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Architecture: difficulty-first + ML confirmation + safety floors.
Usage:
from aco.learned_router import TrainedRouter
router = TrainedRouter.from_pretrained("narcolepticchicken/agent-cost-optimizer")
tier, confidence = router.predict("Write a Python function", "coding", difficulty=3)
"""
import json
import os
import pickle
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from collections import defaultdict
try:
import numpy as np
import xgboost as xgb
HAS_ML = True
except ImportError:
HAS_ML = False
TASK_TYPES = ["quick_answer","coding","research","document_drafting",
"legal_regulated","tool_heavy","retrieval_heavy",
"long_horizon","unknown_ambiguous"]
TT2IDX = {t:i for i,t in enumerate(TASK_TYPES)}
CODE_KW = ["python","javascript","code","function","bug","debug","refactor",
"implement","test","compile","runtime","class","module","async","thread"]
LEGAL_KW = ["contract","legal","compliance","gdpr","privacy","policy","regulatory","liability"]
RESEARCH_KW = ["research","find sources","literature","investigate","compare","analyze","survey"]
TOOL_KW = ["search","fetch","retrieve","query","api","database","scrape","aggregate"]
LONG_KW = ["plan","project","roadmap","orchestrate","multi-step","migrate","pipeline","deploy"]
MATH_KW = ["calculate","compute","solve","equation","formula","optimize","probability"]
# Default safety floors per task type
TASK_FLOOR = {
"legal_regulated":4,"long_horizon":3,"research":3,"coding":3,
"unknown_ambiguous":3,"quick_answer":1,"document_drafting":2,
"tool_heavy":2,"retrieval_heavy":2,
}
class TrainedRouter:
"""Production trained router: difficulty-first + ML confirmation + safety floors."""
def __init__(self, tier_clfs: Dict, feat_keys: List[str],
tier_config: Dict, escalation_threshold: float = 0.55):
self.tier_clfs = tier_clfs
self.feat_keys = feat_keys
self.tier_config = tier_config
self.tier_cost = {int(k):v for k,v in tier_config["tier_cost"].items()}
self.task_floor = tier_config.get("task_floor", TASK_FLOOR)
self.escalation_threshold = escalation_threshold
self._trained = True
def extract_features(self, request: str, task_type: str, difficulty: int = 3) -> Dict:
r = request.lower()
f = {"req_len":len(request),"num_words":len(request.split()),
"has_code":int(any(k in r for k in CODE_KW)),
"n_code":sum(1 for k in CODE_KW if k in r),
"has_legal":int(any(k in r for k in LEGAL_KW)),
"n_legal":sum(1 for k in LEGAL_KW if k in r),
"has_research":int(any(k in r for k in RESEARCH_KW)),
"n_research":sum(1 for k in RESEARCH_KW if k in r),
"has_tool":int(any(k in r for k in TOOL_KW)),
"n_tool":sum(1 for k in TOOL_KW if k in r),
"has_long":int(any(k in r for k in LONG_KW)),
"has_math":int(any(k in r for k in MATH_KW)),
"tt_idx":TT2IDX.get(task_type,8),"difficulty":difficulty}
for tt in TASK_TYPES:
f[f"tt_{tt}"] = int(task_type == tt)
return f
def _feats_to_vec(self, feats: Dict):
import numpy as np
return np.array([float(feats.get(k, 0.0)) for k in self.feat_keys], dtype=np.float32)
def predict(self, request: str, task_type: str, difficulty: int = 3,
escalation_threshold: Optional[float] = None) -> Tuple[int, float]:
"""Predict optimal tier using difficulty-first + ML confirmation.
Returns: (tier, confidence)
"""
threshold = escalation_threshold or self.escalation_threshold
# Step 1: difficulty -> base_tier
base_tier = min(difficulty + 1, 5)
# Step 2: apply safety floor
floor = self.task_floor.get(task_type, 2)
base_tier = max(base_tier, floor)
if not HAS_ML or not self._trained:
return base_tier, 0.6
# Step 3: ML confirmation
feats = self.extract_features(request, task_type, difficulty)
x = self._feats_to_vec(feats).reshape(1, -1)
p_success = self.tier_clfs[base_tier].predict_proba(x)[0, 1]
confidence = p_success
# Step 4: escalate if P(success) too low
while p_success < threshold and base_tier < 5:
base_tier += 1
p_success = self.tier_clfs[base_tier].predict_proba(x)[0, 1]
confidence = p_success
return base_tier, float(confidence)
@classmethod
def from_pretrained(cls, repo_id: str, escalation_threshold: float = 0.55,
cache_dir: Optional[str] = None):
"""Load trained router from HuggingFace Hub."""
from huggingface_hub import hf_hub_download
bundle_path = hf_hub_download(
repo_id=repo_id, filename="router_models/router_bundle.pkl",
cache_dir=cache_dir,
)
with open(bundle_path, "rb") as f:
import pickle
bundle = pickle.load(f)
return cls(
tier_clfs={int(k): v for k, v in bundle["tier_clfs"].items()},
feat_keys=bundle["feat_keys"],
tier_config=bundle["tier_config"],
escalation_threshold=escalation_threshold,
)
@classmethod
def from_local(cls, model_dir: str, escalation_threshold: float = 0.55):
"""Load from local directory."""
bundle_path = os.path.join(model_dir, "router_bundle.pkl")
with open(bundle_path, "rb") as f:
import pickle
bundle = pickle.load(f)
return cls(
tier_clfs={int(k): v for k, v in bundle["tier_clfs"].items()},
feat_keys=bundle["feat_keys"],
tier_config=bundle["tier_config"],
escalation_threshold=escalation_threshold,
)
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