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Text of the page (random words):
answer feedback dialogflow cx google cloud documentation skip to main content technology areas close ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage cross product tools close access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools console english deutsch español español américa latina français indonesia italiano português português brasil עברית 中文 简体 中文 繁體 日本語 한국어 sign in dialogflow dialogflow cx start free dialogflow site selector dialogflow common documentation edition comparison support and resources for building generative playbook based flow based or dialogflow es agents dialogflow cx documentation guides examples and references for building advanced agents with flows and playbooks dialogflow es documentation guides examples and references for building agents with intents conversational agents console new user interface replacing the dialogflow cx console for building agents with generative playbooks and flows dialogflow cx console deprecated user interface for building agents with flows dialogflow es console user interface for building agents with intents guides reference support resources technology areas more dialogflow more guides reference support resources cross product tools more console dialogflow cx introduction conversational agents console dialogflow cx console get started generative vs deterministic setup and cleanup build an agent using playbooks build an agent using flows create a webhook interactions with an integration interactions with the api basic concepts important terms conversational agents console overview dialogflow cx console overview agents prebuilt agents playbook based prebuilt agents overview airline support department of motor vehicles movie expert retail shopping assistant travel flow based prebuilt agents playbook concepts playbooks playbook goals playbook instructions code blocks playbook conditional actions playbook examples playbook parameters playbook tools playbook settings playbook best practices playbook evaluations data store concepts data stores data store tools data store tool settings data store performance improvements data store hybrid agents data store search configuration flow concepts flow based agent basics flows pages state handlers intents entities entities entity options system entities custom entities session entities regexp entities fuzzy matching parameters fulfillments webhooks generative flow features generators generative fallback prebuilt flow components overview account balance account statement address collection alphanumeric collection arrange appointment authentication authentication retail cancel order credit card cvv collection credit card enrollment credit card expiration date collection credit card number collection credit card statement date of birth collection disputes exchange order feedback fraud and dispute retail financial services steering greeting lock card lost and stolen card make a payment name collection numeric collection offers and deals open account order status return and refund telecommunications steering telephone number collection transaction history waiting room zip code collection advanced concepts agent settings speech speech models voice cloning speech adaptation advanced speech settings speech model migration q1 2024 speech model migration for phone_call q2 2024 search integrations integrations telephony dialogflow cx phone gateway audiocodes avaya twilio voximplant dtmf for telephony integrations sip trunk integration overview google cloud platform project setup sbc configuration and validation concepts and troubleshooting text based dialogflow messenger dialogflow messenger dialogflow messenger html customizations dialogflow messenger css customizations dialogflow messenger javascript events dialogflow messenger javascript functions dialogflow messenger fulfillment legacy dialogflow messenger messenger from meta workplace from meta line slack google chat avatar soul machines multilingual agents call companion best practices service use best practices general agent design best practices voice agent design best practices logging and history conversation history cloud logging interaction logging export to bigquery change history agent backup audit logging agent quality answer feedback analytics legacy analytics security and data residency access control agent collaboration customer managed encryption keys cmek security settings using vpc service controls mutual tls authentication regionalization and location settings custom ca certificates access transparency testing test cases experiments continuous tests and deployment conversation profiles training sessions git export restore sentiment using multiple projects versions and environments data application levels how to guides migrating from dialogflow es to cx detect intent implement webhooks manage agents with the api manage pages with the api manage intents with the api update data with a fieldmask list data with a filter long running operations tutorials samples deploy a flow based agent on google cloud overview create a prebuilt agent create a webhook service query a database host your agent other samples and learning material ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools site selector dialogflow common documentation dialogflow cx documentation dialogflow es documentation conversational agents console dialogflow cx console dialogflow es console some products and features are in the process of being renamed generative playbook and flow features are also being migrated to a single consolidated console see the details home documentation ai and ml dialogflow dialogflow cx send feedback answer feedback stay organized with collections save and categorize content based on your preferences to help track agent performance dialogflow provides tools for collecting and analyzing end user feedback on agent answers during a conversation enable feedback before collecting answer feedback you must enable the following settings in the general agent settings enable conversation history enable answer feedback collect feedback with dialogflow cx messenger if you use dialogflow cx messenger you can enable answer feedback collection for the chat dialog by setting the following html attribute allow feedback all this will add thumbs up thumb_up and thumbs down thumb_down buttons to the user interface during the conversation an end user can click these buttons to provide feedback on the agent responses if the user selects thumbs down they can optionally provide a reason for the negative feedback collect feedback with custom user interfaces if you have developed a custom user interface you can add feedback collection to your interface and call the sessions submitanswerfeedback method go to the session api reference select a protocol and version for the session reference protocol v3 v3beta1 rest session resource session resource rpc session interface session interface c sessionsclient not available c sessionsclient not available go sessionsclient not available java sessionsclient sessionsclient node js sessionsclient sessionsclient php not available not available python sessionsclient sessionsclient ruby not available not available close custom feedback data structure you can have feedback data stored in your custom data structure filling the answerfeedback customrating field in the sessions submitanswerfeedback method if you use dialogflow cx messenger you can set up custom feedback collection by defining your custom feedback component browse feedback using the dialogflow cx console you can access and filter feedbacks using the conversation history tool read feedback with bigquery conversation history you can analyze the feedback data with bigquery conversation history if you have already created your bigquery table you can alter your existing table with the following sql command if it doesn t have the required bot_answer_feedback column alter table your_dataset_name your_table_name add column bot_answer_feedback json read feedback with the api the conversation history resource contains answerfeedback fields that contain feedback data see the getconversation method in the rpc documentation previous arrow_back audit logging next analytics arrow_forward send feedback except as otherwise noted the content of this page is licensed under the creative commons attribution 4 0 license and code samples are licensed under the apache 2 0 license for details see the google developers site policies java is a registered trademark of oracle and or its affiliates last updated 2026 07 17 utc need to tell us more easy to understand easytounderstand thumb up solved my problem solvedmyproblem thumb up other otherup thumb up hard to understand hardtounderstand thumb down incorrect information or sample code incorrectinformationorsamplecode thumb down missing the information samples i need missingtheinformationsamplesineed thumb down other otherdown thumb down last updated 2026 07 17 utc products and pricing see all products google cloud pricing google cloud marketplace contact sales support community forums support release notes system status resources github getting started with google cloud code samples cloud architecture center training and certification engage blog events x twitter google cloud on youtube google cloud tech on youtube about google privacy site terms google cloud terms manage cookies our third decade of climate action join us sign up for the google cloud newsletter subscribe english deutsch español español américa latina français indonesia italiano português português brasil עברית 中文 简体 中文 繁體 日本語 한국어