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#!/usr/bin/env python3
import yaml
from pathlib import Path
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
class QueryEngine:
def __init__(self):
with open('config.yaml') as f:
cfg = yaml.safe_load(f)
# Embeddings
self.embeddings = HuggingFaceEmbeddings(
model_name=cfg.get('embedding_model', 'sentence-transformers/all-MiniLM-L6-v2'),
model_kwargs={'device': 'cpu'}
)
# ✅ PATH CORRETO HARDCODED!
faiss_path = '/home/user/app/faiss_index'
# Verifica se existe
if not Path(faiss_path).exists():
raise FileNotFoundError(f"FAISS index não encontrado em {faiss_path}")
# Carrega FAISS
self.vectorstore = FAISS.load_local(
faiss_path,
self.embeddings,
allow_dangerous_deserialization=True
)
def search_by_embedding(self, query: str, top_k: int = 10):
results = self.vectorstore.similarity_search_with_score(query, k=top_k)
return {
'query': query,
'total': len(results),
'results': [
{'id': doc.metadata.get('id'), 'ementa': doc.page_content, 'score': float(score)}
for doc, score in results
]
}
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