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#!/usr/bin/env python3
import yaml
import logging
from typing import List, Dict
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class QueryEngine:
def __init__(self, config_path='config.yaml'):
logger.info("Inicializando QueryEngine...")
with open(config_path) as f:
self.config = yaml.safe_load(f)
model_name = self.config.get('embedding_model', 'sentence-transformers/all-MiniLM-L6-v2')
logger.info(f"Modelo: {model_name}")
self.embeddings = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs={'device': 'cpu'}
)
faiss_path = self.config.get('faiss_path', '/app/faiss_index')
logger.info(f"Carregando FAISS de: {faiss_path}")
self.vectorstore = FAISS.load_local(
faiss_path,
self.embeddings,
allow_dangerous_deserialization=True
)
logger.info("✅ QueryEngine pronto!")
def search_by_embedding(self, query: str, top_k: int = 10, return_embeddings: bool = False) -> Dict:
results = self.vectorstore.similarity_search_with_score(query, k=top_k)
formatted = []
for doc, score in results:
formatted.append({
'id': doc.metadata.get('id'),
'ementa': doc.page_content,
'score': float(score),
'metadata': doc.metadata
})
return {
'cluster_id': self.config.get('cluster_id'),
'query': query,
'total_results': len(formatted),
'results': formatted
}
def search_by_keywords(self, keywords: List[str], operator: str = 'AND', top_k: int = 20) -> Dict:
query = ' '.join(keywords)
return self.search_by_embedding(query, top_k)
def search_by_ids(self, ids: List[str], return_embeddings: bool = False) -> Dict:
all_docs = self.vectorstore.similarity_search("", k=10000)
results = []
for doc in all_docs:
if doc.metadata.get('id') in ids:
results.append({
'id': doc.metadata.get('id'),
'ementa': doc.page_content,
'metadata': doc.metadata
})
if len(results) >= len(ids):
break
return {
'cluster_id': self.config.get('cluster_id'),
'total_results': len(results),
'results': results
}
def get_cluster_info(self) -> Dict:
return {
'cluster_id': self.config.get('cluster_id'),
'chunk_range': [self.config.get('chunk_start'), self.config.get('chunk_end')],
'embedding_model': self.config.get('embedding_model'),
'embedding_dim': 384,
'vector_store': 'FAISS',
'backend': 'LangChain + CPU',
'status': 'ready'
}
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