Spaces:
Sleeping
Sleeping
| # Streaming Intent Router Configuration | |
| # HMM-style belief updating for STT chunk processing | |
| intents: | |
| - ESCALATION | |
| - APPOINTMENT | |
| - MEDICATION | |
| - SYMPTOM_CHECK | |
| - BILLING | |
| - GENERAL_INQUIRY | |
| - OTHER | |
| # Prior distribution (initial belief state) | |
| prior: | |
| ESCALATION: 0.05 | |
| APPOINTMENT: 0.15 | |
| MEDICATION: 0.15 | |
| SYMPTOM_CHECK: 0.15 | |
| BILLING: 0.10 | |
| GENERAL_INQUIRY: 0.15 | |
| OTHER: 0.25 | |
| # Transition matrix T[i,j] = P(intent_t=j | intent_{t-1}=i) | |
| # Rows must sum to 1.0 | |
| # High diagonal = "stickiness" (intent tends to persist) | |
| transition_matrix: | |
| ESCALATION: | |
| ESCALATION: 0.90 | |
| APPOINTMENT: 0.01 | |
| MEDICATION: 0.02 | |
| SYMPTOM_CHECK: 0.02 | |
| BILLING: 0.01 | |
| GENERAL_INQUIRY: 0.02 | |
| OTHER: 0.02 | |
| APPOINTMENT: | |
| ESCALATION: 0.02 | |
| APPOINTMENT: 0.80 | |
| MEDICATION: 0.03 | |
| SYMPTOM_CHECK: 0.03 | |
| BILLING: 0.07 | |
| GENERAL_INQUIRY: 0.03 | |
| OTHER: 0.02 | |
| MEDICATION: | |
| ESCALATION: 0.04 | |
| APPOINTMENT: 0.03 | |
| MEDICATION: 0.82 | |
| SYMPTOM_CHECK: 0.04 | |
| BILLING: 0.02 | |
| GENERAL_INQUIRY: 0.03 | |
| OTHER: 0.02 | |
| SYMPTOM_CHECK: | |
| ESCALATION: 0.08 | |
| APPOINTMENT: 0.04 | |
| MEDICATION: 0.05 | |
| SYMPTOM_CHECK: 0.75 | |
| BILLING: 0.02 | |
| GENERAL_INQUIRY: 0.03 | |
| OTHER: 0.03 | |
| BILLING: | |
| ESCALATION: 0.01 | |
| APPOINTMENT: 0.05 | |
| MEDICATION: 0.02 | |
| SYMPTOM_CHECK: 0.02 | |
| BILLING: 0.85 | |
| GENERAL_INQUIRY: 0.03 | |
| OTHER: 0.02 | |
| GENERAL_INQUIRY: | |
| ESCALATION: 0.03 | |
| APPOINTMENT: 0.05 | |
| MEDICATION: 0.05 | |
| SYMPTOM_CHECK: 0.05 | |
| BILLING: 0.04 | |
| GENERAL_INQUIRY: 0.75 | |
| OTHER: 0.03 | |
| OTHER: | |
| ESCALATION: 0.02 | |
| APPOINTMENT: 0.08 | |
| MEDICATION: 0.08 | |
| SYMPTOM_CHECK: 0.08 | |
| BILLING: 0.06 | |
| GENERAL_INQUIRY: 0.08 | |
| OTHER: 0.60 | |
| # Emission model parameters | |
| emission: | |
| alpha: 1.5 # Sharpening exponent for DriveHealthBERT probabilities | |
| epsilon: 1.0e-8 # Smoothing to avoid zero probabilities | |
| # Decision thresholds | |
| thresholds: | |
| theta_hi: 0.85 # Immediate escalation threshold | |
| theta_med: 0.60 # Consecutive-steps escalation threshold | |
| theta_lock: 0.70 # Commit threshold for non-escalation intents | |
| K: 3 # Required consecutive steps for stability | |
| # Rolling window parameters | |
| window: | |
| max_tokens: 64 # Default max tokens in rolling window | |
| max_tokens_limit: 128 # Hard limit for max_tokens | |
| # Debounce parameters | |
| debounce: | |
| debounce_ms: 150 # Minimum ms between updates (unless is_final) | |
| min_change_chars: 3 # Minimum character change to trigger update | |
| # Model parameters | |
| model: | |
| max_length: 48 # Max sequence length for BioClinicalBERT inference (reduced for latency) | |
| # SPRT (Sequential Probability Ratio Test) parameters | |
| # Used for statistically-principled escalation decisions | |
| sprt: | |
| alpha: 0.05 # Type I error target (false escalation rate) | |
| beta: 0.10 # Type II error target (missed escalation rate) | |
| p0: 0.20 # H0: baseline escalation probability (non-escalation) | |
| p1: 0.60 # H1: expected escalation probability (true escalation) | |
| # Note: Run scripts/evaluate_sprt_streaming.py --estimate_p0_p1 to tune these | |