query_engine.py

#!/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'
        }