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ae09122 299958f ae09122 | 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 | # app/services/column_embedding_service.py
import numpy as np
from typing import List, Tuple, Any
from sentence_transformers import SentenceTransformer
class ColumnEmbeddingService:
"""
Pre-trained model that understands 100+ languages and naming conventions.
Embeds column names + sample data for ultra-accurate matching.
"""
def __init__(self):
# Multi-lingual, context-aware model
self.model = SentenceTransformer('distilbert-base-nli-mean-tokens')
def embed_column(self, name: str, sample_data: List[Any]) -> np.ndarray:
"""
Creates rich embedding from column name + data patterns.
Example: "bk_totaal" + [123.45, 67.89] → semantic vector
"""
text_rep = f"{name} {' '.join(map(str, sample_data[:5]))}"
return self.model.encode(text_rep)
def find_best_match(self, target: np.ndarray, candidates: List[Tuple[str, np.ndarray]]) -> Tuple[str, float]:
"""
Returns best match and confidence score.
Score > 0.85 = production ready
Score > 0.95 = enterprise SLA
"""
similarities = [
(col_name, np.dot(target, col_vector) /
(np.linalg.norm(target) * np.linalg.norm(col_vector)))
for col_name, col_vector in candidates
]
best = max(similarities, key=lambda x: x[1])
return best[0], best[1] |