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Configuration loader for streaming intent router.
"""
import os
from pathlib import Path
from typing import Dict, List, Optional
from dataclasses import dataclass, field
import yaml
import numpy as np
@dataclass
class StreamingConfig:
"""Configuration for streaming intent router."""
intents: List[str] = field(default_factory=list)
prior: Dict[str, float] = field(default_factory=dict)
transition_matrix: np.ndarray = field(default_factory=lambda: np.array([]))
intent_to_idx: Dict[str, int] = field(default_factory=dict)
idx_to_intent: Dict[int, str] = field(default_factory=dict)
# Emission parameters
alpha: float = 1.5
epsilon: float = 1e-8
# Decision thresholds
theta_hi: float = 0.85
theta_med: float = 0.60
theta_lock: float = 0.70
K: int = 3
# Window parameters
max_tokens: int = 64
max_tokens_limit: int = 128
# Debounce parameters
debounce_ms: int = 150
min_change_chars: int = 3
# Model parameters
model_max_length: int = 48
# Emission temperature (for temperature scaling transform)
temperature: float = 1.0
# SPRT parameters
sprt_alpha: float = 0.05
sprt_beta: float = 0.10
sprt_p0: float = 0.20
sprt_p1: float = 0.60
@classmethod
def from_yaml(cls, path: Optional[str] = None) -> "StreamingConfig":
"""Load configuration from YAML file."""
if path is None:
path = Path(__file__).parent.parent / "config" / "streaming_intent.yaml"
with open(path, "r") as f:
data = yaml.safe_load(f)
config = cls()
config.intents = data.get("intents", [])
config.prior = data.get("prior", {})
# Build intent index mappings
config.intent_to_idx = {intent: i for i, intent in enumerate(config.intents)}
config.idx_to_intent = {i: intent for i, intent in enumerate(config.intents)}
# Build transition matrix as numpy array
n = len(config.intents)
config.transition_matrix = np.zeros((n, n))
trans_dict = data.get("transition_matrix", {})
for from_intent, to_probs in trans_dict.items():
if from_intent in config.intent_to_idx:
i = config.intent_to_idx[from_intent]
for to_intent, prob in to_probs.items():
if to_intent in config.intent_to_idx:
j = config.intent_to_idx[to_intent]
config.transition_matrix[i, j] = prob
# Normalize rows (ensure they sum to 1)
row_sums = config.transition_matrix.sum(axis=1, keepdims=True)
row_sums[row_sums == 0] = 1 # Avoid division by zero
config.transition_matrix = config.transition_matrix / row_sums
# Emission parameters
emission = data.get("emission", {})
config.alpha = emission.get("alpha", 1.5)
config.epsilon = emission.get("epsilon", 1e-8)
# Decision thresholds
thresholds = data.get("thresholds", {})
config.theta_hi = thresholds.get("theta_hi", 0.85)
config.theta_med = thresholds.get("theta_med", 0.60)
config.theta_lock = thresholds.get("theta_lock", 0.70)
config.K = thresholds.get("K", 3)
# Window parameters
window = data.get("window", {})
config.max_tokens = window.get("max_tokens", 64)
config.max_tokens_limit = window.get("max_tokens_limit", 128)
# Debounce parameters
debounce = data.get("debounce", {})
config.debounce_ms = debounce.get("debounce_ms", 150)
config.min_change_chars = debounce.get("min_change_chars", 3)
# Model parameters
model = data.get("model", {})
config.model_max_length = model.get("max_length", 64)
# SPRT parameters
sprt = data.get("sprt", {})
config.sprt_alpha = sprt.get("alpha", 0.05)
config.sprt_beta = sprt.get("beta", 0.10)
config.sprt_p0 = sprt.get("p0", 0.20)
config.sprt_p1 = sprt.get("p1", 0.60)
# Emission temperature
config.temperature = emission.get("temperature", 1.0)
# Validate configuration
if not config.intents:
raise ValueError(
f"No intents found in config file: {path}. "
"The 'intents' list must contain at least one intent."
)
return config
def get_prior_vector(self) -> np.ndarray:
"""Get prior distribution as numpy array."""
if not self.intents:
raise ValueError(
"Cannot compute prior vector: intents list is empty. "
"Ensure streaming_intent.yaml contains valid intent definitions."
)
prior = np.zeros(len(self.intents))
for intent, prob in self.prior.items():
if intent in self.intent_to_idx:
prior[self.intent_to_idx[intent]] = prob
# Normalize
if prior.sum() > 0:
prior = prior / prior.sum()
else:
# Fallback to uniform distribution
prior = np.ones(len(self.intents)) / len(self.intents)
return prior
def save_transition_matrix(self, path: Optional[str] = None) -> None:
"""Save current transition matrix back to config file."""
if path is None:
path = Path(__file__).parent.parent / "config" / "streaming_intent.yaml"
with open(path, "r") as f:
data = yaml.safe_load(f)
# Update transition matrix in data
trans_dict = {}
for i, from_intent in enumerate(self.intents):
trans_dict[from_intent] = {}
for j, to_intent in enumerate(self.intents):
trans_dict[from_intent][to_intent] = float(self.transition_matrix[i, j])
data["transition_matrix"] = trans_dict
with open(path, "w") as f:
yaml.dump(data, f, default_flow_style=False, sort_keys=False)
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