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Embedding service: compute semantic similarity between resume and JD.
Strategy (two-tier):
1. Try sentence-transformers (all-MiniLM-L6-v2) for quality embeddings.
2. Fall back to TF-IDF cosine similarity (sklearn) if model unavailable.
The model is loaded lazily so the server starts instantly even if the
first analysis takes a few extra seconds.
"""
from __future__ import annotations
import logging
import threading
from typing import Any
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
logger = logging.getLogger(__name__)
# ── Lazy sentence-transformers loader ────────────────────────────────────
_model_lock = threading.Lock()
_model: Any = None # SentenceTransformer or None
_model_loaded = False # whether we've attempted loading
_ST_MODEL = "all-MiniLM-L6-v2"
def _try_load_st_model() -> Any | None:
"""Attempt to load the SentenceTransformer model once."""
global _model, _model_loaded
with _model_lock:
if _model_loaded:
return _model
_model_loaded = True
try:
from sentence_transformers import SentenceTransformer # noqa: PLC0415
logger.info("Loading SentenceTransformer model %s …", _ST_MODEL)
_model = SentenceTransformer(_ST_MODEL)
logger.info("SentenceTransformer model ready.")
except Exception as exc:
logger.warning("SentenceTransformer unavailable (%s); using TF-IDF fallback.", exc)
_model = None
return _model
# ── Public API ────────────────────────────────────────────────────────────
def compute_similarity(text_a: str, text_b: str) -> float:
"""
Return a cosine similarity score in [0, 1] between two texts.
Uses sentence-transformers if available, else TF-IDF.
"""
if not text_a.strip() or not text_b.strip():
return 0.0
model = _try_load_st_model()
if model is not None:
return _st_similarity(model, text_a, text_b)
return _tfidf_similarity(text_a, text_b)
def compute_section_similarities(
sections: dict[str, str], jd_text: str
) -> dict[str, float]:
"""
Compute per-section similarity against the JD.
Returns dict of {section_name: score_0_to_1}.
"""
results: dict[str, float] = {}
for name, content in sections.items():
if content.strip():
results[name] = compute_similarity(content, jd_text)
return results
def _st_similarity(model: Any, a: str, b: str) -> float:
embs = model.encode([a, b], convert_to_numpy=True)
score = cosine_similarity(embs[0:1], embs[1:2])[0][0]
return float(np.clip(score, 0.0, 1.0))
def _tfidf_similarity(a: str, b: str) -> float:
try:
vec = TfidfVectorizer(
ngram_range=(1, 2),
stop_words="english",
max_features=8000,
)
mat = vec.fit_transform([a, b])
score = cosine_similarity(mat[0:1], mat[1:2])[0][0]
return float(np.clip(score, 0.0, 1.0))
except Exception as exc:
logger.error("TF-IDF similarity failed: %s", exc)
return 0.0
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