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#!/usr/bin/env python3
"""
Query Engine - Busca semântica usando FAISS
SUBSTITUA por sua implementação real
"""
import faiss
import json
import numpy as np
from pathlib import Path
from sentence_transformers import SentenceTransformer
class QueryEngine:
def __init__(
self,
faiss_index_path="/home/user/app/faiss_index",
jsonl_path="/tmp/work/all_filtered.jsonl",
model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
):
print(f"🔧 Carregando Query Engine...")
# Carrega modelo de embeddings
self.model = SentenceTransformer(model_name)
print(f" ✅ Modelo carregado: {model_name}")
# Carrega índice FAISS
index_file = Path(faiss_index_path) / "index.faiss"
if not index_file.exists():
raise FileNotFoundError(f"Índice não encontrado: {index_file}")
self.index = faiss.read_index(str(index_file))
print(f" ✅ FAISS index carregado: {self.index.ntotal} vetores")
# Carrega metadados
self.metadata = []
jsonl_file = Path(jsonl_path)
if jsonl_file.exists():
with open(jsonl_file, 'r', encoding='utf-8') as f:
for line in f:
if line.strip():
self.metadata.append(json.loads(line))
print(f" ✅ {len(self.metadata)} documentos carregados")
def search_by_embedding(self, query: str, top_k: int = 10):
"""Busca por similaridade de embedding"""
# Gera embedding da query
query_embedding = self.model.encode([query])[0]
query_embedding = np.array([query_embedding], dtype=np.float32)
# Busca no FAISS
distances, indices = self.index.search(query_embedding, top_k)
# Prepara resultados
results = []
for i, (dist, idx) in enumerate(zip(distances[0], indices[0])):
if idx < len(self.metadata):
result = self.metadata[idx].copy()
result['score'] = float(1 / (1 + dist)) # Converte distância em score
result['rank'] = i + 1
results.append(result)
return results
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