the message between two entities or message start/end.
Represents feedback the customer has about the quality & correctness of a certain answer in a conversation.
Answer records are records to manage answer history and feedbacks for Dialogflow. Currently, answer record includes: - human agent assistant article suggestion - human agent assistant faq article It doesn’t include: - DetectIntent intent matching - DetectIntent knowledge Answer records are not related to the conversation history in the Dialogflow Console. A Record is generated even when the end-user disables conversation history in the console. Records are created when there’s a human agent assistant suggestion generated. A typical workflow for customers provide feedback to an answer is: 1. For human agent assistant, customers get suggestion via ListSuggestions API. Together with the answers, AnswerRecord.name are returned to the customers. 2. The customer uses the AnswerRecord.name to call the UpdateAnswerRecord method to send feedback about a specific answer that they believe is wrong.
Represents article answer.
Metadata for article suggestion models.
Represents the parameters of human assist query.
Defines the Automated Agent to connect to a conversation.
Represents a response from an automated agent.
The request message for EntityTypes.BatchCreateEntities.
The request message for EntityTypes.BatchDeleteEntities.
The request message for EntityTypes.BatchDeleteEntityTypes.
The request message for Intents.BatchDeleteIntents.
The request message for EntityTypes.BatchUpdateEntities.
The request message for EntityTypes.BatchUpdateEntityTypes.
There is no detailed description.
The request message for ConversationProfiles.ClearFeature.
The request message for Conversations.CompleteConversation.
Dialogflow contexts are similar to natural language context. If a person says to you “they are orange”, you need context in order to understand what “they” is referring to. Similarly, for Dialogflow to handle an end-user expression like that, it needs to be provided with context in order to correctly match an intent. Using contexts, you can control the flow of a conversation. You can configure contexts for an intent by setting input and output contexts, which are identified by string names. When an intent is matched, any configured output contexts for that intent become active. While any contexts are active, Dialogflow is more likely to match intents that are configured with input contexts that correspond to the currently active contexts. For more information about context, see the
Contexts guide.
Represents a conversation. A conversation is an interaction between an agent, including live agents and Dialogflow agents, and a support customer. Conversations can include phone calls and text-based chat sessions.
Represents a conversation dataset that a user imports raw data into. The data inside ConversationDataset can not be changed after ImportConversationData finishes (and calling ImportConversationData on a dataset that already has data is not allowed).
Represents metadata of a conversation.
Represents a conversation model.
Represents evaluation result of a conversation model.
Represents a phone number for telephony integration. It allows for connecting a particular conversation over telephony.
Defines the services to connect to incoming Dialogflow conversations.
The request message for ConversationModels.CreateConversationModelEvaluation
The request message for ConversationModels.DeployConversationModel
The request to detect user’s intent.
The message returned from the DetectIntent method.
Represents a Dialogflow assist answer.
A knowledge document to be used by a KnowledgeBase. For more information, see the
knowledge base guide. Note: The
projects.agent.knowledgeBases.documents resource is deprecated; only use
projects.knowledgeBases.documents.
The status of a reload attempt.
The message in the response that indicates the parameters of DTMF.
Each intent parameter has a type, called the entity type, which dictates exactly how data from an end-user expression is extracted. Dialogflow provides predefined system entities that can match many common types of data. For example, there are system entities for matching dates, times, colors, email addresses, and so on. You can also create your own custom entities for matching custom data. For example, you could define a vegetable entity that can match the types of vegetables available for purchase with a grocery store agent. For more information, see the
Entity guide.
This message is a wrapper around a collection of entity types.
An entity entry for an associated entity type.
You can create multiple versions of your agent and publish them to separate environments. When you edit an agent, you are editing the draft agent. At any point, you can save the draft agent as an agent version, which is an immutable snapshot of your agent. When you save the draft agent, it is published to the default environment. When you create agent versions, you can publish them to custom environments. You can create a variety of custom environments for: - testing - development - production - etc. For more information, see the
versions and environments guide.
The response message for Environments.GetEnvironmentHistory.
Represents an environment history entry.
The configuration for model evaluation.
Smart compose specific configuration for evaluation job.
Smart reply specific configuration for evaluation job.
Events allow for matching intents by event name instead of the natural language input. For instance, input `` can trigger a personalized welcome response. The parameter name may be used by the agent in the response: "Hello #welcome_event.name! What can I do for you today?".
The request message for Agents.ExportAgent.
Request message for Documents.ExportDocument.
Represents answer from “frequently asked questions”.
By default, your agent responds to a matched intent with a static response. As an alternative, you can provide a more dynamic response by using fulfillment. When you enable fulfillment for an intent, Dialogflow responds to that intent by calling a service that you define. For example, if an end-user wants to schedule a haircut on Friday, your service can check your database and respond to the end-user with availability information for Friday. For more information, see the
fulfillment guide.
Whether fulfillment is enabled for the specific feature.
Represents configuration for a generic web service. Dialogflow supports two mechanisms for authentications: - Basic authentication with username and password. - Authentication with additional authentication headers. More information could be found at: https://cloud.google.com/dialogflow/docs/fulfillment-configure.
Google Cloud Storage location for the output.
Google Cloud Storage location for the inputs.
The request message for Conversations.GenerateStatelessSummary.
The minimum amount of information required to generate a Summary without having a Conversation resource created.
The response message for Conversations.GenerateStatelessSummary.
Generated summary for a conversation.
Defines the Human Agent Assist to connect to a conversation.
Custom conversation models used in agent assist feature. Supported feature: ARTICLE_SUGGESTION, SMART_COMPOSE, SMART_REPLY, CONVERSATION_SUMMARIZATION.
Config to process conversation.
Configuration for analyses to run on each conversation message.
Detail human agent assistant config.
Config for suggestion features.
Config for suggestion query.
Settings that determine how to filter recent conversation context when generating suggestions.
Dialogflow source setting. Supported feature: DIALOGFLOW_ASSIST.
The configuration used for human agent side Dialogflow assist suggestion.
Document source settings. Supported features: SMART_REPLY, SMART_COMPOSE.
Knowledge base source settings. Supported features: ARTICLE_SUGGESTION, FAQ.
Custom sections to return when requesting a summary of a conversation. This is only supported when baseline_model_version == ‘2.0’. Supported features: CONVERSATION_SUMMARIZATION, CONVERSATION_SUMMARIZATION_VOICE.
Settings of suggestion trigger.
Defines the hand off to a live agent, typically on which external agent service provider to connect to a conversation. Currently, this feature is not general available, please contact Google to get access.
Configuration specific to LivePerson (https://www.liveperson.com).
Configuration specific to Salesforce Live Agent.
The request message for Agents.ImportAgent.
The request message for ConversationDatasets.ImportConversationData.
The template used for importing documents.
Request message for Documents.ImportDocuments.
Instructs the speech recognizer how to process the audio content.
Represents the configuration of importing a set of conversation files in Google Cloud Storage.
InputDataset used to create model or do evaluation. NextID:5
An intent categorizes an end-user’s intention for one conversation turn. For each agent, you define many intents, where your combined intents can handle a complete conversation. When an end-user writes or says something, referred to as an end-user expression or end-user input, Dialogflow matches the end-user input to the best intent in your agent. Matching an intent is also known as intent classification. For more information, see the
intent guide.
This message is a wrapper around a collection of intents.
Represents a single followup intent in the chain.
A rich response message. Corresponds to the intent
Response field in the Dialogflow console. For more information, see
Rich response messages.
The basic card message. Useful for displaying information.
The button object that appears at the bottom of a card.
Opens the given URI.
Browse Carousel Card for Actions on Google. https://developers.google.com/actions/assistant/responses#browsing_carousel
Browsing carousel tile
Actions on Google action to open a given url.
The card response message.
Contains information about a button.
The card for presenting a carousel of options to select from.
An item in the carousel.
Column properties for TableCard.
The image response message.
The suggestion chip message that allows the user to jump out to the app or website associated with this agent.
The card for presenting a list of options to select from.
An item in the list.
The media content card for Actions on Google.
Response media object for media content card.
The quick replies response message.
Additional info about the select item for when it is triggered in a dialog.
The simple response message containing speech or text.
The collection of simple response candidates. This message in QueryResult.fulfillment_messages and WebhookResponse.fulfillment_messages should contain only one SimpleResponse.
The suggestion chip message that the user can tap to quickly post a reply to the conversation.
The collection of suggestions.
Table card for Actions on Google.
Cell of TableCardRow.
Row of TableCard.
The text response message.
Represents intent parameters.
Represents an intent suggestion.
Represents an example that the agent is trained on.
Represents a part of a training phrase.
A knowledge base represents a collection of knowledge documents that you provide to Dialogflow. Your knowledge documents contain information that may be useful during conversations with end-users. Some Dialogflow features use knowledge bases when looking for a response to an end-user input. For more information, see the
knowledge base guide. Note: The
projects.agent.knowledgeBases resource is deprecated; only use
projects.knowledgeBases.
Response message for AnswerRecords.ListAnswerRecords.
The response message for Contexts.ListContexts.
The response message for ConversationDatasets.ListConversationDatasets.
The response message for ConversationModels.ListConversationModelEvaluations
The response message for ConversationModels.ListConversationModels
The response message for ConversationProfiles.ListConversationProfiles.
The response message for Conversations.ListConversations.
Response message for Documents.ListDocuments.
The response message for EntityTypes.ListEntityTypes.
The response message for Environments.ListEnvironments.
The response message for Intents.ListIntents.
Response message for KnowledgeBases.ListKnowledgeBases.
The response message for Conversations.ListMessages.
The response message for Participants.ListParticipants.
The response message for SessionEntityTypes.ListSessionEntityTypes.
The response message for Versions.ListVersions.
Defines logging behavior for conversation lifecycle events.
Represents a message posted into a conversation.
Represents the result of annotation for the message.
Defines notification behavior.
Represents the natural language speech audio to be played to the end user.
Instructs the speech synthesizer on how to generate the output audio content. If this audio config is supplied in a request, it overrides all existing text-to-speech settings applied to the agent.
Represents a conversation participant (human agent, virtual agent, end-user).
Represents the query input. It can contain either: 1. An audio config which instructs the speech recognizer how to process the speech audio. 2. A conversational query in the form of text. 3. An event that specifies which intent to trigger.
Represents the parameters of the conversational query.
Represents the result of conversational query or event processing.
Request message for Documents.ReloadDocument.
The request message for Agents.RestoreAgent.
The response message for Agents.SearchAgents.
Represents a SearchKnowledge answer.
The sources of the answers.
The request message for Conversations.SearchKnowledge.
The response message for Conversations.SearchKnowledge.
The sentiment, such as positive/negative feeling or association, for a unit of analysis, such as the query text. See: https://cloud.google.com/natural-language/docs/basics#interpreting_sentiment_analysis_values for how to interpret the result.
Configures the types of sentiment analysis to perform.
The result of sentiment analysis. Sentiment analysis inspects user input and identifies the prevailing subjective opinion, especially to determine a user’s attitude as positive, negative, or neutral. For Participants.DetectIntent, it needs to be configured in DetectIntentRequest.query_params. For Participants.StreamingDetectIntent, it needs to be configured in StreamingDetectIntentRequest.query_params. And for Participants.AnalyzeContent and Participants.StreamingAnalyzeContent, it needs to be configured in ConversationProfile.human_agent_assistant_config
A session represents a conversation between a Dialogflow agent and an end-user. You can create special entities, called session entities, during a session. Session entities can extend or replace custom entity types and only exist during the session that they were created for. All session data, including session entities, is stored by Dialogflow for 20 minutes. For more information, see the
session entity guide.
The request message for ConversationProfiles.SetSuggestionFeature.
Represents a smart reply answer.
The evaluation metrics for smart reply model.
Evaluation metrics when retrieving n smart replies with the model.
Metadata for smart reply models.
Hints for the speech recognizer to help with recognition in a specific conversation state.
Configures speech transcription for ConversationProfile.
The request message for Participants.SuggestArticles.
The response message for Participants.SuggestArticles.
The request message for Conversations.SuggestConversationSummary.
The response message for Conversations.SuggestConversationSummary.
Generated summary for a conversation.
The request message for Participants.SuggestFaqAnswers.
The request message for Participants.SuggestFaqAnswers.
The request message for Participants.SuggestSmartReplies.
The response message for Participants.SuggestSmartReplies.
The type of Human Agent Assistant API suggestion to perform, and the maximum number of results to return for that type. Multiple Feature objects can be specified in the features list.
Represents the selection of a suggestion.
One response of different type of suggestion response which is used in the response of Participants.AnalyzeContent and Participants.AnalyzeContent, as well as HumanAgentAssistantEvent.
Configuration of how speech should be synthesized.
Auxiliary proto messages. Represents the natural language text to be processed.
Instructs the speech synthesizer on how to generate the output audio content.
The request message for Agents.TrainAgent.
The request message for ConversationModels.UndeployConversationModel
Represents a single validation error.
Represents the output of agent validation.
You can create multiple versions of your agent and publish them to separate environments. When you edit an agent, you are editing the draft agent. At any point, you can save the draft agent as an agent version, which is an immutable snapshot of your agent. When you save the draft agent, it is published to the default environment. When you create agent versions, you can publish them to custom environments. You can create a variety of custom environments for: - testing - development - production - etc. For more information, see the
versions and environments guide.
Description of which voice to use for speech synthesis.
The response message for Locations.ListLocations.
A resource that represents a Google Cloud location.
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.
An object that represents a latitude/longitude pair. This is expressed as a pair of doubles to represent degrees latitude and degrees longitude. Unless specified otherwise, this object must conform to the WGS84 standard. Values must be within normalized ranges.
Updates/Creates multiple entity types in the specified agent. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: BatchUpdateEntityTypesResponse Note: You should always train an agent prior to sending it queries. See the
training documentation.
Creates an entity type in the specified agent. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Deletes the specified entity type. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Updates or creates multiple entities in the specified entity type. This method does not affect entities in the entity type that aren’t explicitly specified in the request. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: An
Empty message Note: You should always train an agent prior to sending it queries. See the
training documentation.
Retrieves the specified entity type.
Returns the list of all entity types in the specified agent.
Updates the specified entity type. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Creates an agent environment.
Deletes the specified agent environment.
Retrieves the specified agent environment.
Gets the history of the specified environment.
Returns the list of all intents in the specified agent.
Returns the list of all non-default environments of the specified agent.
Updates the specified agent environment. This method allows you to deploy new agent versions into the environment. When an environment is pointed to a new agent version by setting environment.agent_version, the environment is temporarily set to the LOADING state. During that time, the environment continues serving the previous version of the agent. After the new agent version is done loading, the environment is set back to the RUNNING state. You can use “-” as Environment ID in environment name to update an agent version in the default environment. WARNING: this will negate all recent changes to the draft agent and can’t be undone. You may want to save the draft agent to a version before calling this method.
Creates a context. If the specified context already exists, overrides the context.
Deletes the specified context.
Retrieves the specified context.
Returns the list of all contexts in the specified session.
Updates the specified context.
Deletes all active contexts in the specified session.
Processes a natural language query and returns structured, actionable data as a result. This method is not idempotent, because it may cause contexts and session entity types to be updated, which in turn might affect results of future queries. If you might use
Agent Assist or other CCAI products now or in the future, consider using AnalyzeContent instead of
DetectIntent.
AnalyzeContent has additional functionality for Agent Assist and other CCAI products. Note: Always use agent versions for production traffic. See
Versions and environments.
Creates a session entity type. If the specified session entity type already exists, overrides the session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Deletes the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Retrieves the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Returns the list of all session entity types in the specified session. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Updates the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Exports the specified agent to a ZIP file. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: ExportAgentResponse
Retrieves the fulfillment.
Gets agent validation result. Agent validation is performed during training time and is updated automatically when training is completed.
Imports the specified agent from a ZIP file. Uploads new intents and entity types without deleting the existing ones. Intents and entity types with the same name are replaced with the new versions from ImportAgentRequest. After the import, the imported draft agent will be trained automatically (unless disabled in agent settings). However, once the import is done, training may not be completed yet. Please call TrainAgent and wait for the operation it returns in order to train explicitly. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: An
Empty message The operation only tracks when importing is complete, not when it is done training. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Updates/Creates multiple intents in the specified agent. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: BatchUpdateIntentsResponse Note: You should always train an agent prior to sending it queries. See the
training documentation.
Creates an intent in the specified agent. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Deletes the specified intent and its direct or indirect followup intents. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Retrieves the specified intent.
Returns the list of all intents in the specified agent.
Updates the specified intent. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Creates a knowledge base.
Deletes the specified knowledge base.
Creates a new document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document
Deletes the specified document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: An
Empty messageRetrieves the specified document.
Returns the list of all documents of the knowledge base.
Updates the specified document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document
Reloads the specified document from its specified source, content_uri or content. The previously loaded content of the document will be deleted. Note: Even when the content of the document has not changed, there still may be side effects because of internal implementation changes. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document Note: The
projects.agent.knowledgeBases.documents resource is deprecated; only use
projects.knowledgeBases.documents.
Retrieves the specified knowledge base.
Returns the list of all knowledge bases of the specified agent.
Updates the specified knowledge base.
Restores the specified agent from a ZIP file. Replaces the current agent version with a new one. All the intents and entity types in the older version are deleted. After the restore, the restored draft agent will be trained automatically (unless disabled in agent settings). However, once the restore is done, training may not be completed yet. Please call TrainAgent and wait for the operation it returns in order to train explicitly. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: An
Empty message The operation only tracks when restoring is complete, not when it is done training. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Returns the list of agents. Since there is at most one conversational agent per project, this method is useful primarily for listing all agents across projects the caller has access to. One can achieve that with a wildcard project collection id “-”. Refer to
List Sub-Collections.
Creates a context. If the specified context already exists, overrides the context.
Deletes the specified context.
Retrieves the specified context.
Returns the list of all contexts in the specified session.
Updates the specified context.
Deletes all active contexts in the specified session.
Processes a natural language query and returns structured, actionable data as a result. This method is not idempotent, because it may cause contexts and session entity types to be updated, which in turn might affect results of future queries. If you might use
Agent Assist or other CCAI products now or in the future, consider using AnalyzeContent instead of
DetectIntent.
AnalyzeContent has additional functionality for Agent Assist and other CCAI products. Note: Always use agent versions for production traffic. See
Versions and environments.
Creates a session entity type. If the specified session entity type already exists, overrides the session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Deletes the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Retrieves the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Returns the list of all session entity types in the specified session. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Updates the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Updates the fulfillment.
Creates an agent version. The new version points to the agent instance in the “default” environment.
Delete the specified agent version.
Retrieves the specified agent version.
Returns the list of all versions of the specified agent.
Updates the specified agent version. Note that this method does not allow you to update the state of the agent the given version points to. It allows you to update only mutable properties of the version resource.
Returns the list of all answer records in the specified project in reverse chronological order.
Updates the specified answer record.
Completes the specified conversation. Finished conversations are purged from the database after 30 days.
Creates a new conversation. Conversations are auto-completed after 24 hours. Conversation Lifecycle: There are two stages during a conversation: Automated Agent Stage and Assist Stage. For Automated Agent Stage, there will be a dialogflow agent responding to user queries. For Assist Stage, there’s no dialogflow agent responding to user queries. But we will provide suggestions which are generated from conversation. If Conversation.conversation_profile is configured for a dialogflow agent, conversation will start from Automated Agent Stage, otherwise, it will start from Assist Stage. And during Automated Agent Stage, once an Intent with Intent.live_agent_handoff is triggered, conversation will transfer to Assist Stage.
Retrieves the specified conversation dataset.
Import data into the specified conversation dataset. Note that it is not allowed to import data to a conversation dataset that already has data in it. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: ImportConversationDataOperationMetadata -
response: ImportConversationDataOperationResponse
Returns the list of all conversation datasets in the specified project and location.
Retrieves the specific conversation.
Returns the list of all conversations in the specified project.
Lists messages that belong to a given conversation. messages are ordered by create_time in descending order. To fetch updates without duplication, send request with filter create_time_epoch_microseconds > [first item's create_time of previous request] and empty page_token.
Creates a model. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: CreateConversationModelOperationMetadata -
response: ConversationModel
Deletes a model. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: DeleteConversationModelOperationMetadata -
response: An
Empty messageDeploys a model. If a model is already deployed, deploying it has no effect. A model can only serve prediction requests after it gets deployed. For article suggestion, custom model will not be used unless it is deployed. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: DeployConversationModelOperationMetadata -
response: An
Empty messageGets an evaluation of conversation model.
Lists evaluations of a conversation model.
Gets conversation model.
Lists conversation models.
Undeploys a model. If the model is not deployed this method has no effect. If the model is currently being used: - For article suggestion, article suggestion will fallback to the default model if model is undeployed. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: UndeployConversationModelOperationMetadata -
response: An
Empty messageAdds a text (chat, for example), or audio (phone recording, for example) message from a participant into the conversation. Note: Always use agent versions for production traffic sent to virtual agents. See
Versions and environments.
Creates a new participant in a conversation.
Retrieves a conversation participant.
Returns the list of all participants in the specified conversation.
Updates the specified participant.
Gets suggested articles for a participant based on specific historical messages.
Gets suggested faq answers for a participant based on specific historical messages.
Gets smart replies for a participant based on specific historical messages.
Clears a suggestion feature from a conversation profile for the given participant role. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: ClearSuggestionFeatureConfigOperationMetadata -
response: ConversationProfile
Creates a conversation profile in the specified project. ConversationProfile.CreateTime and ConversationProfile.UpdateTime aren’t populated in the response. You can retrieve them via GetConversationProfile API.
Deletes the specified conversation profile.
Retrieves the specified conversation profile.
Returns the list of all conversation profiles in the specified project.
Updates the specified conversation profile. ConversationProfile.CreateTime and ConversationProfile.UpdateTime aren’t populated in the response. You can retrieve them via GetConversationProfile API.
Adds or updates a suggestion feature in a conversation profile. If the conversation profile contains the type of suggestion feature for the participant role, it will update it. Otherwise it will insert the suggestion feature. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: SetSuggestionFeatureConfigOperationMetadata -
response: ConversationProfile If a long running operation to add or update suggestion feature config for the same conversation profile, participant role and suggestion feature type exists, please cancel the existing long running operation before sending such request, otherwise the request will be rejected.
Get answers for the given query based on knowledge documents.
Suggests summary for a conversation based on specific historical messages. The range of the messages to be used for summary can be specified in the request.
Deletes the specified agent.
Retrieves the specified agent.
Creates a knowledge base.
Deletes the specified knowledge base.
Creates a new document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document
Deletes the specified document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: An
Empty messageExports a smart messaging candidate document into the specified destination. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document
Retrieves the specified document.
Creates documents by importing data from external sources. Dialogflow supports up to 350 documents in each request. If you try to import more, Dialogflow will return an error. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: ImportDocumentsResponse
Returns the list of all documents of the knowledge base.
Updates the specified document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document
Reloads the specified document from its specified source, content_uri or content. The previously loaded content of the document will be deleted. Note: Even when the content of the document has not changed, there still may be side effects because of internal implementation changes. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document Note: The
projects.agent.knowledgeBases.documents resource is deprecated; only use
projects.knowledgeBases.documents.
Retrieves the specified knowledge base.
Returns the list of all knowledge bases of the specified agent.
Updates the specified knowledge base.
Updates/Creates multiple entity types in the specified agent. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: BatchUpdateEntityTypesResponse Note: You should always train an agent prior to sending it queries. See the
training documentation.
Creates an entity type in the specified agent. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Deletes the specified entity type. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Updates or creates multiple entities in the specified entity type. This method does not affect entities in the entity type that aren’t explicitly specified in the request. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: An
Empty message Note: You should always train an agent prior to sending it queries. See the
training documentation.
Retrieves the specified entity type.
Returns the list of all entity types in the specified agent.
Updates the specified entity type. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Creates an agent environment.
Deletes the specified agent environment.
Retrieves the specified agent environment.
Gets the history of the specified environment.
Returns the list of all intents in the specified agent.
Returns the list of all non-default environments of the specified agent.
Updates the specified agent environment. This method allows you to deploy new agent versions into the environment. When an environment is pointed to a new agent version by setting environment.agent_version, the environment is temporarily set to the LOADING state. During that time, the environment continues serving the previous version of the agent. After the new agent version is done loading, the environment is set back to the RUNNING state. You can use “-” as Environment ID in environment name to update an agent version in the default environment. WARNING: this will negate all recent changes to the draft agent and can’t be undone. You may want to save the draft agent to a version before calling this method.
Creates a context. If the specified context already exists, overrides the context.
Deletes the specified context.
Retrieves the specified context.
Returns the list of all contexts in the specified session.
Updates the specified context.
Deletes all active contexts in the specified session.
Processes a natural language query and returns structured, actionable data as a result. This method is not idempotent, because it may cause contexts and session entity types to be updated, which in turn might affect results of future queries. If you might use
Agent Assist or other CCAI products now or in the future, consider using AnalyzeContent instead of
DetectIntent.
AnalyzeContent has additional functionality for Agent Assist and other CCAI products. Note: Always use agent versions for production traffic. See
Versions and environments.
Creates a session entity type. If the specified session entity type already exists, overrides the session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Deletes the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Retrieves the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Returns the list of all session entity types in the specified session. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Updates the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Exports the specified agent to a ZIP file. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: ExportAgentResponse
Retrieves the fulfillment.
Gets agent validation result. Agent validation is performed during training time and is updated automatically when training is completed.
Imports the specified agent from a ZIP file. Uploads new intents and entity types without deleting the existing ones. Intents and entity types with the same name are replaced with the new versions from ImportAgentRequest. After the import, the imported draft agent will be trained automatically (unless disabled in agent settings). However, once the import is done, training may not be completed yet. Please call TrainAgent and wait for the operation it returns in order to train explicitly. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: An
Empty message The operation only tracks when importing is complete, not when it is done training. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Updates/Creates multiple intents in the specified agent. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: BatchUpdateIntentsResponse Note: You should always train an agent prior to sending it queries. See the
training documentation.
Creates an intent in the specified agent. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Deletes the specified intent and its direct or indirect followup intents. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Retrieves the specified intent.
Returns the list of all intents in the specified agent.
Updates the specified intent. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Restores the specified agent from a ZIP file. Replaces the current agent version with a new one. All the intents and entity types in the older version are deleted. After the restore, the restored draft agent will be trained automatically (unless disabled in agent settings). However, once the restore is done, training may not be completed yet. Please call TrainAgent and wait for the operation it returns in order to train explicitly. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: An empty
Struct message -
response: An
Empty message The operation only tracks when restoring is complete, not when it is done training. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Returns the list of agents. Since there is at most one conversational agent per project, this method is useful primarily for listing all agents across projects the caller has access to. One can achieve that with a wildcard project collection id “-”. Refer to
List Sub-Collections.
Creates a context. If the specified context already exists, overrides the context.
Deletes the specified context.
Retrieves the specified context.
Returns the list of all contexts in the specified session.
Updates the specified context.
Deletes all active contexts in the specified session.
Processes a natural language query and returns structured, actionable data as a result. This method is not idempotent, because it may cause contexts and session entity types to be updated, which in turn might affect results of future queries. If you might use
Agent Assist or other CCAI products now or in the future, consider using AnalyzeContent instead of
DetectIntent.
AnalyzeContent has additional functionality for Agent Assist and other CCAI products. Note: Always use agent versions for production traffic. See
Versions and environments.
Creates a session entity type. If the specified session entity type already exists, overrides the session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Deletes the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Retrieves the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Returns the list of all session entity types in the specified session. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Updates the specified session entity type. This method doesn’t work with Google Assistant integration. Contact Dialogflow support if you need to use session entities with Google Assistant integration.
Updates the fulfillment.
Creates an agent version. The new version points to the agent instance in the “default” environment.
Delete the specified agent version.
Retrieves the specified agent version.
Returns the list of all versions of the specified agent.
Updates the specified agent version. Note that this method does not allow you to update the state of the agent the given version points to. It allows you to update only mutable properties of the version resource.
Returns the list of all answer records in the specified project in reverse chronological order.
Updates the specified answer record.
Completes the specified conversation. Finished conversations are purged from the database after 30 days.
Creates a new conversation. Conversations are auto-completed after 24 hours. Conversation Lifecycle: There are two stages during a conversation: Automated Agent Stage and Assist Stage. For Automated Agent Stage, there will be a dialogflow agent responding to user queries. For Assist Stage, there’s no dialogflow agent responding to user queries. But we will provide suggestions which are generated from conversation. If Conversation.conversation_profile is configured for a dialogflow agent, conversation will start from Automated Agent Stage, otherwise, it will start from Assist Stage. And during Automated Agent Stage, once an Intent with Intent.live_agent_handoff is triggered, conversation will transfer to Assist Stage.
Creates a new conversation dataset. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: CreateConversationDatasetOperationMetadata -
response: ConversationDataset
Deletes the specified conversation dataset. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: DeleteConversationDatasetOperationMetadata -
response: An
Empty messageRetrieves the specified conversation dataset.
Import data into the specified conversation dataset. Note that it is not allowed to import data to a conversation dataset that already has data in it. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: ImportConversationDataOperationMetadata -
response: ImportConversationDataOperationResponse
Returns the list of all conversation datasets in the specified project and location.
Retrieves the specific conversation.
Returns the list of all conversations in the specified project.
Lists messages that belong to a given conversation. messages are ordered by create_time in descending order. To fetch updates without duplication, send request with filter create_time_epoch_microseconds > [first item's create_time of previous request] and empty page_token.
Creates a model. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: CreateConversationModelOperationMetadata -
response: ConversationModel
Deletes a model. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: DeleteConversationModelOperationMetadata -
response: An
Empty messageDeploys a model. If a model is already deployed, deploying it has no effect. A model can only serve prediction requests after it gets deployed. For article suggestion, custom model will not be used unless it is deployed. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: DeployConversationModelOperationMetadata -
response: An
Empty messageCreates evaluation of a conversation model.
Gets an evaluation of conversation model.
Lists evaluations of a conversation model.
Gets conversation model.
Lists conversation models.
Undeploys a model. If the model is not deployed this method has no effect. If the model is currently being used: - For article suggestion, article suggestion will fallback to the default model if model is undeployed. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: UndeployConversationModelOperationMetadata -
response: An
Empty messageAdds a text (chat, for example), or audio (phone recording, for example) message from a participant into the conversation. Note: Always use agent versions for production traffic sent to virtual agents. See
Versions and environments.
Creates a new participant in a conversation.
Retrieves a conversation participant.
Returns the list of all participants in the specified conversation.
Updates the specified participant.
Gets suggested articles for a participant based on specific historical messages.
Gets suggested faq answers for a participant based on specific historical messages.
Gets smart replies for a participant based on specific historical messages.
Clears a suggestion feature from a conversation profile for the given participant role. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: ClearSuggestionFeatureConfigOperationMetadata -
response: ConversationProfile
Creates a conversation profile in the specified project. ConversationProfile.CreateTime and ConversationProfile.UpdateTime aren’t populated in the response. You can retrieve them via GetConversationProfile API.
Deletes the specified conversation profile.
Retrieves the specified conversation profile.
Returns the list of all conversation profiles in the specified project.
Updates the specified conversation profile. ConversationProfile.CreateTime and ConversationProfile.UpdateTime aren’t populated in the response. You can retrieve them via GetConversationProfile API.
Adds or updates a suggestion feature in a conversation profile. If the conversation profile contains the type of suggestion feature for the participant role, it will update it. Otherwise it will insert the suggestion feature. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: SetSuggestionFeatureConfigOperationMetadata -
response: ConversationProfile If a long running operation to add or update suggestion feature config for the same conversation profile, participant role and suggestion feature type exists, please cancel the existing long running operation before sending such request, otherwise the request will be rejected.
Get answers for the given query based on knowledge documents.
Suggests summary for a conversation based on specific historical messages. The range of the messages to be used for summary can be specified in the request.
Deletes the specified agent.
Retrieves the specified agent.
Gets information about a location.
Creates a knowledge base.
Deletes the specified knowledge base.
Creates a new document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document
Deletes the specified document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: An
Empty messageExports a smart messaging candidate document into the specified destination. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document
Retrieves the specified document.
Creates documents by importing data from external sources. Dialogflow supports up to 350 documents in each request. If you try to import more, Dialogflow will return an error. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: ImportDocumentsResponse
Returns the list of all documents of the knowledge base.
Updates the specified document. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document
Reloads the specified document from its specified source, content_uri or content. The previously loaded content of the document will be deleted. Note: Even when the content of the document has not changed, there still may be side effects because of internal implementation changes. This method is a
long-running operation. The returned
Operation type has the following method-specific fields: -
metadata: KnowledgeOperationMetadata -
response: Document Note: The
projects.agent.knowledgeBases.documents resource is deprecated; only use
projects.knowledgeBases.documents.
Retrieves the specified knowledge base.
Returns the list of all knowledge bases of the specified agent.
Updates the specified knowledge base.
Lists information about the supported locations for this service.
Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED.
Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.
Lists operations that match the specified filter in the request. If the server doesn’t support this method, it returns UNIMPLEMENTED.
Creates/updates the specified agent. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Generates and returns a summary for a conversation that does not have a resource created for it.
Get answers for the given query based on knowledge documents.
A builder providing access to all methods supported on
project resources.
It is not used directly, but through the
Dialogflow hub.
Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED.
Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.
Lists operations that match the specified filter in the request. If the server doesn’t support this method, it returns UNIMPLEMENTED.
Creates/updates the specified agent. Note: You should always train an agent prior to sending it queries. See the
training documentation.
Generates and returns a summary for a conversation that does not have a resource created for it.
Get answers for the given query based on knowledge documents.