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Generators</div></h1></div></div><div class="nRhiJb-fmcmS-oXtfBe dEogG">November 23, 2024</div></div></section><div class="EKklye"><div class="nRhiJb-DARUcf ZWw7T"><div class="npzWPc"><div class="dzoHJ"><div class="nRhiJb-DX2B6 nRhiJb-DX2B6-OWXEXe-h30Snd"><div class="nRhiJb-j5y3u"><ul class="nRhiJb-Qijihe phRaUe" role="list"><li class="hpHPGf"><a class="nRhiJb-ARYxNe" href="https://x.com/intent/tweet?text=Create%20a%20self-escalating%20chatbot%20in%20Conversational%20Agents%20using%20Webhook%20and%20Generators%20@googlecloud&url=https://cloud.google.com/blog/topics/developers-practitioners/create-a-self-escalating-chatbot-in-conversational-agents" track-name="x"track-type="social share"track-metadata-eventdetail="x.com/intent/tweet?text=Create a self-escalating chatbot in Conversational Agents using Webhook and Generators%20@googlecloud&url=cloud.google.com/blog/topics/developers-practitioners/create-a-self-escalating-chatbot-in-conversational-agents"track-metadata-module="social 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href="https://cloudonair.withgoogle.com/summit_series_24?utm_source=cgc-blog&utm_medium=blog&utm_campaign=summit-series-2024&utm_content=summit-series-left-hand-rail-cta&utm_term=-" track-metadata-module="left rail"track-metadata-module_headline="google cloud summit series" track-name="register"track-type="button"track-metadata-position="cta"track-metadata-eventdetail="cloudonair.withgoogle.com/summit_series_24?utm_source=cgc-blog&utm_medium=blog&utm_campaign=summit-series-2024&utm_content=summit-series-left-hand-rail-cta&utm_term=-">Register </a></div></div><div class="OYL9D nRhiJb-kR0ZEf-OWXEXe-GV1x9e-OiUrBf" jsname="tx2NYc"><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">As conversational AI becomes a core part of the user experience, it's crucial for application developers to build chatbots that not only provide accurate information, but also know the right time to escalate to a human agent. </span></p> <p><span style="vertical-align:baseline">This blog post will show you how to create a self-escalating chatbot using Google Cloud's generative AI solutions such as Vertex AI, Conversational Agents (Dialogflow CX), and others. The solution offers several benefits:</span></p> <ul> <li><strong style="vertical-align:baseline">Improved user experience:</strong><span style="vertical-align:baseline"> Users receive accurate information and timely assistance, even for complex questions.</span></li> <li><strong style="vertical-align:baseline">Reduced agent workload:</strong><span style="vertical-align:baseline"> Agents receive concise summaries of previous interactions, allowing them to address issues efficiently.</span></li> <li><strong style="vertical-align:baseline">Enhanced chatbot capabilities:</strong><span style="vertical-align:baseline"> The chatbot can learn from escalated queries, continuously improving its ability to handle future interactions.</span></li> <li><strong style="vertical-align:baseline">Increased scalability and security:</strong><span style="vertical-align:baseline"> Cloud Run Functions (CRF) provides a scalable and secure platform for running the webhook function.</span></li> </ul> <p><span style="vertical-align:baseline">Let’s get started.</span></p></span></section><section class="k6ctOd"><aside class="IwVNTb"><div class="ecJG2e nRhiJb-ObfsIf"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-OiUrBf nRhiJb-snVHke-ibL1re-X66g3b nRhiJb-snVHke-R6PoUb-UFsB2c"><h4 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c">$300 in free credit to try Google Cloud developer tools</h4><p class="nRhiJb-qJTHM nRhiJb-DbgRPb-R6PoUb-ma6Yeb"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p>Build and test your proof of concept with $300 in free credit for new customers. Plus, all customers get free monthly usage of 20+ products, including AI APIs.</p></span></p><a class="nRhiJb-LgbsSe nRhiJb-LgbsSe-OWXEXe-MNynB-o6Shpd LZTd1c" href="http://console.cloud.google.com/freetrial?redirectPath=/welcome" target="_blank" rel="noopener" track-name="start building for free"track-type="button"track-metadata-position="body"track-metadata-eventdetail="http://console.cloud.google.com/freetrial?redirectPath=/welcome"track-metadata-module="aside"track-metadata-module_headline="$300 in free credit to try google cloud developer tools" track-name="start building for free"track-type="button"track-metadata-position="body"track-metadata-eventdetail="http://console.cloud.google.com/freetrial?redirectPath=/welcome">Start building for free </a></div></div></aside></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><strong style="vertical-align:baseline">1. Build the knowledge base</strong></p> <p><span style="vertical-align:baseline">Let’s say we want a chatbot to answer questions related to tourism in India. We’ll start by leveraging </span><strong style="vertical-align:baseline">Vertex AI Agent Builder</strong><span style="vertical-align:baseline"> and </span><strong style="vertical-align:baseline">Conversational Agents (Dialogflow CX)</strong><span style="vertical-align:baseline"> to create it.</span></p> <ul> <li><strong style="vertical-align:baseline">Unstructured datastore:</strong><span style="vertical-align:baseline"> We index an article on "Tourism in India" as an unstructured datastore within Vertex AI. This allows the chatbot to access and retrieve relevant information from the article in real time, providing comprehensive answers to user queries.</span></li> <li><strong style="vertical-align:baseline">Conversational Agents (Dialogflow CX):</strong><span style="vertical-align:baseline"> We design the conversational flow using Conversational Agents (Dialogflow CX), enabling the chatbot to understand user intent and respond appropriately.</span></li> </ul> <p><strong style="vertical-align:baseline">2. Gauge user satisfaction</strong></p> <p><span style="vertical-align:baseline">To ensure user satisfaction, we incorporate a crucial step in the conversation flow: asking the user if they are satisfied with the chatbot's response. This is done using "yes" and "no" chips specified as part of custom payload, providing a clear and intuitive way for users to express their feedback.</span></p> <p><strong style="vertical-align:baseline">3. Escalate with generative AI</strong></p> <p><span style="vertical-align:baseline">If the user expresses dissatisfaction by clicking "no," the chatbot initiates the escalation process. This is where the power of generative AI comes in.</span></p> <ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Generators:</strong><span style="vertical-align:baseline"> We create a generator in Conversational Agents (Dialogflow CX) named "Summarize_mail" that utilizes a </span><a href="https://developers.google.com/machine-learning/resources/prompt-eng#direct_prompting_zero-shot" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">zero-shot prompt</span></a><span style="vertical-align:baseline"> (direct prompt with no examples) to summarize the conversation. This summary is then used to generate the content of an email, providing context to the human agent.</span></p> </li> </ul> <p><span style="vertical-align:baseline">Here's the zero-shot prompt we use:</span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="You are an English expert in summarizing the text in form of a very short mail. Summarize the conversation and write it in form of a concise e-mail which will be forwarded to an agent as a ticket. The mail should be on point, properly formatted and written in formal tone with a polite closure. Keep the text as less as possible. Also, specify the conversation messages below the summary which are present in the conversation. The conversation is as follows: $conversation The feedback of the user about the issue is $last-user-utterance."><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">You are an English expert in summarizing the text in form of a very short mail. Summarize the conversation and write it in form of a concise e-mail which will be forwarded to an agent as a ticket. The mail should be on point, properly formatted and written in formal tone with a polite closure. Keep the text as less as possible. Also, specify the conversation messages below the summary which are present in the conversation. The conversation is as follows: $conversation The feedback of the user about the issue is $last-user-utterance.</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Model configuration:</strong><span style="vertical-align:baseline"> This generator utilizes the </span><a href="https://ai.google.dev/gemini-api/docs/models/gemini" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Gemini-1.5-flash model </span></a><span style="vertical-align:baseline">with a </span><a href="https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/adjust-parameter-values#temperature"><span style="text-decoration:underline;vertical-align:baseline">temperature</span></a><span style="vertical-align:baseline"> of 0.3, helping to ensure coherent and informative summaries.</span></p> </li> </ul> <p><strong style="vertical-align:baseline">4. Trigger the email with Cloud Run Functions (CRF)</strong></p> <p><span style="vertical-align:baseline">To send the email, we use a </span><strong style="vertical-align:baseline">webhook</strong><span style="vertical-align:baseline"> to connect our Conversational Agents (Dialogflow CX) agent to a serverless function deployed on </span><strong style="vertical-align:baseline">Cloud Run Functions</strong><span style="vertical-align:baseline">. This function handles the email sending logic.</span></p> <p><span style="vertical-align:baseline">We utilize the Cloud Run Functions console inline editor to write and deploy the following Python code:</span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="import functions_framework import http.client import smtplib, ssl from flask import Flask, jsonify from email.message import EmailMessage # Cloud Run Functions to handle webhook requests from Conversational Agents (Dialogflow CX) @functions_framework.http def handle_webhook(request): """ Handles webhook requests from Conversational Agents (Dialogflow CX) and sends an email summarizing the conversation to a human agent. Args: request: The incoming HTTP request containing the webhook payload. Returns: A JSON response indicating the status of the email sending process. """ port = 465 # For SSL smtp_server = "smtp.gmail.com" sender_email = '<sender_agent_mail_id>' receiver_email = '<receiver_agent_mail_id>' password = '<sender_agent_password>' cc_email = 'the logged-in user mail id' # Include the user's email # Extract the conversation summary from the webhook payload req = request.get_json() message = req['sessionInfo']['parameters']['$request.generative.output'] # Create and send the email msg = EmailMessage() msg.set_content(message) msg['Subject'] = 'Action Needed: Customer Escalation' msg['From'] = sender_email msg['To'] = receiver_email msg['Cc'] = cc_email # Add the user to the email for transparency try: # Establish a secure connection to the SMTP server server = smtplib.SMTP_SSL(smtp_server, port) server.login(sender_email, password) server.send_message(msg) server.quit() # Return a success message to Conversational Agents (Dialogflow CX) return jsonify( { 'fulfillment_response': { 'messages': [ { 'text': { 'text': ['The mail is successfully sent!'] } } ] } } ) except Exception as e: # Handle potential errors during email sending print(f"Error sending email: {e}") return jsonify( { 'fulfillment_response': { 'messages': [ { 'text': { 'text': ['There was an error sending the email. Please try again later.'] } } ] } } )"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg>lang-py</div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-language="lang-py" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">import functions_framework import http.client import smtplib, ssl from flask import Flask, jsonify from email.message import EmailMessage # Cloud Run Functions to handle webhook requests from Conversational Agents (Dialogflow CX) @functions_framework.http def handle_webhook(request): """ Handles webhook requests from Conversational Agents (Dialogflow CX) and sends an email summarizing the conversation to a human agent. Args: request: The incoming HTTP request containing the webhook payload. Returns: A JSON response indicating the status of the email sending process. """ port = 465 # For SSL smtp_server = "smtp.gmail.com" sender_email = '<sender_agent_mail_id>' receiver_email = '<receiver_agent_mail_id>' password = '<sender_agent_password>' cc_email = 'the logged-in user mail id' # Include the user's email # Extract the conversation summary from the webhook payload req = request.get_json() message = req['sessionInfo']['parameters']['$request.generative.output'] # Create and send the email msg = EmailMessage() msg.set_content(message) msg['Subject'] = 'Action Needed: Customer Escalation' msg['From'] = sender_email msg['To'] = receiver_email msg['Cc'] = cc_email # Add the user to the email for transparency try: # Establish a secure connection to the SMTP server server = smtplib.SMTP_SSL(smtp_server, port) server.login(sender_email, password) server.send_message(msg) server.quit() # Return a success message to Conversational Agents (Dialogflow CX) return jsonify( { 'fulfillment_response': { 'messages': [ { 'text': { 'text': ['The mail is successfully sent!'] } } ] } } ) except Exception as e: # Handle potential errors during email sending print(f"Error sending email: {e}") return jsonify( { 'fulfillment_response': { 'messages': [ { 'text': { 'text': ['There was an error sending the email. Please try again later.'] } } ] } } )</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Cloud Run Functions (CRF) configuration:</strong><span style="vertical-align:baseline"> We configure the CRF with the following settings:</span></p> </li> <ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">Runtime: Python 3.12</span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">Environment: 2nd Gen </span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">Entry point: handle_webhook</span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">Trigger type: HTTPS</span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">Memory allocated: 256 MiB</span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">Ingress settings: Allow all traffic</span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">Requirements (requirements.txt): </span><code style="vertical-align:baseline">functions-framework==3.*</code></p> </li> </ul> </ul> <p><span style="vertical-align:baseline">Not sure which CRF version to choose for Dialogflow CX Webhooks? Here’s a quick way to decide:</span></p> <ol> <li style="list-style-type:decimal;vertical-align:baseline"> <p><span style="vertical-align:baseline">1st Gen: Simpler to set up and deploy, suitable for basic webhooks with less demanding performance requirements.</span></p> </li> <li style="list-style-type:decimal;vertical-align:baseline"> <p><span style="vertical-align:baseline">2nd Gen: Offers more flexibility, control, and scalability, making it a better choice for complex webhooks or high-traffic scenarios.</span></p> </li> </ol> <p><span style="vertical-align:baseline">More details</span><span style="vertical-align:baseline"> </span><a href="https://cloud.google.com/functions/docs/concepts/version-comparison"><span style="text-decoration:underline;vertical-align:baseline">here</span></a></p> <p><strong style="vertical-align:baseline">5. Connect the pieces</strong></p> <p><span style="vertical-align:baseline">Specify the URL of our deployed CRF as the webhook URL in Conversational Agents (Dialogflow CX), ensuring that the escalation process is triggered correctly when needed.</span></p> <h3><strong style="vertical-align:baseline">Here’s how it all comes together</strong></h3> <p><span style="vertical-align:baseline">This breakdown provides a comprehensive understanding of the Conversational Agents (Dialogflow CX) flow design, emphasizing the escalation process and the role of the CRF. Remember to adapt the flow, messages, and email content to suit your specific needs and branding.</span></p> <p><strong style="vertical-align:baseline">Flow Name:</strong><span style="vertical-align:baseline"> (e.g., "Customer Support Flow")</span></p> <p><strong style="vertical-align:baseline">Pages: </strong><span style="vertical-align:baseline">There are two pages, ‘Start Page’ and ‘Escalate’.</span></p> <p><strong style="vertical-align:baseline">1. Start Page</strong></p> <ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Purpose:</strong><span style="vertical-align:baseline"> Initiates the conversation with the welcome intent greeting the user and gauges user satisfaction.</span></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Greeting entry fulfillment:</strong></p> </li> <ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Agent says:</strong><span style="vertical-align:baseline"> "Hi </span><span style="vertical-align:baseline">name</span><span style="vertical-align:baseline">! Please let us know how we can help you."</span></p> </li> </ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Datastore response entry fulfillment:</strong></p> </li> <ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Agent says:</strong><span style="vertical-align:baseline"> "Are you satisfied with the response?"</span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Custom payload:</strong><span style="vertical-align:baseline"> (This creates the "Yes" and "No" chips)<br></span><span style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Oxygen,Ubuntu,Cantarell,'Open Sans','Helvetica Neue',sans-serif"><br>JSON</span></p> </li> </ul> </ul></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="{ "richContent": [ [ { "options": [ { "text": "Yes" }, { "text": "No" } ], "type": "chips" } ] ] }"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg>lang-py</div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-language="lang-py" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">{ "richContent": [ [ { "options": [ { "text": "Yes" }, { "text": "No" } ], "type": "chips" } ] ] }</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Routes:</strong></p> </li> <ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Condition:</strong><span style="vertical-align:baseline"> "Yes" chip selected</span></p> </li> <ul> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Transition:</strong><span style="vertical-align:baseline"> Start page (Marks the conversation as successful)</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Agent says:</strong><span style="vertical-align:baseline"> "Thank you! Is there anything else we can help with?”</span></p> </li> </ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Condition:</strong><span style="vertical-align:baseline"> "No" chip selected</span></p> </li> <ul> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Transition:</strong><span style="vertical-align:baseline"> "Escalate" Page</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Agent says:</strong><span style="vertical-align:baseline"> "Sorry to hear that!" (Acknowledges user dissatisfaction)</span></p> </li> </ul> </ul> </ul> <p><strong style="vertical-align:baseline">2. Escalate</strong></p> <ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Purpose:</strong><span style="vertical-align:baseline"> Offers the user the option to escalate to a human agent.</span></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Entry fulfillment:</strong></p> </li> <ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Agent says:</strong><span style="vertical-align:baseline"> "Would you like to escalate?"</span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Custom payload:</strong><span style="vertical-align:baseline"> (Same "Yes" and "No" chips as the "Start Page")</span></p> </li> </ul> </ul></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="{ "richContent": [ [ { "options": [ { "text": "Yes" }, { "text": "No" } ], "type": "chips" } ] ] }"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">{ "richContent": [ [ { "options": [ { "text": "Yes" }, { "text": "No" } ], "type": "chips" } ] ] }</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Routes:</strong></p> </li> <ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Condition:</strong><span style="vertical-align:baseline"> "No" chip selected</span></p> </li> <ul> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Transition:</strong><span style="vertical-align:baseline"> "Start Page"</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Agent says:</strong><span style="vertical-align:baseline"> "Sure, Thank you!" (Allows the user to continue with the bot)</span></p> </li> </ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Condition:</strong><span style="vertical-align:baseline"> "Yes" chip selected</span></p> </li> <ul> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Transition:</strong><span style="vertical-align:baseline"> "Escalation Webhook"</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Webhook:</strong><span style="vertical-align:baseline"> Cloud Run Functions (Triggers the escalation process)</span></p> </li> </ul> </ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Intents:</strong></p> </li> <ul> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">We describe the two intents ‘confirm.yes’ and ‘confirm.no’ with training phrases described as ‘yes’ and ‘no’. This corresponds to the user clicking on ‘yes’ and ‘no’ chips or simply writing the phrases or the similar ones. </span></p> </li> <li style="list-style-type:circle;vertical-align:baseline"> <p><span style="vertical-align:baseline">To learn more about Intents, please click </span><a href="https://cloud.google.com/dialogflow/cx/docs/concept/intent"><span style="text-decoration:underline;vertical-align:baseline">here</span></a><span style="vertical-align:baseline">.</span></p> </li> </ul> </ul> <h3><strong style="vertical-align:baseline">Cloud Run Functions (Escalation Webhook)</strong></h3> <ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Trigger:</strong><span style="vertical-align:baseline"> HTTPS Eventarc trigger (Activated when the "Escalate" page transitions to the webhook)</span></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Functionality:</strong></p> </li> <ol> <li style="list-style-type:decimal;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Gather conversation history:</strong><span style="vertical-align:baseline"> Retrieve and process the complete conversation history from the Conversational Agents (Dialogflow CX) session using the $conversation in the generator prompt which captures the conversation between the agent and the user, excluding the very last user utterance and the agent's utterances thereafter.</span></p> </li> <li style="list-style-type:decimal;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Summarize conversation:</strong><span style="vertical-align:baseline"> Generate a concise summary of the conversation ($conversation), highlighting key user requests or issues.</span></p> </li> <li style="list-style-type:decimal;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Extract user information:</strong><span style="vertical-align:baseline"> Obtain the user's email address (and any other relevant details) from the Conversational Agents (Dialogflow CX) session or your user database.</span></p> </li> <li style="list-style-type:decimal;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Compose email:</strong><span style="vertical-align:baseline"> Create an email with:</span></p> </li> <ul> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Subject:</strong><span style="vertical-align:baseline"> (e.g., "Escalated Conversation from [User Email]")</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Recipient:</strong><span style="vertical-align:baseline"> The support agent's email address</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">CC:</strong><span style="vertical-align:baseline"> The user's email address</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Body:</strong></p> </li> <ul> <li style="list-style-type:square;vertical-align:baseline"> <p><span style="vertical-align:baseline">User information (e.g., name, ID)</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><span style="vertical-align:baseline">Conversation summary</span></p> </li> <li style="list-style-type:square;vertical-align:baseline"> <p><span style="vertical-align:baseline">Link to the full conversation in Conversational Agents (Dialogflow CX) (if available)</span></p> </li> </ul> </ul> <li style="list-style-type:decimal;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Send email:</strong><span style="vertical-align:baseline"> Use an email sending library (e.g., </span><code style="vertical-align:baseline">sendgrid</code><span style="vertical-align:baseline">, </span><code style="vertical-align:baseline">mailgun,smtp</code><span style="vertical-align:baseline">) or your email provider's API to send the email.</span></p> </li> <li style="list-style-type:decimal;vertical-align:baseline"> <p><strong style="vertical-align:baseline">Return response (optional):</strong><span style="vertical-align:baseline"> Send a response back to Conversational Agents (Dialogflow CX) to inform the user that their request has been escalated (e.g., "The mail is successfully sent!”, "Your request has been escalated. An agent will contact you shortly.").</span></p> </li> </ol> </ul> <h3><strong style="vertical-align:baseline">Chatbot testing and results</strong></h3> <p><span style="vertical-align:baseline">After completing the above steps, you can click on ‘Publish’ and then ‘Try it now’ to test the chatbot.</span></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Yux49fW.max-1900x1900.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Yux49fW.max-1900x1900.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Yux49fW.max-1900x1900.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Yux49fW.max-1900x1900.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><strong style="vertical-align:baseline">These are a few example user journeys:</strong></p> <p><span style="vertical-align:baseline">1. The user is not satisfied with the response and does not want to escalate as well.</span></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZtKoif" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Sc6qFk7.max-600x600.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Sc6qFk7.max-600x600.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZtKoif" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Sc6qFk7.max-600x600.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Sc6qFk7.max-600x600.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">2. The user is not satisfied with the response and escalates as well. The sample triggered mail is shown below in the right image.</span></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_1DuEUue.max-2000x2000.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_1DuEUue.max-2000x2000.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_1DuEUue.max-2000x2000.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/3_1DuEUue.max-2000x2000.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">This approach shows how you can combine various Google Cloud technologies, including Vertex AI, to build intelligent and user-friendly chatbots. As conversational AI continues to evolve, we can expect even more innovative solutions that enhance customer experiences. To learn more and contribute feedback, visit our Google Cloud community on </span><a href="https://medium.com/google-cloud/vertex-ai-search-and-conversation-s-c-for-wikipedia-pdf-chatbot-056569a2054c" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Medium</span></a><span style="vertical-align:baseline">. </span></p> <h3><strong style="vertical-align:baseline">References</strong></h3> <ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/dialogflow/cx/docs/concept/webhook#gcf"><span style="text-decoration:underline;vertical-align:baseline">Webhooks | Dialogflow CX | Google Cloud</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/dialogflow/cx/docs/concept/generators"><span style="text-decoration:underline;vertical-align:baseline">Generators | Dialogflow CX | Google Cloud</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/dialogflow/cx/docs"><span style="text-decoration:underline;vertical-align:baseline">Conversational Agents (Dialogflow CX) documentation | Google Cloud</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/dialogflow/cx/docs/concept/intent"><span style="text-decoration:underline;vertical-align:baseline">Intents | Dialogflow CX | Google Cloud</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/dialogflow/cx/docs/concept/page"><span style="text-decoration:underline;vertical-align:baseline">Pages | Dialogflow CX | Google Cloud</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/dialogflow/cx/docs/concept/handler"><span style="text-decoration:underline;vertical-align:baseline">State handlers | Routes | Dialogflow CX | Google Cloud</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/dialogflow/cx/docs/concept/flow"><span style="text-decoration:underline;vertical-align:baseline">Flows | Dialogflow CX | Google Cloud</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/functions/docs/calling/eventarc"><span style="text-decoration:underline;vertical-align:baseline">Eventarc triggers | Cloud Run functions Documentation</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/adjust-parameter-values#temperature"><span style="text-decoration:underline;vertical-align:baseline">Experiment with parameter values | Generative AI on Vertex AI | Google Cloud</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://developers.google.com/machine-learning/resources/prompt-eng#direct_prompting_zero-shot" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Prompt Engineering for Generative AI | Machine Learning | Google for Developers</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://ai.google.dev/gemini-api/docs/models/gemini" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Gemini models | Gemini API | Google AI for Developers</span></a></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><a href="https://cloud.google.com/functions/docs/concepts/version-comparison"><span style="text-decoration:underline;vertical-align:baseline">Cloud Run functions version comparison</span></a></p> </li> </ul></span></section><section class="kcBhad"><section class="Fabbec"><span class="WrMNjb">Posted in</span><ul class="FzXI4e"><li class="I4B51b"><a href="https://cloud.google.com/blog/topics/developers-practitioners" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/topics/developers-practitioners"track-metadata-module="tag list"track-metadata-module_headline="posted in">Developers & Practitioners</a></li><li class="I4B51b"><a href="https://cloud.google.com/blog/products/ai-machine-learning" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/ai-machine-learning"track-metadata-module="tag list"track-metadata-module_headline="posted in">AI & Machine Learning</a></li></ul></section></section></div></div></div></div></div><section class="nRhiJb-DARUcf " track-metadata-module="related articles" track-metadata-module_headline="related articles"><div class="nRhiJb-DbgRPb-c5RTEf-ma6Yeb nRhiJb-DbgRPb-wNfPc-cGMI2b"><h5 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc nRhiJb-DbgRPb-II5mzb-cGMI2b">Related articles</h5><section class="m9cUGf HGev3 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part of the user experience, it's crucial for application developers to build chatbots that not only provide accurate information, but also know the right time to escalate to a human agent.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThis blog post will show you how to create a self-escalating chatbot using Google Cloud's generative AI solutions such as Vertex AI, Conversational Agents (Dialogflow CX), and others. The solution offers several benefits:\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eImproved user experience:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Users receive accurate information and timely assistance, even for complex questions.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eReduced agent workload:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Agents receive concise summaries of previous interactions, allowing them to address issues efficiently.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eEnhanced chatbot capabilities:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e The chatbot can learn from escalated queries, continuously improving its ability to handle future interactions.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eIncreased scalability and security:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Cloud Run Functions (CRF) provides a scalable and secure platform for running the webhook function.\u003c/span\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eLet\u2019s get started.\u003c/span\u003e\u003c/p\u003e"]]],[null,["$300 in free credit to try Google Cloud developer tools",null,"Start building for free","http://console.cloud.google.com/freetrial?redirectPath\u003d/welcome",[null,"\u003cp\u003eBuild and test your proof of concept with $300 in free credit for new customers. Plus, all customers get free monthly usage of 20+ products, including AI APIs.\u003c/p\u003e"]]],[null,null,[null,[null,"\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e1. Build the knowledge base\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eLet\u2019s say we want a chatbot to answer questions related to tourism in India. We\u2019ll start by leveraging \u003c/span\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eVertex AI Agent Builder\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e and \u003c/span\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eConversational Agents (Dialogflow CX)\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e to create it.\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eUnstructured datastore:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e We index an article on \"Tourism in India\" as an unstructured datastore within Vertex AI. This allows the chatbot to access and retrieve relevant information from the article in real time, providing comprehensive answers to user queries.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eConversational Agents (Dialogflow CX):\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e We design the conversational flow using Conversational Agents (Dialogflow CX), enabling the chatbot to understand user intent and respond appropriately.\u003c/span\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e2. Gauge user satisfaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eTo ensure user satisfaction, we incorporate a crucial step in the conversation flow: asking the user if they are satisfied with the chatbot's response. This is done using \"yes\" and \"no\" chips specified as part of custom payload, providing a clear and intuitive way for users to express their feedback.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e3. Escalate with generative AI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIf the user expresses dissatisfaction by clicking \"no,\" the chatbot initiates the escalation process. This is where the power of generative AI comes in.\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eGenerators:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e We create a generator in Conversational Agents (Dialogflow CX) named \"Summarize_mail\" that utilizes a \u003c/span\u003e\u003ca href\u003d\"https://developers.google.com/machine-learning/resources/prompt-eng#direct_prompting_zero-shot\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ezero-shot prompt\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e (direct prompt with no examples) to summarize the conversation. This summary is then used to generate the content of an email, providing context to the human agent.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eHere's the zero-shot prompt we use:\u003c/span\u003e\u003c/p\u003e"]]],[["You are an English expert in summarizing the text in form of a very short mail.\r\nSummarize the conversation and write it in form of a concise e-mail which will be forwarded to an agent as a ticket. The mail should be on point, properly formatted and written in formal tone with a polite closure. Keep the text as less as possible.\r\nAlso, specify the conversation messages below the summary which are present in the conversation. The conversation is as follows: $conversation\r\nThe feedback of the user about the issue is $last-user-utterance.",null,[null,""]]],[null,null,[null,[null,"\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eModel configuration:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e This generator utilizes the \u003c/span\u003e\u003ca href\u003d\"https://ai.google.dev/gemini-api/docs/models/gemini\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eGemini-1.5-flash model \u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003ewith a \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/adjust-parameter-values#temperature\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003etemperature\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e of 0.3, helping to ensure coherent and informative summaries.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e4. Trigger the email with Cloud Run Functions (CRF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eTo send the email, we use a \u003c/span\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003ewebhook\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e to connect our Conversational Agents (Dialogflow CX) agent to a serverless function deployed on \u003c/span\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCloud Run Functions\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e. This function handles the email sending logic.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eWe utilize the Cloud Run Functions console inline editor to write and deploy the following Python code:\u003c/span\u003e\u003c/p\u003e"]]],[["import functions_framework \r\nimport http.client \r\nimport smtplib, ssl\r\nfrom flask import Flask, jsonify\r\nfrom email.message import EmailMessage\r\n\r\n# Cloud Run Functions to handle webhook requests from Conversational Agents (Dialogflow CX)\r\n@functions_framework.http \r\ndef handle_webhook(request):\r\n \"\"\"\r\n Handles webhook requests from Conversational Agents (Dialogflow CX) and sends an email \r\n summarizing the conversation to a human agent.\r\n\r\n Args:\r\n request: The incoming HTTP request containing the webhook payload.\r\n\r\n Returns:\r\n A JSON response indicating the status of the email sending process.\r\n \"\"\"\r\n\r\n port \u003d 465 # For SSL\r\n smtp_server \u003d \"smtp.gmail.com\"\r\n \r\n sender_email \u003d '\u003csender_agent_mail_id\u003e'\r\n receiver_email \u003d '\u003creceiver_agent_mail_id\u003e'\r\n password \u003d '\u003csender_agent_password\u003e'\r\n cc_email \u003d 'the logged-in user mail id' # Include the user's email\r\n\r\n # Extract the conversation summary from the webhook payload\r\n req \u003d request.get_json()\r\n message \u003d req['sessionInfo']['parameters']['$request.generative.output']\r\n \r\n # Create and send the email\r\n msg \u003d EmailMessage()\r\n msg.set_content(message)\r\n\r\n msg['Subject'] \u003d 'Action Needed: Customer Escalation'\r\n msg['From'] \u003d sender_email\r\n msg['To'] \u003d receiver_email\r\n msg['Cc'] \u003d cc_email # Add the user to the email for transparency\r\n\r\n try:\r\n # Establish a secure connection to the SMTP server\r\n server \u003d smtplib.SMTP_SSL(smtp_server, port)\r\n server.login(sender_email, password) \r\n server.send_message(msg) \r\n server.quit()\r\n\r\n # Return a success message to Conversational Agents (Dialogflow CX)\r\n return jsonify(\r\n {\r\n 'fulfillment_response': {\r\n 'messages': [\r\n {\r\n 'text': {\r\n 'text': ['The mail is successfully sent!']\r\n }\r\n }\r\n ]\r\n }\r\n }\r\n )\r\n\r\n except Exception as e:\r\n # Handle potential errors during email sending\r\n print(f\"Error sending email: {e}\")\r\n return jsonify(\r\n {\r\n 'fulfillment_response': {\r\n 'messages': [\r\n {\r\n 'text': {\r\n 'text': ['There was an error sending the email. Please try again later.']\r\n }\r\n }\r\n ]\r\n }\r\n }\r\n )","lang-py",[null,""]]],[null,null,[null,[null,"\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCloud Run Functions (CRF) configuration:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e We configure the CRF with the following settings:\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eRuntime: Python 3.12\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eEnvironment: 2nd Gen\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eEntry point: handle_webhook\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eTrigger type: HTTPS\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eMemory allocated: 256 MiB\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIngress settings: Allow all traffic\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eRequirements (requirements.txt): \u003c/span\u003e\u003ccode style\u003d\"vertical-align:baseline\"\u003efunctions-framework\u003d\u003d3.*\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eNot sure which CRF version to choose for Dialogflow CX Webhooks? Here\u2019s a quick way to decide:\u003c/span\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli style\u003d\"list-style-type:decimal;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e1st Gen: Simpler to set up and deploy, suitable for basic webhooks with less demanding performance requirements.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:decimal;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e2nd Gen: Offers more flexibility, control, and scalability, making it a better choice for complex webhooks or high-traffic scenarios.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eMore details\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/functions/docs/concepts/version-comparison\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ehere\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e5. Connect the pieces\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eSpecify the URL of our deployed CRF as the webhook URL in Conversational Agents (Dialogflow CX), ensuring that the escalation process is triggered correctly when needed.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eHere\u2019s how it all comes together\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThis breakdown provides a comprehensive understanding of the Conversational Agents (Dialogflow CX) flow design, emphasizing the escalation process and the role of the CRF. Remember to adapt the flow, messages, and email content to suit your specific needs and branding.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eFlow Name:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e (e.g., \"Customer Support Flow\")\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003ePages: \u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThere are two pages, \u2018Start Page\u2019 and \u2018Escalate\u2019.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e1. Start Page\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003ePurpose:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Initiates the conversation with the welcome intent greeting the user and gauges user satisfaction.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eGreeting entry fulfillment:\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eAgent says:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Hi \u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003ename\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e! Please let us know how we can help you.\"\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eDatastore response entry fulfillment:\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eAgent says:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Are you satisfied with the response?\"\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCustom payload:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e (This creates the \"Yes\" and \"No\" chips)\u003cbr\u003e\u003c/span\u003e\u003cspan style\u003d\"font-family:-apple-system,BlinkMacSystemFont,\u0026#39;Segoe UI\u0026#39;,Roboto,Oxygen,Ubuntu,Cantarell,\u0026#39;Open Sans\u0026#39;,\u0026#39;Helvetica Neue\u0026#39;,sans-serif\"\u003e\u003cbr\u003eJSON\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/ul\u003e"]]],[["{\r\n \"richContent\": [\r\n [\r\n {\r\n \"options\": [\r\n {\r\n \"text\": \"Yes\"\r\n },\r\n {\r\n \"text\": \"No\"\r\n }\r\n ],\r\n \"type\": \"chips\"\r\n }\r\n ]\r\n ]\r\n}","lang-py",[null,""]]],[null,null,[null,[null,"\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eRoutes:\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCondition:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Yes\" chip selected\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eTransition:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Start page (Marks the conversation as successful)\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eAgent says:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Thank you! Is there anything else we can help with?\u201d\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCondition:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"No\" chip selected\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eTransition:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Escalate\" Page\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eAgent says:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Sorry to hear that!\" (Acknowledges user dissatisfaction)\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/ul\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e2. Escalate\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003ePurpose:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Offers the user the option to escalate to a human agent.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eEntry fulfillment:\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eAgent says:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Would you like to escalate?\"\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCustom payload:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e (Same \"Yes\" and \"No\" chips as the \"Start Page\")\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/ul\u003e"]]],[["{\r\n \"richContent\": [\r\n [\r\n {\r\n \"options\": [\r\n {\r\n \"text\": \"Yes\"\r\n },\r\n {\r\n \"text\": \"No\"\r\n }\r\n ],\r\n \"type\": \"chips\"\r\n }\r\n ]\r\n ]\r\n}",null,[null,""]]],[null,null,[null,[null,"\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eRoutes:\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCondition:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"No\" chip selected\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eTransition:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Start Page\"\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eAgent says:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Sure, Thank you!\" (Allows the user to continue with the bot)\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCondition:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Yes\" chip selected\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eTransition:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \"Escalation Webhook\"\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eWebhook:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Cloud Run Functions (Triggers the escalation process)\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/ul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eIntents:\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eWe describe the two intents \u2018confirm.yes\u2019 and \u2018confirm.no\u2019 with training phrases described as \u2018yes\u2019 and \u2018no\u2019. This corresponds to the user clicking on \u2018yes\u2019 and \u2018no\u2019 chips or simply writing the phrases or the similar ones.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:circle;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eTo learn more about Intents, please click \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/dialogflow/cx/docs/concept/intent\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ehere\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCloud Run Functions (Escalation Webhook)\u003c/strong\u003e\u003c/h3\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eTrigger:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e HTTPS Eventarc trigger (Activated when the \"Escalate\" page transitions to the webhook)\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eFunctionality:\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003col\u003e\n\u003cli style\u003d\"list-style-type:decimal;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eGather conversation history:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Retrieve and process the complete conversation history from the Conversational Agents (Dialogflow CX) session using the $conversation in the generator prompt which captures the conversation between the agent and the user, excluding the very last user utterance and the agent's utterances thereafter.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:decimal;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eSummarize conversation:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Generate a concise summary of the conversation ($conversation), highlighting key user requests or issues.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:decimal;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eExtract user information:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Obtain the user's email address (and any other relevant details) from the Conversational Agents (Dialogflow CX) session or your user database.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:decimal;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCompose email:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Create an email with:\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eSubject:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e (e.g., \"Escalated Conversation from [User Email]\")\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eRecipient:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e The support agent's email address\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eCC:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e The user's email address\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eBody:\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eUser information (e.g., name, ID)\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eConversation summary\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:square;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eLink to the full conversation in Conversational Agents (Dialogflow CX) (if available)\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/ul\u003e\n\u003cli style\u003d\"list-style-type:decimal;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eSend email:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Use an email sending library (e.g., \u003c/span\u003e\u003ccode style\u003d\"vertical-align:baseline\"\u003esendgrid\u003c/code\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e, \u003c/span\u003e\u003ccode style\u003d\"vertical-align:baseline\"\u003emailgun,smtp\u003c/code\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e) or your email provider's API to send the email.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:decimal;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eReturn response (optional):\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Send a response back to Conversational Agents (Dialogflow CX) to inform the user that their request has been escalated (e.g., \"The mail is successfully sent!\u201d, \"Your request has been escalated. An agent will contact you shortly.\").\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eChatbot testing and results\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eAfter completing the above steps, you can click on \u2018Publish\u2019 and then \u2018Try it now\u2019 to test the chatbot.\u003c/span\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Yux49fW.max-1900x1900.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Yux49fW.max-1100x1100.png 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Yux49fW.max-1900x1900.png 1814w"," 1060px, 1814px","https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Yux49fW.max-1900x1900.png"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eThese are a few example user journeys:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e1. The user is not satisfied with the response and does not want to escalate as well.\u003c/span\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Sc6qFk7.max-600x600.png",null,null,"https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Sc6qFk7.max-600x600.png"],null,1]]],[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e2. The user is not satisfied with the response and escalates as well. The sample triggered mail is shown below in the right image.\u003c/span\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/3_1DuEUue.max-2000x2000.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/3_1DuEUue.max-1100x1100.png 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/images/3_1DuEUue.max-2000x2000.png 1977w"," 1060px, 1977px","https://storage.googleapis.com/gweb-cloudblog-publish/images/3_1DuEUue.max-2000x2000.png"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThis approach shows how you can combine various Google Cloud technologies, including Vertex AI, to build intelligent and user-friendly chatbots. As conversational AI continues to evolve, we can expect even more innovative solutions that enhance customer experiences. To learn more and contribute feedback, visit our Google Cloud community on \u003c/span\u003e\u003ca href\u003d\"https://medium.com/google-cloud/vertex-ai-search-and-conversation-s-c-for-wikipedia-pdf-chatbot-056569a2054c\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eMedium\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eReferences\u003c/strong\u003e\u003c/h3\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/dialogflow/cx/docs/concept/webhook#gcf\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eWebhooks | Dialogflow CX | Google Cloud\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/dialogflow/cx/docs/concept/generators\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eGenerators | Dialogflow CX | Google Cloud\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/dialogflow/cx/docs\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eConversational Agents (Dialogflow CX) documentation | Google Cloud\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/dialogflow/cx/docs/concept/intent\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eIntents | Dialogflow CX | Google Cloud\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/dialogflow/cx/docs/concept/page\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ePages | Dialogflow CX | Google Cloud\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/dialogflow/cx/docs/concept/handler\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eState handlers | Routes | Dialogflow CX | Google Cloud\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/dialogflow/cx/docs/concept/flow\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eFlows | Dialogflow CX | Google Cloud\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/functions/docs/calling/eventarc\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eEventarc triggers | Cloud Run functions Documentation\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/adjust-parameter-values#temperature\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eExperiment with parameter values | Generative AI on Vertex AI | Google Cloud\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://developers.google.com/machine-learning/resources/prompt-eng#direct_prompting_zero-shot\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ePrompt Engineering for Generative AI | Machine Learning | Google for Developers\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://ai.google.dev/gemini-api/docs/models/gemini\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eGemini models | Gemini API | Google AI for Developers\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003ca href\u003d\"https://cloud.google.com/functions/docs/concepts/version-comparison\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eCloud Run functions version comparison\u003c/span\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e"]]]],[["Developers \u0026 Practitioners","Boost your Continuous Delivery pipeline with Generative AI","Learn how to use Gemini models in Vertex AI to automate code reviews, create release notes, and enhance your software delivery pipeline.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/11_-_Developers__Practitioners_a4Y5EGr.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/11_-_Developers__Practitioners_a4Y5EGr.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/11_-_Developers__Practitioners_a4Y5EGr.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/11_-_Developers__Practitioners_a4Y5EGr.max-700x700.jpg"],null,7,null,"https://cloud.google.com/blog/topics/developers-practitioners/boost-your-continuous-delivery-pipeline-with-generative-ai",null,1,[["Giovanni Galloro","EMEA Solutions Lead, Application Modernization"],["Daniel Strebel","EMEA Solutions Lead, Application Modernization"]],null,"55631"],["Developers \u0026 Practitioners","Build an AI agent for trip planning with Gemini 1.5 Pro: A step-by-step guide","Check out this step-by-step guide to create an AI agent for trip planning with Gemini 1.5 Pro.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/11_-_Developers__Practitioners_a4Y5EGr.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/11_-_Developers__Practitioners_a4Y5EGr.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/11_-_Developers__Practitioners_a4Y5EGr.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/11_-_Developers__Practitioners_a4Y5EGr.max-700x700.jpg"],null,5,null,"https://cloud.google.com/blog/topics/developers-practitioners/learn-how-to-create-an-ai-agent-for-trip-planning-with-gemini-1-5-pro",null,1,[["Kelci Mensah","Cloud Architect, Google"],["Dagmawe Legesse","Cloud Engineer"]],null,"55632"],["AI \u0026 Machine Learning","How Commerzbank is transforming financial advisory workflows with gen AI","Commerzbank has automated its financial advisory workflows using an advanced semi-agentic, multi-step gen AI process on Vertex AI.",["https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif","https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif 324w, https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif"],null,5,null,"https://cloud.google.com/blog/products/ai-machine-learning/how-commerzbank-is-transforming-financial-advisory-workflows-with-gen-ai",null,1,[["Tolga Bast\u00fcrk","Product Owner, Commerzbank"],["Anant Nawalgaria","Sr. Staff ML Engineer, Google"]],null,"55621"],["Containers \u0026 Kubernetes","Data loading best practices for AI/ML inference on GKE","When running AI/ML inference workloads on GKE, learn how to accelerate data loading for inference serving containers and downloading models + weights.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg"],null,6,null,"https://cloud.google.com/blog/products/containers-kubernetes/improve-data-loading-times-for-ml-inference-apps-on-gke",null,1,[["Brian Kaufman","Senior Product Manager GKE AI/ML"],["Akshay Ram","Group Product Manager GKE AI/ML"]],null,"55570"]],null,"Developers \u0026 Practitioners",["Google Cloud Summit Series","Discover the latest in AI, Security, Workspace, App Dev, \u0026 more.","Register","https://cloudonair.withgoogle.com/summit_series_24?utm_source\u003dcgc-blog\u0026utm_medium\u003dblog\u0026utm_campaign\u003dsummit-series-2024\u0026utm_content\u003dsummit-series-left-hand-rail-cta\u0026utm_term\u003d-"],[["Developers \u0026 Practitioners","https://cloud.google.com/blog/topics/developers-practitioners","developers"],["AI \u0026 Machine Learning","https://cloud.google.com/blog/products/ai-machine-learning","ai-machine-learning"]],null,null,9], 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