"""
Knowledge Base management endpoints.
[AC-ASA-01, AC-ASA-02, AC-ASA-08] Document upload, list, and index job status.
[AC-AISVC-59~AC-AISVC-64] Multi-knowledge-base management.
"""

import logging
import uuid
from dataclasses import dataclass
from typing import Annotated, Any, Optional

import tiktoken
from fastapi import APIRouter, BackgroundTasks, Depends, File, Form, Query, UploadFile
from fastapi.responses import JSONResponse
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from app.core.database import get_session
from app.core.exceptions import MissingTenantIdException
from app.core.tenant import get_tenant_id
from app.models import ErrorResponse
from app.models.entities import (
    IndexJob,
    IndexJobStatus,
    KBType,
    KnowledgeBaseCreate,
    KnowledgeBaseUpdate,
)
from app.services.kb import KBService
from app.services.knowledge_base_service import KnowledgeBaseService

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/admin/kb", tags=["KB Management"])


@dataclass
class TextChunk:
    """Text chunk with metadata."""
    text: str
    start_token: int
    end_token: int
    page: int | None = None
    source: str | None = None


def chunk_text_by_lines(
    text: str,
    min_line_length: int = 10,
    source: str | None = None,
) -> list[TextChunk]:
    """
    按行分块，每行作为一个独立的检索单元。

    Args:
        text: 要分块的文本
        min_line_length: 最小行长度，低于此长度的行会被跳过
        source: 来源文件路径（可选）

    Returns:
        分块列表，每个块对应一行文本
    """
    lines = text.split('\n')
    chunks: list[TextChunk] = []

    for i, line in enumerate(lines):
        line = line.strip()

        if len(line) < min_line_length:
            continue

        chunks.append(TextChunk(
            text=line,
            start_token=i,
            end_token=i + 1,
            page=None,
            source=source,
        ))

    return chunks


def chunk_text_with_tiktoken(
    text: str,
    chunk_size: int = 512,
    overlap: int = 100,
    page: int | None = None,
    source: str | None = None,
) -> list[TextChunk]:
    """
    使用 tiktoken 按 token 数分块，支持重叠分块。

    Args:
        text: 要分块的文本
        chunk_size: 每个块的最大 token 数
        overlap: 块之间的重叠 token 数
        page: 页码（可选）
        source: 来源文件路径（可选）

    Returns:
        分块列表，每个块包含文本及起始/结束位置
    """
    encoding = tiktoken.get_encoding("cl100k_base")
    tokens = encoding.encode(text)
    chunks: list[TextChunk] = []
    start = 0

    while start < len(tokens):
        end = min(start + chunk_size, len(tokens))
        chunk_tokens = tokens[start:end]
        chunk_text = encoding.decode(chunk_tokens)
        chunks.append(TextChunk(
            text=chunk_text,
            start_token=start,
            end_token=end,
            page=page,
            source=source,
        ))
        if end == len(tokens):
            break
        start += chunk_size - overlap

    return chunks


def get_current_tenant_id() -> str:
    """Dependency to get current tenant ID or raise exception."""
    tenant_id = get_tenant_id()
    if not tenant_id:
        raise MissingTenantIdException()
    return tenant_id


@router.get(
    "/knowledge-bases",
    operation_id="listKnowledgeBases",
    summary="Query knowledge base list",
    description="[AC-AISVC-60] Get list of knowledge bases for the current tenant with type and status filters.",
    responses={
        200: {"description": "Knowledge base list"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
)
async def list_knowledge_bases(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    kb_type: Annotated[Optional[str], Query()] = None,
    is_enabled: Annotated[Optional[bool], Query()] = None,
) -> JSONResponse:
    """
    [AC-AISVC-60] List all knowledge bases for the current tenant.
    Supports filtering by kb_type and is_enabled status.
    """
    try:
        logger.info(f"[AC-AISVC-60] Listing knowledge bases: tenant={tenant_id}, kb_type={kb_type}, is_enabled={is_enabled}")

        kb_service = KnowledgeBaseService(session)
        logger.info(f"[AC-AISVC-60] KnowledgeBaseService created, calling list_knowledge_bases...")
        knowledge_bases = await kb_service.list_knowledge_bases(
            tenant_id=tenant_id,
            kb_type=kb_type,
            is_enabled=is_enabled,
        )
        logger.info(f"[AC-AISVC-60] Found {len(knowledge_bases)} knowledge bases")

        data = []
        for kb in knowledge_bases:
            data.append({
                "id": str(kb.id),
                "name": kb.name,
                "kbType": kb.kb_type,
                "description": kb.description,
                "priority": kb.priority,
                "isEnabled": kb.is_enabled,
                "docCount": kb.doc_count,
                "createdAt": kb.created_at.isoformat() + "Z",
                "updatedAt": kb.updated_at.isoformat() + "Z",
            })

        logger.info(f"[AC-AISVC-60] Returning {len(data)} knowledge bases")
        return JSONResponse(content={"data": data})
    except Exception as e:
        import traceback
        logger.error(f"[AC-AISVC-60] Error listing knowledge bases: {type(e).__name__}: {e}\n{traceback.format_exc()}")
        raise


@router.post(
    "/knowledge-bases",
    operation_id="createKnowledgeBase",
    summary="Create knowledge base",
    description="[AC-AISVC-59] Create a new knowledge base with specified type and priority.",
    responses={
        201: {"description": "Knowledge base created"},
        400: {"description": "Bad Request - invalid kb_type"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
    status_code=201,
)
async def create_knowledge_base(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    kb_create: KnowledgeBaseCreate,
) -> JSONResponse:
    """
    [AC-AISVC-59] Create a new knowledge base.
    Initializes corresponding Qdrant Collection.
    """
    valid_types = [t.value for t in KBType]
    if kb_create.kb_type not in valid_types:
        return JSONResponse(
            status_code=400,
            content={
                "code": "INVALID_KB_TYPE",
                "message": f"Invalid kb_type: {kb_create.kb_type}",
                "details": {"valid_types": valid_types},
            },
        )

    logger.info(
        f"[AC-AISVC-59] Creating knowledge base: tenant={tenant_id}, "
        f"name={kb_create.name}, type={kb_create.kb_type}"
    )

    kb_service = KnowledgeBaseService(session)
    kb = await kb_service.create_knowledge_base(tenant_id, kb_create)
    await session.commit()

    return JSONResponse(
        status_code=201,
        content={
            "id": str(kb.id),
            "name": kb.name,
            "kbType": kb.kb_type,
            "description": kb.description,
            "priority": kb.priority,
            "isEnabled": kb.is_enabled,
            "docCount": kb.doc_count,
            "createdAt": kb.created_at.isoformat() + "Z",
            "updatedAt": kb.updated_at.isoformat() + "Z",
        },
    )


@router.get(
    "/knowledge-bases/{kb_id}",
    operation_id="getKnowledgeBase",
    summary="Get knowledge base details",
    description="Get detailed information about a specific knowledge base.",
    responses={
        200: {"description": "Knowledge base details"},
        404: {"description": "Knowledge base not found"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
)
async def get_knowledge_base(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    kb_id: str,
) -> JSONResponse:
    """
    Get a specific knowledge base by ID.
    """
    logger.info(f"Getting knowledge base: tenant={tenant_id}, kb_id={kb_id}")

    kb_service = KnowledgeBaseService(session)
    kb = await kb_service.get_knowledge_base(tenant_id, kb_id)

    if not kb:
        return JSONResponse(
            status_code=404,
            content={
                "code": "KB_NOT_FOUND",
                "message": f"Knowledge base {kb_id} not found",
            },
        )

    return JSONResponse(
        content={
            "id": str(kb.id),
            "name": kb.name,
            "kbType": kb.kb_type,
            "description": kb.description,
            "priority": kb.priority,
            "isEnabled": kb.is_enabled,
            "docCount": kb.doc_count,
            "createdAt": kb.created_at.isoformat() + "Z",
            "updatedAt": kb.updated_at.isoformat() + "Z",
        }
    )


@router.put(
    "/knowledge-bases/{kb_id}",
    operation_id="updateKnowledgeBase",
    summary="Update knowledge base",
    description="[AC-AISVC-61] Update knowledge base name, type, description, priority, or enabled status.",
    responses={
        200: {"description": "Knowledge base updated"},
        400: {"description": "Bad Request - invalid kb_type"},
        404: {"description": "Knowledge base not found"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
)
async def update_knowledge_base(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    kb_id: str,
    kb_update: KnowledgeBaseUpdate,
) -> JSONResponse:
    """
    [AC-AISVC-61] Update a knowledge base.
    """
    if kb_update.kb_type is not None:
        valid_types = [t.value for t in KBType]
        if kb_update.kb_type not in valid_types:
            return JSONResponse(
                status_code=400,
                content={
                    "code": "INVALID_KB_TYPE",
                    "message": f"Invalid kb_type: {kb_update.kb_type}",
                    "details": {"valid_types": valid_types},
                },
            )

    logger.info(
        f"[AC-AISVC-61] Updating knowledge base: tenant={tenant_id}, kb_id={kb_id}"
    )

    kb_service = KnowledgeBaseService(session)
    kb = await kb_service.update_knowledge_base(tenant_id, kb_id, kb_update)

    if not kb:
        return JSONResponse(
            status_code=404,
            content={
                "code": "KB_NOT_FOUND",
                "message": f"Knowledge base {kb_id} not found",
            },
        )

    await session.commit()

    return JSONResponse(
        content={
            "id": str(kb.id),
            "name": kb.name,
            "kbType": kb.kb_type,
            "description": kb.description,
            "priority": kb.priority,
            "isEnabled": kb.is_enabled,
            "docCount": kb.doc_count,
            "createdAt": kb.created_at.isoformat() + "Z",
            "updatedAt": kb.updated_at.isoformat() + "Z",
        }
    )


@router.delete(
    "/knowledge-bases/{kb_id}",
    operation_id="deleteKnowledgeBase",
    summary="Delete knowledge base",
    description="[AC-AISVC-62] Delete a knowledge base and its associated documents and Qdrant Collection.",
    responses={
        204: {"description": "Knowledge base deleted"},
        404: {"description": "Knowledge base not found"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
)
async def delete_knowledge_base(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    kb_id: str,
) -> JSONResponse:
    """
    [AC-AISVC-62] Delete a knowledge base.
    Also deletes associated documents and Qdrant Collection.
    """
    logger.info(
        f"[AC-AISVC-62] Deleting knowledge base: tenant={tenant_id}, kb_id={kb_id}"
    )

    kb_service = KnowledgeBaseService(session)
    deleted = await kb_service.delete_knowledge_base(tenant_id, kb_id)

    if not deleted:
        return JSONResponse(
            status_code=404,
            content={
                "code": "KB_NOT_FOUND",
                "message": f"Knowledge base {kb_id} not found",
            },
        )

    await session.commit()

    return JSONResponse(
        status_code=204,
        content=None,
    )


@router.get(
    "/documents",
    operation_id="listDocuments",
    summary="Query document list",
    description="[AC-ASA-08] Get list of documents with pagination and filtering.",
    responses={
        200: {"description": "Document list with pagination"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
)
async def list_documents(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    kb_id: Annotated[Optional[str], Query()] = None,
    status: Annotated[Optional[str], Query()] = None,
    page: int = Query(1, ge=1),
    page_size: int = Query(20, ge=1, le=100),
) -> JSONResponse:
    """
    [AC-ASA-08] List documents with filtering and pagination.
    """
    logger.info(
        f"[AC-ASA-08] Listing documents: tenant={tenant_id}, kb_id={kb_id}, "
        f"status={status}, page={page}, page_size={page_size}"
    )

    kb_service = KBService(session)
    documents, total = await kb_service.list_documents(
        tenant_id=tenant_id,
        kb_id=kb_id,
        status=status,
        page=page,
        page_size=page_size,
    )

    total_pages = (total + page_size - 1) // page_size if total > 0 else 0

    data = []
    for doc in documents:
        job_stmt = select(IndexJob).where(
            IndexJob.tenant_id == tenant_id,
            IndexJob.doc_id == doc.id,
        ).order_by(IndexJob.created_at.desc())
        job_result = await session.execute(job_stmt)
        latest_job = job_result.scalar_one_or_none()

        data.append({
            "docId": str(doc.id),
            "kbId": doc.kb_id,
            "fileName": doc.file_name,
            "status": doc.status,
            "jobId": str(latest_job.id) if latest_job else None,
            "createdAt": doc.created_at.isoformat() + "Z",
            "updatedAt": doc.updated_at.isoformat() + "Z",
        })

    return JSONResponse(
        content={
            "data": data,
            "pagination": {
                "page": page,
                "pageSize": page_size,
                "total": total,
                "totalPages": total_pages,
            },
        }
    )


@router.post(
    "/documents",
    operation_id="uploadDocument",
    summary="Upload/import document",
    description="[AC-ASA-01, AC-AISVC-63, AC-IDSMETA-15] Upload document to specified knowledge base and trigger indexing job.",
    responses={
        202: {"description": "Accepted - async indexing job started"},
        400: {"description": "Bad Request - unsupported format or invalid kb_id"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
)
async def upload_document(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    background_tasks: BackgroundTasks,
    file: UploadFile = File(...),
    kb_id: str = Form(...),
    metadata: str = Form(default="{}", description="元数据 JSON 字符串，根据元数据模式配置动态字段"),
) -> JSONResponse:
    """
    [AC-ASA-01, AC-AISVC-63, AC-IDSMETA-15] Upload document to specified knowledge base.
    Creates KB if not exists, indexes to corresponding Qdrant Collection.
    
    [AC-IDSMETA-15] 支持动态元数据校验:
    - metadata: JSON 格式的元数据，字段根据元数据模式配置
    - 根据 scope=kb_document 的字段定义进行 required/type/enum 校验
    
    示例 metadata:
    - 教育行业: {"grade": "初一", "subject": "语文", "type": "痛点"}
    - 医疗行业: {"department": "内科", "disease_type": "慢性病", "content_type": "科普"}
    """
    import json
    from pathlib import Path

    from app.services.document import get_supported_document_formats
    from app.services.metadata_field_definition_service import MetadataFieldDefinitionService

    logger.info(
        f"[AC-IDSMETA-15] Uploading document: tenant={tenant_id}, "
        f"kb_id={kb_id}, filename={file.filename}"
    )

    file_ext = Path(file.filename or "").suffix.lower()
    supported_formats = get_supported_document_formats()

    if file_ext and file_ext not in supported_formats:
        return JSONResponse(
            status_code=400,
            content={
                "code": "UNSUPPORTED_FORMAT",
                "message": f"Unsupported file format: {file_ext}",
                "details": {
                    "supported_formats": supported_formats,
                },
            },
        )

    try:
        metadata_dict = json.loads(metadata) if metadata else {}
    except json.JSONDecodeError:
        return JSONResponse(
            status_code=400,
            content={
                "code": "INVALID_METADATA",
                "message": "Invalid JSON format for metadata",
            },
        )

    field_def_service = MetadataFieldDefinitionService(session)
    
    is_valid, validation_errors = await field_def_service.validate_metadata_for_create(
        tenant_id, metadata_dict, "kb_document"
    )
    
    if not is_valid:
        logger.warning(f"[AC-IDSMETA-15] Metadata validation failed: {validation_errors}")
        return JSONResponse(
            status_code=400,
            content={
                "code": "METADATA_VALIDATION_ERROR",
                "message": "Metadata validation failed",
                "details": {
                    "errors": validation_errors,
                },
            },
        )
    
    kb_service = KnowledgeBaseService(session)

    try:
        kb = await kb_service.get_knowledge_base(tenant_id, kb_id)
        if not kb:
            kb = await kb_service.get_or_create_default_kb(tenant_id)
            kb_id = str(kb.id)
            logger.info(f"[AC-IDSMETA-15] KB not found, using default: {kb_id}")
        else:
            kb_id = str(kb.id)
    except Exception:
        kb = await kb_service.get_or_create_default_kb(tenant_id)
        kb_id = str(kb.id)

    doc_kb_service = KBService(session)
    file_content = await file.read()
    document, job = await doc_kb_service.upload_document(
        tenant_id=tenant_id,
        kb_id=kb_id,
        file_name=file.filename or "unknown",
        file_content=file_content,
        file_type=file.content_type,
    )

    await kb_service.update_doc_count(tenant_id, kb_id, delta=1)
    await session.commit()

    background_tasks.add_task(
        _index_document, tenant_id, kb_id, str(job.id), str(document.id), file_content, file.filename, metadata_dict
    )

    return JSONResponse(
        status_code=202,
        content={
            "jobId": str(job.id),
            "docId": str(document.id),
            "kbId": kb_id,
            "status": job.status,
            "metadata": metadata_dict,
        },
    )


async def _index_document(
    tenant_id: str,
    kb_id: str,
    job_id: str,
    doc_id: str,
    content: bytes,
    filename: str | None = None,
    metadata: dict[str, Any] | None = None,
):
    """
    Background indexing task.
    [AC-AISVC-33, AC-AISVC-34, AC-AISVC-35, AC-AISVC-63] Uses document parsing and pluggable embedding.
    Indexes to the specified knowledge base's Qdrant Collection.
    
    Args:
        metadata: 动态元数据，字段根据元数据模式配置
    """
    import asyncio
    import tempfile
    from pathlib import Path

    from qdrant_client.models import PointStruct

    from app.core.database import async_session_maker
    from app.core.qdrant_client import get_qdrant_client
    from app.services.document import DocumentParseException, UnsupportedFormatError, parse_document
    from app.services.embedding import get_embedding_provider
    from app.services.kb import KBService

    logger.info(f"[INDEX] Starting indexing: tenant={tenant_id}, kb_id={kb_id}, job_id={job_id}, doc_id={doc_id}, filename={filename}")
    await asyncio.sleep(1)

    async with async_session_maker() as session:
        kb_service = KBService(session)
        try:
            await kb_service.update_job_status(
                tenant_id, job_id, IndexJobStatus.PROCESSING.value, progress=10
            )
            await session.commit()

            parse_result = None
            text = None
            file_ext = Path(filename or "").suffix.lower()
            logger.info(f"[INDEX] File extension: {file_ext}, content size: {len(content)} bytes")

            text_extensions = {".txt", ".md", ".markdown", ".rst", ".log", ".json", ".xml", ".yaml", ".yml"}

            if file_ext in text_extensions or not file_ext:
                logger.info("[INDEX] Treating as text file, trying multiple encodings")
                text = None
                for encoding in ["utf-8", "gbk", "gb2312", "gb18030", "big5", "utf-16", "latin-1"]:
                    try:
                        text = content.decode(encoding)
                        logger.info(f"[INDEX] Successfully decoded with encoding: {encoding}")
                        break
                    except (UnicodeDecodeError, LookupError):
                        continue

                if text is None:
                    text = content.decode("utf-8", errors="replace")
                    logger.warning("[INDEX] Failed to decode with known encodings, using utf-8 with replacement")
            else:
                logger.info("[INDEX] Binary file detected, will parse with document parser")
                await kb_service.update_job_status(
                    tenant_id, job_id, IndexJobStatus.PROCESSING.value, progress=15
                )
                await session.commit()

                with tempfile.NamedTemporaryFile(delete=False, suffix=file_ext) as tmp_file:
                    tmp_file.write(content)
                    tmp_path = tmp_file.name

                logger.info(f"[INDEX] Temp file created: {tmp_path}")

                try:
                    logger.info(f"[INDEX] Starting document parsing for {file_ext}...")
                    parse_result = parse_document(tmp_path)
                    text = parse_result.text
                    logger.info(
                        f"[INDEX] Parsed document SUCCESS: {filename}, "
                        f"chars={len(text)}, format={parse_result.metadata.get('format')}, "
                        f"pages={len(parse_result.pages) if parse_result.pages else 'N/A'}, "
                        f"metadata={parse_result.metadata}"
                    )
                    if len(text) < 100:
                        logger.warning(f"[INDEX] Parsed text is very short, preview: {text[:200]}")
                except UnsupportedFormatError as e:
                    logger.error(f"[INDEX] UnsupportedFormatError: {e}")
                    text = content.decode("utf-8", errors="ignore")
                except DocumentParseException as e:
                    logger.error(f"[INDEX] DocumentParseException: {e}, details={getattr(e, 'details', {})}")
                    text = content.decode("utf-8", errors="ignore")
                except Exception as e:
                    logger.error(f"[INDEX] Unexpected parsing error: {type(e).__name__}: {e}")
                    text = content.decode("utf-8", errors="ignore")
                finally:
                    Path(tmp_path).unlink(missing_ok=True)
                    logger.info("[INDEX] Temp file cleaned up")

            logger.info(f"[INDEX] Final text length: {len(text)} chars")
            if len(text) < 50:
                logger.warning(f"[INDEX] Text too short, preview: {repr(text[:200])}")

            await kb_service.update_job_status(
                tenant_id, job_id, IndexJobStatus.PROCESSING.value, progress=20
            )
            await session.commit()

            logger.info("[INDEX] Getting embedding provider...")
            embedding_provider = await get_embedding_provider()
            logger.info(f"[INDEX] Embedding provider: {type(embedding_provider).__name__}")

            all_chunks: list[TextChunk] = []

            if parse_result and parse_result.pages:
                logger.info(f"[INDEX] PDF with {len(parse_result.pages)} pages, using line-based chunking with page metadata")
                for page in parse_result.pages:
                    page_chunks = chunk_text_by_lines(
                        page.text,
                        min_line_length=10,
                        source=filename,
                    )
                    for pc in page_chunks:
                        pc.page = page.page
                    all_chunks.extend(page_chunks)
                logger.info(f"[INDEX] Total chunks from PDF: {len(all_chunks)}")
            else:
                logger.info("[INDEX] Using line-based chunking")
                all_chunks = chunk_text_by_lines(
                    text,
                    min_line_length=10,
                    source=filename,
                )
                logger.info(f"[INDEX] Total chunks: {len(all_chunks)}")

            qdrant = await get_qdrant_client()
            await qdrant.ensure_kb_collection_exists(tenant_id, kb_id, use_multi_vector=True)

            from app.services.embedding.nomic_provider import NomicEmbeddingProvider
            use_multi_vector = isinstance(embedding_provider, NomicEmbeddingProvider)
            logger.info(f"[INDEX] Using multi-vector format: {use_multi_vector}")

            points = []
            total_chunks = len(all_chunks)
            
            doc_metadata = metadata or {}
            logger.info(f"[INDEX] Document metadata: {doc_metadata}")
            
            for i, chunk in enumerate(all_chunks):
                payload = {
                    "text": chunk.text,
                    "source": doc_id,
                    "kb_id": kb_id,
                    "chunk_index": i,
                    "start_token": chunk.start_token,
                    "end_token": chunk.end_token,
                    "metadata": doc_metadata,
                }
                if chunk.page is not None:
                    payload["page"] = chunk.page
                if chunk.source:
                    payload["filename"] = chunk.source

                if use_multi_vector:
                    embedding_result = await embedding_provider.embed_document(chunk.text)
                    points.append({
                        "id": str(uuid.uuid4()),
                        "vector": {
                            "full": embedding_result.embedding_full,
                            "dim_256": embedding_result.embedding_256,
                            "dim_512": embedding_result.embedding_512,
                        },
                        "payload": payload,
                    })
                else:
                    embedding = await embedding_provider.embed(chunk.text)
                    points.append(
                        PointStruct(
                            id=str(uuid.uuid4()),
                            vector=embedding,
                            payload=payload,
                        )
                    )

                progress = 20 + int((i + 1) / total_chunks * 70)
                if i % 10 == 0 or i == total_chunks - 1:
                    await kb_service.update_job_status(
                        tenant_id, job_id, IndexJobStatus.PROCESSING.value, progress=progress
                    )
                    await session.commit()

            if points:
                logger.info(f"[INDEX] Upserting {len(points)} vectors to Qdrant for kb_id={kb_id}...")
                if use_multi_vector:
                    await qdrant.upsert_multi_vector(tenant_id, points, kb_id=kb_id)
                else:
                    await qdrant.upsert_vectors(tenant_id, points, kb_id=kb_id)

            await kb_service.update_job_status(
                tenant_id, job_id, IndexJobStatus.COMPLETED.value, progress=100
            )
            await session.commit()

            logger.info(
                f"[INDEX] COMPLETED: tenant={tenant_id}, kb_id={kb_id}, "
                f"job_id={job_id}, chunks={len(all_chunks)}, text_len={len(text)}"
            )

        except Exception as e:
            import traceback
            logger.error(f"[INDEX] FAILED: {e}\n{traceback.format_exc()}")
            await session.rollback()
            async with async_session_maker() as error_session:
                kb_service = KBService(error_session)
                await kb_service.update_job_status(
                    tenant_id, job_id, IndexJobStatus.FAILED.value,
                    progress=0, error_msg=str(e)
                )
                await error_session.commit()


@router.get(
    "/index/jobs/{job_id}",
    operation_id="getIndexJob",
    summary="Query index job status",
    description="[AC-ASA-02] Get indexing job status and progress.",
    responses={
        200: {"description": "Job status details"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
)
async def get_index_job(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    job_id: str,
) -> JSONResponse:
    """
    [AC-ASA-02] Get indexing job status with progress.
    """
    logger.info(
        f"[AC-ASA-02] Getting job status: tenant={tenant_id}, job_id={job_id}"
    )

    kb_service = KBService(session)
    job = await kb_service.get_index_job(tenant_id, job_id)

    if not job:
        return JSONResponse(
            status_code=404,
            content={
                "code": "JOB_NOT_FOUND",
                "message": f"Job {job_id} not found",
            },
        )

    return JSONResponse(
        content={
            "jobId": str(job.id),
            "docId": str(job.doc_id),
            "status": job.status,
            "progress": job.progress,
            "errorMsg": job.error_msg,
        }
    )


@router.delete(
    "/documents/{doc_id}",
    operation_id="deleteDocument",
    summary="Delete document",
    description="[AC-ASA-08] Delete a document and its associated files.",
    responses={
        200: {"description": "Document deleted"},
        404: {"description": "Document not found"},
        401: {"description": "Unauthorized", "model": ErrorResponse},
        403: {"description": "Forbidden", "model": ErrorResponse},
    },
)
async def delete_document(
    tenant_id: Annotated[str, Depends(get_current_tenant_id)],
    session: Annotated[AsyncSession, Depends(get_session)],
    doc_id: str,
) -> JSONResponse:
    """
    [AC-ASA-08] Delete a document.
    """
    logger.info(
        f"[AC-ASA-08] Deleting document: tenant={tenant_id}, doc_id={doc_id}"
    )

    kb_service = KBService(session)
    deleted = await kb_service.delete_document(tenant_id, doc_id)

    if not deleted:
        return JSONResponse(
            status_code=404,
            content={
                "code": "DOCUMENT_NOT_FOUND",
                "message": f"Document {doc_id} not found",
            },
        )

    return JSONResponse(
        content={
            "success": True,
            "message": "Document deleted",
        }
    )