def get_store_name(event: MessageEvent) -> str:
    """
    Get the file search store name based on the message source.
    Returns user_id for 1-on-1 chat, group_id for group chat.
    """
    if event.source.type == "user":
        return f"user_{event.source.user_id}"
    elif event.source.type == "group":
        return f"group_{event.source.group_id}"
    elif event.source.type == "room":
        return f"room_{event.source.room_id}"
    else:
        return f"unknown_{event.source.user_id}"

async def ensure_file_search_store_exists(store_name: str) -> tuple[bool, str]:
    """
    Ensure file search store exists, create if not.
    Returns (success, actual_store_name).
    """
    try:
        # List all stores and check if one with our display_name exists
        stores = client.file_search_stores.list()
        for store in stores:
            if hasattr(store, 'display_name') and store.display_name == store_name:
                print(f"File search store '{store_name}' already exists: {store.name}")
                return True, store.name

        # Store doesn't exist, create it
        print(f"Creating file search store with display_name '{store_name}'...")
        store = client.file_search_stores.create(
            config={'display_name': store_name}
        )
        print(f"File search store created: {store.name} (display_name: {store_name})")
        return True, store.name

    except Exception as e:
        print(f"Error ensuring file search store exists: {e}")
        return False, ""

# Cache to store display_name -> actual_name mapping
store_name_cache = {}

# 在上傳時使用 cache
if store_name in store_name_cache:
    actual_store_name = store_name_cache[store_name]
else:
    success, actual_store_name = await ensure_file_search_store_exists(store_name)
    store_name_cache[store_name] = actual_store_name

async def upload_to_file_search_store(file_path: Path, store_name: str, display_name: Optional[str] = None) -> bool:
    """
    Upload a file to Gemini file search store.
    Returns True if successful, False otherwise.
    """
    try:
        # Check cache first
        if store_name in store_name_cache:
            actual_store_name = store_name_cache[store_name]
        else:
            success, actual_store_name = await ensure_file_search_store_exists(store_name)
            if not success:
                return False
            store_name_cache[store_name] = actual_store_name

        # Upload to file search store
        config_dict = {}
        if display_name:
            config_dict['display_name'] = display_name

        operation = client.file_search_stores.upload_to_file_search_store(
            file_search_store_name=actual_store_name,
            file=str(file_path),
            config=config_dict if config_dict else None
        )

        # Wait for operation to complete (with timeout)
        max_wait = 60  # seconds
        elapsed = 0
        while not operation.done and elapsed < max_wait:
            await asyncio.sleep(2)
            operation = client.operations.get(operation)
            elapsed += 2

        if operation.done:
            print(f"File uploaded successfully")
            return True
        else:
            print(f"Upload operation timeout")
            return False

    except Exception as e:
        print(f"Error uploading to file search store: {e}")
        return False

async def query_file_search(query: str, store_name: str) -> str:
    """
    Query the file search store using generate_content.
    Returns the AI response text.
    """
    try:
        # Get actual store name from cache or by searching
        actual_store_name = None

        if store_name in store_name_cache:
            actual_store_name = store_name_cache[store_name]
        else:
            # Try to find the store by display_name
            stores = client.file_search_stores.list()
            for store in stores:
                if hasattr(store, 'display_name') and store.display_name == store_name:
                    actual_store_name = store.name
                    store_name_cache[store_name] = actual_store_name
                    break

        if not actual_store_name:
            # Store doesn't exist - guide user to upload files
            return "📁 您還沒有上傳任何檔案。\n\n請先傳送文件檔案（PDF、DOCX、TXT 等）或圖片給我，上傳完成後就可以開始提問了！"

        # Create FileSearch tool with actual store name
        tool = types.Tool(
            file_search=types.FileSearch(
                file_search_store_names=[actual_store_name]
            )
        )

        # Generate content with file search
        response = client.models.generate_content(
            model=MODEL_NAME,
            contents=query,
            config=types.GenerateContentConfig(
                tools=[tool],
                temperature=0.7,
            )
        )

        if response.text:
            return response.text
        else:
            return "抱歉，我無法從文件中找到相關資訊。"

    except Exception as e:
        print(f"Error querying file search: {e}")
        return f"查詢時發生錯誤：{str(e)}"

async def query_file_search(query: str, store_name: str) -> tuple[str, list]:
    """
    Query the file search store using generate_content.
    Returns (AI response text, list of citations).
    """
    # ... (前面的查詢代碼)

    # Extract grounding metadata (citations)
    citations = []
    try:
        if hasattr(response, 'candidates') and response.candidates:
            candidate = response.candidates[0]
            if hasattr(candidate, 'grounding_metadata') and candidate.grounding_metadata:
                grounding_chunks = candidate.grounding_metadata.grounding_chunks
                for chunk in grounding_chunks:
                    if hasattr(chunk, 'web') and chunk.web:
                        # Web source (網頁來源)
                        citations.append({
                            'type': 'web',
                            'title': getattr(chunk.web, 'title', 'Unknown'),
                            'uri': getattr(chunk.web, 'uri', ''),
                        })
                    elif hasattr(chunk, 'retrieved_context') and chunk.retrieved_context:
                        # File search source (文件來源)
                        citations.append({
                            'type': 'file',
                            'title': getattr(chunk.retrieved_context, 'title', 'Unknown'),
                            'text': getattr(chunk.retrieved_context, 'text', '')[:500],
                        })
        print(f"Found {len(citations)} citations")
    except Exception as citation_error:
        print(f"Error extracting citations: {citation_error}")

    return (response.text, citations)

# Cache to store citations/grounding metadata for each user/group
# Key: store_name, Value: list of grounding chunks
citations_cache = {}

# 查詢完成後，儲存引用資訊
response_text, citations = await query_file_search(query, store_name)

# Store citations in cache (limit to 3 for Quick Reply)
if citations:
    citations_cache[store_name] = citations[:3]
    print(f"Stored {len(citations_cache[store_name])} citations for {store_name}")

# Create Quick Reply buttons for citations
quick_reply = None
if citations:
    quick_reply_items = []
    for i, citation in enumerate(citations[:3], 1):  # Limit to 3 citations
        quick_reply_items.append(
            QuickReplyButton(action=MessageAction(
                label=f"📖 引用{i}",
                text=f"📖 引用{i}"
            ))
        )
    quick_reply = QuickReply(items=quick_reply_items)

# Reply to user with citations
reply_msg = TextSendMessage(text=response_text, quick_reply=quick_reply)

# Check if user wants to view a citation
if query.startswith("📖 引用"):
    # Extract citation number
    citation_num = int(query.replace("📖 引用", "").strip())
    if store_name in citations_cache and 0 < citation_num <= len(citations_cache[store_name]):
        citation = citations_cache[store_name][citation_num - 1]

        # Format citation text
        if citation['type'] == 'file':
            citation_text = f"📖 引用 {citation_num}\n\n"
            citation_text += f"📄 文件：{citation['title']}\n\n"
            citation_text += f"📝 內容：\n{citation['text']}"
            if len(citation.get('text', '')) >= 500:
                citation_text += "\n\n... (內容過長，已截斷)"
        elif citation['type'] == 'web':
            citation_text = f"📖 引用 {citation_num}\n\n"
            citation_text += f"🌐 來源：{citation['title']}\n"
            citation_text += f"🔗 連結：{citation['uri']}"

        reply_msg = TextSendMessage(text=citation_text)

def is_list_files_intent(text: str) -> bool:
    """
    Check if user wants to list files.
    """
    list_keywords = [
        '列出檔案', '列出文件', '顯示檔案', '顯示文件',
        '查看檔案', '查看文件', '檔案列表', '文件列表',
        '有哪些檔案', '有哪些文件', '我的檔案', '我的文件',
        'list files', 'show files', 'my files'
    ]
    text_lower = text.lower().strip()
    return any(keyword in text_lower for keyword in list_keywords)

async def delete_document(document_name: str) -> bool:
    """
    Delete a document from file search store.
    Note: force=True is required to permanently delete documents from File Search Store.
    """
    try:
        # Try to use SDK method first with force=True
        if hasattr(client.file_search_stores, 'documents'):
            # Force delete is required for File Search Store documents
            client.file_search_stores.documents.delete(
                name=document_name,
                config={'force': True}  # ⚠️ 必須加上 force=True
            )
            return True
    except Exception as sdk_error:
        # Fallback to REST API with force parameter
        import requests
        url = f"https://generativelanguage.googleapis.com/v1beta/{document_name}"
        params = {
            'key': GOOGLE_API_KEY,
            'force': 'true'  # ⚠️ 必須加上 force parameter
        }
        response = requests.delete(url, params=params, timeout=10)
        response.raise_for_status()
        return True

async def handle_postback(event: PostbackEvent):
    """
    Handle postback events (e.g., delete file button clicks).
    """
    try:
        # Parse postback data
        data = event.postback.data
        params = dict(param.split('=') for param in data.split('&'))

        action = params.get('action')
        doc_name = params.get('doc_name')

        if action == 'delete_file' and doc_name:
            success = await delete_document(doc_name)

            if success:
                reply_msg = TextSendMessage(
                    text=f"✅ 檔案已刪除成功！\n\n如需查看剩餘檔案，請輸入「列出檔案」。"
                )
            else:
                reply_msg = TextSendMessage(text="❌ 刪除檔案失敗，請稍後再試。")

            await line_bot_api.reply_message(event.reply_token, reply_msg)
    except Exception as e:
        print(f"Error handling postback: {e}")

# 問題代碼：檔案路徑包含中文
file_path = "uploads/123456_會議記錄.pdf"  # ❌ 編碼錯誤

# 解決方案：使用 ASCII 檔名，保留原始名稱供顯示
_, ext = os.path.splitext("會議記錄.pdf")
safe_file_name = f"{message_id}{ext}"  # "123456.pdf" ✅
file_path = UPLOAD_DIR / safe_file_name

# 在 config 中保留原始檔名
config = {'display_name': '會議記錄.pdf'}  # 用於 AI 回答時的引用

# 1. 檢查 cache
if store_name in store_name_cache:
    actual_store_name = store_name_cache[store_name]
else:
    # 2. 檢查是否存在，不存在則建立
    success, actual_store_name = await ensure_file_search_store_exists(store_name)
    # 3. 加入 cache
    store_name_cache[store_name] = actual_store_name

# 4. 使用實際的 store name 上傳
operation = client.file_search_stores.upload_to_file_search_store(
    file_search_store_name=actual_store_name,
    file=str(file_path),
    config=config_dict
)

# 等待上傳完成
max_wait = 60  # seconds
elapsed = 0
while not operation.done and elapsed < max_wait:
    await asyncio.sleep(2)  # 異步等待
    operation = client.operations.get(operation)
    elapsed += 2