# Solution 1: Automated Time Tracking Dashboard - PRODUCTION CONFIG
# Connects to Toggl Track API for real-time employee productivity analytics

data_sources:
  # Toggl Track API v9 - Time entries for workspace
  # Get your API token from: https://track.toggl.com/profile
  # Get workspace ID from: https://api.track.toggl.com/api/v9/workspaces
  toggl_entries:
    type: rest
    url: "https://api.track.toggl.com/api/v9/me/time_entries"
    headers:
      Authorization: "Basic ${TOGGL_API_TOKEN}"  # Base64 encoded token:api_token
      Content-Type: "application/json"
    options:
      params:
        start_date: "2024-01-01T00:00:00Z"  # Adjust date range as needed
        end_date: "2024-12-31T23:59:59Z"
      timeout: 30

processing:
  # Step 1: Filter recent time entries
  contextual_filtering:
    toggl_entries:
      - field: "start"
        condition: ">= 2024-01-01"
        new_name: "recent_entries"

  # Step 2: Calculate overall statistics
  content_summarization:
    recent_entries:
      method: "statistical"
      fields: ["duration"]
      summarize: ["sum", "mean", "count", "max", "min"]

  # Step 3: Aggregate metrics by employee
  advanced_operations:
    employee_hours:
      source: "recent_entries"
      group_by: "user_name"
      aggregate:
        total_seconds: "SUM(duration)"
        total_entries: "COUNT(*)"
        avg_seconds: "AVG(duration)"
        max_seconds: "MAX(duration)"
      sort: "-total_seconds"

    # Aggregate metrics by project
    project_hours:
      source: "recent_entries"
      group_by: "project_name"
      aggregate:
        total_seconds: "SUM(duration)"
        entry_count: "COUNT(*)"
        avg_seconds: "AVG(duration)"
      sort: "-total_seconds"

ai_interface:
  model:
    type: rest
    url: "https://api.openai.com/v1/chat/completions"
    method: POST
    headers:
      Authorization: "Bearer ${OPENAI_API_KEY}"
      Content-Type: "application/json"
    options:
      model: "gpt-4"
      temperature: 0.3
      max_tokens: 2000

  default_context:
    company: "${COMPANY_NAME}"
    analysis_period: "2024"

  prompts:
    time_tracking_analysis:
      system: "You are a productivity analyst specializing in workforce time management and workload optimization."
      user_template: |
        # Time Tracking Analysis for {{ company }} - {{ analysis_period }}

        ## Overall Statistics
        {% if recent_entries_summary is defined %}
        - Total time entries: {{ recent_entries_summary.duration_count }}
        - Total hours tracked: {{ ((recent_entries_summary.duration_sum | default(0)) / 3600) | round(1) }} hours
        - Average entry duration: {{ ((recent_entries_summary.duration_mean | default(0)) / 3600) | round(1) }} hours
        - Longest single entry: {{ ((recent_entries_summary.duration_max | default(0)) / 3600) | round(1) }} hours
        {% else %}
        Statistics pending data processing...
        {% endif %}

        ## Employee Performance Summary
        {% if employee_hours is defined and employee_hours %}
        {% for employee in employee_hours %}
        **{{ employee.user_name }}**:
        - Total Hours: {{ ((employee.total_seconds | default(0)) / 3600) | round(1) }} hours
        - Time Entries: {{ employee.total_entries | default(0) }}
        - Average Entry: {{ ((employee.avg_seconds | default(0)) / 3600) | round(1) }} hours
        - Longest Entry: {{ ((employee.max_seconds | default(0)) / 3600) | round(1) }} hours
        {% if ((employee.total_seconds | default(0)) / 3600) > 45 %}⚠️ OVERTIME RISK - Worked {{ ((employee.total_seconds | default(0)) / 3600) | round(1) }} hours{% endif %}

        {% endfor %}
        {% else %}
        Employee data aggregation in progress...
        {% endif %}

        ## Project Resource Allocation
        {% if project_hours is defined and project_hours %}
        {% for project in project_hours %}
        **{{ project.project_name }}**:
        - Total Hours: {{ ((project.total_seconds | default(0)) / 3600) | round(1) }} hours
        - Time Entries: {{ project.entry_count | default(0) }}
        - Average Entry: {{ ((project.avg_seconds | default(0)) / 3600) | round(1) }} hours

        {% endfor %}
        {% else %}
        Project data aggregation in progress...
        {% endif %}

        ## Analysis Request
        Based on this time tracking data, provide:

        1. **Productivity Patterns**: Identify which employees are most productive and efficient
        2. **Overtime Risk Assessment**: Flag employees at risk of burnout from excessive hours
        3. **Workload Balancing Recommendations**: Suggest how to redistribute work for better team balance
        4. **Billable Time Optimization**: Identify opportunities to increase billable hours
        5. **Action Items**: Specific recommendations for managers to improve time allocation

        Focus on actionable insights that improve team productivity and prevent burnout.

      response_format: "text"

output:
  type: file
  path: "output/time-tracking-analysis.json"
  format: json