searchdata.py

"""
DATA & VECTOR DB ENGINEER - Người số 1
Nhiệm vụ: Xử lý đầu vào và trí nhớ cho hệ thống
1. Kết nối Google Drive API tải file đáp án (HỖ TRỢ SUB-FOLDER)
2. Xử lý thô văn bản
3. Chunking (cắt nhỏ)
4. Embedding và đẩy lên Qdrant
5. Viết hàm search_context()
"""

import os
import re
import logging
from typing import List, Dict, Optional
from dataclasses import dataclass
import unicodedata
from pathlib import Path

# === CORE PACKAGES ===
import numpy as np
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from qdrant_client.models import (
    Distance, VectorParams, PointStruct
)

# === GOOGLE DRIVE - DÙNG SERVICE ACCOUNT ===
from google.oauth2 import service_account
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload

# === LOGGING ===
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# ============================================================
# 1. CẤU HÌNH
# ============================================================

@dataclass
class Config:
    """Cấu hình cho Data & Vector DB Engineer"""
    
    # Google Drive - Dùng Service Account
    GOOGLE_CREDENTIALS_FILE: str = "service-account-key.json"
    GOOGLE_FOLDER_ID: str = "1RLjyoxo88y0wpQNgTG4uhFPdWQyRBmIn"
    
    # Qdrant
    QDRANT_HOST: str = "localhost"
    QDRANT_PORT: int = 6333
    QDRANT_COLLECTION_NAME: str = "van_mau"
    
    # Embedding Model
    EMBEDDING_MODEL_NAME: str = "dangvantuan/vietnamese-embedding"
    
    # Chunking
    CHUNK_SIZE: int = 300
    CHUNK_OVERLAP: int = 50
    
    # Paths
    RAW_DIR: str = "data/raw"
    
    def __post_init__(self):
        Path(self.RAW_DIR).mkdir(parents=True, exist_ok=True)

# ============================================================
# 2. XỬ LÝ VĂN BẢN
# ============================================================

class TextProcessor:
    """Xử lý thô văn bản: Chuẩn hóa font, xóa khoảng trắng thừa"""
    
    @staticmethod
    def normalize_unicode(text: str) -> str:
        return unicodedata.normalize('NFC', text)
    
    @staticmethod
    def remove_extra_spaces(text: str) -> str:
        text = re.sub(r'\s+', ' ', text)
        return text.strip()
    
    @staticmethod
    def clean_text(text: str) -> str:
        text = TextProcessor.normalize_unicode(text)
        text = TextProcessor.remove_extra_spaces(text)
        return text

# ============================================================
# 3. CHUNKING
# ============================================================

class ChunkingStrategy:
    """Chiến thuật cắt bài văn chuẩn thành đoạn nhỏ"""
    
    def __init__(self, chunk_size: int = 300, overlap: int = 50):
        self.chunk_size = chunk_size
        self.overlap = overlap
    
    def chunk_smart(self, text: str) -> List[str]:
        """Chiến thuật thông minh: ưu tiên cắt theo đoạn văn"""
        paragraphs = re.split(r'\n\s*\n', text)
        paragraphs = [p.strip() for p in paragraphs if p.strip()]
        
        chunks = []
        for para in paragraphs:
            if len(para) <= self.chunk_size:
                chunks.append(para)
            else:
                sub_chunks = self._chunk_by_size(para)
                chunks.extend(sub_chunks)
        
        return chunks
    
    def _chunk_by_size(self, text: str) -> List[str]:
        """Cắt theo kích thước cố định với chồng lấn"""
        chunks = []
        start = 0
        text_len = len(text)
        
        while start < text_len:
            end = min(start + self.chunk_size, text_len)
            
            if end < text_len:
                for sep in ['. ', '; ', ', ', ' ']:
                    pos = text.rfind(sep, start, end)
                    if pos != -1:
                        end = pos + len(sep)
                        break
            
            chunk = text[start:end].strip()
            if chunk:
                chunks.append(chunk)
            
            start = max(start + 1, end - self.overlap)
        
        return chunks

# ============================================================
# 4. GOOGLE DRIVE API - HỖ TRỢ SUB-FOLDER
# ============================================================

class GoogleDriveManager:
    """Kết nối Google Drive API - Hỗ trợ đệ quy vào sub-folder"""
    
    def __init__(self, credentials_file: str = "service-account-key.json"):
        self.credentials_file = credentials_file
        self.service = None
        self._authenticate()
    
    def _authenticate(self):
        """Xác thực với Google Drive API - Dùng Service Account"""
        try:
            if not os.path.exists(self.credentials_file):
                raise FileNotFoundError(
                    f"Không tìm thấy file {self.credentials_file}! "
                    "Hãy tạo Service Account và tải key JSON về."
                )
            
            creds = service_account.Credentials.from_service_account_file(
                self.credentials_file,
                scopes=['https://www.googleapis.com/auth/drive.readonly']
            )
            
            self.service = build('drive', 'v3', credentials=creds)
            logger.info("✅ Google Drive authenticated with Service Account.")
            
        except Exception as e:
            logger.error(f"❌ Authentication failed: {e}")
            raise
    
    def list_all_files_recursive(self, folder_id: str, prefix: str = "") -> List[Dict]:
        """
        🔥 ĐỆ QUY - Liệt kê TẤT CẢ file trong folder và tất cả sub-folder
        """
        all_files = []
        
        try:
            query = f"'{folder_id}' in parents and trashed = false"
            results = self.service.files().list(
                q=query,
                fields="files(id, name, mimeType, size, createdTime, modifiedTime)",
                pageSize=100
            ).execute()
            
            items = results.get('files', [])
            
            for item in items:
                # Nếu là folder -> đệ quy vào sâu bên trong
                if item.get('mimeType') == 'application/vnd.google-apps.folder':
                    logger.info(f"📁 Đang đào sâu vào: {prefix}{item['name']}/")
                    sub_files = self.list_all_files_recursive(
                        item['id'], 
                        prefix=f"{prefix}{item['name']}/"
                    )
                    all_files.extend(sub_files)
                else:
                    # Nếu là file -> thêm vào danh sách
                    item['full_path'] = f"{prefix}{item['name']}"
                    all_files.append(item)
                    logger.info(f"  📄 Found: {item['full_path']} (ID: {item['id']})")
            
        except Exception as e:
            logger.error(f"❌ Error listing files in folder {folder_id}: {e}")
        
        return all_files
    
    def download_all_files_recursive(self, folder_id: str, destination: str) -> List[str]:
        """
        🔥 Tải TẤT CẢ file trong folder và tất cả sub-folder
        Giữ nguyên cấu trúc thư mục
        """
        logger.info("🔍 Đang quét toàn bộ folder và sub-folder...")
        all_files = self.list_all_files_recursive(folder_id)
        
        if not all_files:
            logger.warning("⚠️ Không tìm thấy file nào trong folder hoặc sub-folder.")
            return []
        
        logger.info(f"📊 Tổng số file tìm thấy: {len(all_files)}")
        
        downloaded_files = []
        
        for file_info in all_files:
            full_path = file_info.get('full_path', file_info['name'])
            file_path = os.path.join(destination, full_path)
            
            # Tạo thư mục cha nếu chưa tồn tại
            os.makedirs(os.path.dirname(file_path), exist_ok=True)
            
            # Bỏ qua Google Workspace files (Docs, Sheets, v.v.)
            if file_info.get('mimeType', '').startswith('application/vnd.google-apps'):
                logger.warning(f"⚠️  Skipping Google Workspace file: {full_path}")
                continue
            
            try:
                request = self.service.files().get_media(fileId=file_info['id'])
                with open(file_path, 'wb') as f:
                    downloader = MediaIoBaseDownload(f, request)
                    done = False
                    while not done:
                        status, done = downloader.next_chunk()
                        logger.info(f"⬇️  Downloading {full_path}: {int(status.progress() * 100)}%")
                
                downloaded_files.append(file_path)
                logger.info(f"✅ Downloaded: {full_path}")
                
            except Exception as e:
                logger.error(f"❌ Error downloading {full_path}: {e}")
        
        return downloaded_files
    
    # Wrapper để tương thích với code cũ
    def download_all_files(self, folder_id: str, destination: str) -> List[str]:
        return self.download_all_files_recursive(folder_id, destination)

# ============================================================
# 5. ĐỌC FILE
# ============================================================

class DocumentReader:
    """Đọc nội dung file đáp án"""
    
    @staticmethod
    def read_file(file_path: str) -> str:
        try:
            with open(file_path, 'r', encoding='utf-8') as f:
                return f.read()
        except Exception as e:
            logger.error(f"❌ Error reading {file_path}: {e}")
            return ""

# ============================================================
# 6. EMBEDDING
# ============================================================

class EmbeddingModel:
    
    """Biến văn bản thành Vector"""
    
    def __init__(self, model_name: str = "dangvantuan/vietnamese-embedding"):
        logger.info(f"🧠 Loading embedding model: {model_name}...")
        self.model = SentenceTransformer(model_name)
        self.vector_size = self.model.get_sentence_embedding_dimension()
        logger.info(f"✅ Model loaded. Vector size: {self.vector_size}")
    
    def encode(self, texts: List[str]) -> np.ndarray:
        if isinstance(texts, str):
            texts = [texts]
        return self.model.encode(texts, convert_to_numpy=True)

# ============================================================
# 7. QDRANT - ĐÃ SỬA LỖI
# ============================================================

class QdrantManager:
    """Quản lý vector database Qdrant"""
    def __init__(self, host: str = "localhost", port: int = 6333, 
                 collection_name: str = "van_mau", vector_size: int = 384):
        self.client = QdrantClient(path="./qdrant_data")
        self.collection_name = collection_name
        self.vector_size = vector_size
        logger.info(f"✅ Connected to Qdrant at {host}:{port}")
    
    def create_collection(self, force: bool = False):
        """Tạo collection để lưu vector"""
        collections = self.client.get_collections().collections
        exists = any(c.name == self.collection_name for c in collections)
        
        if exists:
            if force:
                self.client.delete_collection(self.collection_name)
                logger.info(f"🗑️  Deleted existing collection: {self.collection_name}")
            else:
                logger.info(f"Collection '{self.collection_name}' already exists.")
                return
        
        self.client.create_collection(
            collection_name=self.collection_name,
            vectors_config=VectorParams(
                size=self.vector_size,
                distance=Distance.COSINE
            )
        )
        logger.info(f"✅ Collection '{self.collection_name}' created.")
    
    def upsert_chunks(self, chunks: List[Dict], embedding_model: EmbeddingModel):
        """Đẩy vector lên Qdrant"""
        if not chunks:
            logger.warning("No chunks to upsert.")
            return
        
        contents = [chunk['content'] for chunk in chunks]
        embeddings = embedding_model.encode(contents)
        
        points = []
        for i, chunk in enumerate(chunks):
            point = PointStruct(
                id=i,
                vector=embeddings[i].tolist(),
                payload={
                    "content": chunk['content'],
                    "document_id": chunk['document_id'],
                    "file_name": chunk.get('file_name', ''),
                    "chunk_index": chunk.get('chunk_index', i)
                }
            )
            points.append(point)
        
        self.client.upsert(
            collection_name=self.collection_name,
            points=points
        )
        logger.info(f"✅ Upserted {len(points)} chunks to Qdrant.")
    
    def search(self, query_vector: List[float], limit: int = 3) -> List[Dict]:
        """
        Tìm kiếm vector tương tự trong Qdrant
        🔥 ĐÃ SỬA: Dùng query_points() cho phiên bản mới
        """
        try:
            # Thử dùng API mới (qdrant-client >= 1.8.0)
            results = self.client.query_points(
                collection_name=self.collection_name,
                query=query_vector,
                limit=limit
            )
            
            return [
                {
                    "content": hit.payload["content"],
                    "score": hit.score,
                    "document_id": hit.payload.get("document_id", ""),
                    "file_name": hit.payload.get("file_name", "")
                }
                for hit in results.points
            ]
        except AttributeError:
            # Fallback cho API cũ (qdrant-client < 1.8.0)
            results = self.client.search(
                collection_name=self.collection_name,
                query_vector=query_vector,
                limit=limit
            )
            
            return [
                {
                    "content": hit.payload["content"],
                    "score": hit.score,
                    "document_id": hit.payload.get("document_id", ""),
                    "file_name": hit.payload.get("file_name", "")
                }
                for hit in results
            ]

# ============================================================
# 8. HÀM search_context
# ============================================================

class ContextRetriever:
    """Truy xuất ngữ cảnh đáp án chuẩn từ Qdrant"""
    
    def __init__(self, qdrant: QdrantManager, embedding: EmbeddingModel):
        self.qdrant = qdrant
        self.embedding = embedding
        self.text_processor = TextProcessor()
    
    def search_context(self, bai_van_hoc_sinh: str, limit: int = 3) -> List[str]:
        """
        Hàm chính: Đưa vào bài văn học sinh, trả ra đoạn đáp án chuẩn tương ứng
        """
        cleaned_query = self.text_processor.clean_text(bai_van_hoc_sinh)
        query_vector = self.embedding.encode([cleaned_query])[0]
        results = self.qdrant.search(
            query_vector=query_vector.tolist(),
            limit=limit
        )
        contexts = [result['content'] for result in results]
        logger.info(f"🔍 Found {len(contexts)} relevant context chunks.")
        return contexts

# ============================================================
# 9. PIPELINE CHÍNH
# ============================================================

class DataPipeline:
    """Pipeline của Data & Vector DB Engineer"""
    
    def __init__(self, config: Config):
        self.config = config
        self.text_processor = TextProcessor()
        self.chunking = ChunkingStrategy(
            chunk_size=config.CHUNK_SIZE,
            overlap=config.CHUNK_OVERLAP
        )
        self.gdrive = GoogleDriveManager(
            credentials_file=config.GOOGLE_CREDENTIALS_FILE
        )
        self.document_reader = DocumentReader()
        self.embedding_model = EmbeddingModel(config.EMBEDDING_MODEL_NAME)
        self.qdrant = QdrantManager(
            host=config.QDRANT_HOST,
            port=config.QDRANT_PORT,
            collection_name=config.QDRANT_COLLECTION_NAME,
            vector_size=self.embedding_model.vector_size
        )
    
    def run(self, force_reload: bool = False):
        """Chạy toàn bộ pipeline xử lý dữ liệu"""
        logger.info("=" * 60)
        logger.info(" DATA & VECTOR DB ENGINEER - BẮT ĐẦU")
        logger.info("=" * 60)
        
        # Nhiệm vụ 1: Tải file từ Google Drive (ĐỆ QUY)
        logger.info("\n⬇️  Nhiệm vụ 1: Tải file đáp án từ Google Drive")
        downloaded_files = self.gdrive.download_all_files_recursive(
            self.config.GOOGLE_FOLDER_ID,
            self.config.RAW_DIR
        )
        
        if not downloaded_files:
            logger.error("❌ Không có file nào được tải về.")
            logger.error("   Kiểm tra: 1) Folder ID đúng, 2) Service Account đã được share quyền.")
            return
        
        # Nhiệm vụ 2 & 3: Xử lý văn bản và Chunking
        logger.info("\n  Nhiệm vụ 2 & 3: Xử lý văn bản và Chunking")
        all_chunks = []
        
        for file_path in downloaded_files:
            # Chỉ xử lý file text (có đuôi .txt, .md, .csv, v.v.)
            if not any(file_path.endswith(ext) for ext in ['.txt', '.md', '.csv', '.json', '.html', '.xml']):
                logger.info(f"⏭️  Bỏ qua file không phải text: {os.path.basename(file_path)}")
                continue
            
            raw_text = self.document_reader.read_file(file_path)
            if not raw_text:
                continue
            
            cleaned_text = self.text_processor.clean_text(raw_text)
            chunks_text = self.chunking.chunk_smart(cleaned_text)
            
            file_name = os.path.basename(file_path)
            for i, chunk_text in enumerate(chunks_text):
                if len(chunk_text.strip()) < 10:
                    continue
                all_chunks.append({
                    'content': chunk_text,
                    'document_id': f"doc_{len(all_chunks)}",
                    'file_name': file_name,
                    'chunk_index': i
                })
            
            logger.info(f"✅ Processed {file_name}: {len(chunks_text)} chunks")
        
        # Nhiệm vụ 4: Embedding và đẩy lên Qdrant
        logger.info("\n🧠 Nhiệm vụ 4: Embedding và đẩy lên Qdrant")
        self.qdrant.create_collection(force=force_reload)
        self.qdrant.upsert_chunks(all_chunks, self.embedding_model)
        
        logger.info("\n" + "=" * 60)
        logger.info(f"✅ DATA PIPELINE HOÀN TẤT!")
        logger.info(f"📊 Tổng số chunks đã xử lý: {len(all_chunks)}")
        logger.info("=" * 60)
    
    def get_retriever(self) -> ContextRetriever:
        return ContextRetriever(self.qdrant, self.embedding_model)

# ============================================================
# 10. MAIN
# ============================================================

def main():
    """Chạy pipeline của Data & Vector DB Engineer"""
    
    config = Config()
    
    print("\n" + "="*60)
    print("👤 DATA & VECTOR DB ENGINEER - Người số 1")
    print("="*60)
    print(f"\n📁 Google Drive Folder ID: {config.GOOGLE_FOLDER_ID}")
    print(f"📄 Credentials: {config.GOOGLE_CREDENTIALS_FILE}")
    print(f"🗄️  Qdrant: {config.QDRANT_HOST}:{config.QDRANT_PORT}")
    print(f"📚 Collection: {config.QDRANT_COLLECTION_NAME}")
    print(f"🧠 Embedding Model: {config.EMBEDDING_MODEL_NAME}")
    print(f"✂️  Chunk size: {config.CHUNK_SIZE} (overlap: {config.CHUNK_OVERLAP})")
    print("="*60 + "\n")
    
    # Chạy pipeline
    pipeline = DataPipeline(config)
    pipeline.run(force_reload=True)
    
    # Test thử search_context
    print("\n" + "="*60)
    print("🧪 TEST search_context")
    print("="*60)
    
    retriever = pipeline.get_retriever()
    test_query = "Hãy phân tích nhân vật Chí Phèo"
    results = retriever.search_context(test_query, limit=2)
    
    print(f"\n🔍 Query: '{test_query}'")
    print(f"📝 Found {len(results)} results:\n")
    for i, result in enumerate(results, 1):
        print(f"{i}. {result[:300]}...\n")

if __name__ == "__main__":
    main()