## PRD: Predictive Rate Limiting Dashboard

This PRD outlines the development of a dashboard to visualize and monitor the performance of the predictive rate limiting system in AIVA. The dashboard will provide key metrics and visualizations to understand the impact of predictive rate limiting compared to the existing static rate limiting.

### GPM-076: Dashboard Foundation - Basic UI Setup
**Goal:** Establish the basic UI framework for the predictive rate limiting dashboard using a suitable framework (e.g., Flask, Django, Streamlit, or a similar tool).
**Acceptance Criteria:**
- A basic web page is rendered with a title "Predictive Rate Limiting Dashboard".
- The page includes placeholders for key metrics and visualizations (e.g., empty charts, text areas).
- The UI framework is configured for future integration with data sources.
**Files:** `dashboard/app.py`, `dashboard/templates/index.html`, `dashboard/requirements.txt`
**Spawns:** Implement basic authentication and authorization for the dashboard.

### GPM-077: Real-time Request Volume Visualization
**Goal:** Display a real-time graph of total request volume over time.
**Acceptance Criteria:**
- A line graph is displayed on the dashboard.
- The graph shows the total number of requests received per minute (or a suitable interval).
- The graph updates automatically at a defined interval (e.g., every 5 seconds).
- The graph displays a reasonable time window (e.g., the last 15 minutes).
**Files:** `dashboard/app.py`, `dashboard/templates/index.html`, `dashboard/static/js/chart_updater.js`
**Spawns:** Add ability to filter the request volume graph by different request types (e.g., API endpoints).

### GPM-078: Predictive vs. Static Rate Limiting Comparison Chart
**Goal:** Visualize the number of requests throttled by the predictive rate limiting system versus the static rate limiting system.
**Acceptance Criteria:**
- A bar chart or similar visualization is displayed on the dashboard.
- The chart compares the number of requests throttled by each system over a defined period (e.g., hourly, daily).
- The data for both systems is sourced from appropriate logging or metrics.
- The user can select the time granularity (hourly, daily, weekly).
**Files:** `dashboard/app.py`, `dashboard/templates/index.html`, `dashboard/utils/data_fetcher.py`
**Spawns:** Implement alerting on significant differences in throttling rates between the two systems.

### GPM-079: Response Latency Monitoring
**Goal:** Display the average response latency of requests under both rate limiting systems.
**Acceptance Criteria:**
- Two line graphs are displayed, one for response latency under predictive rate limiting and another for static rate limiting.
- The graphs show the average response latency over time.
- The graphs update automatically at a defined interval.
- Data source for response latency comes from existing AIVA performance monitoring.
**Files:** `dashboard/app.py`, `dashboard/templates/index.html`, `dashboard/utils/data_fetcher.py`
**Spawns:** Investigate correlation between rate limiting and response latency.

### GPM-080: Predictive Model Performance Metrics
**Goal:** Display key performance metrics for the predictive model itself (e.g., accuracy, precision, recall).
**Acceptance Criteria:**
- Dedicated section for displaying the model's performance metrics.
- Metrics are updated at regular intervals (e.g., daily or weekly).
- Clear definitions of each metric are provided (tooltip or inline explanation).
- The source of these metrics is the model training and evaluation pipeline.
**Files:** `dashboard/app.py`, `dashboard/templates/index.html`, `dashboard/utils/model_metrics.py`
**Spawns:** Add anomaly detection for model performance metrics.

### GPM-081: Configuration Parameter Display
**Goal:** Display the key configuration parameters of both the predictive and static rate limiting systems.
**Acceptance Criteria:**
- A table or list displays the current configuration parameters.
- Parameters include, but are not limited to: rate limits, model thresholds, and training schedule.
- The source of these parameters is the AIVA configuration management system.
- It is clear which parameters relate to which rate limiting system.
**Files:** `dashboard/app.py`, `dashboard/templates/index.html`, `dashboard/utils/config_fetcher.py`
**Spawns:** Allow authorized users to modify rate limiting parameters directly from the dashboard.

### GPM-082: Dashboard User Roles and Permissions
**Goal:** Implement basic user roles and permissions to control access to dashboard features.
**Acceptance Criteria:**
- At least two user roles are defined (e.g., "viewer" and "administrator").
- Viewers can only see data, while administrators can also modify configurations.
- Authentication is handled through an existing AIVA authentication mechanism (if available).
- Users are assigned roles upon login.
**Files:** `dashboard/app.py`, `dashboard/auth/auth_manager.py`
**Spawns:** Integrate with AIVA's existing access control lists for fine-grained permissions.
