app/law_document_chunker.py

import re
import os
import uuid
from typing import List, Dict, Optional, Tuple, Any
from dataclasses import dataclass
from loguru import logger
from .supabase_db import SupabaseClient
from .embedding import EmbeddingClient
from .config import get_settings

@dataclass
class ChunkMetadata:
    """Metadata cho một chunk."""
    id: str
    content: str
    vanbanid: int
    cha: Optional[str] = None
    document_title: str = ""
    article_number: Optional[int] = None
    article_title: str = ""
    clause_number: str = ""
    sub_clause_letter: str = ""
    context_summary: str = ""

class LawDocumentChunker:
    """Module xử lý chunking văn bản luật và tích hợp với Supabase."""
    
    def __init__(self):
        """Khởi tạo chunker với các regex patterns."""
        settings = get_settings()
        self.supabase_client = SupabaseClient(settings.supabase_url, settings.supabase_key)
        self.embedding_client = EmbeddingClient()
        self.llm_client: Optional[Any] = None
        
        # Regex patterns cho các cấp độ cấu trúc - SỬA LẠI ĐỂ CHÍNH XÁC HƠN
        # Đảm bảo mỗi pattern có đúng số group
        self.PHAN_REGEX = r"^(Phần|PHẦN|Phần thứ)\s+(\d+|[IVXLCDM]+|nhất|hai|ba|tư|năm|sáu|bảy|tám|chín|mười)\.?\s*(.*)"
        self.PHU_LUC_REGEX = r"^(Phụ lục|PHỤ LỤC)\s+(\d+|[A-Z]+)\.?\s*(.*)"
        self.CHUONG_REGEX = r"^(Chương|CHƯƠNG)\s+(\d+|[IVXLCDM]+)\.?\s*(.*)"
        self.MUC_REGEX = r"^(Mục|MỤC)\s+(\d+)\.?\s*(.*)"
        self.DIEU_REGEX = r"^Điều\s+(\d+)\.\s*(.*)"
        self.KHOAN_REGEX = r"^\s*(\d+(\.\d+)*)\.\s*(.*)"
        self.DIEM_REGEX_A = r"^\s*([a-zđ])\)\s*(.*)"
        self.DIEM_REGEX_NUM = r"^\s*(\d+\.\d+\.\d+)\.\s*(.*)"
        
        # Cấu hình chunking
        self.CHUNK_SIZE = 500
        self.CHUNK_OVERLAP = 100
        
        logger.info("[CHUNKER] Initialized LawDocumentChunker")

    def _create_data_directory(self):
        """Tạo thư mục data nếu chưa tồn tại."""
        data_dir = "data"
        if not os.path.exists(data_dir): # noqa
            os.makedirs(data_dir)
            logger.info(f"[CHUNKER] Created directory: {data_dir}")
        return data_dir

    def _extract_document_title(self, file_path: str) -> str:
        """Trích xuất tiêu đề văn bản từ tên file."""
        filename = os.path.basename(file_path)
        # Loại bỏ extension
        name_without_ext = os.path.splitext(filename)[0]
        # Thay _ bằng khoảng trắng và viết hoa chữ cái đầu
        title = name_without_ext.replace('_', ' ').title() # noqa
        logger.info(f"[CHUNKER] Extracted document title: {title}")
        return title

    def _read_document(self, file_path: str) -> str:
        """Đọc nội dung văn bản từ file."""
        try:
            with open(file_path, 'r', encoding='utf-8') as f:
                content = f.read()
            logger.debug(f"[CHUNKER] Read document: {file_path}, length: {len(content)}")
            return content
        except Exception as e:
            logger.error(f"[CHUNKER] Error reading file {file_path}: {e}")
            raise

    def _detect_structure_level(self, line: str) -> Tuple[str, Optional[str], Optional[str]]:
        """Phát hiện cấp độ cấu trúc của một dòng."""
        line = line.strip()
        
        try:
            # Phần
            match = re.match(self.PHAN_REGEX, line, re.IGNORECASE)
            if match:
                return "PHAN", match.group(1), match.group(2)
            
            # Phụ lục
            match = re.match(self.PHU_LUC_REGEX, line, re.IGNORECASE)
            if match:
                return "PHU_LUC", match.group(1), match.group(2)
            
            # Chương
            match = re.match(self.CHUONG_REGEX, line, re.IGNORECASE)
            if match:
                return "CHUONG", match.group(1), match.group(2)
            
            # Mục
            match = re.match(self.MUC_REGEX, line, re.IGNORECASE)
            if match:
                return "MUC", match.group(1), match.group(2)
            
            # Điều
            match = re.match(self.DIEU_REGEX, line)
            if match:
                return "DIEU", match.group(1), match.group(2)
            
            # Khoản
            match = re.match(self.KHOAN_REGEX, line)
            if match:
                clause_num = match.group(1)
                # Kiểm tra không phải điểm (có từ 3 số trở lên)
                if len(clause_num.split('.')) < 3:
                    return "KHOAN", clause_num, match.group(3)
            
            # Điểm chữ cái
            match = re.match(self.DIEM_REGEX_A, line)
            if match:
                return "DIEM", match.group(1), match.group(2)
            
            # Điểm số
            match = re.match(self.DIEM_REGEX_NUM, line)
            if match:
                return "DIEM", match.group(1), match.group(2)
            
            return "CONTENT", None, None
            
        except Exception as e:
            logger.error(f"[CHUNKER] Error in _detect_structure_level for line '{line}': {e}")
            return "CONTENT", None, None

    def _build_structure_summary(self, article_number, clause_number, sub_clause_letter):
        if sub_clause_letter and clause_number and article_number:
            return f"Điểm {sub_clause_letter} Khoản {clause_number} Điều {article_number}"
        elif clause_number and article_number:
            return f"Khoản {clause_number} Điều {article_number}"
        elif article_number:
            return f"Điều {article_number}"
        return ""

    def _create_chunk_metadata(self, content: str, level: str, level_value: Optional[str], 
                              parent_id: Optional[str], vanbanid: int, 
                              document_title: str, chunk_stack: List[Tuple[str, str, Optional[str], str]], chunk_dict: dict) -> 'ChunkMetadata':
        """Tạo metadata cho chunk."""
        chunk_id = str(uuid.uuid4())
        metadata = ChunkMetadata(
            id=chunk_id,
            content=content,
            vanbanid=vanbanid,
            cha=parent_id,
            document_title=document_title
        )
        # Điền metadata từ chunk hiện tại
        if level == "DIEU" and level_value:
            metadata.article_number = int(level_value) if level_value.isdigit() else None
            metadata.article_title = content.split('\n')[0].strip() if content else ""
        elif level == "KHOAN" and level_value:
            metadata.clause_number = level_value
        elif level == "DIEM" and level_value:
            metadata.sub_clause_letter = level_value
        # Điền metadata từ parent chunks nếu có
        logger.debug(f"[CHUNKER] Creating chunk with level: {level}, parent_id: {parent_id}, stack_size: {len(chunk_stack)}")
        if chunk_dict is not None and parent_id:
            self._fill_metadata_from_parents(metadata, parent_id, chunk_dict)
        else:
            logger.debug(f"[CHUNKER] Skipping metadata fill - no parent_id or chunk_dict")
        # Gán context_summary theo format pháp lý
        metadata.context_summary = self._build_structure_summary(
            metadata.article_number, metadata.clause_number, metadata.sub_clause_letter #
        )
        logger.debug(f"[CHUNKER] Final metadata for chunk {chunk_id[:8]}... - Level: {level}, Article: {metadata.article_number}, Clause: {metadata.clause_number}, Point: {metadata.sub_clause_letter}")
        return metadata

    def _fill_metadata_from_parents(self, metadata: ChunkMetadata, parent_id: str, chunk_dict: Dict[str, ChunkMetadata]):
        """
        Điền metadata từ parent và ancestor (cha, ông, ...), sử dụng dict id->chunk.
        """
        parent = chunk_dict.get(parent_id)
        if not parent:
            logger.warning(f"[CHUNKER] Parent chunk {parent_id} not found in chunk_dict")
            return
        # Điền từ cha
        if parent.article_number and not metadata.article_number:
            metadata.article_number = parent.article_number
        if parent.article_title and not metadata.article_title:
            metadata.article_title = parent.article_title #
        if parent.clause_number and not metadata.clause_number:
            metadata.clause_number = parent.clause_number
        if parent.sub_clause_letter and not metadata.sub_clause_letter:
            metadata.sub_clause_letter = parent.sub_clause_letter
        # Nếu cha là Khoản, tìm ông là Điều
        if parent.clause_number and not metadata.article_number: # noqa
            grandparent = chunk_dict.get(parent.cha) if parent.cha else None
            if grandparent and grandparent.article_number:
                metadata.article_number = grandparent.article_number
            if grandparent and grandparent.article_title:
                metadata.article_title = grandparent.article_title

    def _split_into_chunks(self, text: str, chunk_size: int, overlap: int) -> List[str]:
        """Chia text thành các chunk với overlap."""
        chunks = []
        start = 0
        
        while start < len(text):
            end = start + chunk_size
            chunk = text[start:end]
            
            # Tìm vị trí kết thúc chunk tốt nhất (cuối câu hoặc cuối từ)
            if end < len(text):
                # Tìm dấu chấm hoặc xuống dòng gần nhất
                last_period = chunk.rfind('.')
                last_newline = chunk.rfind('\n')
                best_break = max(last_period, last_newline)
                
                if best_break > start + chunk_size * 0.7:  # Chỉ break nếu không quá sớm
                    end = start + best_break + 1
                    chunk = text[start:end]
            
            chunks.append(chunk)
            start = end - overlap
            
            if start >= len(text):
                break
        
        return chunks

    def _process_document_recursive(self, content: str, vanbanid: int, 
                                  document_title: str) -> List[ChunkMetadata]:
        """Xử lý văn bản theo cấu trúc phân cấp."""
        lines = content.split('\n')
        chunks = []
        chunk_stack = []  # (chunk_id, level, level_value, content)
        chunk_dict = {}   # id -> ChunkMetadata
        current_chunk_content = ""
        current_level = None
        current_level_value = None
        current_parent = None
        current_level_priority = None
        level_priority = {
            "PHAN": 1,
            "PHU_LUC": 1,
            "CHUONG": 2,
            "MUC": 3,
            "DIEU": 4,
            "KHOAN": 5,
            "DIEM": 6,
            "CONTENT": 7
        }
        preamble_done = False
        for line in lines:
            level, level_value, _ = self._detect_structure_level(line)
            line_priority = level_priority.get(level, 7)
            # Nếu là dòng đầu tiên hoặc preamble
            if not preamble_done and (level == "CONTENT" or not level_value):
                current_chunk_content += line + "\n"
                current_level = "CONTENT"
                current_level_value = None
                current_parent = None
                current_level_priority = 7
                continue
            if not preamble_done and (level != "CONTENT" and level_value):
                # Kết thúc preamble
                if current_chunk_content.strip():
                    metadata = self._create_chunk_metadata(
                        current_chunk_content.strip(),
                        "CONTENT",
                        None,
                        None,
                        vanbanid,
                        document_title,
                        chunk_stack,
                        chunk_dict
                    )
                    chunks.append(metadata)
                    chunk_stack.append((metadata.id, "CONTENT", None, current_chunk_content.strip()))
                    chunk_dict[metadata.id] = metadata
                preamble_done = True
                current_chunk_content = ""
                current_level = level
                current_level_value = level_value
                current_level_priority = line_priority
                current_parent = self._find_parent_for_level(chunk_stack, level, level_priority)
                current_chunk_content += line + "\n"
                continue
            # Nếu gặp level mới
            if level != "CONTENT" and level_value:
                if current_level is not None and current_level_priority is not None and line_priority <= current_level_priority:
                    # Kết thúc chunk hiện tại
                    if current_chunk_content.strip():
                        metadata = self._create_chunk_metadata(
                            current_chunk_content.strip(),
                            str(current_level),
                            current_level_value,
                            current_parent,
                            vanbanid,
                            document_title,
                            chunk_stack,
                            chunk_dict
                        )
                        chunks.append(metadata)
                        chunk_stack.append((metadata.id, str(current_level), current_level_value, current_chunk_content.strip()))
                        chunk_dict[metadata.id] = metadata
                    # Bắt đầu chunk mới
                    current_parent = self._find_parent_for_level(chunk_stack, level, level_priority)
                    current_chunk_content = line + "\n"
                    current_level = level
                    current_level_value = level_value
                    current_level_priority = line_priority
                else:
                    # Level mới nhưng priority cao hơn (ví dụ: Mục trong Chương)
                    if current_chunk_content.strip() and current_level is not None:
                        metadata = self._create_chunk_metadata(
                            current_chunk_content.strip(),
                            str(current_level),
                            current_level_value,
                            current_parent,
                            vanbanid,
                            document_title,
                            chunk_stack,
                            chunk_dict
                        )
                        chunks.append(metadata)
                        chunk_stack.append((metadata.id, str(current_level), current_level_value, current_chunk_content.strip()))
                        chunk_dict[metadata.id] = metadata
                    current_parent = self._find_parent_for_level(chunk_stack, level, level_priority)
                    current_chunk_content = line + "\n"
                    current_level = level
                    current_level_value = level_value
                    current_level_priority = line_priority
            else:
                # CONTENT nối vào chunk hiện tại
                current_chunk_content += line + "\n"
                # Nếu chunk quá lớn thì chia nhỏ
                if len(current_chunk_content) > self.CHUNK_SIZE and current_level is not None:
                    sub_chunks = self._split_into_chunks(current_chunk_content, self.CHUNK_SIZE, self.CHUNK_OVERLAP)
                    for sub_chunk in sub_chunks:
                        metadata = self._create_chunk_metadata(
                            sub_chunk.strip(),
                            str(current_level),
                            current_level_value,
                            current_parent,
                            vanbanid,
                            document_title,
                            chunk_stack,
                            chunk_dict
                        )
                        chunks.append(metadata)
                        chunk_stack.append((metadata.id, str(current_level), current_level_value, sub_chunk.strip()))
                        chunk_dict[metadata.id] = metadata
                    current_chunk_content = ""
        # Lưu chunk cuối cùng
        if current_chunk_content.strip() and current_level is not None:
            metadata = self._create_chunk_metadata(
                current_chunk_content.strip(),
                str(current_level),
                current_level_value,
                current_parent,
                vanbanid,
                document_title,
                chunk_stack,
                chunk_dict
            )
            chunks.append(metadata)
            chunk_stack.append((metadata.id, str(current_level), current_level_value, current_chunk_content.strip()))
            chunk_dict[metadata.id] = metadata
        root_count = sum(1 for chunk in chunks if chunk.cha is None)
        logger.info(f"[CHUNKER] Created {len(chunks)} chunks, {root_count} root chunks")
        for i, chunk in enumerate(chunks[:10]):
            logger.debug(f"[CHUNKER] Chunk {i+1}: {chunk.content[:100]}... -> Parent: {chunk.cha}")
        if len(chunks) > 10:
            logger.debug(f"[CHUNKER] ... and {len(chunks) - 10} more chunks")
        return chunks
    
    def _find_parent_for_level(self, chunk_stack: List[Tuple[str, str, Optional[str], str]], 
                              current_level: str, level_priority: Dict[str, int]) -> Optional[str]:
        """
        Tìm parent gần nhất có level cao hơn (priority thấp hơn) cho level hiện tại, kiểm tra hợp lệ cha-con.
        """
        current_priority = level_priority.get(current_level, 999)
        valid_parents = {
            "MUC": ["CHUONG", "PHAN"],
            "DIEU": ["MUC", "CHUONG", "PHAN"],
            "CHUONG": ["PHAN"],
            # Các level khác giữ nguyên logic cũ
        }
        for chunk_id, level, level_value, content in reversed(chunk_stack):
            if level_priority.get(level, 999) < current_priority:
                if current_level in valid_parents:
                    if level in valid_parents[current_level]:
                        return chunk_id
                else:
                    return chunk_id
        return None

    async def _create_embeddings_for_chunks(self, chunks: List[ChunkMetadata]) -> int:
        """Tạo embeddings cho các chunks và lưu ngay lập tức vào Supabase."""
        logger.info(f"[CHUNKER] Creating embeddings and storing {len(chunks)} chunks")
        
        success_count = 0
        failed_count = 0
        
        # Debug: Log chi tiết metadata của từng chunk
        logger.debug(f"[CHUNKER] === DETAILED METADATA ANALYSIS ===")
        for i, chunk in enumerate(chunks[:20]):  # Log 20 chunks đầu tiên
            logger.info(f"[CHUNKER] Chunk {i+1}:")
            logger.info(f"  - ID: {chunk.id[:8]}...")
            logger.info(f"  - Content: {chunk.content[:100]}...")
            logger.info(f"  - Parent: {chunk.cha}")
            logger.info(f"  - Article: {chunk.article_number}")
            logger.info(f"  - Article Title: {chunk.article_title}")
            logger.info(f"  - Clause: {chunk.clause_number}")
            logger.info(f"  - Point: {chunk.sub_clause_letter}")
            logger.info(f"  - Document: {chunk.document_title}")
            logger.info(f"  ---")
        
        for i, chunk in enumerate(chunks, 1):
            try:
                # Tạo embedding
                embedding = await self.embedding_client.create_embedding(chunk.content, task_type="retrieval_document")
                # Sinh semantic summary bằng LLM
                semantic_summary = await self._create_semantic_summary_with_llm(chunk.content)
                
                # Chuẩn bị data cho Supabase
                chunk_dict = {
                    'id': chunk.id,
                    'content': chunk.content,
                    'embedding': embedding if embedding is not None else [0.0] * 768,  # Sử dụng embedding thực tế nếu có
                    'vanbanid': chunk.vanbanid, # noqa
                    'cha': chunk.cha,
                    'document_title': chunk.document_title,
                    'article_number': chunk.article_number,
                    'article_title': chunk.article_title,
                    'clause_number': chunk.clause_number,
                    'sub_clause_letter': chunk.sub_clause_letter,
                    'context_summary': f"Structure: {chunk.context_summary}|Semantic: {semantic_summary}"
                }
                
                # Lưu ngay lập tức vào Supabase
                success = self.supabase_client.store_document_chunk(chunk_dict)
                if success:
                    success_count += 1 # noqa
                    if i % 100 == 0:  # Log mỗi 100 chunks
                        logger.info(f"[CHUNKER] Stored chunk {i}/{len(chunks)}: {chunk.id[:8]}...")
                else:
                    failed_count += 1
                    logger.error(f"[CHUNKER] Failed to store chunk {chunk.id}")
                
            except Exception as e:
                failed_count += 1
                logger.error(f"[CHUNKER] Error processing chunk {chunk.id}: {e}")
                continue
        
        logger.info(f"[CHUNKER] Successfully processed {success_count}/{len(chunks)} chunks, {failed_count} failed")
        return success_count

    async def _store_chunks_to_supabase(self, chunk_data: List[Dict]) -> bool:
        """Legacy method - không còn sử dụng."""
        logger.warning("[CHUNKER] _store_chunks_to_supabase is deprecated, use _create_embeddings_for_chunks instead")
        return True

    async def process_law_document(self, file_path: str, document_id: int) -> bool:
        """
        Hàm chính để xử lý văn bản luật.
        
        Args:
            file_path: Đường dẫn đến file văn bản luật
            document_id: ID duy nhất của văn bản luật
            
        Returns:
            bool: True nếu thành công, False nếu thất bại
        """
        try:
            logger.info(f"[CHUNKER] Starting processing for file: {file_path}, document_id: {document_id}")
            
            # 1. Tạo thư mục data nếu cần
            self._create_data_directory()
            
            # 2. Kiểm tra file tồn tại
            if not os.path.exists(file_path):
                logger.error(f"[CHUNKER] File not found: {file_path}")
                return False
            
            # 3. Đọc văn bản
            content = self._read_document(file_path)
            
            # 4. Trích xuất tiêu đề
            document_title = self._extract_document_title(file_path)
            
            # 5. Xử lý chunking theo cấu trúc
            chunks = self._process_document_recursive(content, document_id, document_title)
            
            if not chunks:
                logger.warning(f"[CHUNKER] No chunks created for document {document_id}")
                return False
            
            # 6. Tạo embeddings
            success_count = await self._create_embeddings_for_chunks(chunks)
            
            if success_count == 0:
                logger.error(f"[CHUNKER] No embeddings created for document {document_id}")
                return False
            
            logger.info(f"[CHUNKER] Successfully processed document {document_id} with {success_count} chunks")
            return True
            
        except Exception as e:
            logger.error(f"[CHUNKER] Error processing document {document_id}: {e}") ##
            return False 

    async def _create_semantic_summary_with_llm(self, chunk_content: str) -> str:
        """
        Sinh semantic summary ngắn gọn, súc tích cho chunk bằng LLM.
        """
        if not hasattr(self, "llm_client") or self.llm_client is None:
            logger.warning("[CHUNKER] llm_client chưa được gán, bỏ qua semantic summary.")
            return ""
        prompt = (
            "Tóm tắt thật ngắn gọn, súc tích nội dung luật sau (1-2 câu, không lặp lại tiêu đề, không giải thích):\n"
            f"{chunk_content.strip()}"
        )
        try:
            summary = await self.llm_client.generate_text(prompt)
            return summary.strip() if summary else ""
        except Exception as e:
            logger.error(f"[CHUNKER] Lỗi khi sinh semantic summary bằng LLM: {e}")
            return ""