ChatGPT HelpDesk.md

Hello can I use a Google API to use the Google internal helpdesk?

While there isn't a Google API specifically designed to handle full-fledged interactions with Google's internal helpdesk system, here's a breakdown of the possibilities and workarounds:

What Google does provide:

• Cloud Support API: This API is primarily meant for Google Cloud customers with Premium Support. It enables you to integrate case management data from Google's support system into your own internal ticketing systems. This offers streamlining and syncing of support information. (https://www.youtube.com/watch?v=OWtkkQvM3ZY)
• Support Hub (Google Cloud): The Support Hub is where you can find resources and potentially submit support cases if you have a Google Cloud account with the appropriate support plan. (https://cloud.google.com/support-hub)

Workarounds/Alternative Solutions:

• Google Forms Integration:
  • Create a detailed Google Form for your employees to submit internal support requests.
  • Connect the form to a Google Sheet to track and organize requests.
  • Set up automation (using Google Apps Script or other tools) to send email notifications for new requests to the designated team or individuals.
• 3rd-Party Ticketing Systems: Integrate a dedicated helpdesk solution (Zendesk, Freshdesk, Jira Service Management, Mojo Helpdesk, etc.) with Google Workspace. Many of these tools offer features to pull in emails from Gmail to create tickets, enhancing support processes.

Considerations

• Scale of your needs: If you have a simple helpdesk requirement, a Google Forms-based solution might work. For more complex handling or a large volume of support requests, using a dedicated help desk system would be preferable.
• Authentication: Any solution will need a way to authenticate your internal users (likely using your Google Workspace credentials).

Important Note: Google's internal tools and APIs for its own helpdesk operations are likely not publicly exposed.

Let me know if you'd like help brainstorming how to implement a helpdesk solution using the options above!

Thank you for confirming. I am a founder of a B2B technical software support services (wrench.chat). My customers are Google's customers because we support the power users, developers, and admins who use Looker. Right now I am testing HubDesk and GitHub as my customer facing helpdesk solution. We use GitHub for development and testing so there's no additional cost. HubDesk will receive emails sent to support@wrench.chat and create a GitHub issue, attach @reply to the email for tracking. Pretty simple solution but effective. We also want to use Slack because many of our customers use Slack. Finally, our primary method of communication with the customer is Google Chat. So we need to tie all these together with an automation so that we maintain a sing thread of activity per issue no matter the source or type of information type (email, slack message, chat, google meet, doc, sheets, and issue). Where is that thread stored and how is it created and maintained for integrity? I'd like to use Looker for reporting and Looker actions as well since that's our primary work environment. I'm comfortable developing in the Console but prefer working in VS Codium and I'm comfortable with Apps Scripts. And if can help me get this one that would be great too.

Building a Unified Customer Thread with Automation

You're on the right track with your current setup using HubDesk and GitHub for email-to-ticket creation. Let's explore how to integrate Slack, Google Chat, and automate a single thread of activity across these platforms while keeping Looker as your central reporting hub.

Here's a breakdown of the approach:

Centralized Thread Storage and Automation:

1. Google Sheet: This will act as your central thread storage. Each row can represent an issue, with columns for capturing details like:

   • Issue ID (linked to GitHub issue)
   • Source (Email, Slack, Chat, etc.)
   • Timestamp
   • Message Content
   • Assignee (if applicable)
   • Additional relevant data

2. Google Apps Script: This script will be the workhorse for your automation. It will handle:

   • Monitoring: Continuously check for new messages in Slack channels and Google Chat rooms designated for support.
   • Parsing: Extract relevant information from the message (content, sender, etc.).
   • HubDesk & GitHub Integration: Use HubDesk's API to create tickets for emails and update the corresponding GitHub issue with the message content and @reply tag.
   • Spreadsheet Update: Add a new row to the Google Sheet with the extracted message details and source.

Integration with Looker:

1. Looker Connection: Connect Looker to your Google Sheet to access the issue thread data.

2. Looker Dashboards and Actions:

   • Dashboards: Create dashboards in Looker to visualize issue trends, analyze communication channels used, track resolution times, etc.
   • Actions: Develop Looker actions to automate tasks based on specific criteria. For example, an action could trigger an email notification to the assignee when a high-priority issue sits unresolved for a certain timeframe.

Development Tools:

• Google Apps Script: Use this for the core automation logic. It integrates well with Google Workspace and offers easy deployment.
• VS Codium (Optional): Develop the script in VS Codium for a familiar coding environment.

Implementation Steps:

1. Design your Google Sheet: Define the columns and format for storing thread data.
2. Develop the Google Apps Script: Implement the functionalities mentioned above. Utilize libraries like the Gmail API for monitoring emails and the Google Chat API for monitoring chat messages. HubDesk and GitHub APIs will be needed for interacting with those platforms.
3. Set up Looker Connection & Build Dashboards/Actions: Connect Looker to your Google Sheet and design reports and actions as needed.

Benefits:

• Centralized Thread: All communication regarding an issue is consolidated in the Google Sheet, providing a complete picture.
• Seamless Workflow: Automation reduces manual work and streamlines communication across platforms.
• Looker Integration: Leverage Looker for powerful reporting and taking actions based on insights.

Additional Considerations:

• Security: Implement proper access controls for your Google Sheet and Apps Script to ensure data security.
• Scalability: As your customer base grows, consider using a dedicated cloud-based solution for managing helpdesk workflows.

This approach provides a solid foundation for a unified customer thread. Remember to test thoroughly and refine the automation as needed.

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This is a good start.

Instead of Sheets, I want to use BigQuery as our database but we can use Sheets for ad hoc reporting. Also, instead of calling customer issues (and all the information related to them) let's call them cards since that's familiar concept in the Google lexicon. Let's specify the purpose of each app involved in this workflow and the jobs they have and how they are integrated. I will need a Kanban type of app to manage the cards and make them visible to the customer. Let's go in this order so it's easy to follow when we're developing: Apps Scripts, Chat, Slack, Gmail, Groups, Calendar, Meet, Groups, GitHub, HubDesk, Docs, Sheets. Let's document each step with YAML style pseudcode that we can translate later into Javascript. Let's go.

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Unified Customer Thread with BigQuery & Kanban App

Here's the breakdown of each application's role and its integration points, documented in YAML-style pseudocode for easier translation to JavaScript later:

1. Google Apps Script (Central Automation Engine):

YAML

# Triggered by new message events from Chat, Slack, and Gmail
onMessage(source, message_data) {
  # Extract relevant data (content, sender, etc.) from message_data
  message_details = parseMessage(message_data, source);

  # Check if message relates to an existing card (search BigQuery)
  card_id = findCardByDetails(message_details);

  if (card_id) {
    # Update existing card in BigQuery
    updateCard(card_id, message_details);
  } else {
    # Create a new card in BigQuery
    card_id = createCard(message_details);
  }

  # Update Kanban app with card details (use API or webhooks)
  updateKanbanCard(card_id, message_details);

  # Additional actions based on message content (e.g., assign owner, update GitHub issue)
  handleSpecificActions(message_details);
}

# Helper functions for parsing messages, interacting with BigQuery, Kanban app, etc.

Use code with caution.

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2. Google Chat & Slack:

• Purpose: Provide channels for customer communication.
• Jobs:
  • Users interact with support representatives through these platforms.
  • New messages trigger the Apps Script function onMessage.

3. Gmail:

• Purpose: Receive customer support emails sent to support@wrench.chat.
• Jobs:
  • Forwards emails to HubDesk (using HubDesk's forwarding functionality).
  • HubDesk triggers the creation of a ticket and potentially a GitHub issue.
  • The Apps Script (triggered by HubDesk's notification) retrieves the email content and associates it with the corresponding card in BigQuery (via HubDesk ticket ID).

4. BigQuery:

• Purpose: Centralized database for storing all card data.
• Jobs:
  • Stores card information (ID, creation time, message history, source, assignee, etc.).
  • Accessible by Apps Script for CRUD (Create, Read, Update, Delete) operations on cards.

5. Kanban App (e.g., Trello, Asana):

• Purpose: Visualize card progress and manage workflow.
• Jobs:
  • Displays cards with relevant details (title, status, assignee).
  • Updates card status and assignee based on information received from the Apps Script.
  • Provides a customer-facing view of the card (optional, with limited access).

6. Groups, Calendar, Meet (Not Directly Used in Automation):

• Purpose: Can be used for internal team collaboration and scheduling meetings related to specific cards.
• Jobs:
  • Groups can be used for discussions or knowledge-sharing within the support team.
  • Calendar and Meet can be used to schedule meetings or calls with customers related to specific cards. Information about these interactions can be manually added to the card in BigQuery through the Kanban app or directly through the Apps Script.

7. GitHub (Optional):

• Purpose: Can be used for tracking code-related issues reported by customers.
• Jobs:
  • Can be integrated with HubDesk to automatically create GitHub issues for relevant tickets.
  • Updates to the card in BigQuery can trigger updates to the corresponding GitHub issue (via Apps Script).

8. HubDesk (Optional, primarily for email integration):

• Purpose: Can be used as a ticketing system if desired.
• Jobs:
  • Receives support emails and creates tickets.
  • Triggers the Apps Script function to associate the email content with the corresponding card in BigQuery.

9. Docs & Sheets (For Ad-hoc Reporting):

• Purpose: Used for creating reports or dashboards based on card data in BigQuery.
• Jobs:
  • No direct role in the automation workflow.
  • Apps Script or Looker can be used to extract data from BigQuery and populate Docs or Sheets for custom reporting needs.

Integration Points:

• Apps Script acts as the central automation engine, interacting with all other applications.
• Triggers in Chat, Slack, and Gmail initiate the Apps Script function onMessage.
• Apps Script communicates with BigQuery for CRUD operations on cards.
• The Kanban app updates its card details based on information received from the Apps Script.

This is a high-level overview. The specific implementation details will depend on the chosen Kanban app and its API

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Please write the pseudocode for each step. Specify how a Card is created and how we can use labels in almost every app to to make these Cards more effective as containers of information.

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Absolutely! Here's a more detailed breakdown of the pseudocode with an emphasis on card creation and the use of labels.

1. Apps Script:

YAML

# Trigger functions (adjust based on your setup)
onChatMessage(message_data):
  handleMessage("chat", message_data)

onSlackMessage(message_data):
  handleMessage("slack", message_data)

onHubDeskNotification(notification_data):
  handleMessage("email", notification_data)

# Main handling function
handleMessage(source, message_data):
  message_details = parseMessage(message_data, source) 
  card = findCardByDetails(message_details) 

  if not card:
    card = createCard(message_details) 

  updateCard(card.id, message_details) 
  updateKanbanCard(card.id, card) 
  handleSpecificActions(message_details) 

# Card Creation
createCard(message_details):
  # Generate a unique card ID
  card_id = generateCardId() 

  # Set initial card data 
  card = {
    id: card_id,
    title: extractTitle(message_details),
    source: source,
    labels: extractLabels(message_details), 
    status: "new",  
    messages: [message_details],
    # ...other fields as needed: assignee, creation_time, etc.
  }

  # Insert card into BigQuery
  insertCardIntoBigQuery(card) 

  return card

# Updating a Card
updateCard(card_id, message_details):
  # Update the relevant fields
  card.messages.append(message_details)  
  card.labels = mergeLabels(card.labels, extractLabels(message_details)) 

  # Additional updates as needed (status changes, assignee, etc.)

  # Update card in BigQuery
  updateCardInBigQuery(card_id, card) 

Use code with caution.

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2. Label Handling:

• Labels as a Concept: Labels can be represented as a simple array of strings within the card data structure in BigQuery. Each app you integrate with should allow the mapping of their labels or tags to this array.

• extractLabels() function: Implement logic to extract labels or tags from the message content, taking into account the specific syntax of each application (e.g., Hashtags for Slack, etc.).

• mergeLabels() function: This function would combine existing labels with new ones, ensure uniqueness, and potentially enforce a set of predefined labels for better organization.

3. Integration with Other Apps:

The details of integrating with Slack, Chat, Kanban app, etc., would depend on the specific APIs and webhooks they provide. The core idea is that whenever there's a change in a card (new message, status update, etc.), the Apps Script updates the corresponding information in the other applications.

Example: Creating a Card from a Slack Message:

1. A user posts a support question in a designated Slack channel.
2. Slack triggers your Apps Script webhook with the message data.
3. Apps Script's onSlackMessage() function is executed.
4. handleMessage() processes the message and determines no existing card matches it.
5. createCard() is called, generating a new card in BigQuery with:
   • A unique card_id.
   • Title extracted from the Slack message.
   • Source set to "slack".
   • Labels extracted from Slack message (if applicable).
   • Initial status set to "new".
6. Apps Script updates the Kanban app (e.g., Trello) to create a new card with details synced from BigQuery.

Let me know if you'd like examples for specific integration points, or want to refine the pseudocode further.

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I want to explain the use of Cards a bit more. We use Google Groups as the source of truth for our customers, vendors, partners, leads, prospects so every Contact needs a Card that can be referenced by one of our team members the first time that Customer contacts. The customer opens chat, sees we're available, sends their first message to us and our team can reply with 'Hello {{ firstname }} because the Customer's Card popped up with the message. If a customer has asked for help from us through our channels five times there should six cards, one of the customer and one for each issue.

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Certainly! Here's an expanded explanation of Cards considering Google Groups as the source of truth and incorporating multiple issues per customer:

Cards as Contact and Issue Containers:

In your scenario, Cards will act as a two-pronged information hub:

1. Customer Contact Information: Data from Google Groups will populate these Cards, providing a central repository for each customer's details (name, company, contact information, labels, etc.).
2. Customer Issue Tracking: Each time a customer contacts you through any channel (Chat, Slack, email, etc.), a new "issue card" will be created under the customer's main Card in BigQuery. This creates a threaded structure, where each issue card is linked to the parent customer card.

Here's a breakdown of the Card structure and how it facilitates personalized interactions:

• Customer Card:

  • id: Unique identifier for the customer.
  • contact_info: Information from Google Groups (name, email, company, etc.).
  • labels: Labels for categorization (e.g., customer type, industry).
  • issue_cards: Array containing references (IDs) to all the customer's issue cards.

• Issue Card:

  • id: Unique identifier for the specific issue.
  • parent_card_id: Reference to the customer's main Card (linking the issue to the customer).
  • source: Channel where the issue originated (Chat, Slack, email, etc.).
  • messages: Array containing a chronological history of all messages related to the issue.
  • status: Current status of the issue (new, ongoing, resolved).
  • labels: Labels specific to the issue (e.g., urgency, technical area).

Benefits for Personalized Service:

• Pre-Populated Information: When a customer initiates contact (Chat, for example), your Apps Script can retrieve the customer's Card based on their email address or other identifiers. This allows the Chat window to display the customer's name (e.g., "Hello {{firstname}}") for a personalized greeting.
• Issue History: All communication related to a specific issue is consolidated within the corresponding issue card. This allows team members to quickly understand the context and progress of the issue.
• Label-Based Organization: Labels can be used to categorize both customer cards and issue cards. This helps identify customer types, prioritize issues, and filter information for more focused views.

Example: Customer Initiates Contact:

1. A customer you support through a Google Group sends a message in your Chat window.
2. The Apps Script identifies the customer's email address from the chat.
3. It retrieves the customer's Card from BigQuery based on the email address.
4. If a Card exists, the script extracts the customer's first name and populates the chat window with a greeting like "Hello {{firstname}}".
5. Additionally, the script checks if an issue card already exists for this specific chat interaction.
   • If no issue card exists, a new one is created and linked to the customer's Card.

Additional Considerations:

• Merging Duplicate Cards: Implement logic to handle situations where a customer might have multiple Cards due to inconsistencies in data from Google Groups.
• Data Security: Ensure proper access controls are in place for BigQuery to protect customer information.

By leveraging Cards as a central repository for customer and issue information, you can provide a more personalized and efficient support experience for your team.

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Let's define the schema for Cards using the Google App APIs with YAML examples e.g. Fields: type and Metadata, that Cards can have child Cards and parent Cards, and finally what a customer card might look like in a Google Workspace application.

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Here's the schema for Cards in the context of your Google Workspace application, with YAML examples and considerations for child/parent relationships. Since we're using BigQuery, we'll focus on the conceptual schema rather than a direct Google API representation.

Core Card Schema:

YAML

card:
  type: object
  required:
    - id
    - type
  properties:
    id:
      type: string
      description: Unique identifier for the card.
    type:
      type: string
      enum:
        - customer
        - issue
    source:
      type: string 
      description: The channel from which the card originated (Chat, Slack, email, etc.).
    status:
      type: string
      enum:
         - new
         - in_progress
         - resolved
         - closed
    title:
      type: string
      description: Short descriptive title for the card.
    labels:
      type: array
      items:
        type: string
      description: Labels for categorization and filtering.
    messages:
      type: array
      items:
        type: object
        properties:
         content: string
         timestamp: string
         sender: string
    metadata:
      type: object
      description: Additional metadata fields as needed (e.g., creation date, assignee).

Use code with caution.

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Relationships:

• Customer <-> Issue Cards: This is represented using a parent_card_id field in the issue card.

YAML

# Issue Card Example
issue_card:
  # ... inherits other properties from 'card' schema
  properties:
  parent_card_id:
    type: string
    description: ID of the associated customer card.

Use code with caution.

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Customer Card Example:

YAML

customer_card:
  id: "customer-1234"
  type: "customer"
  source: "google_groups"  # Since the source of truth is Groups
  title: "ACME Corporation"
  labels: ["enterprise", "tech"]
  metadata:
    company: "ACME Corp"
    contact_name: "Jane Doe"
    contact_email: "jane.doe@acme.com"
    # ... other relevant customer details

Use code with caution.

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Considerations:

• Schema Flexibility: The metadata field allows adding custom details as needed. Consider your reporting requirements and the data you might need in your Kanban app to structure this field.
• Data Consistency: Implement logic in your Apps Script to ensure consistency between this schema and the actual data stored in BigQuery.
• Labels: Define a controlled vocabulary or allowed list of labels for easier filtering and reporting.

Workspace Interface:

The visual representation of a customer card would likely be within your Kanban app. Additionally, you might consider developing a Workspace add-on to display card information as a sidebar within Gmail, Chat, etc.

Let me know if you'd like to refine the schema further or have additional use cases you want to accommodate!

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Let's consider a Super-Card. This is an enhanced card for a some entity, in this case a person, me. The card contains the usual information about a person, name, email, etc. as well availability icons for Chat, Voice, Meet, Slack that green if available, yellow available but slow to respond, red if not available, a button to schedule a Google Meet on my calendar, a button to start a Chat with me, a button to start a chat with Miguel AI our Vertex AI chat app trained on Looker, issues I am currently working (sanitized for privacy and security). Visitors would need to be authenticated but this Super-Card would always be available so customers could reach me. What else should be on this card? should I host this on Google Sites? And please write the pseudocode for this as well.

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Super-Card for Availability and Communication

The Super-Card concept sounds valuable for providing a central point of contact and presence for you. Here's a breakdown of the functionalities, considerations, and pseudocode for its implementation.

Super-Card Features:

• Contact Information: Name, email, etc.
• Availability Icons: Chat, Voice, Meet, Slack icons that change color based on real-time status (green = available, yellow = slow response, red = unavailable).
• Action Buttons:
  • Schedule a Google Meet: Triggers Google Calendar integration to allow scheduling meetings.
  • Start Chat: Opens a Chat window to initiate direct communication.
  • Start Chat with Miguel AI: Launches the Vertex AI chat interface.
• Current Issues: Lists the issues you're currently working on (sanitized for privacy and security).

Hosting Options:

While Google Sites is a possibility, it might not be the most dynamic option for this scenario. Here are some alternatives:

• Google Apps Script with a Custom Web App: This approach offers full control over the Super-Card's functionality and user interface. You can leverage libraries like the Google Calendar API and Google Chat API for real-time integrations.
• Third-Party Service: Services like Calendly or HubSpot Meetings provide pre-built solutions for scheduling meetings and managing availability. However, customization might be limited.

Authentication:

For accessing sensitive information like your current issues, implementing user authentication using Google Workspace login or similar mechanisms is recommended.

Pseudocode (using Google Apps Script with a Custom Web App):

YAML

# Function to generate the Super-Card HTML content
function getSuperCardHtml() {
  availability_icons = getAvailabilityStatus();
  current_issues = getSanitizedIssues();

  # Generate HTML with user information, icons, buttons, and issue list
  return `
    <div>
      <h2>${getName()}</h2>
      <p>...</p>  <div>Availability: ${availability_icons}</div>
      <button onclick="openChat()">Start Chat</button>
      <button onclick="scheduleMeet()">Schedule Meeting</button>
      <button onclick="openMiguelChat()">Start Chat with Miguel AI</button>
      <h3>Current Issues</h3>
      <ul>
        ${listCurrentIssues(current_issues)}
      </ul>
    </div>
  `;
}

# Function to retrieve availability status from calendar/presence data
function getAvailabilityStatus() {
  // Implement logic to check calendar events and presence information
  // Return a string representing the availability icons (HTML or CSS classes)
}

# Function to retrieve a sanitized list of current issues
function getSanitizedIssues() {
  // Implement logic to retrieve your current issues from a data source
  // Sanitize sensitive information and return a list of issue summaries
}

# Helper functions for opening Chat window, scheduling a meeting, etc.

# Serve the Super-Card HTML content when the web app is accessed
doGet(e) {
  htmlContent = getSuperCardHtml();
  e.outputContent(htmlContent);
}

Use code with caution.

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Additional Considerations:

• Security: Implement proper authorization checks to ensure only authenticated users can access your Super-Card and sensitive information.
• Real-time Updates: Consider using websockets or server-sent events for near real-time updates to availability icons and potentially issue lists.
• Mobile Responsiveness: Ensure your Super-Card displays well on mobile devices.

This pseudocode provides a starting point. You'll need to fill in the specific details of how to retrieve your availability status, issues, and implement the action buttons based on the chosen hosting platform.

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Since we're using Apps Scripts for most of the work, would this be considered an application?

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Yes, a system built primarily on Google Apps Script, as you've described, can be considered an application. Let's break down why:

• Functionality: Apps Script allows you to implement complex logic, automate workflows, and integrate with various Google Workspace services. Your system for managing customer interactions and creating a "Super-Card" represents a set of functionalities similar to a traditional application.
• Interface: While Apps Script itself doesn't have a built-in visual interface builder, you can use the following for creating a user interface:
  • HTML Service: Allows you to create web apps with custom HTML, CSS, and JavaScript, enabling dynamic interfaces like your proposed "Super-Card".
  • Google Sheets UI: Apps Script can interact with Google Sheets user interfaces, allowing you to build custom dashboard-like apps within a familiar environment.
  • Google Workspace Add-ons: Apps Scripts can power sidebars and custom features within Gmail, Calendar, Docs, etc.
• Deployment as a Web App: Apps Script projects can be published as web applications, allowing users to access their functionality via a URL.

Key Considerations:

• Scalability: Apps Script-based applications have limitations in terms of execution quotas and concurrent usage. If you anticipate high traffic or complex workloads, you might need to supplement your solution with traditional App Engine or cloud-based services.
• Maintainability: As with any application, well-structured Apps Script code is essential. Focus on modularity and code organization to enhance maintainability as your project grows.

In Summary: While the term "application" is broad, a sophisticated and well-developed Apps Script project can indeed be considered an application – especially for streamlining processes and extending the functionality of Google Workspace.

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So let's define our application as something like HelpDesk is a Javascript app for Google Workspace that uses APIs to integrate (all apps go here) and, provide a customer portal has the following features..... and an admin env to manage all the cards... something like that. Please expand and detail the major integration points between applications like Slack and GitHub or the Admin Env and BigQuery, not more than a 15 word sentence for each integration point.

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Absolutely! Here's a definition of your HelpDesk application with specific attention to the integration points:

HelpDesk Application Definition:

HelpDesk is a JavaScript app for Google Workspace that integrates with Slack, GitHub, Google Chat, Gmail, Groups, BigQuery, and other related services to provide a customer portal and an admin environment for managing support requests.

Major Integration Points:

• Slack: HelpDesk receives support requests from Slack channels and creates corresponding cards in BigQuery.
• GitHub: Support issues are automatically reflected in GitHub issue trackers for development teams.
• Google Chat: HelpDesk utilizes Chat for real-time communication between support agents and customers.
• Gmail: HelpDesk receives customer support emails and creates cards, potentially triggering relevant GitHub issues.
• Groups: HelpDesk uses Groups as a source of customer data, creating cards for existing contacts.
• BigQuery: HelpDesk stores all card data (customer info, conversations, issue history) in BigQuery for centralized management.
• Customer Portal: The customer portal displays open and resolved issues, and offers communication options with support representatives.
• Admin Environment: The admin environment allows for managing cards, assigning support agents, and viewing reports/analytics powered by BigQuery data.

Key Technologies:

• Google Apps Script: Core automation logic, card management, integration with Google Workspace services.
• JavaScript/Web Technologies: Development of the customer portal and admin environment (HTML, CSS, JavaScript).
• Cloud Functions/App Engine (optional): For scaling or tasks not suitable for Apps Script limitations.

Let me know if you'd like to explore a specific integration point in more detail or add additional application features!

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Can all of this exist on https://script.google.com/ or should we use GitHub for a repo?

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Here's a breakdown of the pros and cons of each approach, plus a hybrid solution to consider:

Option 1: Fully on script.google.com

• Pros:

  • Easy to Start: Apps Script development is built-in to your Google Workspace. No additional setup is required.
  • Workspace Integration: Apps Script has seamless integration with services like Sheets, Docs, Calendar, etc.

• Cons:

  • Version Control: script.google.com's versioning is limited, making collaboration and reverting code changes harder.
  • Code Management: No robust code organization system – a multi-file Apps Script project can get unmanageable quickly.
  • Testing: Local development and testing are tricky with Apps Script's editor.

Option 2: GitHub Repo

• Pros:

  • Robust Version Control: Full Git functionality for tracking changes, collaboration, and branching.
  • Flexible Tooling: Use your preferred code editor, linters, and testing frameworks.
  • Community and Open-Source: Benefit from existing libraries and community support for JavaScript development.

• Cons:

  • Deployment Process: Requires using tools like CLASP (Command Line Apps Script Projects) to deploy from your repo to script.google.com.
  • Potential Friction: Initial setup and syncing with script.google.com adds a bit of complexity.

Hybrid Option (Recommended):

1. Develop in GitHub: Use GitHub for version control, coding, and testing.
2. Use CLASP: Employ CLASP to push code to Apps Script during development and for final deployment.
3. Web Apps in script.google.com: Build web apps (customer portal, admin UI) directly in script.google.com, where their integration with Google services is simplified.

Why Hybrid is Best:

This gives you the best of both worlds:

• Superior development and collaboration: Git and your chosen code editor.
• Simplified web app development: Within the Apps Script environment.
• Maintainable system: Your codebase is well-organized and versioned.

Additional Notes:

• Consider how many people are involved. If it's a solo project, script.google.com alone might suffice in the early stages.
• As your project grows and collaboration becomes more important, migrating to the hybrid model is fairly straightforward.

Let me know if you'd like help outlining the setup of the hybrid approach!

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It's a solo job so let's start with Option 1 and plan a migration to Option 3. What is the first thing we should do in Apps Script?

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Here's a plan for starting with Option 1 (all within script.google.com) and outlining a path towards Option 3 (hybrid model).

Step 1: Project Setup in Apps Script

1. Create a New Project: Navigate to https://script.google.com/ and start a new Apps Script project. Give it a descriptive name, like "HelpDesk".
2. Core Function: Create an initial onMessage() function (or similar depending on your trigger) to serve as the starting point. This function will handle incoming messages and begin the process of card creation.

Step 2: Establish Card Schema

1. BigQuery Table: Design the basic schema for your Cards table in BigQuery. Consider the fields we've outlined previously (id, type, source, title, labels, messages, etc.). Create this table in your designated BigQuery project.
2. Data Structure: Within your onMessage() function, create a temporary JavaScript object to represent a new Card based on your schema.

Step 3: Basic Integration

1. Choose a Starting Point: Pick one communication channel for initial integration, likely Slack or Chat, since you can simulate messages easily.
2. Set up Webhook/Trigger: Configure either a Slack webhook or enable relevant Chat event triggers to call your Apps Script function.
3. Parse Messages: Write code in onMessage() to extract relevant information (sender, message content, etc.) from the incoming message data.
4. Populate the Card Object: Map the extracted message data to your temporary Card object.
5. Connect to BigQuery: Within the onMessage() function, establish a connection to your BigQuery project and insert a new row into your Cards table using the populated card object.

Step 4: Planning Your Migration

1. Version Control: Even with script.google.com alone, utilize the built-in versioning system diligently. Comment changes and take snapshots as you work. This will ease the transition later.
2. Modularization: Think about breaking down your project into logically separate files within script.google.com. This will make refactoring for the hybrid setup smoother.
3. CLASP Familiarization: Start reading up on CLASP (Command Line Apps Script Projects) to understand the project structure and deployment process.

Why Start This Way

This approach lets you prototype rapidly and see concrete results with your card management core, while setting a foundation for a smooth migration later.

Next Steps

Once you have the basic integration working, focus on:

• Expanding to other channels (Gmail, etc.).
• Implementing logic for updating existing cards instead of only creating new ones.
• Designing a rudimentary web UI to view Cards data directly within Apps Script.

Let me know when you're ready to tackle specific integration points or discuss the migration to the Hybrid Option in more detail!

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Please write performant javascript for the function onMessage()

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Absolutely! Here's how to write performant JavaScript for your onMessage() function, along with explanations and optimizations:

Base Structure

JavaScript

function onMessage(event) {
  const messageData = event.message; // Assuming 'message' is the property containing data 
  const source = messageData.source || "unknown"; // Determine source if possible

  // 1. Early Filtering
  if (source === "ignore") { 
    return; // Abort processing for unnecessary sources
  }

  // 2. Card Handling
  const card = findOrCreateCard(messageData); 

  // 3. Message Appending
  card.messages.push(messageData); 

  // 4. BigQuery Update (Batching)
  updateCardInBigQuery(card); 

  // 5. Additional Actions
  handleAdditionalActions(messageData);
}

Use code with caution.

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Performance Optimizations

1. Early Filtering: If certain sources are irrelevant, immediately return from the function to conserve resources.

2. Efficient Card Lookup: Implement a fast way to find existing cards (e.g., by indexing a relevant field in memory if your dataset is manageable). Minimize BigQuery lookups as much as possible.

3. Message Appending: Simple array operations like push are generally fast.

4. BigQuery Batching: Instead of updating BigQuery after every message, accumulate updates and write to BigQuery in batches. This dramatically reduces API calls and improves performance.

5. Asynchronous Operations: If handleAdditionalActions involves things like sending notifications or external API calls, use asynchronous functions (promises, async/await) to avoid blocking the main execution thread.

Additional Considerations

• App Script Quotas: Be aware of Apps Script's execution time limits and URL fetch quotas. Batching updates to BigQuery helps stay within these constraints.
• Data Structure: If your cards grow large, consider how to store them efficiently in BigQuery (e.g., nested message objects vs. separate message entries).
• Profiling (If Needed): For very large-scale applications, profiling tools can help identify specific bottlenecks in your code.

Example Optimization: BigQuery Batching

JavaScript

let updateBatch = []; // Store updates temporarily

function onMessage(event) {
  // ... other logic ...

  updateBatch.push(card);

  if (updateBatch.length >= 50 || event.source === "flush") { // Update every 50 messages or on a special source
    updateCardsInBigQuery(updateBatch); 
    updateBatch = []; 
  }
}

Use code with caution.

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Important: Performance is always relative to the scale of your data and workload. Start with these optimizations, and if you encounter bottlenecks, adjust based on your specific use case.

Let me know if you want to dive into optimizations for specific parts of the onMessage() function!

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Let's assume this scenario: a customer (johnq@startup.io) is getting the following LookML error: Unknown or inaccessible field "user_order_facts.lifetime_orders" referenced in "users.lifetime_orders". Check for typos and missing joins. so he emails us at support@wrench.chat asking for help and includes the error in the body of the email. What does HelpDesk do before I even open the email?

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Here's a breakdown of what HelpDesk would do upon receiving the support email from johnq@startup.io, leveraging the automation you've built:

1. Email Arrival and HubDesk Integration

• Email arrives at support@wrench.chat and is forwarded to HubDesk (or your email-based ticketing system).
• HubDesk creates a ticket, extracts the email content, and potentially populates relevant fields (sender, subject line, etc.).
• HubDesk triggers a webhook or API call to notify your Apps Script.

2. Apps Script Automation Kicks In

• Your Apps Script receives the notification with email/ticket data.
• Message Parsing with NLP: Analyze the ticket content for:
  • Recognizing LookML-specific errors. This could use regular expressions or more sophisticated Natural Language Processing (NLP) techniques to identify LookML syntax.
  • Extracting relevant entities: "user_order_facts.lifetime_orders", "users.lifetime_orders", etc.
• BigQuery Lookup:
  • Check if a customer card exists for johnq@startup.io.
  • If a card exists, retrieve any past issue cards related to LookML or data modeling. This might help understand if the issue is a recurring one.

3. Card Creation/Update

• New or Existing Card:
  • If no customer card exists for johnq@startup.io, create a new one, populating it with available information from the email and/or Groups data.
  • Create a new issue card under johnq@startup.io's main card.
• Card Enrichment:
  • Add the LookML error message, extracted entities, and any historical context to the issue card.
  • Assign labels like "LookML", "error", and potentially a priority label ("urgent" if certain keywords are detected).

Result: Enhanced Ticket

By the time you open the email/ticket:

• A card is associated with johnq@startup.io, either a new one or an existing one updated with the latest issue.
• The ticket will contain the original error message with relevant keywords and entities highlighted (if your NLP extraction is in place).
• Prioritized, labeled issues make it easier to triage and determine how urgently you need to respond.

Additional Possibilities (Not Yet Implemented)

• Knowledge Base Search: Your system could search an internal knowledge base for articles related to the "lifetime_orders" field, potentially surfacing a solution automatically.
• GitHub Search: If connected to the customer's GitHub repo, it could search for references to "lifetime_orders" to check for recent changes in their project.

Let me know if you would like to expand on any of these steps or explore the additional possibilities!

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Here are my notes: from Hubdesk documentation

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Unfortunately, I can't provide specific insights without seeing your HubDesk documentation notes. To create the best possible guidance, please share the following key points from your notes:

1. Event Triggers:

• What specific actions in HubDesk trigger notifications or webhooks to your Apps Script? (e.g., new ticket creation, ticket update, etc.)
• Does HubDesk allow flexibility in choosing what data is sent as part of the notification?

2. Available Data:

• What essential data fields will be included in the notification payload from HubDesk? This might include:
  • Sender's email address
  • Ticket subject
  • Ticket body (the original email content)
  • Ticket ID or reference number
  • Any custom fields you've configured in HubDesk

3. Configuration Options

• Can you create custom webhooks in HubDesk to send data to your Apps Script endpoint?
• Does HubDesk allow filtering notifications (e.g., only for certain ticket types, or with specific keywords)?

Example:

To illustrate how this information would be used, let's assume the following:

• Trigger: A new ticket is created in HubDesk.
• Data: Notification payload includes sender's email, ticket subject, body, and ticket ID.
• Configuration: You can set up a webhook in HubDesk pointing to your Apps Script URL.

How I Would Adapt the Guidance

1. Refine the onMessage() function in your Apps Script to accept a notification object representing the data structure above.
2. Guide you on extracting information from the notification object.
3. Focus on the ticket body section, advising you on text analysis to detect LookML errors.

Please provide the relevant parts of your HubDesk notes, and I'll tailor the guidance and code examples accordingly!

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Forward Emails to HubDesk You configure email forwarding in your own email account, not in HubDesk. Exactly how this is done depends on the email provider you're using. A number of email providers allow you to create email forwarding rules so that you can select the incoming mail that should be forwarded to your HubDesk account. HubDesk does not support multi-forwarding, or forwarding that goes through multiple locations before being sent to the HubDesk support address. In the event that multi-forwarding is configured, the requester will be the first address that HubDesk can find in the Reply:To or From: fields in the email headers. This could produce inconsistent results and is not supported. This article covers way to forward email for : Google Workspace Office 365 Google Workspace (formerly GSuite) You can receive support requests at a Gmail or Google Workspace account, and forward them to your HubDesk email address. Automatically forwarding emails from your Gmail account With Gmail, you can choose to forward all of your new messages to another email address, or only forward certain types of messages. For details of how to configure forwarding, see Automatically forward Gmail messages to another account on Google's website. Redirecting incoming emails to another email account You can add an alias to an email address to route incoming emails from one address to another. For example, you might have an employee leave your organization, but you still need to make sure that you don’t miss any messages from your customers. For details about how to redirect incoming emails, see Redirect incoming messages to another email address on Google's website. Office 365 or Outlook.com You can receive support requests at an Outlook email account, and forward it to your HubDesk email address. This works with email addresses using both Outlook.com and Outlook hooked into an Exchange server. Forwarding email from Outlook.com To set up forwarding from your Outlook.com mailbox, follow the instructions in Turn on or off automatic forwarding in Outlook.com on Microsoft's website. Forwarding email from Microsoft Exchange To forward email to HubDesk using Outlook hooked in to an Exchange server, you can set up a redirect rule. To do so, follow the instructions in Microsoft Outlook: Automatically forward my messages to another e-mail account. Another option is to set up email forwarding on your Exchange mailbox as follows: Create a contact in Active Directory called HubDesk Support with the email address you specified when setting up your inbox. Enable forwarding on on that email in the user properties in Active Directory. For detailed instructions, refer to How to configure a mailbox to forward mail to a mail-enabled contact.

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Great! This information provides a clear starting point for integrating HubDesk with your HelpDesk system. Here's how to break down the process and adjust our previous guidance:

Understanding HubDesk's Limitations:

• No Direct Webhooks: It seems HubDesk's primary integration mechanism is email forwarding. This means we need to design your Apps Script to parse incoming emails effectively.
• Multi-forwarding Issues: Be mindful of this while setting up forwarding rules in your email providers to avoid inconsistent results.

Adjusting the Workflow:

1. Configure Email Forwarding: Choose the most appropriate method for forwarding support emails based on your email provider (Gmail, Office 365, etc.). Ensure you're forwarding directly to your HubDesk email address.

2. Enhance Apps Script for Email Parsing:

   • Modify the onMessage() function (or create a new email-specific function) to parse the content of forwarded emails.
   • Extract critical information: sender's email, subject, ticket body (original email).
   • HubDesk might also include additional metadata (like ticket ID) in the forwarded email headers. Research how to access email headers in Apps Script for potential extra data.

3. Continue Existing Logic: Once critical data is extracted, the rest of your card creation/update logic remains largely the same:

   • Search for an existing customer card based on the sender's email.
   • Create a new issue card under the customer card.
   • Analyze the ticket body for LookML errors and relevant entities.
   • Update BigQuery accordingly.

Additional Considerations:

• HubDesk Documentation on Headers: See if your HubDesk documentation provides more detail about how they format forwarded emails and what data is included in the headers. This will make parsing more reliable.
• Gmail API Alternative: If the email parsing becomes complex, consider using the Gmail API directly in Apps Script. This gives more granular access to message data, but it potentially adds another layer of complexity.

Example Code Snippet (Illustrative):

JavaScript

function onEmailReceived(email) {
  const senderEmail = email.getFrom(); // May need to adjust header parsing
  const subject = email.getSubject();
  const body = email.getBody(); 

  // ... rest of your logic to find/create cards, analyze LookML errors... 
}

Use code with caution.

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Next Steps:

1. Test Email Forwarding: Set up forwarding and send test emails to your HubDesk support address.
2. Examine Email Format: Inspect the raw format of the forwarded emails in HubDesk to understand how HubDesk structures them and what header information is available.

Let's work on the email parsing code once you have a clearer picture of the email format you're receiving from HubDesk!

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Are the cards created each their own file created dynamically when queried?

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No, cards in your HelpDesk application are not individual files. Here's why:

• BigQuery Storage: You've designed your system to store card data in BigQuery, a database. Databases manage data in tables, where each row generally represents a distinct item (in this case, a card).

• Dynamic Creation: While the BigQuery table is a pre-existing structure, the card entries (rows) are created dynamically in response to incoming messages:

  1. A new message triggers your Apps Script code.
  2. The code either finds an existing card in the BigQuery table or generates a new card entry (a row) with a unique ID.
  3. The message data is added to the appropriate card.

Advantages of BigQuery:

• Centralization: Storing all your cards in one BigQuery table makes management and querying efficient.
• Flexibility: The table schema allows you to add/modify the fields you store about each card without having to manage individual files.
• Querying Power: BigQuery offers SQL-like querying to find, filter, and aggregate information across your cards.

Analogy:

Think of your BigQuery table like a spreadsheet:

• The spreadsheet itself is the BigQuery table you create to define the card structure.
• Each row in the spreadsheet represents an individual card.
• New cards are added as new rows are inserted by your Apps Script.

File-Based Systems (Not the current method):

In some scenarios, you could represent data as individual files. However, for a dynamic system like your HelpDesk, this quickly becomes unmanageable:

• Finding a specific card would require searching through many files.
• Querying across multiple cards would become very inefficient.
• Ensuring data consistency across files would be a challenge.

Let me know if you'd like to explore scenarios where a file-based system might be suitable. However, for your current HelpDesk design, BigQuery is the appropriate choice!

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I like BigQuery for this. Let's specify using Google Cloud Storage for anything not appropriate for BQ (image, video, etc.). So the data for the cards will exist in at least two places: GitHub and BQ, correct?

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Yes, you're starting to see how a multi-pronged data storage strategy fits into your HelpDesk application. Let's clarify and refine the concept:

Core Data in BigQuery

The primary store for your card data will remain BigQuery. This is ideal for the following reasons:

• Structured Data: BigQuery is optimized for structured and semi-structured data, aligning perfectly with your card schema (IDs, source, metadata, message history in JSON, etc.).
• Querying and Analytics: BigQuery allows for complex queries to filter cards, generate reports, and analyze trends related to support requests.
• Links to External Resources: Instead of storing external content (images, videos, etc.) directly in BigQuery, you would store references (URLs) to those files hosted elsewhere, like Cloud Storage.

GitHub's Role

It's important to distinguish two potential use cases of GitHub here:

1. Code Development: This is the primary use case for GitHub – managing your HelpDesk application's codebase, versioning, and collaboration.

2. Issue Tracking (Customer Code): If your customers are developers who submit code-related support issues, GitHub's issue tracker might make sense for managing the technical details relating to those specific requests. This creates a link between their GitHub issue and your internal card in BigQuery.

Google Cloud Storage for Unstructured Content

Google Cloud Storage is well-suited for unstructured data like images, videos, large documents, etc. Here's how it fits in:

• Handling Uploads: If customers can attach files to support requests, you can implement a mechanism to receive those attachments and save them to Cloud Storage.
• Reference in BigQuery: Your card records in BigQuery would not store the file content itself, but rather store a link (URL) to the file residing in Cloud Storage.
• Retrieval: When displaying a card (e.g., in your admin environment), if associated files exist, you'd use the URL stored in BigQuery to fetch the content from Cloud Storage and display it.

Caveat: If image or video analysis is critical to your HelpDesk workflow, storing them in Cloud Storage enables you to use additional Google Cloud AI services (Vision API, Video Intelligence API) to extract insights from those files and enrich your card data.

Let's enhance your card schema in BigQuery to accommodate these additions. Please let me know if you would like to explore the code aspect of handling file uploads and integrating Cloud Storage!

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What are the costs associated with running HelpDesk that are incremental to what I am already paying?

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1

[

](https://stackoverflow.com/questions/41725250/does-google-app-script-propertiesservice-support-serialized-data-or-arrays)

Here's a breakdown of the primary incremental costs you might incur when running your HelpDesk application, beyond your existing Google Workspace subscription.

1. Google BigQuery

• Storage: Costs are based on the amount of data stored in your Cards table. BigQuery offers a free tier, but you'll likely exceed this. The cost depends on the number of cards, message volume, and the size of your metadata.

• Querying: BigQuery charges for the amount of data scanned by your queries. Simple card lookups will be inexpensive, but complex analytics across a large dataset will incur higher costs.

• BigQuery Pricing Details: https://cloud.google.com/bigquery/pricing

2. Google Cloud Storage

• Storage: Cost is based on the amount of data (images, videos, etc.) you store.

• Network: Egress charges (data leaving Cloud Storage) apply when you retrieve files. This will likely be minimal unless you're serving high-resolution images/videos frequently.

• Cloud Storage Pricing Details: https://cloud.google.com/storage/pricing

3. Google Apps Script (Potential Costs)

• Execution Quotas: Apps Script has limits on daily execution time. You're unlikely to hit these unless you have a very high volume of support requests.

• URL Fetch Quotas: Apps Script limits the amount of data fetched from external services. Excessive API calls to external systems (if you implement those features) could hit this limit.

• Apps Script Quotas: https://developers.google.com/apps-script/guides/services/quotas

4. Other Services (If Utilized)

• Cloud Functions: If you migrate parts of your automation to Cloud Functions for scalability, they have a free tier and then usage-based pricing.
• AI/ML APIs: Using Cloud Vision API or similar services to analyze images/videos would incur costs based on the number of API requests.

Important Considerations:

• Start Small: Your initial costs will likely be minimal. Google's free tiers should cover early usage.
• Monitoring: It's essential to monitor your BigQuery and Cloud Storage costs as your usage grows so you can make adjustments as needed.
• Optimizations: The performance optimizations we discussed for your Apps Script code will also play a role in controlling BigQuery query costs.

Cost Estimation:

It's difficult to provide a precise cost estimate without knowing your expected volume of cards and message frequency. However, for a moderate-scale HelpDesk, you could likely run your system comfortably for under $50 per month (potentially much less) after the free tiers are accounted for.

Do you have a ballpark estimate of the number of customers and support requests you anticipate? I can help provide a more tailored cost projection.

Sources

info

1. stackoverflow.com/questions/41725250/does-google-app-script-propertiesservice-support-serialized-data-or-arrays

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So we are asserting that if there is no activity there is no incremental cost correct?

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Mostly correct! Let's break down the nuances to ensure accuracy:

Zero Activity:

• BigQuery Storage: Even if there's no active usage, you'll still pay for the storage of existing card data. However, this should be a small cost unless you accumulate a massive number of inactive cards.
• Cloud Storage: Similarly, storing customer-attached files would incur storage costs, even if they aren't being accessed.
• Apps Script, Cloud Functions: There would be zero execution cost if nothing is being triggered.

Very Low Activity:

• BigQuery Querying: Simple card lookups occasionally would fall into the free tier of BigQuery or result in negligible charges.
• Apps Script & Cloud Functions: Minimal usage within quotas would likely not incur costs.

Situations Where Inactivity Could Result in Costs:

• Runaway Queries: A poorly written query in your code that accidentally scans a huge amount of data in BigQuery could result in charges, even without frequent use of the system.
• External API Integrations: If your HelpDesk polls external systems frequently (regardless of whether there are new support requests), those API calls could contribute to costs depending on the API pricing models.

Key Points:

• Dominant Cost: Storage will likely be your primary ongoing cost, even with periods of low activity.
• Usage-Based: The system is primarily designed with usage-based costs (querying, API calls). True "inactivity" should result in minimal charges.
• Optimization: Well-written Apps Script code and efficient BigQuery design keeps the usage-based costs in check.

In essence, while not strictly "zero" cost during inactivity, a well-designed HelpDesk system should have very low incremental costs during periods of low activity.

Let me know if you'd like to discuss potential cost-saving strategies or specific scenarios you're concerned about!

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Also, customers have to be authenticated to reach us on our Slack channel, Chat, Meet but anyone can email support@wrench.chat so we need to verify the sender is a paying customer before creating an issue in github, correct?

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Yes, you're absolutely correct! Let's break down why this is important and how you could implement the verification.

Why Verify Paying Customers

1. Focus on Support: Ensuring the person emailing support@wrench.chat is a paying customer prevents you from handling support issues for non-paying users, allowing you to prioritize those who have a support agreement.
2. GitHub Issue Management: Limiting issue creation to paying customers helps manage your workflow on GitHub, preventing cluttering of your issue tracker with requests you might not be obligated to address.

Verification Mechanism

Here's one approach to achieve verification:

1. Customer Database: Maintain a database or list within your Apps Script that stores email addresses of paying customers. This could be synced from your billing system or even a simple Google Sheet initially.
2. Email Lookup: When a new email arrives at support@wrench.chat, your Apps Script would:
   • Extract the sender's email address.
   • Check if this email exists in your customer database.
3. Conditional Issue Creation:
   • If verified: Proceed with creating the GitHub issue and the rest of your HelpDesk workflow.
   • If not verified:
     • Option 1: Send an automated email to the sender indicating they need to have an active support plan for issue creation.
     • Option 2: Hold the message in a queue for manual review by a support agent.

Integration Points

• HubDesk: If HubDesk extracts the sender's email into a field you can access, the verification could happen directly within your Apps Script triggered by HubDesk.
• GitHub API: You'll need to use the GitHub API to create issues programmatically.
• Customer Database: Choose how you maintain your paying customer data (Google Sheet, external system, etc.).

Additional Considerations

• Security: Protect your customer data and access mechanisms.
• Edge Cases: Decide how to handle edge cases like a paying customer using a new email address for the first time.
• Communication: Have clear communication on your website about support channels available to paying customers.

Let's refine this verification process based on your specific setup and how you manage paying customer data. Are you using a particular billing or CRM system?

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I only accept phone calls on my Google Voice number for sceduled appts as well.

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Got it! Here's how to adjust your HelpDesk system to accommodate the scheduled calls element while maintaining the paying customer verification.

Assumptions:

• You are using Google Calendar for scheduling appointments.
• Paying customers schedule calls through a designated system or process (form, another calendar link, etc.).

Integrating Calendar Data

1. Calendar API in Apps Script: Enable the Google Calendar API within your Apps Script project to access your Google Voice call appointments.
2. Regular Checks: Set up your Apps Script on a trigger (time-driven, perhaps every hour or few hours). The script would:
   • Fetch upcoming Google Voice call appointments from your Calendar.
   • Extract the phone number associated with each appointment.

Customer Verification (Enhanced)

1. Maintain a List: In addition to email, store the phone numbers of paying customers allowed to receive scheduled calls.
2. When a Call Comes In:
   • Google Voice API (If Possible): If the Google Voice API allows you to capture the caller ID during an incoming call, integrate that to get the caller's phone number.
   • Manual Input (Otherwise): If not, provide your team with a way to input the caller's phone number when answering the call.
3. Verification Step: Before fully engaging with the caller, your Apps Script or system would check the provided phone number against the list of paying customers.

Workflow Outcomes

• Verified Customer: Proceed to create a Card as usual or log details about the call directly on an existing Card (if one exists).
• Unverified Customer:
  • Politely inform them of your support policy for scheduled calls.
  • Potentially offer alternative support channels for non-urgent queries.

Challenges & Refinements

• Google Voice API: Investigate if Google Voice API integration is feasible to get the caller ID automatically.
• Data Consistency: Ensure your scheduling process populates the customer's phone number in your Calendar events for accurate matching.
• Fallback Mechanism: If the Google Voice API is limited, have a quick interface (a Sheet or a simple form) where your team can input the phone number as soon as they answer the call.

Let's explore specific APIs and mechanisms if you'd like to make this process more automated and seamless!

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That's helpful thank you. Here are the assumptions: A company has two Looker Developers. The people are my customers but the company pays me. So when we say Customers we are referring to the people I am interacting with. Any given given customer has a 12% chance of initiating contact with me. Whether they engage or not after that doesn't really matter about the immediate processes after. Here are some rough numbers: + avg email 86KB + three image attachments 150KB each + image file from the Contact's profile we use for their Card 2.2 MB (unless we compress it) Each issue has on average 10 emails total There is 90% probability of a chat with 50 messages There is 20% of a Google Meet which generate a 45min recording Please calculate the cost for an average month (22 work days) with 10 companies. And let me know if you would like clarification or additional assumptions.

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Absolutely! Let's break down the cost calculation with your provided assumptions.

Assumptions Summary

• Customers: We're focusing on the individual developers you support at paying companies.
• Contact Probability: Each developer has a 12% chance of contacting you within a month.
• Data Volumes:
  • Email: 86KB per email
  • Image Attachments: 150KB each (x3)
  • Profile Image: 2.2MB
  • Emails per issue: 10
  • Chat Messages: 50 (with 90% probability)
  • Meet Recording: 45 minutes (with 20% probability)

Calculations

We'll calculate costs for a single customer and then scale it up.

1. BigQuery Storage

   • Text Data: Let's assume each email & chat message equates to roughly 500 bytes of text data. 10 emails + 50 chat messages = ~30KB per issue.
   • Card Metadata: Estimate a modest 1KB per card for fields like ID, status, etc.
   • Total Per Issue: ~31KB
   • Monthly Storage (1 customer): Let's assume 2 issues per month * 31KB = ~62KB
   • Image Storage: Deferred to Cloud Storage (calculated next)

2. Cloud Storage

   • Attachments per Issue: 3 * 150KB = 450KB
   • Profile Image: 2.2MB (potentially compressed)
   • Monthly Storage (1 customer): Assume 2 issues * (450KB + 2.2MB) = ~5.3MB

3. Google Meet Recording Storage

   • Assumptions Needed:
     • Recording Resolution/Quality
     • Compression applied by Google Meet
   • Placeholder: Let's assume, conservatively, 500MB per recording
   • Monthly Storage (1 customer): 20% probability * 2 issues * 500MB = 200MB

4. BigQuery Queries, Apps Script Costs: Let's allocate a budget of $5 per customer per month initially for these variable costs. We'll revisit if this proves insufficient.

Monthly Cost Per Customer

• BigQuery Storage: Use the BigQuery pricing calculator https://cloud.google.com/bigquery/pricing with 62KB of storage as the input. Cost is likely minimal.
• Cloud Storage: Use the Cloud Storage pricing calculator https://cloud.google.com/storage/pricing with around 5.5MB monthly usage.
• Meet Storage: Factor in ~200MB.
• Querying and Apps Script: $5 budget

Total Monthly Cost (10 companies)

• Extrapolate the 'per customer' costs calculated above and multiply by 10.

Important Notes

• Rough Estimate: This is a best-effort estimate with numerous assumptions about data sizes. It provides a ballpark idea.
• Monitoring is Key: Once your system is live, closely monitor your actual BigQuery and Cloud Storage costs.
• Optimization:
  • Compress profile images
  • Archive old data
  • Refine BigQuery queries to reduce costs.

Let me know if you want to refine any of the assumptions (email sizes, recording resolution, etc.). I can recalculate accordingly!

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so what's the total cost per 10 companies per month for querying, storage, and total? approximations are fine.

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Absolutely! Let's approximate the total cost as follows:

Assumptions in place:

• Your previous data assumptions hold true.
• We'll stick with the $5 budget for queries and Apps Script per customer, anticipating these costs to remain under control initially.

Cost Breakdown (Per Customer, Per Month)

• BigQuery Storage: Minimal, likely under $0.50 per customer, per month.
• Cloud Storage: This will dominate the cost. With image attachments and potential Meet recordings, a conservative estimate would place this between $2 to $5 per customer, per month.
• Querying & Apps Script: Budget of $5, revisit if needed.

Total Approximation (10 Companies)

• Conservative Estimate:
  • 10 customers * ($0.50 + $5 + $5) = ~$105
• Less Conservative Estimate (Smaller Images, No Meet Recordings):
  • 10 customers * ($0.50 + $2 + $5) = ~$75

Key Points

• Image & Recording Impact: Reducing profile image sizes and the frequency of Google Meet recordings significantly lowers Cloud Storage costs.
• Real-World Monitoring: These are approximations. Once your HelpDesk is live, the essential step is tracking your actual usage on BigQuery and Cloud Storage to get accurate costs.

Optimization Potential

• Image Compression: Aggressively optimizing profile images can make a big difference.
• Archiving: Implement a process to move older data to cheaper storage options (Cloud Storage Nearline, etc.) after a certain period.

Let's revisit these calculations if you discover your assumptions about email sizes, image usage, or Meet frequency need adjustments!

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Could I pas these costs down to my customers as usage fees?

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Yes, you could potentially pass these costs down to your customers as usage fees. Here's a breakdown of how to approach this and the factors to consider:

Usage-Based Pricing Model

Instead of a flat monthly or annual subscription fee, structure your pricing model to include a base fee and additional charges based on their resource usage. Potential usage metrics could include:

• Storage: The amount of data stored for each customer in BigQuery and Cloud Storage (emails, images, recordings).
• Support Volume: Number of support issues or support interactions above a certain threshold within a billing period.
• Premium Features: If you develop features that incur high costs (ex: image analysis), you could tier your service and charge more for access to those features.

Considerations

• Transparency: Clearly communicate your pricing structure to your customers upfront. Explain what constitutes billable usage and provide them with tools or a dashboard to monitor their usage throughout the month.
• Competitive Landscape: Research how similar support services structure their pricing. You want to stay competitive while ensuring your costs are covered.
• Value Proposition: Emphasize the value your HelpDesk system provides. Customers will be more receptive to usage-based fees if the value clearly outweighs the additional costs.
• Tiered Pricing: Consider offering different pricing tiers based on anticipated usage levels. This allows smaller customers a lower cost of entry while enabling you to recoup costs from high-volume customers.
• Predictability: While pure usage-based fees seem fair, some customers might prefer more predictable costs. Consider a hybrid model with a base fee covering a certain level of usage and then overage charges beyond that.

Advantages of Usage-Based Pricing

• Scalability: Your revenue aligns with the cost of serving each customer.
• Fairness: Customers who use more resources pay more.
• Incentivizes Efficiency: Could potentially encourage customers to optimize their requests.

Challenges

• Complexity: Implementing usage tracking and billing can add complexity to your system.
• Customer Perception: Some customers may dislike unpredictable cost fluctuations.

Recommendation

Start with a simple usage-based model focused on storage costs (most predictable). Monitor your system and customer feedback to gauge the success of this model and determine if you need to expand your fee structure.

Let me know if you'd like to brainstorm specific fee structures or discuss ways to implement usage tracking within your HelpDesk application!

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right now i charge $499 per month for Live Support for a company with 2 developers. It's $99 per additional person but on average thre are two people per company that are requesting help from a service like mine. i charge $999 for weekly office hours which is 90 minutes per week for a company where i will troubleshoot problems, answer questions, present on a topic related to Looker or BI in general, for a companies Looker users or executive team; finally I have a Vertix AI chat app that a developer can subscribe to for $99 per license per month (no login sharing). There are about 13,000 companies globally that have purchased Looker, i expect there are 7,800 active companies in the US (where my Google Ads and english only support will be most welcomed) with one average 2 developers each. Assuming these price points are effective, what would be a commensurate usage price?

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Here's how we can approach calculating a commensurate usage price for your HelpDesk system, considering your existing pricing structure and the cost estimates we discussed earlier:

Current Pricing Structure

• Live Support: $499/month (base) + $99/month per additional developer (usage-based)
• Weekly Office Hours: Flat fee of $999/month
• Vertex AI Chat App: $99/month per license (usage-based)

HelpDesk Usage Costs (Recap)

• The estimated cost per customer (10 companies) ranged from $75 to $105 per month, depending on image sizes and Meet recordings.

Considerations for Usage Pricing

• Target Cost: Aim to recover your estimated HelpDesk system costs (around $7.50 to $10.50 per customer) through usage fees.
• Simplicity: While you can get very granular with usage metrics, a simpler model might be easier to manage initially.
• Value Proposition: Focus on the value your HelpDesk provides beyond just storage – faster resolution times, proactive support, etc.

Possible Usage Fee Models

Model 1: Flat Fee per Developer

• Simplicity: Add a flat monthly fee per developer who uses your HelpDesk system, on top of your base Live Support fee.
• Example: Set a fee of $10-$15 per developer, per month for HelpDesk access.
• Advantage: Easy to understand and manage.
• Disadvantage: Doesn't directly reflect individual usage levels.

Model 2: Tiered Usage

• Offer different pricing tiers: Basic (limited interactions), Standard (moderate interactions), and Premium (high volume support).
• Base your tiers on: Estimated number of support issues, storage used, or a combination of factors.
• Advantage: Reflects usage levels more accurately.
• Disadvantage: More complex to explain and manage for both you and your customers.

Recommendation:

Start with a flat fee per developer model (Model 1) to keep things simple initially. Set the fee within the range of $10-$15 per developer to cover your estimated HelpDesk costs while offering value within your existing pricing structure.

Here's an example pricing structure incorporating the flat fee:

• Live Support: $499/month (base) + $99/month per additional developer + $10-$15 per developer for HelpDesk access
• Weekly Office Hours: $999/month (flat fee)
• Vertex AI Chat App: $99/month per license

Monitor and Adapt

• Track your actual HelpDesk usage and costs over time.
• Gather customer feedback on the usage fees.
• You can refine your model later by:
  • Adjusting the flat fee per developer.
  • Transitioning to a tiered usage model if it proves more sustainable.

Additional Considerations

• Free Tier: You might consider a limited free tier for the HelpDesk system to incentivize customer adoption.
• Data Visibility: If you implement usage-based pricing, provide your customers with clear dashboards or reports where they can monitor their usage and estimated costs.

By closely monitoring usage and customer feedback, you can adapt your pricing model to ensure long-term sustainability while delivering value to your customers.

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i dont think usage pricing will work.

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That's perfectly alright! Here's why a pure usage-based model might not be the best fit and how we can explore alternative pricing strategies:

Reasons Why Usage Pricing Might Not Be Ideal

• Unpredictability for Customers: Fluctuating costs based on support volume or storage could create budgeting challenges for your customers.
• Complexity: Implementing granular usage tracking and billing adds overhead to your system.
• Potentially High Barrier to Entry: Charging for every bit of storage and interaction could dissuade smaller companies from trying your HelpDesk system.

Alternative Approaches

Let's focus on pricing models that provide more predictability and align well with your existing service offerings:

1. Feature Bundling

   • Package HelpDesk as Premium Support: Include your HelpDesk system as part of an enhanced support tier priced higher than your base Live Support. This positions HelpDesk as a value-added service.
   • Advantages:
     • Simplicity: No complex usage tracking.
     • Attractive Value: Bundling offers a compelling reason for customers to upgrade to the higher-priced tier.

2. Value-Based Tiered Pricing

   • Focus on Outcomes: Emphasize the benefits of your HelpDesk system (faster resolution times, reduced downtime, proactive insights).
   • Create Tiers: Offer different tiers based on the level of support volume and advanced features (image analysis on request, integration with 3rd party knowledge bases, etc.)
   • Advantages:
     • Customer Choice: Provides flexibility for customers depending on their needs.
     • **Upsell Potential: ** Creates a path for customers to upgrade to higher tiers as their usage grows.

3. Annual Contracts or Discounts

   • Offer Incentives: Provide discounts for annual contracts or upfront payments. This guarantees revenue for you and lowers the perceived monthly cost for customers.
   • Advantages:
     • Predictable Revenue: Improves forecasting.
     • Appealing to Customers: Discounts can be attractive.

Hybrid Model

You could combine aspects of these models. For example:

• Tiered Pricing: Different pricing tiers based on the number of supported developers and feature sets.
• Annual Contract Discounts: Incentivize longer-term commitments with discounts within each tier.

Important Questions for You

1. How much predictability is desired by your ideal customers?
2. What additional features could you add to the HelpDesk to justify a higher price point in a premium tier?
3. Would annual contracts be attractive to your target market?

Let's brainstorm! Tell me more about your target customer profile and their pain points, and we can tailor a pricing strategy that fits your goals and their preferences.

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Can I make the (Apps Script) HelpDesk app available as web app on a custom domain wrench.chat since that is also my workspace domain?

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Yes, you can absolutely deploy your Apps Script HelpDesk app as a web app and make it available on your custom domain (wrench.chat). Here's a breakdown of the steps involved:

1. Develop Web App UI in Apps Script

• HTML Service: Use the HTML Service within Apps Script to design the front-end interface of your HelpDesk app (customer portal, admin view, etc.).
• Functionality: Define doGet() and (if needed) doPost() functions to handle web requests and display data dynamically.

2. Publish as a Web App

• Project Settings: In the Apps Script Editor:

  • Go to Publish -> Deploy as web app...
  • Important: Choose "Execute the app as: Me" and "Who has access to the app: Anyone" (or "Anyone within your Workspace domain" if you prefer to restrict access).

• Obtain Web App URL: Apps Script will provide you with a long, unintuitive web app URL.

3. Setup Domain Mapping

You'll need the ability to manage DNS records for your wrench.chat domain. Here are the general steps:

• CNAME Record: Create a CNAME record pointing a subdomain (e.g., helpdesk.wrench.chat) to the long Apps Script web app URL.
• Verification (Potentially): Your domain registrar might require you to verify ownership for security.

Example using Google Domains (If your registrar is different, the steps will vary slightly)

1. Go to your Google Domains dashboard and manage your wrench.chat domain.
2. Navigate to the DNS settings.
3. Add a custom resource record:
   • Type: CNAME
   • Hostname: helpdesk (or your preferred subdomain)
   • Data: The long Apps Script web app URL

4. Propagation: DNS changes can take some time (up to 24-48 hours) to propagate.

Additional Considerations

• User Authentication: If you want to restrict access to the HelpDesk web app, you'll need to implement authentication mechanisms within your Apps Script code. This could leverage Google Workspace login or an independent system you set up.
• Custom Look and Feel: While HTML Service allows you to create the interface, fully customizing the appearance might require additional CSS and JavaScript. You can host these files on Cloud Storage and include them in your web app.
• HTTPS: If you have an SSL/TLS certificate for your wrench.chat domain, you should be able to set up HTTPS for your HelpDesk web app as well, providing better security.

Specific Guidance

I'm ready to assist you through the detailed steps of DNS configuration. Here's what I'll need:

• Confirm Your Domain Registrar: Where is your wrench.chat domain registered (Google Domains, GoDaddy, etc.)?
• Authentication Preference: Do you want the HelpDesk web app to be open to the public, restricted to your Workspace users, or accessible with a different login system?

Let's work through this together!

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Ok, I think we're ready to codify a plan. There is a lot to cover. BRB

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Absolutely! Let's break down the codification of your HelpDesk plan into manageable phases. Since there are numerous areas to address, we'll prioritize and create an actionable roadmap.

Phase 1: Core HelpDesk Functionality

Focus: Establish the foundation of card creation, issue tracking, and integrations.

1. BigQuery Schema Design:
   • Card Fields: Refine the list of essential fields for Customer Cards and Issue Cards (ID, source, status, labels, message history, metadata, etc.).
   • Relationship: Determine how to explicitly link Issue Cards to their parent Customer Card.
2. Apps Script Foundation:

• onMessage() function: Enhance the code to handle email (via HubDesk or potentially Gmail API), extract relevant info, and create/update cards in BigQuery accordingly.
• Basic Web UI: Use HTML Service for a rudimentary customer portal to view cards and raise new issues, plus an admin view for your team.

Phase 2: Enhanced Automation and Integrations

Focus: Expand channels and improve the intelligence of your system.

1. Slack & Chat Integration: Set up webhooks/triggers to call your onMessage() function when messages arrive on your designated support channels.
2. GitHub Integration (If Needed): Connect to customer GitHub repos to automatically create Issue Cards for relevant issues (those tagged with a specific label, for example).
3. NLP for LookML Errors: Develop or refine techniques to identify LookML-specific errors within support messages, enriching your cards with insights.

Phase 3: User Experience & Authentication

Focus: Make the HelpDesk easily accessible and secure.

1. Custom Domain: Follow the steps we outlined to make your HelpDesk web app accessible on a subdomain of wrench.chat.
2. Authentication: Determine the best approach (Workspace login, custom system), and implement it to protect access to card data.
3. UI Improvements (Customer Portal): Design a user-friendly interface for customers to view their issues, add comments, and see resolution status.

Phase 4: Optimization & Additional Features

Focus: Performance, cost-saving measures, and advanced functionality as needed.

1. Performance Profiling: If you encounter bottlenecks, use profiling tools in Apps Script to identify and address them.
2. Archiving Strategy: Implement a process to archive old, resolved Issue Cards to reduce BigQuery storage costs.
3. Proactive Features (Potential):
   • Knowledge Base Integration: If you have a knowledge base, connect it to your HelpDesk to suggest potential solutions.
   • Reporting & Analytics: Build dashboards (using Looker, potentially) to visualize support trends and gain insights from card data.

Important Considerations:

• Testing: Build unit testing into your Apps Script code and have a rigorous testing process throughout development.
• Version Control: If you haven't moved to the hybrid Git/Apps Script model yet, now is a good time to start for proper collaboration and change tracking.
• Flexibility: Design with some adaptability in mind, as your customer needs might evolve.

How to Get Started

Let's choose the most critical piece from Phase 1 to tackle first. Do you want to finalize your BigQuery schema or work on email integration with HubDesk/Gmail?

I'm here to guide you through each step, provide detailed code examples, and help you overcome any challenges along the way!

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This is an abstraction of the HelpDesk application, information flow, monthly cost estimate, required integrations by application, YAML pseudocode we've already discussed, example javascript we've already reviewed, a step by step guide to build the HelpDesk web app with Apps Script, and how Gemini will help. This document will also be in our repo for team members to review so they can see how Gemini and Dion built HelpDesk. Please include at a minimum the following: 1. An introduction of HelpDesk, our in-house app for managing customer Issues, creating Cards for Issues and Contacts, and the nerve center of wrench.chat.  2. Unified Customer Thread with BigQuery & Kanban App 3. Here's a more detailed breakdown of the pseudocode with an emphasis on card creation and the use of labels. Apps Script: Label Handling: Integration with Other Apps: Example: Creating a Card from a Slack Message: 4. Cards as Contact and Issue Containers: Here's a breakdown of the Card structure and how it facilitates personalized interactions: Customer Card: Issue Card: Benefits for Personalized Service: Example: Customer Initiates Contact: Additional Considerations: Core Card Schema: Relationships: Workspace Interface: 5. Super-Card for Availability and Communication 6. Pseudocode (using Google Apps Script with a Custom Web App): 7. Base Structure onMessage() 8. Example Optimization: BigQuery Batching 9. Readme for the github repo

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I do not have enough information about that person to help with your request. I am a large language model, and I am able to communicate and generate human-like text in response to a wide range of prompts and questions, but my knowledge about this person is limited. Is there anything else I can do to help you with this request?

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what person are you referring to?

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I apologize for the confusion! It seems I've misinterpreted something in your previous request. Here's how I can help you create the best possible document about HelpDesk:

Document Outline

Let's break down your document structure for maximum clarity and impact.

1. HelpDesk Introduction

• Purpose: Clearly define HelpDesk as your in-house customer support tracking and card management system.
• Key Features: Highlight:
  • Customer issue tracking
  • Centralized card-based customer and issue data
  • Integration with core communication channels (Slack, Chat, email, etc.)

2. Unified Customer Thread

• BigQuery: Explain its role as the central data repository for cards, ensuring comprehensive issue history.
• Kanban App: Describe its function in visualizing card progress and managing workflows.
• Benefits: Emphasize how this combination provides a holistic view of each customer's support journey.

3. Card Creation & Label Usage (Pseudocode Focus)

• Apps Script: Describe its role as the automation engine, handling message parsing, and interacting with various apps and BigQuery.
• Pseudocode:
  • Provide clear YAML examples for functions like onMessage(), handleMessage(), createCard().
  • Emphasis on Labels: Explain how they categorize cards, filter information and enable targeted actions.

4. Cards as Containers

• Types: Differentiate between Customer Cards and Issue Cards, outlining their structures.
• Schema Examples: Provide detailed YAML examples showing essential fields (refer to our previous work).
• Personalized Service: Explain how these comprehensive cards drive better support experiences through contextual conversations.

5. Super-Card

• Concept: Explain how this enhanced card includes availability indicators, preferred contact methods, and more.
• Benefits: Highlight its function in providing your team with a consolidated view for proactive and efficient support.

6. Pseudocode Focus

• Custom Web App: Explain the value of a web app interface built within Apps Script.
• onMessage(): Use our prior example, adding detailed comments for each step (message parsing, card logic, BigQuery interaction).
• BigQuery Batching: Revisit the optimization, providing in-line comments to explain the performance gain.

7. GitHub README.md

• Project Overview: A concise explanation of HelpDesk's purpose and features.
• Setup: Installation instructions (if any) or prerequisites.
• Usage: Key functions and how to leverage them.
• Team: Optionally credit team members for clarity.

Gemini's Role

Since I don't have information about your specific "Gemini" AI, let's reframe this section:

• Future Enhancements:
  • Outline potential areas where AI, NLP, or ML could enhance HelpDesk:
    • Intelligent issue routing
    • Knowledge base article suggestion
    • Support trend analysis
  • Acknowledge this as a roadmap for continuous improvement.

How I Can Help

• Content Generation: I'll provide you with text snippets, code examples (aligned with our previous work), and pseudocode refinements tailored to each section.
• Organization: I'll suggest best practices for structuring your document and README for clarity.
• Review: I'll proofread and give feedback on your drafts to ensure a comprehensive and accurate explanation.

Let's Start! Which section of the document would you like to focus on first? I'm ready to collaborate and build a resource that empowers your team.