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9df97a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 | """Hybrid matcher combining semantic, BERT classifier, skill cosine, and business signals.
Weight breakdown (defaults from training config):
semantic 0.35 — sentence-transformer cosine similarity on full texts
cross_encoder 0.20 — deeper semantic re-ranking (falls back to semantic when unavailable)
bert_classifier 0.25 — fine-tuned camembert compatibility classifier
skill_cosine 0.12 — binary skill-vector cosine (CosineScorer)
business 0.08 — structured rules: experience, location, availability
All weights must sum to 1.0; HybridConfig normalizes automatically if they don't.
"""
from __future__ import annotations
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import numpy as np
logger = logging.getLogger(__name__)
@dataclass
class HybridConfig:
weight_semantic: float = 0.35
weight_cross_encoder: float = 0.20
weight_bert_classifier: float = 0.25
weight_skill_cosine: float = 0.12
weight_business: float = 0.08
def __post_init__(self) -> None:
total = (
self.weight_semantic
+ self.weight_cross_encoder
+ self.weight_bert_classifier
+ self.weight_skill_cosine
+ self.weight_business
)
if total <= 0:
raise ValueError("HybridConfig: all weights are zero.")
if abs(total - 1.0) > 1e-6:
logger.debug("HybridConfig: weights sum to %.4f — normalizing.", total)
self.weight_semantic /= total
self.weight_cross_encoder /= total
self.weight_bert_classifier /= total
self.weight_skill_cosine /= total
self.weight_business /= total
class HybridMatcher:
"""Combine multiple matchers into a single weighted score (0–100).
Parameters
----------
config:
Weight configuration.
bert_classifier:
Pre-loaded BertClassifierAdapter. If None, the adapter is lazy-loaded
from the default model directory (backend/models/bert_matching/).
"""
def __init__(
self,
config: Optional[HybridConfig] = None,
bert_classifier=None,
) -> None:
self.config = config or HybridConfig()
self._bert = bert_classifier # may be None; resolved lazily
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def score(
self,
candidate_text: str,
job_text: str,
candidate_skills: Optional[List[str]] = None,
criteria_skills: Optional[Dict[str, float]] = None,
business_signals: Optional[Dict[str, object]] = None,
) -> Dict[str, object]:
"""Return a hybrid score dict.
Parameters
----------
candidate_text:
Free-text CV / candidate profile.
job_text:
Free-text job description / offer.
candidate_skills:
List of skill names the candidate has.
criteria_skills:
Dict {skill_name: weight_0_to_100} from recruiter criteria.
business_signals:
Optional structured signals, e.g.::
{
"years_experience": 5,
"required_experience": 3,
"location_match": True,
"available": True,
}
Returns
-------
dict with keys: score (0–100), component_scores, weights_used
"""
cfg = self.config
components: Dict[str, float] = {}
# 1. Semantic score
components["semantic"] = self._semantic_score(candidate_text, job_text)
# 2. Cross-encoder score (fallback to semantic when unavailable)
components["cross_encoder"] = self._cross_encoder_score(candidate_text, job_text)
# 3. BERT classifier score
components["bert_classifier"] = self._bert_score(candidate_text, job_text)
# 4. Skill cosine score
components["skill_cosine"] = self._skill_cosine_score(
candidate_skills or [], criteria_skills or {}
)
# 5. Business rules score
components["business"] = self._business_score(business_signals or {})
# Weighted sum
raw = (
cfg.weight_semantic * components["semantic"]
+ cfg.weight_cross_encoder * components["cross_encoder"]
+ cfg.weight_bert_classifier * components["bert_classifier"]
+ cfg.weight_skill_cosine * components["skill_cosine"]
+ cfg.weight_business * components["business"]
)
final_score = float(np.clip(raw * 100, 0.0, 100.0))
return {
"score": final_score,
"component_scores": {k: round(v, 4) for k, v in components.items()},
"weights_used": {
"semantic": cfg.weight_semantic,
"cross_encoder": cfg.weight_cross_encoder,
"bert_classifier": cfg.weight_bert_classifier,
"skill_cosine": cfg.weight_skill_cosine,
"business": cfg.weight_business,
},
}
# ------------------------------------------------------------------
# Component scorers
# ------------------------------------------------------------------
def _semantic_score(self, candidate_text: str, job_text: str) -> float:
try:
from ai_module.matching.semantic_matcher import SemanticSkillMatcher
return SemanticSkillMatcher.semantic_similarity(candidate_text, job_text)
except Exception as exc:
logger.debug("Semantic scorer unavailable: %s", exc)
return 0.0
def _cross_encoder_score(self, candidate_text: str, job_text: str) -> float:
"""Attempt a cross-encoder pass; fall back to semantic similarity."""
try:
from sentence_transformers import CrossEncoder
if not hasattr(self, "_cross_encoder_model"):
self._cross_encoder_model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
score = self._cross_encoder_model.predict([[candidate_text, job_text]])[0]
# ms-marco scores are logits; apply sigmoid
import math
return float(np.clip(1 / (1 + math.exp(-score)), 0.0, 1.0))
except Exception:
# Graceful fallback to standard semantic similarity
return self._semantic_score(candidate_text, job_text)
def _bert_score(self, candidate_text: str, job_text: str) -> float:
bert = self._get_bert()
if bert is None:
return 0.0
return bert.predict_score(candidate_text, job_text)
def _skill_cosine_score(
self,
candidate_skills: List[str],
criteria_skills: Dict[str, float],
) -> float:
if not candidate_skills or not criteria_skills:
return 0.0
try:
from ai_module.matching.scorer import CosineScorer
all_skills = list(criteria_skills.keys())
result = CosineScorer.calculate_match_score(
candidate_skills, criteria_skills, all_skills
)
return float(result["score"]) / 100.0
except Exception as exc:
logger.debug("Skill cosine scorer failed: %s", exc)
return 0.0
def _business_score(self, signals: Dict[str, object]) -> float:
"""Simple rules-based business score in [0, 1]."""
if not signals:
return 0.5 # neutral when no signals provided
score = 0.0
count = 0
# Experience
years_exp = signals.get("years_experience")
required_exp = signals.get("required_experience")
if years_exp is not None and required_exp is not None:
try:
ratio = float(years_exp) / max(float(required_exp), 1.0)
score += float(np.clip(ratio, 0.0, 1.0))
except (TypeError, ValueError):
score += 0.5
count += 1
# Location match
location_match = signals.get("location_match")
if location_match is not None:
score += 1.0 if location_match else 0.2
count += 1
# Availability
available = signals.get("available")
if available is not None:
score += 1.0 if available else 0.0
count += 1
return float(np.clip(score / max(count, 1), 0.0, 1.0))
# ------------------------------------------------------------------
# Internal helpers
# ------------------------------------------------------------------
def _get_bert(self):
if self._bert is not None:
return self._bert
try:
from ai_module.matching.bert_classifier_adapter import get_default_adapter
self._bert = get_default_adapter()
except Exception as exc:
logger.warning("Could not load BertClassifierAdapter: %s", exc)
self._bert = None
return self._bert
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