{"repo": "autolab-sai-sjtu/autopage", "files": {".env": {"bebd9007ee35871fc7dad316f1bf8efbee6dfe29": {"t": 1785674038.807483, "n": 679, "h": "2d18f3855e4c564c", "c": "\n\n#qwen\n# export QWEN_API_KEY='your_qwen_api_key' # openai\n# export QWEN_API_BASE_URL='https://dashscope.aliyuncs.com/compatible-mode/v1' # openai\n\n# deepseek\n# export OPENAI_API_KEY='your_deepseek_api_key'\n# export OPENAI_API_BASE_URL='https://api.deepseek.com'\n\n# export OPENAI_API_KEY='your_openai_api_key' # openai\n# export OPENAI_API_BASE_URL='https://api.openai.com' # openai\n\n# gemini\n# export GEMINI_API_KEY=\"your_gemini_key\"\n\n# ZhiPu glm\n# export ZHIPUAI_API_KEY='your_glm_api_key'\n# export OPENAI_API_BASE_URL='https://open.bigmodel.cn/api/paas/v4/'\n\n\n# export OPENROUTER_API_KEY=\"your_openrouter_key\"\n# export OPENROUTER_API_BASE_URL=\"https://openrouter.ai/api/v1\"\n\n\n\n"}, "df7f5beaea02d1d7e482b81ad26374c1f709105a": {"t": 1785747552.6111557, "n": 679, "h": "2d18f3855e4c564c", "c": "\n\n#qwen\n# export QWEN_API_KEY='your_qwen_api_key' # openai\n# export QWEN_API_BASE_URL='https://dashscope.aliyuncs.com/compatible-mode/v1' # openai\n\n# deepseek\n# export OPENAI_API_KEY='your_deepseek_api_key'\n# export OPENAI_API_BASE_URL='https://api.deepseek.com'\n\n# export OPENAI_API_KEY='your_openai_api_key' # openai\n# export OPENAI_API_BASE_URL='https://api.openai.com' # openai\n\n# gemini\n# export GEMINI_API_KEY=\"your_gemini_key\"\n\n# ZhiPu glm\n# export ZHIPUAI_API_KEY='your_glm_api_key'\n# export OPENAI_API_BASE_URL='https://open.bigmodel.cn/api/paas/v4/'\n\n\n# export OPENROUTER_API_KEY=\"your_openrouter_key\"\n# export OPENROUTER_API_BASE_URL=\"https://openrouter.ai/api/v1\"\n\n\n\n"}, "ecd0ca309215d137e33bd278abaa8d73d250957b": {"t": 1785846821.0229964, "n": 679, "h": "2d18f3855e4c564c", "c": "\n\n#qwen\n# export QWEN_API_KEY='your_qwen_api_key' # openai\n# export QWEN_API_BASE_URL='https://dashscope.aliyuncs.com/compatible-mode/v1' # openai\n\n# deepseek\n# export OPENAI_API_KEY='your_deepseek_api_key'\n# export OPENAI_API_BASE_URL='https://api.deepseek.com'\n\n# export OPENAI_API_KEY='your_openai_api_key' # openai\n# export OPENAI_API_BASE_URL='https://api.openai.com' # openai\n\n# gemini\n# export GEMINI_API_KEY=\"your_gemini_key\"\n\n# ZhiPu glm\n# export ZHIPUAI_API_KEY='your_glm_api_key'\n# export OPENAI_API_BASE_URL='https://open.bigmodel.cn/api/paas/v4/'\n\n\n# export OPENROUTER_API_KEY=\"your_openrouter_key\"\n# export OPENROUTER_API_BASE_URL=\"https://openrouter.ai/api/v1\"\n\n\n\n"}}, "app.py": {"bebd9007ee35871fc7dad316f1bf8efbee6dfe29": {"t": 1785674160.9252, "n": 63193, "h": "af0414489db141ed", "c": "import gradio as gr\nimport os\nimport json\nfrom pathlib import Path\nimport base64\nimport re\nfrom threading import Thread\nfrom http.server import HTTPServer, SimpleHTTPRequestHandler\nimport socket\nfrom dotenv import load_dotenv\nfrom ProjectPageAgent.parse_paper import parse_paper_for_project_page, save_parsed_content\nfrom ProjectPageAgent.html_finder import HtmlFinder\nfrom ProjectPageAgent.content_planner import ProjectPageContentPlanner\nfrom ProjectPageAgent.html_generator import ProjectPageHTMLGenerator, to_url\nfrom utils.wei_utils import get_agent_config\nimport os\nimport subprocess\n\nfrom ProjectPageAgent.content_planner import filter_references\nfrom utils.src.utils import run_sync_screenshots\nfrom ProjectPageAgent.main_pipline import matching, copy_static_files\n\nload_dotenv()\n\n\n\ndef get_agent_config_with_keys(model_type, openai_api_key=\"\", gemini_api_key=\"\", \n                              qwen_api_key=\"\", zhipuai_api_key=\"\", openrouter_api_key=\"\"):\n    \"\"\"\n    Get agent configuration with user-provided API keys.\n    Falls back to environment variables if user keys are not provided.\n    Note: This function sets environment variables but does NOT restore them.\n    The environment variables will remain set for the duration of the application.\n    \"\"\"\n    # Set environment variables with user-provided keys\n    api_keys = {\n        'OPENAI_API_KEY': openai_api_key,\n        'GEMINI_API_KEY': gemini_api_key, \n        'QWEN_API_KEY': qwen_api_key,\n        'ZHIPUAI_API_KEY': zhipuai_api_key,\n        'OPENROUTER_API_KEY': openrouter_api_key\n    }\n    \n    # Set new API keys in environment\n    for key, value in api_keys.items():\n        if value and value.strip():\n            os.environ[key] = value\n    \n    # Get agent config with the new API keys\n    config = get_agent_config(model_type)\n    return config\n\ndef validate_api_keys(model_name_t, model_name_v, openai_api_key, gemini_api_key, \n                     qwen_api_key, zhipuai_api_key, openrouter_api_key):\n    \"\"\"\n    Validate that required API keys are provided for the selected models.\n    \"\"\"\n    errors = []\n    \n    # Check text model requirements\n    if model_name_t in ['4o', '4o-mini', 'gpt-4.1', 'gpt-4.1-mini', 'o1', 'o3', 'o3-mini']:\n        if not openai_api_key or not openai_api_key.strip():\n            errors.append(\"OpenAI API key is required for GPT models\")\n    elif model_name_t in ['gemini', 'gemini-2.5-pro', 'gemini-2.5-flash']:\n        if not gemini_api_key or not gemini_api_key.strip():\n            errors.append(\"Gemini API key is required for Gemini models\")\n    elif model_name_t in ['qwen', 'qwen-plus', 'qwen-max', 'qwen-long']:\n        if not qwen_api_key or not qwen_api_key.strip():\n            errors.append(\"Qwen API key is required for Qwen models\")\n    elif model_name_t.startswith('openrouter_'):\n        if not openrouter_api_key or not openrouter_api_key.strip():\n            errors.append(\"OpenRouter API key is required for OpenRouter models\")\n    \n    # Check vision model requirements\n    if model_name_v in ['4o', '4o-mini']:\n        if not openai_api_key or not openai_api_key.strip():\n            errors.append(\"OpenAI API key is required for GPT vision models\")\n    elif model_name_v in ['gemini', 'gemini-2.5-pro', 'gemini-2.5-flash']:\n        if not gemini_api_key or not gemini_api_key.strip():\n            errors.append(\"Gemini API key is required for Gemini vision models\")\n    elif model_name_v in ['qwen-vl-max', 'qwen-2.5-vl-72b']:\n        if not qwen_api_key or not qwen_api_key.strip():\n            errors.append(\"Qwen API key is required for Qwen vision models\")\n    elif model_name_v.startswith('openrouter_'):\n        if not openrouter_api_key or not openrouter_api_key.strip():\n            errors.append(\"OpenRouter API key is required for OpenRouter vision models\")\n    \n    return errors\n\n# Global Variables\ncurrent_html_dir = None\npreview_server = None\npreview_port = None\ntemplate_preview_servers = []\n\nclass CustomHTTPRequestHandler(SimpleHTTPRequestHandler):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, directory=current_html_dir, **kwargs)\n    \n    def log_message(self, format, *args):\n        pass\n\ndef find_free_port(start_port=8000, max_attempts=100):\n    for port in range(start_port, start_port + max_attempts):\n        try:\n            with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n                s.bind(('', port))\n                return port\n        except OSError:\n            continue\n    raise RuntimeError(f\"Could not find available port\")\n\ndef start_preview_server(html_dir):\n    global current_html_dir, preview_server, preview_port\n    stop_preview_server()\n    current_html_dir = html_dir\n    preview_port = find_free_port()\n    preview_server = HTTPServer(('0.0.0.0', preview_port), CustomHTTPRequestHandler)\n    server_thread = Thread(target=preview_server.serve_forever, daemon=True)\n    server_thread.start()\n    return preview_port\n\ndef stop_preview_server():\n    global preview_server, preview_port\n    if preview_server:\n        preview_server.shutdown()\n        preview_server = None\n        preview_port = None\n\ndef start_ephemeral_server_for_dir(html_dir):\n    port = find_free_port()\n    class _TempHandler(SimpleHTTPRequestHandler):\n        def __init__(self, *args, **kwargs):\n            super().__init__(*args, directory=html_dir, **kwargs)\n        def log_message(self, format, *args):\n            pass\n    srv = HTTPServer(('0.0.0.0', port), _TempHandler)\n    t = Thread(target=srv.serve_forever, daemon=True)\n    t.start()\n    template_preview_servers.append((srv, port))\n    return port\n\ndef stop_all_template_preview_servers():\n    global template_preview_servers\n    for srv, _ in template_preview_servers:\n        try:\n            srv.shutdown()\n        except Exception:\n            pass\n    template_preview_servers = []\n\nclass GenerationArgs:\n    def __init__(self, paper_path, model_name_t, model_name_v, template_root, \n                 template_dir, template_file, output_dir, style_preference, tmp_dir,\n                 full_content_check_times, background_color, has_navigation, \n                 has_hero_section, title_color, page_density, image_layout, \n                 html_check_times, resume, human_input):\n        self.paper_path = paper_path\n        self.model_name_t = model_name_t\n        self.model_name_v = model_name_v\n        self.template_root = template_root\n        self.template_dir = template_dir\n        self.template_file = template_file\n        self.output_dir = output_dir\n        self.style_preference = style_preference\n        self.tmp_dir = tmp_dir\n        self.full_content_check_times = full_content_check_times\n        self.background_color = background_color\n        self.has_navigation = has_navigation\n        self.has_hero_section = has_hero_section\n        self.title_color = title_color\n        self.page_density = page_density\n        self.image_layout = image_layout\n        self.html_check_times = html_check_times\n        self.resume = resume\n        self.human_input = human_input\n        self.paper_name = None\n\n# ==================== Formatting Functions ====================\n\ndef format_section_to_markdown(section_data):\n    \"\"\"\n    Convert Section JSON to beautifully formatted Markdown\n    \n    Args:\n        section_data: Section JSON data\n    \n    Returns:\n        str: Formatted Markdown string\n    \"\"\"\n    if not section_data:\n        return \"No data available\"\n    \n    md_lines = []\n    \n    # Title\n    md_lines.append(\"# 📄 Paper Page Structure Preview\\n\")\n    \n    # Basic Information\n    if \"title\" in section_data:\n        md_lines.append(f\"## 📌 Title\\n**{section_data['title']}**\\n\")\n    \n    if \"authors\" in section_data:\n        md_lines.append(f\"## 👥 Authors\\n{section_data['authors']}\\n\")\n    \n    if \"affiliation\" in section_data:\n        md_lines.append(f\"## 🏛️ Affiliation\\n{section_data['affiliation']}\\n\")\n    \n    # Other Sections\n    md_lines.append(\"## 📑 Page Sections\\n\")\n    \n    section_count = 0\n    for key, value in section_data.items():\n        if key in [\"title\", \"authors\", \"affiliation\"]:\n            continue\n        \n        section_count += 1\n        \n        # Section Title\n        section_title = key.replace(\"_\", \" \").title()\n        md_lines.append(f\"### {section_count}. {section_title}\\n\")\n        \n        # Section Content\n        if isinstance(value, dict):\n            # If dictionary, process recursively\n            for sub_key, sub_value in value.items():\n                sub_title = sub_key.replace(\"_\", \" \").title()\n                md_lines.append(f\"**{sub_title}**: {sub_value}\\n\")\n        elif isinstance(value, list):\n            # If list\n            for item in value:\n                if isinstance(item, str):\n                    md_lines.append(f\"- {item}\\n\")\n                elif isinstance(item, dict):\n                    for k, v in item.items():\n                        md_lines.append(f\"- **{k}**: {v}\\n\")\n        else:\n            # Simple value\n            md_lines.append(f\"{value}\\n\")\n        \n        md_lines.append(\"\")  # Empty line\n    \n    # Add Statistics\n    md_lines.append(\"---\\n\")\n    md_lines.append(f\"**📊 Total {section_count} sections**\\n\")\n    \n    return \"\\n\".join(md_lines)\n\n\ndef format_full_content_to_markdown(content_data, figures=None):\n    \"\"\"\n    Convert Full Content JSON to beautifully formatted Markdown\n    \n    Args:\n        content_data: Full Content JSON data\n        figures: Images and tables data (optional)\n    \n    Returns:\n        str: Formatted Markdown string\n    \"\"\"\n    if not content_data:\n        return \"No data available\"\n    \n    md_lines = []\n    \n    # Title\n    md_lines.append(\"# 📄 Full Content Preview\\n\")\n    \n    # Basic Information\n    if \"title\" in content_data:\n        md_lines.append(f\"# {content_data['title']}\\n\")\n    \n    if \"authors\" in content_data:\n        md_lines.append(f\"**Authors**: {content_data['authors']}\\n\")\n    \n    if \"affiliation\" in content_data:\n        md_lines.append(f\"**Affiliation**: {content_data['affiliation']}\\n\")\n    \n    md_lines.append(\"---\\n\")\n    \n    # Process Each Section\n    section_count = 0\n    image_count = 0\n    table_count = 0\n    \n    for key, value in content_data.items():\n        if key in [\"title\", \"authors\", \"affiliation\"]:\n            continue\n        \n        section_count += 1\n        \n        # Section Title\n        section_title = key.replace(\"_\", \" \").title()\n        md_lines.append(f\"## {section_count}. {section_title}\\n\")\n        \n        # Process Content\n        if isinstance(value, dict):\n            # Process dictionary type content\n            for sub_key, sub_value in value.items():\n                if sub_key.lower() in ['content', 'description', 'text']:\n                    # Main text content\n                    md_lines.append(f\"{sub_value}\\n\")\n                elif sub_key.lower() in ['image', 'figure', 'img']:\n                    # Image\n                    image_count += 1\n                    if isinstance(sub_value, dict):\n                        caption = sub_value.get('caption', f'Figure {image_count}')\n                        path = sub_value.get('path', '')\n                        md_lines.append(f\"\\n**🖼️ {caption}**\\n\")\n                        if path:\n                            md_lines.append(f\"*Image path: `{path}`*\\n\")\n                    else:\n                        md_lines.append(f\"\\n**🖼️ Figure {image_count}**: {sub_value}\\n\")\n                elif sub_key.lower() in ['table']:\n                    # Table\n                    table_count += 1\n                    md_lines.append(f\"\\n**📊 Table {table_count}**\\n\")\n                    if isinstance(sub_value, dict):\n                        caption = sub_value.get('caption', f'Table {table_count}')\n                        md_lines.append(f\"*{caption}*\\n\")\n                    else:\n                        md_lines.append(f\"{sub_value}\\n\")\n                elif sub_key.lower() in ['code']:\n                    # Code block\n                    md_lines.append(f\"\\n```\\n{sub_value}\\n```\\n\")\n                else:\n                    # Other subtitles\n                    sub_title = sub_key.replace(\"_\", \" \").title()\n                    md_lines.append(f\"\\n### {sub_title}\\n\")\n                    md_lines.append(f\"{sub_value}\\n\")\n        \n        elif isinstance(value, list):\n            # Process list type content\n            for idx, item in enumerate(value):\n                if isinstance(item, dict):\n                    # Dictionary items in list\n                    if 'title' in item or 'name' in item:\n                        item_title = item.get('title', item.get('name', f'Item {idx+1}'))\n                        md_lines.append(f\"\\n### {item_title}\\n\")\n                    \n                    for k, v in item.items():\n                        if k not in ['title', 'name']:\n                            if k.lower() in ['content', 'description', 'text']:\n                                md_lines.append(f\"{v}\\n\")\n                            elif k.lower() in ['image', 'figure']:\n                                image_count += 1\n                                md_lines.append(f\"\\n**🖼️ Figure {image_count}**: {v}\\n\")\n                            elif k.lower() == 'table':\n                                table_count += 1\n                                md_lines.append(f\"\\n**📊 Table {table_count}**: {v}\\n\")\n                            else:\n                                k_title = k.replace(\"_\", \" \").title()\n                                md_lines.append(f\"**{k_title}**: {v}\\n\")\n                else:\n                    # Simple list item\n                    md_lines.append(f\"- {item}\\n\")\n        \n        else:\n            # Simple text value\n            md_lines.append(f\"{value}\\n\")\n        \n        md_lines.append(\"\")  # Empty line between sections\n    \n    # Add Statistics\n    md_lines.append(\"\\n---\\n\")\n    stats = []\n    stats.append(f\"📊 **Statistics**\")\n    stats.append(f\"- Sections: {section_count}\")\n    if image_count > 0:\n        stats.append(f\"- Images: {image_count}\")\n    if table_count > 0:\n        stats.append(f\"- Tables: {table_count}\")\n    \n    # If figures data is provided, add more information\n    if figures:\n        if 'images' in figures and figures['images']:\n            stats.append(f\"- Available images: {len(figures['images'])}\")\n        if 'tables' in figures and figures['tables']:\n            stats.append(f\"- Available tables: {len(figures['tables'])}\")\n    \n    md_lines.append(\"\\n\".join(stats))\n    md_lines.append(\"\\n\")\n    \n    return \"\\n\".join(md_lines)\n\n# ==================== Global State Management ====================\n\nclass GenerationState:\n    def __init__(self):\n        self.reset()\n    \n    def reset(self):\n        self.args = None\n        self.paper_content = None\n        self.figures = None\n        self.generated_section = None\n        self.text_page_content = None\n        self.generated_content = None\n        self.html_content = None\n        self.html_file_path = None\n        self.html_dir = None\n        self.planner = None\n        self.html_generator = None\n        self.agent_config_t = None\n        self.total_input_tokens_t = 0\n        self.total_output_tokens_t = 0\n        self.current_stage = \"init\"\n        self.preview_url = None\n\nstate = GenerationState()\n\ndef create_project_zip(project_dir, output_dir, paper_name):\n    \"\"\"\n    Create project archive\n    \n    Args:\n        project_dir: Project directory path\n        output_dir: Output directory\n        paper_name: Paper name\n    \n    Returns:\n        str: Archive path, None if failed\n    \"\"\"\n    import zipfile\n    \n    zip_filename = f\"{paper_name}_project_page.zip\"\n    zip_path = os.path.join(output_dir, zip_filename)\n    \n    print(f\"Creating project archive: {zip_path}\")\n    \n    try:\n        with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:\n            # Traverse project directory, add all files\n            for root, dirs, files in os.walk(project_dir):\n                for file in files:\n                    file_path = os.path.join(root, file)\n                    # Calculate relative path\n                    arcname = os.path.relpath(file_path, output_dir)\n                    zipf.write(file_path, arcname)\n                    \n        print(f\"Archive created successfully: {zip_path}\")\n        \n        # Get archive size\n        zip_size = os.path.getsize(zip_path)\n        zip_size_mb = zip_size / (1024 * 1024)\n        print(f\"Archive size: {zip_size_mb:.2f} MB\")\n        \n        return zip_path\n        \n    except Exception as e:\n        print(f\"Archive creation failed: {e}\")\n        return None\n\ndef start_generation(pdf_file, model_name_t, model_name_v, template_root, \n                    template_dir, template_file, output_dir, style_preference, \n                    tmp_dir, full_content_check_times, background_color, \n                    has_navigation, has_hero_section, title_color, page_density, \n                    image_layout, html_check_times, resume, human_input, \n                    template_choice_value, openai_api_key, gemini_api_key, \n                    qwen_api_key, zhipuai_api_key, openrouter_api_key):\n    \"\"\"Start generation process\"\"\"\n    if pdf_file is None:\n        return \"❌ Please upload a PDF file\", gr.update(visible=False), \"\", \"\", gr.update(), gr.update(), \"\"\n    \n    # Validate API keys\n    validation_errors = validate_api_keys(\n        model_name_t, model_name_v, openai_api_key, gemini_api_key, \n        qwen_api_key, zhipuai_api_key, openrouter_api_key\n    )\n    \n    if validation_errors:\n        error_msg = \"❌ API Key Validation Failed:\\n\" + \"\\n\".join(f\"• {error}\" for error in validation_errors)\n        return error_msg, gr.update(visible=False), \"\", \"\", gr.update(), gr.update(), \"\"\n    \n    state.reset()\n    \n    # Handle template selection\n    if not (template_dir and str(template_dir).strip()):\n        if not template_choice_value:\n            stop_all_template_preview_servers()\n            template_requirement = {\n                \"background_color\": background_color,\n                \"has_hero_section\": has_hero_section,\n                \"Page density\": page_density,\n                \"image_layout\": image_layout,\n                \"has_navigation\": has_navigation,\n                \"title_color\": title_color\n            }\n            try:\n                matched = matching(template_requirement)\n            except Exception as e:\n                return f\"❌ Template recommendation failed: {e}\", gr.update(visible=False), \"\", \"\", gr.update(choices=[], value=None), gr.update(visible=False, value=\"\"), \"\"\n            \n            html_finder_ = HtmlFinder()\n            with open('templates/template_link.json','r') as f:\n                template_link = json.load(f)\n            previews = []\n            for name in matched:\n                t_dir = os.path.join(template_root, name)\n                try:\n                    html_path = html_finder_.find_html(t_dir)\n                    if not os.path.exists(html_path):\n                        continue\n                    html_dir = os.path.dirname(os.path.abspath(html_path))\n                    filename = os.path.basename(html_path)\n                    port = start_ephemeral_server_for_dir(html_dir)\n                    url = template_link[name]\n                    previews.append((name, html_path, url))\n                except Exception:\n                    continue\n            \n            if not previews:\n                return \"❌ No previewable templates found\", gr.update(visible=False), \"\", \"\", gr.update(choices=[], value=None), gr.update(visible=False, value=\"\"), \"\"\n            \n            md_lines = [\"### 🔍 Please select a template to preview before clicking **Start Generation**\", \"\"]\n            for name, _, url in previews:\n                md_lines.append(f\"- **{name}** → [{url}]({url})\")\n            md = \"\\n\".join(md_lines)\n            \n            return \"Recommended 3 templates, please select one to continue\", gr.update(visible=False), \"\", \"\", gr.update(choices=[n for n, _, _ in previews], value=None), gr.update(visible=True, value=md), \"\"\n        \n        template_dir = os.path.join(template_root, template_choice_value)\n    \n    # Create arguments object\n    args = GenerationArgs(\n        paper_path=pdf_file.name,\n        model_name_t=model_name_t,\n        model_name_v=model_name_v,\n        template_root=template_root,\n        template_dir=template_dir,\n        template_file=template_file,\n        output_dir=output_dir,\n        style_preference=style_preference,\n        tmp_dir=tmp_dir,\n        full_content_check_times=full_content_check_times,\n        background_color=background_color,\n        has_navigation=has_navigation,\n        has_hero_section=has_hero_section,\n        title_color=title_color,\n        page_density=page_density,\n        image_layout=image_layout,\n        html_check_times=html_check_times,\n        resume=resume,\n        human_input=human_input\n    )\n    \n    if not args.template_dir:\n        return \"❌ Please select a template\", gr.update(visible=False), \"\", \"\", gr.update(), gr.update(), \"\"\n    \n    if not args.template_file:\n        html_finder_ = HtmlFinder()\n        args.template_file = html_finder_.find_html(args.template_dir)\n    \n    paper_name = args.paper_path.split('/')[-1].replace('.pdf', '') if '/' in args.paper_path else args.paper_path.replace('.pdf', '')\n    args.paper_name = paper_name\n    \n    os.makedirs(args.tmp_dir, exist_ok=True)\n    \n    try:\n        # Initialization\n        agent_config_t = get_agent_config_with_keys(\n            args.model_name_t, openai_api_key, gemini_api_key, \n            qwen_api_key, zhipuai_api_key, openrouter_api_key\n        )\n        state.agent_config_t = agent_config_t\n        state.args = args\n        \n        # Step 1: Parse PDF\n        print(\"=\"*50)\n        print(\"STEP 1: Parsing Research Paper\")\n        print(\"=\"*50)\n        \n        raw_content_path = f'project_contents/{args.paper_name}_raw_content.json'\n        if not os.path.exists(raw_content_path):\n            agent_config_v = get_agent_config_with_keys(\n                args.model_name_v, openai_api_key, gemini_api_key, \n                qwen_api_key, zhipuai_api_key, openrouter_api_key\n            )\n            input_token, output_token, raw_result, images, tables = parse_paper_for_project_page(args, agent_config_t)\n            state.total_input_tokens_t += input_token\n            state.total_output_tokens_t += output_token\n            raw_content_path, _ = save_parsed_content(args, raw_result, images, tables, input_token, output_token)\n        \n        with open(raw_content_path, 'r') as f:\n            paper_content = json.load(f)\n        \n        images = paper_content.get('images', [])\n        tables = paper_content.get('tables', [])\n        figures = {'images': images, 'tables': tables}\n        paper_content = paper_content.get('markdown_content', \"\")\n        \n        state.paper_content = paper_content\n        state.figures = figures\n        \n        # Step 2: Filter content\n        print(\"=\"*50)\n        print(\"STEP 2: Filtering Content\")\n        print(\"=\"*50)\n        \n        planner = ProjectPageContentPlanner(agent_config_t, args)\n        state.planner = planner\n        \n        paper_content, figures, input_token, output_token = planner.filter_raw_content(paper_content, figures)\n        state.total_input_tokens_t += input_token\n        state.total_output_tokens_t += output_token\n        state.paper_content = paper_content\n        state.figures = figures\n        \n        # Step 3: Generate Section\n        print(\"=\"*50)\n        print(\"STEP 3: Generating Sections\")\n        print(\"=\"*50)\n        \n        state.current_stage = \"section\"\n        \n        generated_section, input_token, output_token = generate_section_initial()\n        state.total_input_tokens_t += input_token\n        state.total_output_tokens_t += output_token\n        \n        # Use Markdown formatting\n        section_display_md = format_section_to_markdown(generated_section)\n        section_display_json = json.dumps(generated_section, indent=2, ensure_ascii=False)\n        \n        return (\n            f\"✅ Section generation completed, please review and provide feedback\\n\\nTokens: {input_token} → {output_token}\",\n            gr.update(visible=True),  # feedback_section\n            section_display_md,       # Markdown format\n            section_display_json,     # JSON format (hidden)\n            gr.update(),\n            gr.update(visible=False, value=\"\"),\n            \"\"\n        )\n        \n    except Exception as e:\n        import traceback\n        error_msg = f\"❌ Generation failed: {str(e)}\\n{traceback.format_exc()}\"\n        return error_msg, gr.update(visible=False), \"\", \"\", gr.update(), gr.update(), \"\"\n\ndef create_dynamic_page_dict(sections):\n        poster_dict = {\n            \"title\": \"Title of the paper\",\n            \"authors\": \"Authors of the paper\",\n            \"affiliation\": \"Affiliation of the authors\",\n        }\n        poster_dict.update(sections)\n        return poster_dict\ndef generate_section_initial():\n    \"\"\"Generate initial Section\"\"\"\n    import yaml\n    from jinja2 import Environment, StrictUndefined\n    from utils.wei_utils import account_token\n    from utils.src.utils import get_json_from_response\n    \n    with open('utils/prompt_templates/page_templates/section_generation.yaml', 'r') as f:\n        planner_config = yaml.safe_load(f)\n    \n    jinja_env = Environment(undefined=StrictUndefined)\n    template = jinja_env.from_string(planner_config[\"template\"])\n    \n    jinja_args = {\n        'paper_content': state.paper_content,\n        'json_format_example': json.dumps(state.paper_content, indent=2)\n    }\n    \n    prompt = template.render(**jinja_args)\n    \n    state.planner.planner_agent.reset()\n    response = state.planner.planner_agent.step(prompt)\n    input_token, output_token = account_token(response)\n    generated_section = get_json_from_response(response.msgs[0].content)\n    generated_section = create_dynamic_page_dict(generated_section)\n    state.generated_section = generated_section\n    \n    generated_path = f'project_contents/{state.args.paper_name}_generated_section.json'\n    with open(generated_path, 'w') as f:\n        json.dump(generated_section, f, indent=4)\n    \n    return generated_section, input_token, output_token\n\ndef submit_section_feedback(feedback_text):\n    \"\"\"Submit Section feedback\"\"\"\n    if not feedback_text or feedback_text.strip().lower() == 'yes':\n        # User satisfied, proceed to next stage\n        result = proceed_to_text_content()\n        status, fc_section_visible, fc_display_visible, fc_display_md, fc_display_json, fc_feedback_visible = result\n        return (\n            status,\n            \"\",  # section_display_md clear\n            \"\",  # section_display_json clear\n            \"\",  # section_feedback_input clear\n            gr.update(visible=False),  # feedback_section hide\n            fc_section_visible,  # feedback_full_content show\n            fc_display_visible,  # full_content_display_md show\n            fc_display_md,  # full_content_display_md content\n            fc_display_json,  # full_content_display_json content\n            fc_feedback_visible  # full_content_feedback_input show\n        )\n    \n    # User provides feedback, modify Section\n    from camel.messages import BaseMessage\n    from utils.wei_utils import account_token\n    from utils.src.utils import get_json_from_response\n    \n    message = BaseMessage.make_assistant_message(\n        role_name='User',\n        content=f'human feedback: {feedback_text}\\n\\nPlease make modifications based on this feedback. Output format as specified above.'\n    )\n    response = state.planner.planner_agent.step(message)\n    input_token, output_token = account_token(response)\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    generated_section = get_json_from_response(response.msgs[0].content)\n    generated_section = create_dynamic_page_dict(generated_section)\n    state.generated_section = generated_section\n    \n    generated_path = f'project_contents/{state.args.paper_name}_generated_section.json'\n    with open(generated_path, 'w') as f:\n        json.dump(generated_section, f, indent=4)\n    \n    # Use Markdown formatting\n    section_display_md = format_section_to_markdown(generated_section)\n    section_display_json = json.dumps(generated_section, indent=2, ensure_ascii=False)\n    \n    return (\n        f\"✅ Section updated, please continue reviewing\\n\\nTokens: {input_token} → {output_token}\",\n        section_display_md,  # Markdown format\n        section_display_json,  # JSON format\n        \"\",  # Clear input box\n        gr.update(visible=True),  # feedback_section keep visible\n        gr.update(visible=False),  # feedback_full_content keep hidden\n        gr.update(visible=False),  # full_content_display_md keep hidden\n        \"\",  # full_content_display_md content\n        \"\",  # full_content_display_json content\n        gr.update(visible=False)  # full_content_feedback_input keep hidden\n    )\n\ndef proceed_to_text_content():\n    \"\"\"Enter Text Content generation stage\"\"\"\n    print(\"=\"*50)\n    print(\"STEP 4: Generating Text Content\")\n    print(\"=\"*50)\n    \n    text_page_content, input_token, output_token = state.planner.text_content_generation(\n        state.paper_content, state.figures, state.generated_section\n    )\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    state.text_page_content = text_page_content\n    \n    # Enter Full Content stage\n    return proceed_to_full_content()\n\ndef proceed_to_full_content():\n    \"\"\"Enter Full Content generation stage\"\"\"\n    print(\"=\"*50)\n    print(\"STEP 5: Generating Full Content\")\n    print(\"=\"*50)\n    \n    state.current_stage = \"full_content\"\n    \n    generated_content, input_token, output_token = generate_full_content_initial()\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    # Use Markdown formatting\n    content_display_md = format_full_content_to_markdown(generated_content, state.figures)\n    content_display_json = json.dumps(generated_content, indent=2, ensure_ascii=False)\n    \n    return (\n        f\"✅ Full Content generation completed, please review and provide feedback\\n\\nTokens: {input_token} → {output_token}\",\n        gr.update(visible=True),   # feedback_full_content show\n        gr.update(visible=True),   # full_content_display_md show\n        content_display_md,        # Markdown format\n        content_display_json,      # JSON format\n        gr.update(visible=True)    # full_content_feedback_input show\n    )\n\ndef generate_full_content_initial():\n    \"\"\"Generate initial Full Content\"\"\"\n    import yaml\n    from jinja2 import Environment, StrictUndefined\n    from utils.wei_utils import account_token\n    from utils.src.utils import get_json_from_response\n    \n    with open('utils/prompt_templates/page_templates/full_content_generation.yaml', 'r') as f:\n        planner_config = yaml.safe_load(f)\n    \n    jinja_env = Environment(undefined=StrictUndefined)\n    template = jinja_env.from_string(planner_config[\"template\"])\n    \n    jinja_args = {\n        'paper_content': state.paper_content,\n        'figures': json.dumps(state.figures, indent=2),\n        'project_page_content': json.dumps(state.text_page_content, indent=2)\n    }\n    \n    prompt = template.render(**jinja_args)\n    \n    state.planner.planner_agent.reset()\n    response = state.planner.planner_agent.step(prompt)\n    input_token, output_token = account_token(response)\n    generated_content = get_json_from_response(response.msgs[0].content)\n    \n    state.generated_content = generated_content\n    \n    first_path = f'project_contents/{state.args.paper_name}_generated_full_content.v0.json'\n    with open(first_path, 'w', encoding='utf-8') as f:\n        json.dump(generated_content, f, ensure_ascii=False, indent=2)\n    \n    return generated_content, input_token, output_token\n\ndef submit_full_content_feedback(feedback_text):\n    \"\"\"Submit Full Content feedback\"\"\"\n    if not feedback_text or feedback_text.strip().lower() == 'yes':\n        # User satisfied, proceed to HTML generation\n        result = proceed_to_html_generation()\n        status, html_feedback_visible, preview_info, preview_url, open_btn_visible = result\n        return (\n            status,\n            \"\",  # full_content_display_md clear\n            \"\",  # full_content_display_json clear\n            \"\",  # full_content_feedback_input clear\n            gr.update(visible=False),  # feedback_full_content hide\n            html_feedback_visible,  # feedback_html show\n            preview_info,  # preview_info_display\n            preview_url,  # preview_url_state\n            open_btn_visible  # open_preview_btn show\n        )\n    \n    # User provides feedback\n    from camel.messages import BaseMessage\n    from utils.wei_utils import account_token\n    from utils.src.utils import get_json_from_response\n    \n    message = BaseMessage.make_assistant_message(\n        role_name='User',\n        content=f'human feedback: {feedback_text}\\n\\nPlease make modifications based on this feedback. Output format as specified above.'\n    )\n    response = state.planner.planner_agent.step(message)\n    input_token, output_token = account_token(response)\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    generated_content = get_json_from_response(response.msgs[0].content)\n    state.generated_content = generated_content\n    \n    final_path = f'project_contents/{state.args.paper_name}_generated_full_content.json'\n    with open(final_path, 'w', encoding='utf-8') as f:\n        json.dump(generated_content, f, ensure_ascii=False, indent=2)\n    \n    # Use Markdown formatting\n    content_display_md = format_full_content_to_markdown(generated_content, state.figures)\n    content_display_json = json.dumps(generated_content, indent=2, ensure_ascii=False)\n    \n    return (\n        f\"✅ Full Content updated, please continue reviewing\\n\\nTokens: {input_token} → {output_token}\",\n        content_display_md,  # Markdown format\n        content_display_json,  # JSON format\n        \"\",  # Clear input box\n        gr.update(visible=True),  # feedback_full_content keep visible\n        gr.update(visible=False),  # feedback_html keep hidden\n        \"\",  # preview_info_display\n        \"\",  # preview_url_state\n        gr.update(visible=False)  # open_preview_btn keep hidden\n    )\n\ndef proceed_to_html_generation():\n    \"\"\"Enter HTML generation stage\"\"\"\n    print(\"=\"*50)\n    print(\"STEP 6: Generating HTML\")\n    print(\"=\"*50)\n    \n    state.current_stage = \"html\"\n    \n    # Copy static files\n    static_dir = copy_static_files(\n        state.args.template_file, \n        state.args.template_dir, \n        state.args.output_dir, \n        state.args.paper_name\n    )\n    \n    # Generate HTML\n    html_relative_path = os.path.relpath(state.args.template_file, state.args.template_dir)\n    html_dir = '/'.join(html_relative_path.strip().split('/')[:-1])\n    state.html_dir = html_dir\n    \n    html_generator = ProjectPageHTMLGenerator(state.agent_config_t, state.args)\n    state.html_generator = html_generator\n    \n    with open(state.args.template_file, 'r', encoding='utf-8') as file:\n        html_template = file.read()\n    \n    # Create assets directory\n    assets_dir = html_generator.create_assets_directory(state.args, html_dir, state.args.output_dir)\n    \n    # Generate HTML\n    html_content, input_token, output_token = html_generator.generate_complete_html(\n        state.args, state.generated_content, html_dir, html_template\n    )\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    # Save HTML (before table modification)\n    html_dir_path = os.path.join(state.args.output_dir, state.args.paper_name, html_dir)\n    os.makedirs(html_dir_path, exist_ok=True)\n    \n    html_file_path_no_modify = os.path.join(html_dir_path, 'index_no_modify_table.html')\n    with open(html_file_path_no_modify, 'w', encoding='utf-8') as file:\n        file.write(html_content)\n    \n    # Generate screenshot (before table modification)\n    screenshot_path_no_modify = os.path.join(html_dir_path, 'page_final_no_modify_table.png')\n    run_sync_screenshots(to_url(html_file_path_no_modify), screenshot_path_no_modify)\n    \n    # Modify tables\n    html_content, input_token, output_token = html_generator.modify_html_table(html_content, html_dir)\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    state.html_content = html_content\n    \n    # Save HTML (after table modification)\n    html_file_path = os.path.join(html_dir_path, 'index.html')\n    with open(html_file_path, 'w', encoding='utf-8') as file:\n        file.write(html_content)\n    \n    state.html_file_path = html_file_path\n    \n    # Generate screenshot (after table modification)\n    run_sync_screenshots(\n        to_url(html_file_path), \n        os.path.join(html_dir_path, 'page_final.png')\n    )\n    \n    # Start preview server\n    html_full_dir = os.path.dirname(os.path.abspath(html_file_path))\n    port = start_preview_server(html_full_dir)\n    preview_url = f\"http://localhost:{port}/index.html\"\n    state.preview_url = preview_url\n    \n    # Create preview info display\n    preview_info = f\"\"\"\n### 🌐 HTML Generation Completed\n\n**Preview URL**: {preview_url}\n\n**Instructions**:\n1. Click the **\"🌐 Open Preview in New Tab\"** button below to view the generated webpage\n2. Carefully review the page in the new tab\n3. If satisfied, enter **'yes'** in the feedback box and submit\n4. If modifications are needed, provide detailed feedback and submit\n\n**Token Usage**: {input_token} → {output_token}\n\"\"\"\n    \n    return (\n        f\"✅ HTML generation completed\\n\\nTokens: {input_token} → {output_token}\",\n        gr.update(visible=True),   # feedback_html show\n        preview_info,              # preview_info_display\n        preview_url,               # preview_url_state\n        gr.update(visible=True)    # open_preview_btn show\n    )\n\ndef submit_html_feedback(feedback_text):\n    \"\"\"Submit HTML feedback\"\"\"\n    if not feedback_text or feedback_text.strip().lower() == 'yes':\n        # User satisfied, complete generation\n        result = finalize_generation()\n        status, html_file = result\n        return (\n            status,\n            \"\",  # preview_info_display clear\n            \"\",  # html_feedback_input clear\n            gr.update(visible=False),  # feedback_html hide\n            gr.update(visible=False),  # open_preview_btn hide\n            html_file  # html_file_output\n        )\n    \n    # User provides feedback\n    html_content, input_token, output_token = state.html_generator.modify_html_from_human_feedback(\n        state.html_content, feedback_text\n    )\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    state.html_content = html_content\n    \n    # Save updated HTML\n    html_dir_path = os.path.dirname(state.html_file_path)\n    \n    # Save as temporary version (for possible feedback iteration)\n    import time\n    timestamp = int(time.time())\n    html_file_feedback = os.path.join(html_dir_path, f'index_feedback_{timestamp}.html')\n    with open(html_file_feedback, 'w', encoding='utf-8') as file:\n        file.write(html_content)\n    \n    # Also update main file\n    with open(state.html_file_path, 'w', encoding='utf-8') as file:\n        file.write(html_content)\n    \n    # Regenerate screenshot\n    screenshot_path = os.path.join(html_dir_path, 'page_final.png')\n    try:\n        run_sync_screenshots(to_url(state.html_file_path), screenshot_path)\n    except Exception as e:\n        print(f\"Screenshot generation failed: {e}\")\n    \n    # Update preview info\n    preview_info = f\"\"\"\n### 🌐 HTML Updated\n\n**Preview URL**: {state.preview_url}\n\n**Instructions**:\n1. Click the **\"🌐 Open Preview in New Tab\"** button below to view the updated webpage\n2. **Refresh the browser** to see the latest version\n3. If satisfied, enter **'yes'** in the feedback box and submit\n4. If further modifications are needed, continue providing feedback\n\n**Token Usage**: {input_token} → {output_token}\n\"\"\"\n    \n    return (\n        f\"✅ HTML updated, please refresh the preview page\\n\\nTokens: {input_token} → {output_token}\",\n        preview_info,              # preview_info_display\n        \"\",  # Clear input box\n        gr.update(visible=True),   # feedback_html keep visible\n        gr.update(visible=True),   # open_preview_btn keep visible\n        None  # html_file_output no download yet\n    )\n\ndef finalize_generation():\n    \"\"\"Complete generation and save final results\"\"\"\n    import time\n    \n    # Ensure final HTML is saved\n    html_dir_path = os.path.dirname(state.html_file_path)\n    \n    # Save final version\n    final_html_path = os.path.join(html_dir_path, 'index_final.html')\n    with open(final_html_path, 'w', encoding='utf-8') as file:\n        file.write(state.html_content)\n    \n    # Also update main file\n    with open(state.html_file_path, 'w', encoding='utf-8') as file:\n        file.write(state.html_content)\n    \n    # Save metadata\n    metadata = state.html_generator.generate_metadata(state.generated_content, state.args)\n    metadata_path = state.html_generator.save_metadata(metadata, state.args, state.args.output_dir)\n    \n    # Create README file\n    readme_path = os.path.join(state.args.output_dir, state.args.paper_name, 'README.md')\n    readme_content = f\"\"\"# {state.args.paper_name} - Project Page\n\n## 📄 Project Information\n\n- **Paper Name**: {state.args.paper_name}\n- **Generation Time**: {time.strftime('%Y-%m-%d %H:%M:%S')}\n- **Text Model**: {state.args.model_name_t}\n- **Vision Model**: {state.args.model_name_v}\n\n## 🚀 Usage\n\n1. Extract this archive to any directory\n2. Open `index.html` to view the project page\n3. All resources (CSS, images, etc.) are included\n\n## 📁 File Structure\n\n- `index.html` - Main page file\n- `index_final.html` - Final confirmed version\n- `assets/` - Image and table resources\n- `css/` or `styles/` - Style files\n- `js/` or `scripts/` - JavaScript files\n- `metadata.json` - Page metadata\n- `generation_log.json` - Generation log\n\n## 💡 Tips\n\n- Recommended browsers: Chrome, Firefox, Safari, Edge\n- For web deployment, simply upload the entire folder\n- Feel free to modify HTML and CSS for customization\n\n---\nGenerated by Paper2ProjectPage\n\"\"\"\n    \n    with open(readme_path, 'w', encoding='utf-8') as f:\n        f.write(readme_content)\n    \n    # Save generation log\n    log_data = {\n        'paper_name': state.args.paper_name,\n        'paper_path': state.args.paper_path,\n        'models': {\n            'text_model': state.args.model_name_t,\n            'vision_model': state.args.model_name_v\n        },\n        'token_usage': {\n            'text_input_tokens': state.total_input_tokens_t,\n            'text_output_tokens': state.total_output_tokens_t\n        },\n        'output_files': {\n            'html_file': state.html_file_path,\n            'final_html_file': final_html_path,\n            'metadata_file': metadata_path,\n            'readme_file': readme_path\n        },\n        'timestamp': time.strftime('%Y-%m-%d %H:%M:%S')\n    }\n    \n    log_path = f\"{state.args.output_dir}/{state.args.paper_name}/generation_log.json\"\n    with open(log_path, 'w') as f:\n        json.dump(log_data, f, indent=4, ensure_ascii=False)\n    \n    # Create project archive\n    project_dir = os.path.join(state.args.output_dir, state.args.paper_name)\n    zip_path = create_project_zip(project_dir, state.args.output_dir, state.args.paper_name)\n    \n    if zip_path and os.path.exists(zip_path):\n        # Get archive size\n        zip_size = os.path.getsize(zip_path)\n        zip_size_mb = zip_size / (1024 * 1024)\n        zip_filename = os.path.basename(zip_path)\n        \n        success_msg = f\"\"\"\n✅ Project page generation completed!\n\n📁 Output directory: {state.args.output_dir}/{state.args.paper_name}\n🌐 HTML file: {state.html_file_path}\n🌐 Final version: {final_html_path}\n📋 Metadata: {metadata_path}\n📖 README: {readme_path}\n📊 Log file: {log_path}\n📦 Archive: {zip_filename} ({zip_size_mb:.2f} MB)\n🔢 Total token usage: {state.total_input_tokens_t} → {state.total_output_tokens_t}\n\n🎉 All feedback completed, page successfully generated!\nClick the button below to download the complete project archive (including HTML, CSS, images, README, and all resources).\n\"\"\"\n        \n        return (\n            success_msg,\n            zip_path  # Return archive for download\n        )\n        \n    else:\n        error_msg = f\"\"\"\n⚠️ Project page generated, but archive creation failed!\n\n📁 Output directory: {state.args.output_dir}/{state.args.paper_name}\n🌐 HTML file: {state.html_file_path}\n📋 Metadata: {metadata_path}\n\nYou can manually retrieve all files from the output directory {project_dir}.\n\"\"\"\n        return (\n            error_msg,\n            state.html_file_path  # Return HTML file\n        )\n\n# ==================== Gradio Interface ====================\n\n# Custom CSS for better English font rendering\ncustom_css = \"\"\"\n@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap');\n\n* {\n    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif !important;\n}\n\ncode, pre, .code {\n    font-family: 'JetBrains Mono', 'Courier New', Consolas, Monaco, monospace !important;\n}\n\nh1, h2, h3, h4, h5, h6 {\n    font-weight: 600 !important;\n    letter-spacing: -0.02em !important;\n}\n\n.markdown-text {\n    line-height: 1.7 !important;\n    font-size: 15px !important;\n}\n\n.gr-button {\n    font-weight: 500 !important;\n    letter-spacing: 0.01em !important;\n}\n\n.gr-input, .gr-textarea {\n    font-size: 14px !important;\n    line-height: 1.6 !important;\n}\n\n.gr-box {\n    border-radius: 8px !important;\n}\n\n/* Better spacing for English content */\n.gr-markdown p {\n    margin-bottom: 0.8em !important;\n}\n\n.gr-markdown ul, .gr-markdown ol {\n    margin-left: 1.2em !important;\n}\n\n.gr-markdown li {\n    margin-bottom: 0.4em !important;\n}\n\"\"\"\n\nwith gr.Blocks(title=\"Paper2ProjectPage Generator\", theme=gr.themes.Soft(), css=custom_css) as demo:\n    \n    gr.Markdown(\"\"\"\n    # 📄 AutoPage Generator with Interactive Feedback\n    \n    Upload your research paper PDF and generate beautiful project pages through multi-round interactive feedback\n    \"\"\")\n    \n    with gr.Row():\n        with gr.Column(scale=1):\n            # PDF Upload\n            pdf_input = gr.File(\n                label=\"📎 Upload PDF Paper\",\n                file_types=[\".pdf\"],\n                type=\"filepath\"\n            )\n            \n            gr.Markdown(\"### 🔑 API Keys Configuration\")\n            gr.Markdown(\"\"\"\n            **⚠️ Security Notice**: Your API keys are only stored in memory during the session and are never saved to disk.\n            \n            **💡 Note**: You only need to fill in ONE API key corresponding to the models you selected below.\n            \n            **📋 How to get API keys:**\n            - **OpenAI**: Get your API key from [OpenAI Platform](https://platform.openai.com/api-keys)\n            - **Gemini**: Get your API key from [Google AI Studio](https://aistudio.google.com/app/apikey)\n            - **Qwen**: Get your API key from [DashScope](https://dashscope.console.aliyun.com/apiKey)\n            - **ZhipuAI**: Get your API key from [ZhipuAI Console](https://open.bigmodel.cn/usercenter/apikeys)\n            - **OpenRouter**: Get your API key from [OpenRouter](https://openrouter.ai/keys)\n            \n            **🚀 For HuggingFace Spaces**: You can also set these as environment variables in your Space settings.\n            \"\"\")\n            \n            with gr.Row():\n                openai_api_key = gr.Textbox(\n                    label=\"OpenAI API Key\",\n                    value=os.getenv(\"OPENAI_API_KEY\", \"\"),\n                    type=\"password\",\n                    placeholder=\"sk-...\",\n                    info=\"Required for GPT models\"\n                )\n                gemini_api_key = gr.Textbox(\n                    label=\"Gemini API Key\", \n                    value=os.getenv(\"GEMINI_API_KEY\", \"\"),\n                    type=\"password\",\n                    placeholder=\"AI...\",\n                    info=\"Required for Gemini models\"\n                )\n            \n            with gr.Row():\n                qwen_api_key = gr.Textbox(\n                    label=\"Qwen API Key\",\n                    value=os.getenv(\"QWEN_API_KEY\", \"\"),\n                    type=\"password\", \n                    placeholder=\"sk-...\",\n                    info=\"Required for Qwen models\"\n                )\n                zhipuai_api_key = gr.Textbox(\n                    label=\"ZhipuAI API Key\",\n                    value=os.getenv(\"ZHIPUAI_API_KEY\", \"\"),\n                    type=\"password\",\n                    placeholder=\"...\",\n                    info=\"Required for GLM models\"\n                )\n            \n            openrouter_api_key = gr.Textbox(\n                label=\"OpenRouter API Key\",\n                value=os.getenv(\"OPENROUTER_API_KEY\", \"\"),\n                type=\"password\",\n                placeholder=\"sk-or-...\",\n                info=\"Required for OpenRouter models\"\n            )\n            \n            gr.Markdown(\"### 🤖 Model Configuration\")\n            \n            # Text Model Options\n            text_model_options = [\n                (\"GPT-4o\", \"4o\"),\n                (\"GPT-4o Mini\", \"4o-mini\"),\n                (\"GPT-4.1\", \"gpt-4.1\"),\n                (\"GPT-4.1 Mini\", \"gpt-4.1-mini\"),\n                (\"O1\", \"o1\"),\n                (\"O3\", \"o3\"),\n                (\"O3 Mini\", \"o3-mini\"),\n                (\"Gemini 2.5 Pro\", \"gemini\"),\n                (\"Gemini 2.5 Pro (Alt)\", \"gemini-2.5-pro\"),\n                (\"Gemini 2.5 Flash\", \"gemini-2.5-flash\"),\n                (\"Qwen\", \"qwen\"),\n                (\"Qwen Plus\", \"qwen-plus\"),\n                (\"Qwen Max\", \"qwen-max\"),\n                (\"Qwen Long\", \"qwen-long\"),\n                (\"OpenRouter Qwen Plus\", \"openrouter_qwen-plus\"),\n                (\"OpenRouter GPT-4o Mini\", \"openrouter_gpt-4o-mini\"),\n                (\"OpenRouter Gemini 2.5 Flash\", \"openrouter_gemini-2.5-flash\"),\n                (\"OpenRouter O3\", \"openrouter_openai/o3\"),\n                (\"OpenRouter Claude Sonnet 4.5\", \"openrouter_claude-sonnet-4.5\"),\n            ]\n            \n            # Vision Model Options\n            vision_model_options = [\n                (\"GPT-4o\", \"4o\"),\n                (\"GPT-4o Mini\", \"4o-mini\"),\n                (\"Gemini 2.5 Pro\", \"gemini\"),\n                (\"Gemini 2.5 Pro (Alt)\", \"gemini-2.5-pro\"),\n                (\"Gemini 2.5 Flash\", \"gemini-2.5-flash\"),\n                (\"Qwen VL Max\", \"qwen-vl-max\"),\n                (\"Qwen 2.5 VL 72B\", \"qwen-2.5-vl-72b\"),\n                (\"OpenRouter Qwen VL 72B\", \"openrouter_qwen_vl_72b\"),\n                (\"OpenRouter Qwen VL 7B\", \"openrouter_qwen_vl_7b\"),\n                (\"OpenRouter Qwen VL Max\", \"openrouter_qwen-vl-max\"),\n                (\"OpenRouter Gemini 2.5 Flash\", \"openrouter_gemini-2.5-flash\"),\n            ]\n            \n            with gr.Row():\n                model_name_t = gr.Dropdown(\n                    label=\"Text Model\",\n                    choices=text_model_options,\n                    value=\"gemini\",\n                    info=\"Select model for text processing\"\n                )\n                model_name_v = gr.Dropdown(\n                    label=\"Vision Model\", \n                    choices=vision_model_options,\n                    value=\"gemini\",\n                    info=\"Select model for vision processing\"\n                )\n            \n            # Hidden backend configuration fields\n            template_root = gr.Textbox(\n                label=\"Template Root\",\n                value=\"templates\",\n                visible=False\n            )\n            template_dir = gr.Textbox(\n                label=\"Template Directory\",\n                value=\"\",\n                visible=False\n            )\n            template_file = gr.Textbox(\n                label=\"Template File\",\n                value=\"\",\n                visible=False\n            )\n            output_dir = gr.Textbox(\n                label=\"Output Directory\",\n                value=\"generated_project_pages\",\n                visible=False\n            )\n            style_preference = gr.Textbox(\n                label=\"Style Preference JSON\",\n                value=\"\",\n                visible=False\n            )\n            tmp_dir = gr.Textbox(\n                label=\"Temporary Directory\",\n                value=\"tmp\",\n                visible=False\n            )\n            \n            # Hidden parameters with default values\n            resume = gr.Radio(\n                label=\"Resume From Step\",\n                choices=['parse_pdf', 'generate_content','full_content_check', 'generate_html', 'html_check','modify_table','html_feedback'],\n                value='parse_pdf',\n                visible=False\n            )\n            \n            human_input = gr.Radio(\n                label=\"Enable Human Feedback\",\n                choices=[0, 1],\n                value=1,\n                visible=False\n            )\n            \n            full_content_check_times = gr.Number(\n                label=\"Full Content Check Times\",\n                value=1,\n                precision=0,\n                visible=False\n            )\n            \n            html_check_times = gr.Number(\n                label=\"HTML Check Times\",\n                value=1,\n                precision=0,\n                visible=False\n            )\n            \n        with gr.Column(scale=1):\n            gr.Markdown(\"\"\"\n            ### 💡 User Guide\n            \n            1. **Upload PDF**: Select your research paper PDF file\n            2. **Configure API Key**: Fill in ONE API key for your selected models\n            3. **Select Models**: Choose text and vision models from dropdowns\n            4. **Configure Style**: Adjust style preferences below\n            5. **Start Generation**: Click the \"Start Generation\" button\n            6. **Three-Stage Feedback**:\n               - 📝 **Section Feedback**: Review the generated page structure (Markdown preview + JSON data)\n               - 📄 **Full Content Feedback**: Review the generated complete content (Markdown preview + JSON data)\n               - 🌐 **HTML Feedback**: View the generated webpage in a new tab\n            7. **Download Results**: Download the complete project archive after completion\n            \n            ⚠️ **Tips**: \n            - Each stage supports multiple rounds of feedback until you're satisfied\n            - Enter 'yes' to proceed to the next stage\n            - The final ZIP download includes the complete project folder with all resources\n            \"\"\")\n            \n            gr.Markdown(\"### 🎨 Style Configuration\")\n            \n            with gr.Row():\n                with gr.Column():\n                    background_color = gr.Radio(\n                        label=\"Background Color\",\n                        choices=[\"light\", \"dark\"],\n                        value=\"light\",\n                        info=\"Background color theme\"\n                    )\n                    \n                    has_navigation = gr.Radio(\n                        label=\"Has Navigation\",\n                        choices=[\"yes\", \"no\"],\n                        value=\"yes\",\n                        info=\"Include navigation bar\"\n                    )\n                    \n                    has_hero_section = gr.Radio(\n                        label=\"Has Hero Section\",\n                        choices=[\"yes\", \"no\"],\n                        value=\"yes\",\n                        info=\"Include hero/header section\"\n                    )\n                \n                with gr.Column():\n                    title_color = gr.Radio(\n                        label=\"Title Color\",\n                        choices=[\"pure\", \"colorful\"],\n                        value=\"pure\",\n                        info=\"Title color style\"\n                    )\n                    \n                    page_density = gr.Radio(\n                        label=\"Page Density\",\n                        choices=[\"spacious\", \"compact\"],\n                        value=\"spacious\",\n                        info=\"Page spacing density\"\n                    )\n                    \n                    image_layout = gr.Radio(\n                        label=\"Image Layout\",\n                        choices=[\"rotation\", \"parallelism\"],\n                        value=\"parallelism\",\n                        info=\"Image layout style\"\n                    )\n            \n            gr.Markdown(\"### 📁 Template Selection\")\n            \n            template_choice = gr.Radio(\n                label=\"Recommended Templates\",\n                choices=[],\n                value=None,\n                info=\"Select from recommended templates\"\n            )\n            \n            template_preview_links = gr.Markdown(\n                value=\"\",\n                visible=False\n            )\n    \n    # Start Generation Button\n    start_btn = gr.Button(\"🚀 Start Generation\", variant=\"primary\", size=\"lg\")\n    \n    # Status Output\n    status_output = gr.Textbox(\n        label=\"📊 Generation Status\",\n        lines=5,\n        interactive=False\n    )\n    \n    # Section Feedback Area\n    with gr.Group(visible=False) as feedback_section:\n        gr.Markdown(\"### 📝 Section Generation Results\")\n        gr.Markdown(\"Please review the generated section structure. If satisfied, enter **'yes'**, otherwise provide modification feedback:\")\n        \n        with gr.Tabs():\n            with gr.Tab(\"📖 Preview (Markdown)\"):\n                section_display_md = gr.Markdown(\n                    label=\"Section Preview\",\n                    value=\"\"\n                )\n            with gr.Tab(\"📋 Raw Data (JSON)\"):\n                section_display_json = gr.Code(\n                    label=\"Section JSON\",\n                    language=\"json\",\n                    value=\"\",\n                    lines=15\n                )\n        \n        section_feedback_input = gr.TextArea(\n            label=\"Your Feedback\",\n            placeholder=\"Enter 'yes' to continue, or provide modification feedback...\",\n            lines=3\n        )\n        section_submit_btn = gr.Button(\"Submit Feedback\", variant=\"primary\")\n    \n    # Full Content Feedback Area\n    with gr.Group(visible=False) as feedback_full_content:\n        gr.Markdown(\"### 📄 Full Content Generation Results\")\n        gr.Markdown(\"Please review the generated full content. If satisfied, enter **'yes'**, otherwise provide modification feedback:\")\n        \n        with gr.Tabs():\n            with gr.Tab(\"📖 Preview (Markdown)\"):\n                full_content_display_md = gr.Markdown(\n                    label=\"Full Content Preview\",\n                    value=\"\"\n                )\n            with gr.Tab(\"📋 Raw Data (JSON)\"):\n                full_content_display_json = gr.Code(\n                    label=\"Full Content JSON\",\n                    language=\"json\",\n                    value=\"\",\n                    lines=15\n                )\n        \n        full_content_feedback_input = gr.TextArea(\n            label=\"Your Feedback\",\n            placeholder=\"Enter 'yes' to continue, or provide modification feedback...\",\n            lines=3\n        )\n        full_content_submit_btn = gr.Button(\"Submit Feedback\", variant=\"primary\")\n    \n    # HTML Feedback Area\n    with gr.Group(visible=False) as feedback_html:\n        gr.Markdown(\"### 🌐 HTML Generation Results\")\n        \n        # Preview Info Display\n        preview_info_display = gr.Markdown(\n            value=\"\",\n            label=\"Preview Information\"\n        )\n        \n        # Preview URL (hidden state for JS)\n        preview_url_state = gr.Textbox(visible=False)\n        \n        # Open Preview in New Tab Button\n        open_preview_btn = gr.Button(\n            \"🌐 Open Preview in New Tab\",\n            variant=\"secondary\",\n            size=\"lg\",\n            visible=False\n        )\n        \n        gr.Markdown(\"---\")\n        \n        # Feedback Input Area\n        html_feedback_input = gr.TextArea(\n            label=\"Your Feedback\",\n            placeholder=\"Enter 'yes' to finalize, or provide modification feedback...\",\n            lines=3\n        )\n        html_submit_btn = gr.Button(\"Submit Feedback\", variant=\"primary\")\n    \n    # Final Output\n    html_file_output = gr.File(\n        label=\"📥 Download Project Archive\",\n        interactive=False\n    )\n    \n    # Bind Events\n    start_btn.click(\n        fn=start_generation,\n        inputs=[\n            pdf_input, model_name_t, model_name_v, template_root,\n            template_dir, template_file, output_dir, style_preference,\n            tmp_dir, full_content_check_times, background_color,\n            has_navigation, has_hero_section, title_color, page_density,\n            image_layout, html_check_times, resume, human_input,\n            template_choice, openai_api_key, gemini_api_key, \n            qwen_api_key, zhipuai_api_key, openrouter_api_key\n        ],\n        outputs=[\n            status_output,\n            feedback_section,\n            section_display_md,\n            section_display_json,\n            template_choice,\n            template_preview_links,\n            section_feedback_input\n        ]\n    )\n    \n    section_submit_btn.click(\n        fn=submit_section_feedback,\n        inputs=[section_feedback_input],\n        outputs=[\n            status_output,\n            section_display_md,\n            section_display_json,\n            section_feedback_input,\n            feedback_section,\n            feedback_full_content,\n            full_content_display_md,\n            full_content_display_md,\n            full_content_display_json,\n            full_content_feedback_input\n        ]\n    )\n    \n    full_content_submit_btn.click(\n        fn=submit_full_content_feedback,\n        inputs=[full_content_feedback_input],\n        outputs=[\n            status_output,\n            full_content_display_md,\n            full_content_display_json,\n            full_content_feedback_input,\n            feedback_full_content,\n            feedback_html,\n            preview_info_display,\n            preview_url_state,\n            open_preview_btn\n        ]\n    )\n    \n    html_submit_btn.click(\n        fn=submit_html_feedback,\n        inputs=[html_feedback_input],\n        outputs=[\n            status_output,\n            preview_info_display,\n            html_feedback_input,\n            feedback_html,\n            open_preview_btn,\n            html_file_output\n        ]\n    )\n    \n    # Open Preview Button - Use JavaScript to open in new tab\n    open_preview_btn.click(\n        fn=None,\n        inputs=[preview_url_state],\n        outputs=None,\n        js=\"(url) => window.open(url, '_blank')\"\n    )\n\n# Launch Application\nif __name__ == \"__main__\":\n    demo.launch(\n        server_name=\"0.0.0.0\",\n        server_port=7860,\n        share=False,\n        show_error=True\n    )"}, "ecd0ca309215d137e33bd278abaa8d73d250957b": {"t": 1785846841.2051988, "n": 63193, "h": "af0414489db141ed", "c": "import gradio as gr\nimport os\nimport json\nfrom pathlib import Path\nimport base64\nimport re\nfrom threading import Thread\nfrom http.server import HTTPServer, SimpleHTTPRequestHandler\nimport socket\nfrom dotenv import load_dotenv\nfrom ProjectPageAgent.parse_paper import parse_paper_for_project_page, save_parsed_content\nfrom ProjectPageAgent.html_finder import HtmlFinder\nfrom ProjectPageAgent.content_planner import ProjectPageContentPlanner\nfrom ProjectPageAgent.html_generator import ProjectPageHTMLGenerator, to_url\nfrom utils.wei_utils import get_agent_config\nimport os\nimport subprocess\n\nfrom ProjectPageAgent.content_planner import filter_references\nfrom utils.src.utils import run_sync_screenshots\nfrom ProjectPageAgent.main_pipline import matching, copy_static_files\n\nload_dotenv()\n\n\n\ndef get_agent_config_with_keys(model_type, openai_api_key=\"\", gemini_api_key=\"\", \n                              qwen_api_key=\"\", zhipuai_api_key=\"\", openrouter_api_key=\"\"):\n    \"\"\"\n    Get agent configuration with user-provided API keys.\n    Falls back to environment variables if user keys are not provided.\n    Note: This function sets environment variables but does NOT restore them.\n    The environment variables will remain set for the duration of the application.\n    \"\"\"\n    # Set environment variables with user-provided keys\n    api_keys = {\n        'OPENAI_API_KEY': openai_api_key,\n        'GEMINI_API_KEY': gemini_api_key, \n        'QWEN_API_KEY': qwen_api_key,\n        'ZHIPUAI_API_KEY': zhipuai_api_key,\n        'OPENROUTER_API_KEY': openrouter_api_key\n    }\n    \n    # Set new API keys in environment\n    for key, value in api_keys.items():\n        if value and value.strip():\n            os.environ[key] = value\n    \n    # Get agent config with the new API keys\n    config = get_agent_config(model_type)\n    return config\n\ndef validate_api_keys(model_name_t, model_name_v, openai_api_key, gemini_api_key, \n                     qwen_api_key, zhipuai_api_key, openrouter_api_key):\n    \"\"\"\n    Validate that required API keys are provided for the selected models.\n    \"\"\"\n    errors = []\n    \n    # Check text model requirements\n    if model_name_t in ['4o', '4o-mini', 'gpt-4.1', 'gpt-4.1-mini', 'o1', 'o3', 'o3-mini']:\n        if not openai_api_key or not openai_api_key.strip():\n            errors.append(\"OpenAI API key is required for GPT models\")\n    elif model_name_t in ['gemini', 'gemini-2.5-pro', 'gemini-2.5-flash']:\n        if not gemini_api_key or not gemini_api_key.strip():\n            errors.append(\"Gemini API key is required for Gemini models\")\n    elif model_name_t in ['qwen', 'qwen-plus', 'qwen-max', 'qwen-long']:\n        if not qwen_api_key or not qwen_api_key.strip():\n            errors.append(\"Qwen API key is required for Qwen models\")\n    elif model_name_t.startswith('openrouter_'):\n        if not openrouter_api_key or not openrouter_api_key.strip():\n            errors.append(\"OpenRouter API key is required for OpenRouter models\")\n    \n    # Check vision model requirements\n    if model_name_v in ['4o', '4o-mini']:\n        if not openai_api_key or not openai_api_key.strip():\n            errors.append(\"OpenAI API key is required for GPT vision models\")\n    elif model_name_v in ['gemini', 'gemini-2.5-pro', 'gemini-2.5-flash']:\n        if not gemini_api_key or not gemini_api_key.strip():\n            errors.append(\"Gemini API key is required for Gemini vision models\")\n    elif model_name_v in ['qwen-vl-max', 'qwen-2.5-vl-72b']:\n        if not qwen_api_key or not qwen_api_key.strip():\n            errors.append(\"Qwen API key is required for Qwen vision models\")\n    elif model_name_v.startswith('openrouter_'):\n        if not openrouter_api_key or not openrouter_api_key.strip():\n            errors.append(\"OpenRouter API key is required for OpenRouter vision models\")\n    \n    return errors\n\n# Global Variables\ncurrent_html_dir = None\npreview_server = None\npreview_port = None\ntemplate_preview_servers = []\n\nclass CustomHTTPRequestHandler(SimpleHTTPRequestHandler):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, directory=current_html_dir, **kwargs)\n    \n    def log_message(self, format, *args):\n        pass\n\ndef find_free_port(start_port=8000, max_attempts=100):\n    for port in range(start_port, start_port + max_attempts):\n        try:\n            with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n                s.bind(('', port))\n                return port\n        except OSError:\n            continue\n    raise RuntimeError(f\"Could not find available port\")\n\ndef start_preview_server(html_dir):\n    global current_html_dir, preview_server, preview_port\n    stop_preview_server()\n    current_html_dir = html_dir\n    preview_port = find_free_port()\n    preview_server = HTTPServer(('0.0.0.0', preview_port), CustomHTTPRequestHandler)\n    server_thread = Thread(target=preview_server.serve_forever, daemon=True)\n    server_thread.start()\n    return preview_port\n\ndef stop_preview_server():\n    global preview_server, preview_port\n    if preview_server:\n        preview_server.shutdown()\n        preview_server = None\n        preview_port = None\n\ndef start_ephemeral_server_for_dir(html_dir):\n    port = find_free_port()\n    class _TempHandler(SimpleHTTPRequestHandler):\n        def __init__(self, *args, **kwargs):\n            super().__init__(*args, directory=html_dir, **kwargs)\n        def log_message(self, format, *args):\n            pass\n    srv = HTTPServer(('0.0.0.0', port), _TempHandler)\n    t = Thread(target=srv.serve_forever, daemon=True)\n    t.start()\n    template_preview_servers.append((srv, port))\n    return port\n\ndef stop_all_template_preview_servers():\n    global template_preview_servers\n    for srv, _ in template_preview_servers:\n        try:\n            srv.shutdown()\n        except Exception:\n            pass\n    template_preview_servers = []\n\nclass GenerationArgs:\n    def __init__(self, paper_path, model_name_t, model_name_v, template_root, \n                 template_dir, template_file, output_dir, style_preference, tmp_dir,\n                 full_content_check_times, background_color, has_navigation, \n                 has_hero_section, title_color, page_density, image_layout, \n                 html_check_times, resume, human_input):\n        self.paper_path = paper_path\n        self.model_name_t = model_name_t\n        self.model_name_v = model_name_v\n        self.template_root = template_root\n        self.template_dir = template_dir\n        self.template_file = template_file\n        self.output_dir = output_dir\n        self.style_preference = style_preference\n        self.tmp_dir = tmp_dir\n        self.full_content_check_times = full_content_check_times\n        self.background_color = background_color\n        self.has_navigation = has_navigation\n        self.has_hero_section = has_hero_section\n        self.title_color = title_color\n        self.page_density = page_density\n        self.image_layout = image_layout\n        self.html_check_times = html_check_times\n        self.resume = resume\n        self.human_input = human_input\n        self.paper_name = None\n\n# ==================== Formatting Functions ====================\n\ndef format_section_to_markdown(section_data):\n    \"\"\"\n    Convert Section JSON to beautifully formatted Markdown\n    \n    Args:\n        section_data: Section JSON data\n    \n    Returns:\n        str: Formatted Markdown string\n    \"\"\"\n    if not section_data:\n        return \"No data available\"\n    \n    md_lines = []\n    \n    # Title\n    md_lines.append(\"# 📄 Paper Page Structure Preview\\n\")\n    \n    # Basic Information\n    if \"title\" in section_data:\n        md_lines.append(f\"## 📌 Title\\n**{section_data['title']}**\\n\")\n    \n    if \"authors\" in section_data:\n        md_lines.append(f\"## 👥 Authors\\n{section_data['authors']}\\n\")\n    \n    if \"affiliation\" in section_data:\n        md_lines.append(f\"## 🏛️ Affiliation\\n{section_data['affiliation']}\\n\")\n    \n    # Other Sections\n    md_lines.append(\"## 📑 Page Sections\\n\")\n    \n    section_count = 0\n    for key, value in section_data.items():\n        if key in [\"title\", \"authors\", \"affiliation\"]:\n            continue\n        \n        section_count += 1\n        \n        # Section Title\n        section_title = key.replace(\"_\", \" \").title()\n        md_lines.append(f\"### {section_count}. {section_title}\\n\")\n        \n        # Section Content\n        if isinstance(value, dict):\n            # If dictionary, process recursively\n            for sub_key, sub_value in value.items():\n                sub_title = sub_key.replace(\"_\", \" \").title()\n                md_lines.append(f\"**{sub_title}**: {sub_value}\\n\")\n        elif isinstance(value, list):\n            # If list\n            for item in value:\n                if isinstance(item, str):\n                    md_lines.append(f\"- {item}\\n\")\n                elif isinstance(item, dict):\n                    for k, v in item.items():\n                        md_lines.append(f\"- **{k}**: {v}\\n\")\n        else:\n            # Simple value\n            md_lines.append(f\"{value}\\n\")\n        \n        md_lines.append(\"\")  # Empty line\n    \n    # Add Statistics\n    md_lines.append(\"---\\n\")\n    md_lines.append(f\"**📊 Total {section_count} sections**\\n\")\n    \n    return \"\\n\".join(md_lines)\n\n\ndef format_full_content_to_markdown(content_data, figures=None):\n    \"\"\"\n    Convert Full Content JSON to beautifully formatted Markdown\n    \n    Args:\n        content_data: Full Content JSON data\n        figures: Images and tables data (optional)\n    \n    Returns:\n        str: Formatted Markdown string\n    \"\"\"\n    if not content_data:\n        return \"No data available\"\n    \n    md_lines = []\n    \n    # Title\n    md_lines.append(\"# 📄 Full Content Preview\\n\")\n    \n    # Basic Information\n    if \"title\" in content_data:\n        md_lines.append(f\"# {content_data['title']}\\n\")\n    \n    if \"authors\" in content_data:\n        md_lines.append(f\"**Authors**: {content_data['authors']}\\n\")\n    \n    if \"affiliation\" in content_data:\n        md_lines.append(f\"**Affiliation**: {content_data['affiliation']}\\n\")\n    \n    md_lines.append(\"---\\n\")\n    \n    # Process Each Section\n    section_count = 0\n    image_count = 0\n    table_count = 0\n    \n    for key, value in content_data.items():\n        if key in [\"title\", \"authors\", \"affiliation\"]:\n            continue\n        \n        section_count += 1\n        \n        # Section Title\n        section_title = key.replace(\"_\", \" \").title()\n        md_lines.append(f\"## {section_count}. {section_title}\\n\")\n        \n        # Process Content\n        if isinstance(value, dict):\n            # Process dictionary type content\n            for sub_key, sub_value in value.items():\n                if sub_key.lower() in ['content', 'description', 'text']:\n                    # Main text content\n                    md_lines.append(f\"{sub_value}\\n\")\n                elif sub_key.lower() in ['image', 'figure', 'img']:\n                    # Image\n                    image_count += 1\n                    if isinstance(sub_value, dict):\n                        caption = sub_value.get('caption', f'Figure {image_count}')\n                        path = sub_value.get('path', '')\n                        md_lines.append(f\"\\n**🖼️ {caption}**\\n\")\n                        if path:\n                            md_lines.append(f\"*Image path: `{path}`*\\n\")\n                    else:\n                        md_lines.append(f\"\\n**🖼️ Figure {image_count}**: {sub_value}\\n\")\n                elif sub_key.lower() in ['table']:\n                    # Table\n                    table_count += 1\n                    md_lines.append(f\"\\n**📊 Table {table_count}**\\n\")\n                    if isinstance(sub_value, dict):\n                        caption = sub_value.get('caption', f'Table {table_count}')\n                        md_lines.append(f\"*{caption}*\\n\")\n                    else:\n                        md_lines.append(f\"{sub_value}\\n\")\n                elif sub_key.lower() in ['code']:\n                    # Code block\n                    md_lines.append(f\"\\n```\\n{sub_value}\\n```\\n\")\n                else:\n                    # Other subtitles\n                    sub_title = sub_key.replace(\"_\", \" \").title()\n                    md_lines.append(f\"\\n### {sub_title}\\n\")\n                    md_lines.append(f\"{sub_value}\\n\")\n        \n        elif isinstance(value, list):\n            # Process list type content\n            for idx, item in enumerate(value):\n                if isinstance(item, dict):\n                    # Dictionary items in list\n                    if 'title' in item or 'name' in item:\n                        item_title = item.get('title', item.get('name', f'Item {idx+1}'))\n                        md_lines.append(f\"\\n### {item_title}\\n\")\n                    \n                    for k, v in item.items():\n                        if k not in ['title', 'name']:\n                            if k.lower() in ['content', 'description', 'text']:\n                                md_lines.append(f\"{v}\\n\")\n                            elif k.lower() in ['image', 'figure']:\n                                image_count += 1\n                                md_lines.append(f\"\\n**🖼️ Figure {image_count}**: {v}\\n\")\n                            elif k.lower() == 'table':\n                                table_count += 1\n                                md_lines.append(f\"\\n**📊 Table {table_count}**: {v}\\n\")\n                            else:\n                                k_title = k.replace(\"_\", \" \").title()\n                                md_lines.append(f\"**{k_title}**: {v}\\n\")\n                else:\n                    # Simple list item\n                    md_lines.append(f\"- {item}\\n\")\n        \n        else:\n            # Simple text value\n            md_lines.append(f\"{value}\\n\")\n        \n        md_lines.append(\"\")  # Empty line between sections\n    \n    # Add Statistics\n    md_lines.append(\"\\n---\\n\")\n    stats = []\n    stats.append(f\"📊 **Statistics**\")\n    stats.append(f\"- Sections: {section_count}\")\n    if image_count > 0:\n        stats.append(f\"- Images: {image_count}\")\n    if table_count > 0:\n        stats.append(f\"- Tables: {table_count}\")\n    \n    # If figures data is provided, add more information\n    if figures:\n        if 'images' in figures and figures['images']:\n            stats.append(f\"- Available images: {len(figures['images'])}\")\n        if 'tables' in figures and figures['tables']:\n            stats.append(f\"- Available tables: {len(figures['tables'])}\")\n    \n    md_lines.append(\"\\n\".join(stats))\n    md_lines.append(\"\\n\")\n    \n    return \"\\n\".join(md_lines)\n\n# ==================== Global State Management ====================\n\nclass GenerationState:\n    def __init__(self):\n        self.reset()\n    \n    def reset(self):\n        self.args = None\n        self.paper_content = None\n        self.figures = None\n        self.generated_section = None\n        self.text_page_content = None\n        self.generated_content = None\n        self.html_content = None\n        self.html_file_path = None\n        self.html_dir = None\n        self.planner = None\n        self.html_generator = None\n        self.agent_config_t = None\n        self.total_input_tokens_t = 0\n        self.total_output_tokens_t = 0\n        self.current_stage = \"init\"\n        self.preview_url = None\n\nstate = GenerationState()\n\ndef create_project_zip(project_dir, output_dir, paper_name):\n    \"\"\"\n    Create project archive\n    \n    Args:\n        project_dir: Project directory path\n        output_dir: Output directory\n        paper_name: Paper name\n    \n    Returns:\n        str: Archive path, None if failed\n    \"\"\"\n    import zipfile\n    \n    zip_filename = f\"{paper_name}_project_page.zip\"\n    zip_path = os.path.join(output_dir, zip_filename)\n    \n    print(f\"Creating project archive: {zip_path}\")\n    \n    try:\n        with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:\n            # Traverse project directory, add all files\n            for root, dirs, files in os.walk(project_dir):\n                for file in files:\n                    file_path = os.path.join(root, file)\n                    # Calculate relative path\n                    arcname = os.path.relpath(file_path, output_dir)\n                    zipf.write(file_path, arcname)\n                    \n        print(f\"Archive created successfully: {zip_path}\")\n        \n        # Get archive size\n        zip_size = os.path.getsize(zip_path)\n        zip_size_mb = zip_size / (1024 * 1024)\n        print(f\"Archive size: {zip_size_mb:.2f} MB\")\n        \n        return zip_path\n        \n    except Exception as e:\n        print(f\"Archive creation failed: {e}\")\n        return None\n\ndef start_generation(pdf_file, model_name_t, model_name_v, template_root, \n                    template_dir, template_file, output_dir, style_preference, \n                    tmp_dir, full_content_check_times, background_color, \n                    has_navigation, has_hero_section, title_color, page_density, \n                    image_layout, html_check_times, resume, human_input, \n                    template_choice_value, openai_api_key, gemini_api_key, \n                    qwen_api_key, zhipuai_api_key, openrouter_api_key):\n    \"\"\"Start generation process\"\"\"\n    if pdf_file is None:\n        return \"❌ Please upload a PDF file\", gr.update(visible=False), \"\", \"\", gr.update(), gr.update(), \"\"\n    \n    # Validate API keys\n    validation_errors = validate_api_keys(\n        model_name_t, model_name_v, openai_api_key, gemini_api_key, \n        qwen_api_key, zhipuai_api_key, openrouter_api_key\n    )\n    \n    if validation_errors:\n        error_msg = \"❌ API Key Validation Failed:\\n\" + \"\\n\".join(f\"• {error}\" for error in validation_errors)\n        return error_msg, gr.update(visible=False), \"\", \"\", gr.update(), gr.update(), \"\"\n    \n    state.reset()\n    \n    # Handle template selection\n    if not (template_dir and str(template_dir).strip()):\n        if not template_choice_value:\n            stop_all_template_preview_servers()\n            template_requirement = {\n                \"background_color\": background_color,\n                \"has_hero_section\": has_hero_section,\n                \"Page density\": page_density,\n                \"image_layout\": image_layout,\n                \"has_navigation\": has_navigation,\n                \"title_color\": title_color\n            }\n            try:\n                matched = matching(template_requirement)\n            except Exception as e:\n                return f\"❌ Template recommendation failed: {e}\", gr.update(visible=False), \"\", \"\", gr.update(choices=[], value=None), gr.update(visible=False, value=\"\"), \"\"\n            \n            html_finder_ = HtmlFinder()\n            with open('templates/template_link.json','r') as f:\n                template_link = json.load(f)\n            previews = []\n            for name in matched:\n                t_dir = os.path.join(template_root, name)\n                try:\n                    html_path = html_finder_.find_html(t_dir)\n                    if not os.path.exists(html_path):\n                        continue\n                    html_dir = os.path.dirname(os.path.abspath(html_path))\n                    filename = os.path.basename(html_path)\n                    port = start_ephemeral_server_for_dir(html_dir)\n                    url = template_link[name]\n                    previews.append((name, html_path, url))\n                except Exception:\n                    continue\n            \n            if not previews:\n                return \"❌ No previewable templates found\", gr.update(visible=False), \"\", \"\", gr.update(choices=[], value=None), gr.update(visible=False, value=\"\"), \"\"\n            \n            md_lines = [\"### 🔍 Please select a template to preview before clicking **Start Generation**\", \"\"]\n            for name, _, url in previews:\n                md_lines.append(f\"- **{name}** → [{url}]({url})\")\n            md = \"\\n\".join(md_lines)\n            \n            return \"Recommended 3 templates, please select one to continue\", gr.update(visible=False), \"\", \"\", gr.update(choices=[n for n, _, _ in previews], value=None), gr.update(visible=True, value=md), \"\"\n        \n        template_dir = os.path.join(template_root, template_choice_value)\n    \n    # Create arguments object\n    args = GenerationArgs(\n        paper_path=pdf_file.name,\n        model_name_t=model_name_t,\n        model_name_v=model_name_v,\n        template_root=template_root,\n        template_dir=template_dir,\n        template_file=template_file,\n        output_dir=output_dir,\n        style_preference=style_preference,\n        tmp_dir=tmp_dir,\n        full_content_check_times=full_content_check_times,\n        background_color=background_color,\n        has_navigation=has_navigation,\n        has_hero_section=has_hero_section,\n        title_color=title_color,\n        page_density=page_density,\n        image_layout=image_layout,\n        html_check_times=html_check_times,\n        resume=resume,\n        human_input=human_input\n    )\n    \n    if not args.template_dir:\n        return \"❌ Please select a template\", gr.update(visible=False), \"\", \"\", gr.update(), gr.update(), \"\"\n    \n    if not args.template_file:\n        html_finder_ = HtmlFinder()\n        args.template_file = html_finder_.find_html(args.template_dir)\n    \n    paper_name = args.paper_path.split('/')[-1].replace('.pdf', '') if '/' in args.paper_path else args.paper_path.replace('.pdf', '')\n    args.paper_name = paper_name\n    \n    os.makedirs(args.tmp_dir, exist_ok=True)\n    \n    try:\n        # Initialization\n        agent_config_t = get_agent_config_with_keys(\n            args.model_name_t, openai_api_key, gemini_api_key, \n            qwen_api_key, zhipuai_api_key, openrouter_api_key\n        )\n        state.agent_config_t = agent_config_t\n        state.args = args\n        \n        # Step 1: Parse PDF\n        print(\"=\"*50)\n        print(\"STEP 1: Parsing Research Paper\")\n        print(\"=\"*50)\n        \n        raw_content_path = f'project_contents/{args.paper_name}_raw_content.json'\n        if not os.path.exists(raw_content_path):\n            agent_config_v = get_agent_config_with_keys(\n                args.model_name_v, openai_api_key, gemini_api_key, \n                qwen_api_key, zhipuai_api_key, openrouter_api_key\n            )\n            input_token, output_token, raw_result, images, tables = parse_paper_for_project_page(args, agent_config_t)\n            state.total_input_tokens_t += input_token\n            state.total_output_tokens_t += output_token\n            raw_content_path, _ = save_parsed_content(args, raw_result, images, tables, input_token, output_token)\n        \n        with open(raw_content_path, 'r') as f:\n            paper_content = json.load(f)\n        \n        images = paper_content.get('images', [])\n        tables = paper_content.get('tables', [])\n        figures = {'images': images, 'tables': tables}\n        paper_content = paper_content.get('markdown_content', \"\")\n        \n        state.paper_content = paper_content\n        state.figures = figures\n        \n        # Step 2: Filter content\n        print(\"=\"*50)\n        print(\"STEP 2: Filtering Content\")\n        print(\"=\"*50)\n        \n        planner = ProjectPageContentPlanner(agent_config_t, args)\n        state.planner = planner\n        \n        paper_content, figures, input_token, output_token = planner.filter_raw_content(paper_content, figures)\n        state.total_input_tokens_t += input_token\n        state.total_output_tokens_t += output_token\n        state.paper_content = paper_content\n        state.figures = figures\n        \n        # Step 3: Generate Section\n        print(\"=\"*50)\n        print(\"STEP 3: Generating Sections\")\n        print(\"=\"*50)\n        \n        state.current_stage = \"section\"\n        \n        generated_section, input_token, output_token = generate_section_initial()\n        state.total_input_tokens_t += input_token\n        state.total_output_tokens_t += output_token\n        \n        # Use Markdown formatting\n        section_display_md = format_section_to_markdown(generated_section)\n        section_display_json = json.dumps(generated_section, indent=2, ensure_ascii=False)\n        \n        return (\n            f\"✅ Section generation completed, please review and provide feedback\\n\\nTokens: {input_token} → {output_token}\",\n            gr.update(visible=True),  # feedback_section\n            section_display_md,       # Markdown format\n            section_display_json,     # JSON format (hidden)\n            gr.update(),\n            gr.update(visible=False, value=\"\"),\n            \"\"\n        )\n        \n    except Exception as e:\n        import traceback\n        error_msg = f\"❌ Generation failed: {str(e)}\\n{traceback.format_exc()}\"\n        return error_msg, gr.update(visible=False), \"\", \"\", gr.update(), gr.update(), \"\"\n\ndef create_dynamic_page_dict(sections):\n        poster_dict = {\n            \"title\": \"Title of the paper\",\n            \"authors\": \"Authors of the paper\",\n            \"affiliation\": \"Affiliation of the authors\",\n        }\n        poster_dict.update(sections)\n        return poster_dict\ndef generate_section_initial():\n    \"\"\"Generate initial Section\"\"\"\n    import yaml\n    from jinja2 import Environment, StrictUndefined\n    from utils.wei_utils import account_token\n    from utils.src.utils import get_json_from_response\n    \n    with open('utils/prompt_templates/page_templates/section_generation.yaml', 'r') as f:\n        planner_config = yaml.safe_load(f)\n    \n    jinja_env = Environment(undefined=StrictUndefined)\n    template = jinja_env.from_string(planner_config[\"template\"])\n    \n    jinja_args = {\n        'paper_content': state.paper_content,\n        'json_format_example': json.dumps(state.paper_content, indent=2)\n    }\n    \n    prompt = template.render(**jinja_args)\n    \n    state.planner.planner_agent.reset()\n    response = state.planner.planner_agent.step(prompt)\n    input_token, output_token = account_token(response)\n    generated_section = get_json_from_response(response.msgs[0].content)\n    generated_section = create_dynamic_page_dict(generated_section)\n    state.generated_section = generated_section\n    \n    generated_path = f'project_contents/{state.args.paper_name}_generated_section.json'\n    with open(generated_path, 'w') as f:\n        json.dump(generated_section, f, indent=4)\n    \n    return generated_section, input_token, output_token\n\ndef submit_section_feedback(feedback_text):\n    \"\"\"Submit Section feedback\"\"\"\n    if not feedback_text or feedback_text.strip().lower() == 'yes':\n        # User satisfied, proceed to next stage\n        result = proceed_to_text_content()\n        status, fc_section_visible, fc_display_visible, fc_display_md, fc_display_json, fc_feedback_visible = result\n        return (\n            status,\n            \"\",  # section_display_md clear\n            \"\",  # section_display_json clear\n            \"\",  # section_feedback_input clear\n            gr.update(visible=False),  # feedback_section hide\n            fc_section_visible,  # feedback_full_content show\n            fc_display_visible,  # full_content_display_md show\n            fc_display_md,  # full_content_display_md content\n            fc_display_json,  # full_content_display_json content\n            fc_feedback_visible  # full_content_feedback_input show\n        )\n    \n    # User provides feedback, modify Section\n    from camel.messages import BaseMessage\n    from utils.wei_utils import account_token\n    from utils.src.utils import get_json_from_response\n    \n    message = BaseMessage.make_assistant_message(\n        role_name='User',\n        content=f'human feedback: {feedback_text}\\n\\nPlease make modifications based on this feedback. Output format as specified above.'\n    )\n    response = state.planner.planner_agent.step(message)\n    input_token, output_token = account_token(response)\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    generated_section = get_json_from_response(response.msgs[0].content)\n    generated_section = create_dynamic_page_dict(generated_section)\n    state.generated_section = generated_section\n    \n    generated_path = f'project_contents/{state.args.paper_name}_generated_section.json'\n    with open(generated_path, 'w') as f:\n        json.dump(generated_section, f, indent=4)\n    \n    # Use Markdown formatting\n    section_display_md = format_section_to_markdown(generated_section)\n    section_display_json = json.dumps(generated_section, indent=2, ensure_ascii=False)\n    \n    return (\n        f\"✅ Section updated, please continue reviewing\\n\\nTokens: {input_token} → {output_token}\",\n        section_display_md,  # Markdown format\n        section_display_json,  # JSON format\n        \"\",  # Clear input box\n        gr.update(visible=True),  # feedback_section keep visible\n        gr.update(visible=False),  # feedback_full_content keep hidden\n        gr.update(visible=False),  # full_content_display_md keep hidden\n        \"\",  # full_content_display_md content\n        \"\",  # full_content_display_json content\n        gr.update(visible=False)  # full_content_feedback_input keep hidden\n    )\n\ndef proceed_to_text_content():\n    \"\"\"Enter Text Content generation stage\"\"\"\n    print(\"=\"*50)\n    print(\"STEP 4: Generating Text Content\")\n    print(\"=\"*50)\n    \n    text_page_content, input_token, output_token = state.planner.text_content_generation(\n        state.paper_content, state.figures, state.generated_section\n    )\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    state.text_page_content = text_page_content\n    \n    # Enter Full Content stage\n    return proceed_to_full_content()\n\ndef proceed_to_full_content():\n    \"\"\"Enter Full Content generation stage\"\"\"\n    print(\"=\"*50)\n    print(\"STEP 5: Generating Full Content\")\n    print(\"=\"*50)\n    \n    state.current_stage = \"full_content\"\n    \n    generated_content, input_token, output_token = generate_full_content_initial()\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    # Use Markdown formatting\n    content_display_md = format_full_content_to_markdown(generated_content, state.figures)\n    content_display_json = json.dumps(generated_content, indent=2, ensure_ascii=False)\n    \n    return (\n        f\"✅ Full Content generation completed, please review and provide feedback\\n\\nTokens: {input_token} → {output_token}\",\n        gr.update(visible=True),   # feedback_full_content show\n        gr.update(visible=True),   # full_content_display_md show\n        content_display_md,        # Markdown format\n        content_display_json,      # JSON format\n        gr.update(visible=True)    # full_content_feedback_input show\n    )\n\ndef generate_full_content_initial():\n    \"\"\"Generate initial Full Content\"\"\"\n    import yaml\n    from jinja2 import Environment, StrictUndefined\n    from utils.wei_utils import account_token\n    from utils.src.utils import get_json_from_response\n    \n    with open('utils/prompt_templates/page_templates/full_content_generation.yaml', 'r') as f:\n        planner_config = yaml.safe_load(f)\n    \n    jinja_env = Environment(undefined=StrictUndefined)\n    template = jinja_env.from_string(planner_config[\"template\"])\n    \n    jinja_args = {\n        'paper_content': state.paper_content,\n        'figures': json.dumps(state.figures, indent=2),\n        'project_page_content': json.dumps(state.text_page_content, indent=2)\n    }\n    \n    prompt = template.render(**jinja_args)\n    \n    state.planner.planner_agent.reset()\n    response = state.planner.planner_agent.step(prompt)\n    input_token, output_token = account_token(response)\n    generated_content = get_json_from_response(response.msgs[0].content)\n    \n    state.generated_content = generated_content\n    \n    first_path = f'project_contents/{state.args.paper_name}_generated_full_content.v0.json'\n    with open(first_path, 'w', encoding='utf-8') as f:\n        json.dump(generated_content, f, ensure_ascii=False, indent=2)\n    \n    return generated_content, input_token, output_token\n\ndef submit_full_content_feedback(feedback_text):\n    \"\"\"Submit Full Content feedback\"\"\"\n    if not feedback_text or feedback_text.strip().lower() == 'yes':\n        # User satisfied, proceed to HTML generation\n        result = proceed_to_html_generation()\n        status, html_feedback_visible, preview_info, preview_url, open_btn_visible = result\n        return (\n            status,\n            \"\",  # full_content_display_md clear\n            \"\",  # full_content_display_json clear\n            \"\",  # full_content_feedback_input clear\n            gr.update(visible=False),  # feedback_full_content hide\n            html_feedback_visible,  # feedback_html show\n            preview_info,  # preview_info_display\n            preview_url,  # preview_url_state\n            open_btn_visible  # open_preview_btn show\n        )\n    \n    # User provides feedback\n    from camel.messages import BaseMessage\n    from utils.wei_utils import account_token\n    from utils.src.utils import get_json_from_response\n    \n    message = BaseMessage.make_assistant_message(\n        role_name='User',\n        content=f'human feedback: {feedback_text}\\n\\nPlease make modifications based on this feedback. Output format as specified above.'\n    )\n    response = state.planner.planner_agent.step(message)\n    input_token, output_token = account_token(response)\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    generated_content = get_json_from_response(response.msgs[0].content)\n    state.generated_content = generated_content\n    \n    final_path = f'project_contents/{state.args.paper_name}_generated_full_content.json'\n    with open(final_path, 'w', encoding='utf-8') as f:\n        json.dump(generated_content, f, ensure_ascii=False, indent=2)\n    \n    # Use Markdown formatting\n    content_display_md = format_full_content_to_markdown(generated_content, state.figures)\n    content_display_json = json.dumps(generated_content, indent=2, ensure_ascii=False)\n    \n    return (\n        f\"✅ Full Content updated, please continue reviewing\\n\\nTokens: {input_token} → {output_token}\",\n        content_display_md,  # Markdown format\n        content_display_json,  # JSON format\n        \"\",  # Clear input box\n        gr.update(visible=True),  # feedback_full_content keep visible\n        gr.update(visible=False),  # feedback_html keep hidden\n        \"\",  # preview_info_display\n        \"\",  # preview_url_state\n        gr.update(visible=False)  # open_preview_btn keep hidden\n    )\n\ndef proceed_to_html_generation():\n    \"\"\"Enter HTML generation stage\"\"\"\n    print(\"=\"*50)\n    print(\"STEP 6: Generating HTML\")\n    print(\"=\"*50)\n    \n    state.current_stage = \"html\"\n    \n    # Copy static files\n    static_dir = copy_static_files(\n        state.args.template_file, \n        state.args.template_dir, \n        state.args.output_dir, \n        state.args.paper_name\n    )\n    \n    # Generate HTML\n    html_relative_path = os.path.relpath(state.args.template_file, state.args.template_dir)\n    html_dir = '/'.join(html_relative_path.strip().split('/')[:-1])\n    state.html_dir = html_dir\n    \n    html_generator = ProjectPageHTMLGenerator(state.agent_config_t, state.args)\n    state.html_generator = html_generator\n    \n    with open(state.args.template_file, 'r', encoding='utf-8') as file:\n        html_template = file.read()\n    \n    # Create assets directory\n    assets_dir = html_generator.create_assets_directory(state.args, html_dir, state.args.output_dir)\n    \n    # Generate HTML\n    html_content, input_token, output_token = html_generator.generate_complete_html(\n        state.args, state.generated_content, html_dir, html_template\n    )\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    # Save HTML (before table modification)\n    html_dir_path = os.path.join(state.args.output_dir, state.args.paper_name, html_dir)\n    os.makedirs(html_dir_path, exist_ok=True)\n    \n    html_file_path_no_modify = os.path.join(html_dir_path, 'index_no_modify_table.html')\n    with open(html_file_path_no_modify, 'w', encoding='utf-8') as file:\n        file.write(html_content)\n    \n    # Generate screenshot (before table modification)\n    screenshot_path_no_modify = os.path.join(html_dir_path, 'page_final_no_modify_table.png')\n    run_sync_screenshots(to_url(html_file_path_no_modify), screenshot_path_no_modify)\n    \n    # Modify tables\n    html_content, input_token, output_token = html_generator.modify_html_table(html_content, html_dir)\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    \n    state.html_content = html_content\n    \n    # Save HTML (after table modification)\n    html_file_path = os.path.join(html_dir_path, 'index.html')\n    with open(html_file_path, 'w', encoding='utf-8') as file:\n        file.write(html_content)\n    \n    state.html_file_path = html_file_path\n    \n    # Generate screenshot (after table modification)\n    run_sync_screenshots(\n        to_url(html_file_path), \n        os.path.join(html_dir_path, 'page_final.png')\n    )\n    \n    # Start preview server\n    html_full_dir = os.path.dirname(os.path.abspath(html_file_path))\n    port = start_preview_server(html_full_dir)\n    preview_url = f\"http://localhost:{port}/index.html\"\n    state.preview_url = preview_url\n    \n    # Create preview info display\n    preview_info = f\"\"\"\n### 🌐 HTML Generation Completed\n\n**Preview URL**: {preview_url}\n\n**Instructions**:\n1. Click the **\"🌐 Open Preview in New Tab\"** button below to view the generated webpage\n2. Carefully review the page in the new tab\n3. If satisfied, enter **'yes'** in the feedback box and submit\n4. If modifications are needed, provide detailed feedback and submit\n\n**Token Usage**: {input_token} → {output_token}\n\"\"\"\n    \n    return (\n        f\"✅ HTML generation completed\\n\\nTokens: {input_token} → {output_token}\",\n        gr.update(visible=True),   # feedback_html show\n        preview_info,              # preview_info_display\n        preview_url,               # preview_url_state\n        gr.update(visible=True)    # open_preview_btn show\n    )\n\ndef submit_html_feedback(feedback_text):\n    \"\"\"Submit HTML feedback\"\"\"\n    if not feedback_text or feedback_text.strip().lower() == 'yes':\n        # User satisfied, complete generation\n        result = finalize_generation()\n        status, html_file = result\n        return (\n            status,\n            \"\",  # preview_info_display clear\n            \"\",  # html_feedback_input clear\n            gr.update(visible=False),  # feedback_html hide\n            gr.update(visible=False),  # open_preview_btn hide\n            html_file  # html_file_output\n        )\n    \n    # User provides feedback\n    html_content, input_token, output_token = state.html_generator.modify_html_from_human_feedback(\n        state.html_content, feedback_text\n    )\n    state.total_input_tokens_t += input_token\n    state.total_output_tokens_t += output_token\n    state.html_content = html_content\n    \n    # Save updated HTML\n    html_dir_path = os.path.dirname(state.html_file_path)\n    \n    # Save as temporary version (for possible feedback iteration)\n    import time\n    timestamp = int(time.time())\n    html_file_feedback = os.path.join(html_dir_path, f'index_feedback_{timestamp}.html')\n    with open(html_file_feedback, 'w', encoding='utf-8') as file:\n        file.write(html_content)\n    \n    # Also update main file\n    with open(state.html_file_path, 'w', encoding='utf-8') as file:\n        file.write(html_content)\n    \n    # Regenerate screenshot\n    screenshot_path = os.path.join(html_dir_path, 'page_final.png')\n    try:\n        run_sync_screenshots(to_url(state.html_file_path), screenshot_path)\n    except Exception as e:\n        print(f\"Screenshot generation failed: {e}\")\n    \n    # Update preview info\n    preview_info = f\"\"\"\n### 🌐 HTML Updated\n\n**Preview URL**: {state.preview_url}\n\n**Instructions**:\n1. Click the **\"🌐 Open Preview in New Tab\"** button below to view the updated webpage\n2. **Refresh the browser** to see the latest version\n3. If satisfied, enter **'yes'** in the feedback box and submit\n4. If further modifications are needed, continue providing feedback\n\n**Token Usage**: {input_token} → {output_token}\n\"\"\"\n    \n    return (\n        f\"✅ HTML updated, please refresh the preview page\\n\\nTokens: {input_token} → {output_token}\",\n        preview_info,              # preview_info_display\n        \"\",  # Clear input box\n        gr.update(visible=True),   # feedback_html keep visible\n        gr.update(visible=True),   # open_preview_btn keep visible\n        None  # html_file_output no download yet\n    )\n\ndef finalize_generation():\n    \"\"\"Complete generation and save final results\"\"\"\n    import time\n    \n    # Ensure final HTML is saved\n    html_dir_path = os.path.dirname(state.html_file_path)\n    \n    # Save final version\n    final_html_path = os.path.join(html_dir_path, 'index_final.html')\n    with open(final_html_path, 'w', encoding='utf-8') as file:\n        file.write(state.html_content)\n    \n    # Also update main file\n    with open(state.html_file_path, 'w', encoding='utf-8') as file:\n        file.write(state.html_content)\n    \n    # Save metadata\n    metadata = state.html_generator.generate_metadata(state.generated_content, state.args)\n    metadata_path = state.html_generator.save_metadata(metadata, state.args, state.args.output_dir)\n    \n    # Create README file\n    readme_path = os.path.join(state.args.output_dir, state.args.paper_name, 'README.md')\n    readme_content = f\"\"\"# {state.args.paper_name} - Project Page\n\n## 📄 Project Information\n\n- **Paper Name**: {state.args.paper_name}\n- **Generation Time**: {time.strftime('%Y-%m-%d %H:%M:%S')}\n- **Text Model**: {state.args.model_name_t}\n- **Vision Model**: {state.args.model_name_v}\n\n## 🚀 Usage\n\n1. Extract this archive to any directory\n2. Open `index.html` to view the project page\n3. All resources (CSS, images, etc.) are included\n\n## 📁 File Structure\n\n- `index.html` - Main page file\n- `index_final.html` - Final confirmed version\n- `assets/` - Image and table resources\n- `css/` or `styles/` - Style files\n- `js/` or `scripts/` - JavaScript files\n- `metadata.json` - Page metadata\n- `generation_log.json` - Generation log\n\n## 💡 Tips\n\n- Recommended browsers: Chrome, Firefox, Safari, Edge\n- For web deployment, simply upload the entire folder\n- Feel free to modify HTML and CSS for customization\n\n---\nGenerated by Paper2ProjectPage\n\"\"\"\n    \n    with open(readme_path, 'w', encoding='utf-8') as f:\n        f.write(readme_content)\n    \n    # Save generation log\n    log_data = {\n        'paper_name': state.args.paper_name,\n        'paper_path': state.args.paper_path,\n        'models': {\n            'text_model': state.args.model_name_t,\n            'vision_model': state.args.model_name_v\n        },\n        'token_usage': {\n            'text_input_tokens': state.total_input_tokens_t,\n            'text_output_tokens': state.total_output_tokens_t\n        },\n        'output_files': {\n            'html_file': state.html_file_path,\n            'final_html_file': final_html_path,\n            'metadata_file': metadata_path,\n            'readme_file': readme_path\n        },\n        'timestamp': time.strftime('%Y-%m-%d %H:%M:%S')\n    }\n    \n    log_path = f\"{state.args.output_dir}/{state.args.paper_name}/generation_log.json\"\n    with open(log_path, 'w') as f:\n        json.dump(log_data, f, indent=4, ensure_ascii=False)\n    \n    # Create project archive\n    project_dir = os.path.join(state.args.output_dir, state.args.paper_name)\n    zip_path = create_project_zip(project_dir, state.args.output_dir, state.args.paper_name)\n    \n    if zip_path and os.path.exists(zip_path):\n        # Get archive size\n        zip_size = os.path.getsize(zip_path)\n        zip_size_mb = zip_size / (1024 * 1024)\n        zip_filename = os.path.basename(zip_path)\n        \n        success_msg = f\"\"\"\n✅ Project page generation completed!\n\n📁 Output directory: {state.args.output_dir}/{state.args.paper_name}\n🌐 HTML file: {state.html_file_path}\n🌐 Final version: {final_html_path}\n📋 Metadata: {metadata_path}\n📖 README: {readme_path}\n📊 Log file: {log_path}\n📦 Archive: {zip_filename} ({zip_size_mb:.2f} MB)\n🔢 Total token usage: {state.total_input_tokens_t} → {state.total_output_tokens_t}\n\n🎉 All feedback completed, page successfully generated!\nClick the button below to download the complete project archive (including HTML, CSS, images, README, and all resources).\n\"\"\"\n        \n        return (\n            success_msg,\n            zip_path  # Return archive for download\n        )\n        \n    else:\n        error_msg = f\"\"\"\n⚠️ Project page generated, but archive creation failed!\n\n📁 Output directory: {state.args.output_dir}/{state.args.paper_name}\n🌐 HTML file: {state.html_file_path}\n📋 Metadata: {metadata_path}\n\nYou can manually retrieve all files from the output directory {project_dir}.\n\"\"\"\n        return (\n            error_msg,\n            state.html_file_path  # Return HTML file\n        )\n\n# ==================== Gradio Interface ====================\n\n# Custom CSS for better English font rendering\ncustom_css = \"\"\"\n@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap');\n\n* {\n    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif !important;\n}\n\ncode, pre, .code {\n    font-family: 'JetBrains Mono', 'Courier New', Consolas, Monaco, monospace !important;\n}\n\nh1, h2, h3, h4, h5, h6 {\n    font-weight: 600 !important;\n    letter-spacing: -0.02em !important;\n}\n\n.markdown-text {\n    line-height: 1.7 !important;\n    font-size: 15px !important;\n}\n\n.gr-button {\n    font-weight: 500 !important;\n    letter-spacing: 0.01em !important;\n}\n\n.gr-input, .gr-textarea {\n    font-size: 14px !important;\n    line-height: 1.6 !important;\n}\n\n.gr-box {\n    border-radius: 8px !important;\n}\n\n/* Better spacing for English content */\n.gr-markdown p {\n    margin-bottom: 0.8em !important;\n}\n\n.gr-markdown ul, .gr-markdown ol {\n    margin-left: 1.2em !important;\n}\n\n.gr-markdown li {\n    margin-bottom: 0.4em !important;\n}\n\"\"\"\n\nwith gr.Blocks(title=\"Paper2ProjectPage Generator\", theme=gr.themes.Soft(), css=custom_css) as demo:\n    \n    gr.Markdown(\"\"\"\n    # 📄 AutoPage Generator with Interactive Feedback\n    \n    Upload your research paper PDF and generate beautiful project pages through multi-round interactive feedback\n    \"\"\")\n    \n    with gr.Row():\n        with gr.Column(scale=1):\n            # PDF Upload\n            pdf_input = gr.File(\n                label=\"📎 Upload PDF Paper\",\n                file_types=[\".pdf\"],\n                type=\"filepath\"\n            )\n            \n            gr.Markdown(\"### 🔑 API Keys Configuration\")\n            gr.Markdown(\"\"\"\n            **⚠️ Security Notice**: Your API keys are only stored in memory during the session and are never saved to disk.\n            \n            **💡 Note**: You only need to fill in ONE API key corresponding to the models you selected below.\n            \n            **📋 How to get API keys:**\n            - **OpenAI**: Get your API key from [OpenAI Platform](https://platform.openai.com/api-keys)\n            - **Gemini**: Get your API key from [Google AI Studio](https://aistudio.google.com/app/apikey)\n            - **Qwen**: Get your API key from [DashScope](https://dashscope.console.aliyun.com/apiKey)\n            - **ZhipuAI**: Get your API key from [ZhipuAI Console](https://open.bigmodel.cn/usercenter/apikeys)\n            - **OpenRouter**: Get your API key from [OpenRouter](https://openrouter.ai/keys)\n            \n            **🚀 For HuggingFace Spaces**: You can also set these as environment variables in your Space settings.\n            \"\"\")\n            \n            with gr.Row():\n                openai_api_key = gr.Textbox(\n                    label=\"OpenAI API Key\",\n                    value=os.getenv(\"OPENAI_API_KEY\", \"\"),\n                    type=\"password\",\n                    placeholder=\"sk-...\",\n                    info=\"Required for GPT models\"\n                )\n                gemini_api_key = gr.Textbox(\n                    label=\"Gemini API Key\", \n                    value=os.getenv(\"GEMINI_API_KEY\", \"\"),\n                    type=\"password\",\n                    placeholder=\"AI...\",\n                    info=\"Required for Gemini models\"\n                )\n            \n            with gr.Row():\n                qwen_api_key = gr.Textbox(\n                    label=\"Qwen API Key\",\n                    value=os.getenv(\"QWEN_API_KEY\", \"\"),\n                    type=\"password\", \n                    placeholder=\"sk-...\",\n                    info=\"Required for Qwen models\"\n                )\n                zhipuai_api_key = gr.Textbox(\n                    label=\"ZhipuAI API Key\",\n                    value=os.getenv(\"ZHIPUAI_API_KEY\", \"\"),\n                    type=\"password\",\n                    placeholder=\"...\",\n                    info=\"Required for GLM models\"\n                )\n            \n            openrouter_api_key = gr.Textbox(\n                label=\"OpenRouter API Key\",\n                value=os.getenv(\"OPENROUTER_API_KEY\", \"\"),\n                type=\"password\",\n                placeholder=\"sk-or-...\",\n                info=\"Required for OpenRouter models\"\n            )\n            \n            gr.Markdown(\"### 🤖 Model Configuration\")\n            \n            # Text Model Options\n            text_model_options = [\n                (\"GPT-4o\", \"4o\"),\n                (\"GPT-4o Mini\", \"4o-mini\"),\n                (\"GPT-4.1\", \"gpt-4.1\"),\n                (\"GPT-4.1 Mini\", \"gpt-4.1-mini\"),\n                (\"O1\", \"o1\"),\n                (\"O3\", \"o3\"),\n                (\"O3 Mini\", \"o3-mini\"),\n                (\"Gemini 2.5 Pro\", \"gemini\"),\n                (\"Gemini 2.5 Pro (Alt)\", \"gemini-2.5-pro\"),\n                (\"Gemini 2.5 Flash\", \"gemini-2.5-flash\"),\n                (\"Qwen\", \"qwen\"),\n                (\"Qwen Plus\", \"qwen-plus\"),\n                (\"Qwen Max\", \"qwen-max\"),\n                (\"Qwen Long\", \"qwen-long\"),\n                (\"OpenRouter Qwen Plus\", \"openrouter_qwen-plus\"),\n                (\"OpenRouter GPT-4o Mini\", \"openrouter_gpt-4o-mini\"),\n                (\"OpenRouter Gemini 2.5 Flash\", \"openrouter_gemini-2.5-flash\"),\n                (\"OpenRouter O3\", \"openrouter_openai/o3\"),\n                (\"OpenRouter Claude Sonnet 4.5\", \"openrouter_claude-sonnet-4.5\"),\n            ]\n            \n            # Vision Model Options\n            vision_model_options = [\n                (\"GPT-4o\", \"4o\"),\n                (\"GPT-4o Mini\", \"4o-mini\"),\n                (\"Gemini 2.5 Pro\", \"gemini\"),\n                (\"Gemini 2.5 Pro (Alt)\", \"gemini-2.5-pro\"),\n                (\"Gemini 2.5 Flash\", \"gemini-2.5-flash\"),\n                (\"Qwen VL Max\", \"qwen-vl-max\"),\n                (\"Qwen 2.5 VL 72B\", \"qwen-2.5-vl-72b\"),\n                (\"OpenRouter Qwen VL 72B\", \"openrouter_qwen_vl_72b\"),\n                (\"OpenRouter Qwen VL 7B\", \"openrouter_qwen_vl_7b\"),\n                (\"OpenRouter Qwen VL Max\", \"openrouter_qwen-vl-max\"),\n                (\"OpenRouter Gemini 2.5 Flash\", \"openrouter_gemini-2.5-flash\"),\n            ]\n            \n            with gr.Row():\n                model_name_t = gr.Dropdown(\n                    label=\"Text Model\",\n                    choices=text_model_options,\n                    value=\"gemini\",\n                    info=\"Select model for text processing\"\n                )\n                model_name_v = gr.Dropdown(\n                    label=\"Vision Model\", \n                    choices=vision_model_options,\n                    value=\"gemini\",\n                    info=\"Select model for vision processing\"\n                )\n            \n            # Hidden backend configuration fields\n            template_root = gr.Textbox(\n                label=\"Template Root\",\n                value=\"templates\",\n                visible=False\n            )\n            template_dir = gr.Textbox(\n                label=\"Template Directory\",\n                value=\"\",\n                visible=False\n            )\n            template_file = gr.Textbox(\n                label=\"Template File\",\n                value=\"\",\n                visible=False\n            )\n            output_dir = gr.Textbox(\n                label=\"Output Directory\",\n                value=\"generated_project_pages\",\n                visible=False\n            )\n            style_preference = gr.Textbox(\n                label=\"Style Preference JSON\",\n                value=\"\",\n                visible=False\n            )\n            tmp_dir = gr.Textbox(\n                label=\"Temporary Directory\",\n                value=\"tmp\",\n                visible=False\n            )\n            \n            # Hidden parameters with default values\n            resume = gr.Radio(\n                label=\"Resume From Step\",\n                choices=['parse_pdf', 'generate_content','full_content_check', 'generate_html', 'html_check','modify_table','html_feedback'],\n                value='parse_pdf',\n                visible=False\n            )\n            \n            human_input = gr.Radio(\n                label=\"Enable Human Feedback\",\n                choices=[0, 1],\n                value=1,\n                visible=False\n            )\n            \n            full_content_check_times = gr.Number(\n                label=\"Full Content Check Times\",\n                value=1,\n                precision=0,\n                visible=False\n            )\n            \n            html_check_times = gr.Number(\n                label=\"HTML Check Times\",\n                value=1,\n                precision=0,\n                visible=False\n            )\n            \n        with gr.Column(scale=1):\n            gr.Markdown(\"\"\"\n            ### 💡 User Guide\n            \n            1. **Upload PDF**: Select your research paper PDF file\n            2. **Configure API Key**: Fill in ONE API key for your selected models\n            3. **Select Models**: Choose text and vision models from dropdowns\n            4. **Configure Style**: Adjust style preferences below\n            5. **Start Generation**: Click the \"Start Generation\" button\n            6. **Three-Stage Feedback**:\n               - 📝 **Section Feedback**: Review the generated page structure (Markdown preview + JSON data)\n               - 📄 **Full Content Feedback**: Review the generated complete content (Markdown preview + JSON data)\n               - 🌐 **HTML Feedback**: View the generated webpage in a new tab\n            7. **Download Results**: Download the complete project archive after completion\n            \n            ⚠️ **Tips**: \n            - Each stage supports multiple rounds of feedback until you're satisfied\n            - Enter 'yes' to proceed to the next stage\n            - The final ZIP download includes the complete project folder with all resources\n            \"\"\")\n            \n            gr.Markdown(\"### 🎨 Style Configuration\")\n            \n            with gr.Row():\n                with gr.Column():\n                    background_color = gr.Radio(\n                        label=\"Background Color\",\n                        choices=[\"light\", \"dark\"],\n                        value=\"light\",\n                        info=\"Background color theme\"\n                    )\n                    \n                    has_navigation = gr.Radio(\n                        label=\"Has Navigation\",\n                        choices=[\"yes\", \"no\"],\n                        value=\"yes\",\n                        info=\"Include navigation bar\"\n                    )\n                    \n                    has_hero_section = gr.Radio(\n                        label=\"Has Hero Section\",\n                        choices=[\"yes\", \"no\"],\n                        value=\"yes\",\n                        info=\"Include hero/header section\"\n                    )\n                \n                with gr.Column():\n                    title_color = gr.Radio(\n                        label=\"Title Color\",\n                        choices=[\"pure\", \"colorful\"],\n                        value=\"pure\",\n                        info=\"Title color style\"\n                    )\n                    \n                    page_density = gr.Radio(\n                        label=\"Page Density\",\n                        choices=[\"spacious\", \"compact\"],\n                        value=\"spacious\",\n                        info=\"Page spacing density\"\n                    )\n                    \n                    image_layout = gr.Radio(\n                        label=\"Image Layout\",\n                        choices=[\"rotation\", \"parallelism\"],\n                        value=\"parallelism\",\n                        info=\"Image layout style\"\n                    )\n            \n            gr.Markdown(\"### 📁 Template Selection\")\n            \n            template_choice = gr.Radio(\n                label=\"Recommended Templates\",\n                choices=[],\n                value=None,\n                info=\"Select from recommended templates\"\n            )\n            \n            template_preview_links = gr.Markdown(\n                value=\"\",\n                visible=False\n            )\n    \n    # Start Generation Button\n    start_btn = gr.Button(\"🚀 Start Generation\", variant=\"primary\", size=\"lg\")\n    \n    # Status Output\n    status_output = gr.Textbox(\n        label=\"📊 Generation Status\",\n        lines=5,\n        interactive=False\n    )\n    \n    # Section Feedback Area\n    with gr.Group(visible=False) as feedback_section:\n        gr.Markdown(\"### 📝 Section Generation Results\")\n        gr.Markdown(\"Please review the generated section structure. If satisfied, enter **'yes'**, otherwise provide modification feedback:\")\n        \n        with gr.Tabs():\n            with gr.Tab(\"📖 Preview (Markdown)\"):\n                section_display_md = gr.Markdown(\n                    label=\"Section Preview\",\n                    value=\"\"\n                )\n            with gr.Tab(\"📋 Raw Data (JSON)\"):\n                section_display_json = gr.Code(\n                    label=\"Section JSON\",\n                    language=\"json\",\n                    value=\"\",\n                    lines=15\n                )\n        \n        section_feedback_input = gr.TextArea(\n            label=\"Your Feedback\",\n            placeholder=\"Enter 'yes' to continue, or provide modification feedback...\",\n            lines=3\n        )\n        section_submit_btn = gr.Button(\"Submit Feedback\", variant=\"primary\")\n    \n    # Full Content Feedback Area\n    with gr.Group(visible=False) as feedback_full_content:\n        gr.Markdown(\"### 📄 Full Content Generation Results\")\n        gr.Markdown(\"Please review the generated full content. If satisfied, enter **'yes'**, otherwise provide modification feedback:\")\n        \n        with gr.Tabs():\n            with gr.Tab(\"📖 Preview (Markdown)\"):\n                full_content_display_md = gr.Markdown(\n                    label=\"Full Content Preview\",\n                    value=\"\"\n                )\n            with gr.Tab(\"📋 Raw Data (JSON)\"):\n                full_content_display_json = gr.Code(\n                    label=\"Full Content JSON\",\n                    language=\"json\",\n                    value=\"\",\n                    lines=15\n                )\n        \n        full_content_feedback_input = gr.TextArea(\n            label=\"Your Feedback\",\n            placeholder=\"Enter 'yes' to continue, or provide modification feedback...\",\n            lines=3\n        )\n        full_content_submit_btn = gr.Button(\"Submit Feedback\", variant=\"primary\")\n    \n    # HTML Feedback Area\n    with gr.Group(visible=False) as feedback_html:\n        gr.Markdown(\"### 🌐 HTML Generation Results\")\n        \n        # Preview Info Display\n        preview_info_display = gr.Markdown(\n            value=\"\",\n            label=\"Preview Information\"\n        )\n        \n        # Preview URL (hidden state for JS)\n        preview_url_state = gr.Textbox(visible=False)\n        \n        # Open Preview in New Tab Button\n        open_preview_btn = gr.Button(\n            \"🌐 Open Preview in New Tab\",\n            variant=\"secondary\",\n            size=\"lg\",\n            visible=False\n        )\n        \n        gr.Markdown(\"---\")\n        \n        # Feedback Input Area\n        html_feedback_input = gr.TextArea(\n            label=\"Your Feedback\",\n            placeholder=\"Enter 'yes' to finalize, or provide modification feedback...\",\n            lines=3\n        )\n        html_submit_btn = gr.Button(\"Submit Feedback\", variant=\"primary\")\n    \n    # Final Output\n    html_file_output = gr.File(\n        label=\"📥 Download Project Archive\",\n        interactive=False\n    )\n    \n    # Bind Events\n    start_btn.click(\n        fn=start_generation,\n        inputs=[\n            pdf_input, model_name_t, model_name_v, template_root,\n            template_dir, template_file, output_dir, style_preference,\n            tmp_dir, full_content_check_times, background_color,\n            has_navigation, has_hero_section, title_color, page_density,\n            image_layout, html_check_times, resume, human_input,\n            template_choice, openai_api_key, gemini_api_key, \n            qwen_api_key, zhipuai_api_key, openrouter_api_key\n        ],\n        outputs=[\n            status_output,\n            feedback_section,\n            section_display_md,\n            section_display_json,\n            template_choice,\n            template_preview_links,\n            section_feedback_input\n        ]\n    )\n    \n    section_submit_btn.click(\n        fn=submit_section_feedback,\n        inputs=[section_feedback_input],\n        outputs=[\n            status_output,\n            section_display_md,\n            section_display_json,\n            section_feedback_input,\n            feedback_section,\n            feedback_full_content,\n            full_content_display_md,\n            full_content_display_md,\n            full_content_display_json,\n            full_content_feedback_input\n        ]\n    )\n    \n    full_content_submit_btn.click(\n        fn=submit_full_content_feedback,\n        inputs=[full_content_feedback_input],\n        outputs=[\n            status_output,\n            full_content_display_md,\n            full_content_display_json,\n            full_content_feedback_input,\n            feedback_full_content,\n            feedback_html,\n            preview_info_display,\n            preview_url_state,\n            open_preview_btn\n        ]\n    )\n    \n    html_submit_btn.click(\n        fn=submit_html_feedback,\n        inputs=[html_feedback_input],\n        outputs=[\n            status_output,\n            preview_info_display,\n            html_feedback_input,\n            feedback_html,\n            open_preview_btn,\n            html_file_output\n        ]\n    )\n    \n    # Open Preview Button - Use JavaScript to open in new tab\n    open_preview_btn.click(\n        fn=None,\n        inputs=[preview_url_state],\n        outputs=None,\n        js=\"(url) => window.open(url, '_blank')\"\n    )\n\n# Launch Application\nif __name__ == \"__main__\":\n    demo.launch(\n        server_name=\"0.0.0.0\",\n        server_port=7860,\n        share=False,\n        show_error=True\n    )"}}, "camel/types/enums.py": {"bebd9007ee35871fc7dad316f1bf8efbee6dfe29": {"t": 1785674651.178309, "n": 70958, "h": "31041e19c42e5c5b", "c": "# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========\nimport os\nfrom enum import Enum, EnumMeta\nfrom typing import Union, cast\n\nfrom camel.logger import get_logger\nfrom camel.types.unified_model_type import UnifiedModelType\n\nlogger = get_logger(__name__)\n\n\nclass RoleType(Enum):\n    ASSISTANT = \"assistant\"\n    USER = \"user\"\n    CRITIC = \"critic\"\n    EMBODIMENT = \"embodiment\"\n    DEFAULT = \"default\"\n\n\nclass ModelType(UnifiedModelType, Enum):\n    DEFAULT = os.getenv(\"DEFAULT_MODEL_TYPE\", \"gpt-5-mini\")\n\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    GPT_4 = \"gpt-4\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    GPT_4_5_PREVIEW = \"gpt-4.5-preview\"\n    O1 = \"o1\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    O3_MINI = \"o3-mini\"\n    GPT_4_1 = \"gpt-4.1-2025-04-14\"\n    GPT_4_1_MINI = \"gpt-4.1-mini-2025-04-14\"\n    GPT_4_1_NANO = \"gpt-4.1-nano-2025-04-14\"\n    O4_MINI = \"o4-mini\"\n    O3 = \"o3\"\n    O3_PRO = \"o3-pro\"\n    GPT_5 = \"gpt-5\"\n    GPT_5_MINI = \"gpt-5-mini\"\n    GPT_5_NANO = \"gpt-5-nano\"\n\n    AWS_CLAUDE_3_7_SONNET = \"anthropic.claude-3-7-sonnet-20250219-v1:0\"\n    AWS_CLAUDE_3_5_SONNET = \"anthropic.claude-3-5-sonnet-20241022-v2:0\"\n    AWS_CLAUDE_3_HAIKU = \"anthropic.claude-3-haiku-20240307-v1:0\"\n    AWS_CLAUDE_3_SONNET = \"anthropic.claude-3-sonnet-20240229-v1:0\"\n    AWS_DEEPSEEK_R1 = \"us.deepseek.r1-v1:0\"\n    AWS_LLAMA_3_3_70B_INSTRUCT = \"us.meta.llama3-3-70b-instruct-v1:0\"\n    AWS_LLAMA_3_2_90B_INSTRUCT = \"us.meta.llama3-2-90b-instruct-v1:0\"\n    AWS_LLAMA_3_2_11B_INSTRUCT = \"us.meta.llama3-2-11b-instruct-v1:0\"\n    AWS_CLAUDE_SONNET_4 = \"anthropic.claude-sonnet-4-20250514-v1:0\"\n    AWS_CLAUDE_OPUS_4 = \"anthropic.claude-opus-4-20250514-v1:0\"\n    AWS_CLAUDE_OPUS_4_1 = \"anthropic.claude-opus-4-1-20250805-v1:0\"\n\n    AMD_GPT4 = \"dvue-aoai-001-gpt-4.1\"\n\n    GLM_4 = \"glm-4\"\n    GLM_4V = \"glm-4v\"\n    GLM_4V_FLASH = \"glm-4v-flash\"\n    GLM_4V_PLUS_0111 = \"glm-4v-plus-0111\"\n    GLM_4_PLUS = \"glm-4-plus\"\n    GLM_4_AIR = \"glm-4-air\"\n    GLM_4_AIR_0111 = \"glm-4-air-0111\"\n    GLM_4_AIRX = \"glm-4-airx\"\n    GLM_4_LONG = \"glm-4-long\"\n    GLM_4_FLASHX = \"glm-4-flashx\"\n    GLM_4_FLASH = \"glm-4-flash\"\n    GLM_ZERO_PREVIEW = \"glm-zero-preview\"\n    GLM_3_TURBO = \"glm-3-turbo\"\n\n    # Groq platform models\n    GROQ_LLAMA_3_1_8B = \"llama-3.1-8b-instant\"\n    GROQ_LLAMA_3_3_70B = \"llama-3.3-70b-versatile\"\n    GROQ_LLAMA_3_3_70B_PREVIEW = \"llama-3.3-70b-specdec\"\n    GROQ_LLAMA_3_8B = \"llama3-8b-8192\"\n    GROQ_LLAMA_3_70B = \"llama3-70b-8192\"\n    GROQ_MIXTRAL_8_7B = \"mixtral-8x7b-32768\"\n    GROQ_GEMMA_2_9B_IT = \"gemma2-9b-it\"\n\n    # Nebius AI Studio platform models\n    NEBIUS_GPT_OSS_120B = \"gpt-oss-120b\"\n    NEBIUS_GPT_OSS_20B = \"gpt-oss-20b\"\n    NEBIUS_GLM_4_5 = \"GLM-4.5\"\n    NEBIUS_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3\"\n    NEBIUS_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n    NEBIUS_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n    NEBIUS_MISTRAL_7B_INSTRUCT = \"mistralai/Mistral-7B-Instruct-v0.3\"\n\n    # CometAPI platform models\n    COMETAPI_GPT_5_CHAT_LATEST = \"gpt-5-chat-latest\"\n    COMETAPI_CHATGPT_4O_LATEST = \"chatgpt-4o-latest\"\n    COMETAPI_GPT_5_MINI = \"gpt-5-mini\"\n    COMETAPI_GPT_5_NANO = \"gpt-5-nano\"\n    COMETAPI_GPT_5 = \"gpt-5\"\n    COMETAPI_GPT_4_1 = \"gpt-4.1\"\n    COMETAPI_GPT_4O_MINI = \"gpt-4o-mini\"\n    COMETAPI_O4_MINI_2025_04_16 = \"o4-mini-2025-04-16\"\n    COMETAPI_O3_PRO_2025_06_10 = \"o3-pro-2025-06-10\"\n    COMETAPI_CLAUDE_OPUS_4_1_20250805 = \"claude-opus-4-1-20250805\"\n    COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING = (\n        \"claude-opus-4-1-20250805-thinking\"\n    )\n    COMETAPI_CLAUDE_SONNET_4_20250514 = \"claude-sonnet-4-20250514\"\n    COMETAPI_CLAUDE_SONNET_4_20250514_THINKING = (\n        \"claude-sonnet-4-20250514-thinking\"\n    )\n    COMETAPI_CLAUDE_3_7_SONNET_LATEST = \"claude-3-7-sonnet-latest\"\n    COMETAPI_CLAUDE_3_5_HAIKU_LATEST = \"claude-3-5-haiku-latest\"\n    COMETAPI_GEMINI_2_5_PRO = \"gemini-2.5-pro\"\n    COMETAPI_GEMINI_2_5_FLASH = \"gemini-2.5-flash\"\n    COMETAPI_GEMINI_2_5_FLASH_LITE = \"gemini-2.5-flash-lite\"\n    COMETAPI_GEMINI_2_0_FLASH = \"gemini-2.0-flash\"\n    COMETAPI_GROK_4_0709 = \"grok-4-0709\"\n    COMETAPI_GROK_3 = \"grok-3\"\n    COMETAPI_GROK_3_MINI = \"grok-3-mini\"\n    COMETAPI_GROK_2_IMAGE_1212 = \"grok-2-image-1212\"\n    COMETAPI_DEEPSEEK_V3_1 = \"deepseek-v3.1\"\n    COMETAPI_DEEPSEEK_V3 = \"deepseek-v3\"\n    COMETAPI_DEEPSEEK_R1_0528 = \"deepseek-r1-0528\"\n    COMETAPI_DEEPSEEK_CHAT = \"deepseek-chat\"\n    COMETAPI_DEEPSEEK_REASONER = \"deepseek-reasoner\"\n    COMETAPI_QWEN3_30B_A3B = \"qwen3-30b-a3b\"\n    COMETAPI_QWEN3_CODER_PLUS_2025_07_22 = \"qwen3-coder-plus-2025-07-22\"\n\n    # OpenRouter models\n    OPENROUTER_LLAMA_3_1_405B = \"meta-llama/llama-3.1-405b-instruct\"\n    OPENROUTER_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n    OPENROUTER_LLAMA_4_MAVERICK = \"meta-llama/llama-4-maverick\"\n    OPENROUTER_LLAMA_4_MAVERICK_FREE = \"meta-llama/llama-4-maverick:free\"\n    OPENROUTER_LLAMA_4_SCOUT = \"meta-llama/llama-4-scout\"\n    OPENROUTER_LLAMA_4_SCOUT_FREE = \"meta-llama/llama-4-scout:free\"\n    OPENROUTER_OLYMPICODER_7B = \"open-r1/olympiccoder-7b:free\"\n    OPENROUTER_HORIZON_ALPHA = \"openrouter/horizon-alpha\"\n    OPENROUTER_GROK_4_FAST = \"x-ai/grok-4-fast\"\n    OPENROUTER_GEMINI_2_5_FLASH = \"google/gemini-2.5-flash\"\n    OPENROUTER_GPT_4O_MINI = 'openai/gpt-4o-mini'\n    OPENROUTER_QWEN_PLUS = 'qwen/qwen-plus'\n    OPENROUTER_QWEN_VL_MAX = 'qwen/qwen-vl-max'\n    OPENROUTER_o3 = 'openai/o3'\n    OPENROUTER_CLAUDE_SONNET_4_5 = 'anthropic/claude-sonnet-4.5'\n    \n    # LMStudio models\n    LMSTUDIO_GEMMA_3_1B = \"gemma-3-1b\"\n    LMSTUDIO_GEMMA_3_4B = \"gemma-3-4b\"\n    LMSTUDIO_GEMMA_3_12B = \"gemma-3-12b\"\n    LMSTUDIO_GEMMA_3_27B = \"gemma-3-27b\"\n\n    # TogetherAI platform models support tool calling\n    TOGETHER_LLAMA_3_1_8B = \"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\"\n    TOGETHER_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\"\n    TOGETHER_LLAMA_3_1_405B = \"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\"\n    TOGETHER_LLAMA_3_3_70B = \"meta-llama/Llama-3.3-70B-Instruct-Turbo\"\n    TOGETHER_MIXTRAL_8_7B = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n    TOGETHER_MISTRAL_7B = \"mistralai/Mistral-7B-Instruct-v0.1\"\n    TOGETHER_LLAMA_4_MAVERICK = (\n        \"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\"\n    )\n    TOGETHER_LLAMA_4_SCOUT = \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n\n    # PPIO platform models support tool calling\n    PPIO_DEEPSEEK_PROVER_V2_671B = \"deepseek/deepseek-prover-v2-671b\"\n    PPIO_DEEPSEEK_R1_TURBO = \"deepseek/deepseek-r1-turbo\"\n    PPIO_DEEPSEEK_V3_TURBO = \"deepseek/deepseek-v3-turbo\"\n    PPIO_DEEPSEEK_R1_COMMUNITY = \"deepseek/deepseek-r1/community\"\n    PPIO_DEEPSEEK_V3_COMMUNITY = \"deepseek/deepseek-v3/community\"\n    PPIO_DEEPSEEK_R1 = \"deepseek/deepseek-r1\"\n    PPIO_DEEPSEEK_V3 = \"deepseek/deepseek-v3\"\n    PPIO_QWEN_2_5_72B = \"qwen/qwen-2.5-72b-instruct\"\n    PPIO_BAICHUAN_2_13B_CHAT = \"baichuan/baichuan2-13b-chat\"\n    PPIO_LLAMA_3_3_70B = \"meta-llama/llama-3.3-70b-instruct\"\n    PPIO_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n    PPIO_YI_1_5_34B_CHAT = \"01-ai/yi-1.5-34b-chat\"\n\n    # SambaNova Cloud platform models support tool calling\n    SAMBA_LLAMA_3_1_8B = \"Meta-Llama-3.1-8B-Instruct\"\n    SAMBA_LLAMA_3_1_70B = \"Meta-Llama-3.1-70B-Instruct\"\n    SAMBA_LLAMA_3_1_405B = \"Meta-Llama-3.1-405B-Instruct\"\n\n    # SGLang models support tool calling\n    SGLANG_LLAMA_3_1_8B = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n    SGLANG_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n    SGLANG_LLAMA_3_1_405B = \"meta-llama/Meta-Llama-3.1-405B-Instruct\"\n    SGLANG_LLAMA_3_2_1B = \"meta-llama/Llama-3.2-1B-Instruct\"\n    SGLANG_MIXTRAL_NEMO = \"mistralai/Mistral-Nemo-Instruct-2407\"\n    SGLANG_MISTRAL_7B = \"mistralai/Mistral-7B-Instruct-v0.3\"\n    SGLANG_QWEN_2_5_7B = \"Qwen/Qwen2.5-7B-Instruct\"\n    SGLANG_QWEN_2_5_32B = \"Qwen/Qwen2.5-32B-Instruct\"\n    SGLANG_QWEN_2_5_72B = \"Qwen/Qwen2.5-72B-Instruct\"\n\n    STUB = \"stub\"\n\n    # Legacy anthropic models\n    # NOTE: anthropic legacy models only Claude 2.1 has system prompt support\n    CLAUDE_2_1 = \"claude-2.1\"\n    CLAUDE_2_0 = \"claude-2.0\"\n    CLAUDE_INSTANT_1_2 = \"claude-instant-1.2\"\n\n    # Claude models\n    CLAUDE_3_OPUS = \"claude-3-opus-latest\"\n    CLAUDE_3_SONNET = \"claude-3-sonnet-20240229\"\n    CLAUDE_3_HAIKU = \"claude-3-haiku-20240307\"\n    CLAUDE_3_5_SONNET = \"claude-3-5-sonnet-latest\"\n    CLAUDE_3_5_HAIKU = \"claude-3-5-haiku-latest\"\n    CLAUDE_3_7_SONNET = \"claude-3-7-sonnet-latest\"\n    CLAUDE_SONNET_4 = \"claude-sonnet-4-20250514\"\n    CLAUDE_OPUS_4 = \"claude-opus-4-20250514\"\n    CLAUDE_OPUS_4_1 = \"claude-opus-4-1-20250805\"\n\n    # Netmind models\n    NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT = (\n        \"meta-llama/Llama-4-Maverick-17B-128E-Instruct\"\n    )\n    NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT = (\n        \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n    )\n    NETMIND_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n    NETMIND_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3-0324\"\n    NETMIND_DOUBAO_1_5_PRO = \"doubao/Doubao-1.5-pro\"\n    NETMIND_QWQ_32B = \"Qwen/QwQ-32B\"\n\n    # Nvidia models\n    NVIDIA_NEMOTRON_340B_INSTRUCT = \"nvidia/nemotron-4-340b-instruct\"\n    NVIDIA_NEMOTRON_340B_REWARD = \"nvidia/nemotron-4-340b-reward\"\n    NVIDIA_YI_LARGE = \"01-ai/yi-large\"\n    NVIDIA_MISTRAL_LARGE = \"mistralai/mistral-large\"\n    NVIDIA_MIXTRAL_8X7B = \"mistralai/mixtral-8x7b-instruct\"\n    NVIDIA_LLAMA3_70B = \"meta/llama3-70b\"\n    NVIDIA_LLAMA3_1_8B_INSTRUCT = \"meta/llama-3.1-8b-instruct\"\n    NVIDIA_LLAMA3_1_70B_INSTRUCT = \"meta/llama-3.1-70b-instruct\"\n    NVIDIA_LLAMA3_1_405B_INSTRUCT = \"meta/llama-3.1-405b-instruct\"\n    NVIDIA_LLAMA3_2_1B_INSTRUCT = \"meta/llama-3.2-1b-instruct\"\n    NVIDIA_LLAMA3_2_3B_INSTRUCT = \"meta/llama-3.2-3b-instruct\"\n    NVIDIA_LLAMA3_3_70B_INSTRUCT = \"meta/llama-3.3-70b-instruct\"\n\n    # Gemini models\n    GEMINI_2_5_FLASH = \"gemini-2.5-flash\"\n    GEMINI_2_5_PRO = \"gemini-2.5-pro\"\n    GEMINI_2_0_FLASH = \"gemini-2.0-flash\"\n    GEMINI_2_0_FLASH_EXP = \"gemini-2.0-flash-exp\"\n    GEMINI_2_0_FLASH_THINKING = \"gemini-2.0-flash-thinking-exp\"\n    GEMINI_2_0_PRO_EXP = \"gemini-2.0-pro-exp-02-05\"\n    GEMINI_2_0_FLASH_LITE = \"gemini-2.0-flash-lite\"\n    GEMINI_2_0_FLASH_LITE_PREVIEW = \"gemini-2.0-flash-lite-preview-02-05\"\n    GEMINI_1_5_FLASH = \"gemini-1.5-flash\"\n    GEMINI_1_5_PRO = \"gemini-1.5-pro\"\n\n    # Mistral AI models\n    MISTRAL_3B = \"ministral-3b-latest\"\n    MISTRAL_7B = \"open-mistral-7b\"\n    MISTRAL_8B = \"ministral-8b-latest\"\n    MISTRAL_CODESTRAL = \"codestral-latest\"\n    MISTRAL_CODESTRAL_MAMBA = \"open-codestral-mamba\"\n    MISTRAL_LARGE = \"mistral-large-latest\"\n    MISTRAL_MIXTRAL_8x7B = \"open-mixtral-8x7b\"\n    MISTRAL_MIXTRAL_8x22B = \"open-mixtral-8x22b\"\n    MISTRAL_NEMO = \"open-mistral-nemo\"\n    MISTRAL_PIXTRAL_12B = \"pixtral-12b-2409\"\n    MISTRAL_MEDIUM_3_1 = \"mistral-medium-2508\"\n    MISTRAL_SMALL_3_2 = \"mistral-small-2506\"\n    MAGISTRAL_SMALL_1_2 = \"magistral-small-1.2\"\n    MAGISTRAL_MEDIUM_1_2 = \"magistral-medium-1.2\"\n\n    # Reka models\n    REKA_CORE = \"reka-core\"\n    REKA_FLASH = \"reka-flash\"\n    REKA_EDGE = \"reka-edge\"\n\n    # Cohere models\n    COHERE_COMMAND_R_PLUS = \"command-r-plus\"\n    COHERE_COMMAND_R = \"command-r\"\n    COHERE_COMMAND_LIGHT = \"command-light\"\n    COHERE_COMMAND = \"command\"\n    COHERE_COMMAND_NIGHTLY = \"command-nightly\"\n\n    # Qwen models (Aliyun)\n    QWEN_MAX = \"qwen-max\"\n    QWEN_PLUS = \"qwen-plus\"\n    QWEN_TURBO = \"qwen-turbo\"\n    QWEN_PLUS_LATEST = \"qwen-plus-latest\"\n    QWEN_PLUS_2025_04_28 = \"qwen-plus-2025-04-28\"\n    QWEN_TURBO_LATEST = \"qwen-turbo-latest\"\n    QWEN_TURBO_2025_04_28 = \"qwen-turbo-2025-04-28\"\n    QWEN_LONG = \"qwen-long\"\n    QWEN_VL_MAX = \"qwen-vl-max\"\n    QWEN_VL_PLUS = \"qwen-vl-plus\"\n    QWEN_MATH_PLUS = \"qwen-math-plus\"\n    QWEN_MATH_TURBO = \"qwen-math-turbo\"\n    QWEN_CODER_TURBO = \"qwen-coder-turbo\"\n    QWEN_2_5_CODER_32B = \"qwen2.5-coder-32b-instruct\"\n    QWEN_2_5_VL_72B = \"qwen2.5-vl-72b-instruct\"\n    QWEN_2_5_72B = \"qwen2.5-72b-instruct\"\n    QWEN_2_5_32B = \"qwen2.5-32b-instruct\"\n    QWEN_2_5_14B = \"qwen2.5-14b-instruct\"\n    QWEN_QWQ_32B = \"qwq-32b-preview\"\n    QWEN_QVQ_72B = \"qvq-72b-preview\"\n    QWEN_QWQ_PLUS = \"qwq-plus\"\n    QWEN_3_CODER_PLUS = \"qwen3-coder-plus\"\n\n    # Yi models (01-ai)\n    YI_LIGHTNING = \"yi-lightning\"\n    YI_LARGE = \"yi-large\"\n    YI_MEDIUM = \"yi-medium\"\n    YI_LARGE_TURBO = \"yi-large-turbo\"\n    YI_VISION = \"yi-vision\"\n    YI_MEDIUM_200K = \"yi-medium-200k\"\n    YI_SPARK = \"yi-spark\"\n    YI_LARGE_RAG = \"yi-large-rag\"\n    YI_LARGE_FC = \"yi-large-fc\"\n\n    # DeepSeek models\n    DEEPSEEK_CHAT = \"deepseek-chat\"\n    DEEPSEEK_REASONER = \"deepseek-reasoner\"\n    # InternLM models\n    INTERNLM3_LATEST = \"internlm3-latest\"\n    INTERNLM3_8B_INSTRUCT = \"internlm3-8b-instruct\"\n    INTERNLM2_5_LATEST = \"internlm2.5-latest\"\n    INTERNLM2_PRO_CHAT = \"internlm2-pro-chat\"\n\n    # Moonshot models\n    MOONSHOT_V1_8K = \"moonshot-v1-8k\"\n    MOONSHOT_V1_32K = \"moonshot-v1-32k\"\n    MOONSHOT_V1_128K = \"moonshot-v1-128k\"\n    MOONSHOT_KIMI_K2 = \"kimi-k2-0711-preview\"\n\n    # SiliconFlow models support tool calling\n    SILICONFLOW_DEEPSEEK_V2_5 = \"deepseek-ai/DeepSeek-V2.5\"\n    SILICONFLOW_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3\"\n    SILICONFLOW_INTERN_LM2_5_20B_CHAT = \"internlm/internlm2_5-20b-chat\"\n    SILICONFLOW_INTERN_LM2_5_7B_CHAT = \"internlm/internlm2_5-7b-chat\"\n    SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT = \"Pro/internlm/internlm2_5-7b-chat\"\n    SILICONFLOW_QWEN2_5_72B_INSTRUCT = \"Qwen/Qwen2.5-72B-Instruct\"\n    SILICONFLOW_QWEN2_5_32B_INSTRUCT = \"Qwen/Qwen2.5-32B-Instruct\"\n    SILICONFLOW_QWEN2_5_14B_INSTRUCT = \"Qwen/Qwen2.5-14B-Instruct\"\n    SILICONFLOW_QWEN2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n    SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT = \"Pro/Qwen/Qwen2.5-7B-Instruct\"\n    SILICONFLOW_THUDM_GLM_4_9B_CHAT = \"THUDM/glm-4-9b-chat\"\n    SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT = \"Pro/THUDM/glm-4-9b-chat\"\n\n    # AIML models support tool calling\n    AIML_MIXTRAL_8X7B = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n    AIML_MISTRAL_7B_INSTRUCT = \"mistralai/Mistral-7B-Instruct-v0.1\"\n\n    # Novita platform models support tool calling\n    NOVITA_LLAMA_4_MAVERICK_17B = (\n        \"meta-llama/llama-4-maverick-17b-128e-instruct-fp8\"\n    )\n    NOVITA_LLAMA_4_SCOUT_17B = \"meta-llama/llama-4-scout-17b-16e-instruct\"\n    NOVITA_DEEPSEEK_V3_0324 = \"deepseek/deepseek-v3-0324\"\n    NOVITA_QWEN_2_5_V1_72B = \"qwen/qwen2.5-vl-72b-instruct\"\n    NOVITA_DEEPSEEK_V3_TURBO = \"deepseek/deepseek-v3-turbo\"\n    NOVITA_DEEPSEEK_R1_TURBO = \"deepseek/deepseek-r1-turbo\"\n    NOVITA_GEMMA_3_27B_IT = \"google/gemma-3-27b-it\"\n    NOVITA_QWEN_32B = \"qwen/qwq-32b\"\n    NOVITA_L3_8B_STHENO_V3_2 = \"Sao10K/L3-8B-Stheno-v3.2\"\n    NOVITA_MYTHOMAX_L2_13B = \"gryphe/mythomax-l2-13b\"\n    NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B = (\n        \"deepseek/deepseek-r1-distill-llama-8b\"\n    )\n    NOVITA_DEEPSEEK_V3 = \"deepseek/deepseek_v3\"\n    NOVITA_LLAMA_3_1_8B = \"meta-llama/llama-3.1-8b-instruct\"\n    NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B = (\n        \"deepseek/deepseek-r1-distill-qwen-14b\"\n    )\n    NOVITA_LLAMA_3_3_70B = \"meta-llama/llama-3.3-70b-instruct\"\n    NOVITA_QWEN_2_5_72B = \"qwen/qwen-2.5-72b-instruct\"\n    NOVITA_MISTRAL_NEMO = \"mistralai/mistral-nemo\"\n    NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B = (\n        \"deepseek/deepseek-r1-distill-qwen-32b\"\n    )\n    NOVITA_LLAMA_3_8B = \"meta-llama/llama-3-8b-instruct\"\n    NOVITA_WIZARDLM_2_8X22B = \"microsoft/wizardlm-2-8x22b\"\n    NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B = (\n        \"deepseek/deepseek-r1-distill-llama-70b\"\n    )\n    NOVITA_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n    NOVITA_GEMMA_2_9B_IT = \"google/gemma-2-9b-it\"\n    NOVITA_MISTRAL_7B = \"mistralai/mistral-7b-instruct\"\n    NOVITA_LLAMA_3_70B = \"meta-llama/llama-3-70b-instruct\"\n    NOVITA_DEEPSEEK_R1 = \"deepseek/deepseek-r1\"\n    NOVITA_HERMES_2_PRO_LLAMA_3_8B = \"nousresearch/hermes-2-pro-llama-3-8b\"\n    NOVITA_L3_70B_EURYALE_V2_1 = \"sao10k/l3-70b-euryale-v2.1\"\n    NOVITA_DOLPHIN_MIXTRAL_8X22B = (\n        \"cognitivecomputations/dolphin-mixtral-8x22b\"\n    )\n    NOVITA_AIROBOROS_L2_70B = \"jondurbin/airoboros-l2-70b\"\n    NOVITA_MIDNIGHT_ROSE_70B = \"sophosympatheia/midnight-rose-70b\"\n    NOVITA_L3_8B_LUNARIS = \"sao10k/l3-8b-lunaris\"\n    NOVITA_GLM_4_9B_0414 = \"thudm/glm-4-9b-0414\"\n    NOVITA_GLM_Z1_9B_0414 = \"thudm/glm-z1-9b-0414\"\n    NOVITA_GLM_Z1_32B_0414 = \"thudm/glm-z1-32b-0414\"\n    NOVITA_GLM_4_32B_0414 = \"thudm/glm-4-32b-0414\"\n    NOVITA_GLM_Z1_RUMINATION_32B_0414 = \"thudm/glm-z1-rumination-32b-0414\"\n    NOVITA_QWEN_2_5_7B = \"qwen/qwen2.5-7b-instruct\"\n    NOVITA_LLAMA_3_2_1B = \"meta-llama/llama-3.2-1b-instruct\"\n    NOVITA_LLAMA_3_2_11B_VISION = \"meta-llama/llama-3.2-11b-vision-instruct\"\n    NOVITA_LLAMA_3_2_3B = \"meta-llama/llama-3.2-3b-instruct\"\n    NOVITA_LLAMA_3_1_8B_BF16 = \"meta-llama/llama-3.1-8b-instruct-bf16\"\n    NOVITA_L31_70B_EURYALE_V2_2 = \"sao10k/l31-70b-euryale-v2.2\"\n\n    # ModelScope models support tool calling\n    MODELSCOPE_QWEN_2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n    MODELSCOPE_QWEN_2_5_14B_INSTRUCT = \"Qwen/Qwen2.5-14B-Instruct\"\n    MODELSCOPE_QWEN_2_5_32B_INSTRUCT = \"Qwen/Qwen2.5-32B-Instruct\"\n    MODELSCOPE_QWEN_2_5_72B_INSTRUCT = \"Qwen/Qwen2.5-72B-Instruct\"\n    MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT = \"Qwen/Qwen2.5-Coder-7B-Instruct\"\n    MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT = \"Qwen/Qwen2.5-Coder-14B-Instruct\"\n    MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT = \"Qwen/Qwen2.5-Coder-32B-Instruct\"\n    MODELSCOPE_QWEN_3_235B_A22B = \"Qwen/Qwen3-235B-A22B\"\n    MODELSCOPE_QWEN_3_32B = \"Qwen/Qwen3-32B\"\n    MODELSCOPE_QWQ_32B = \"Qwen/QwQ-32B\"\n    MODELSCOPE_QWQ_32B_PREVIEW = \"Qwen/QwQ-32B-Preview\"\n    MODELSCOPE_LLAMA_3_1_8B_INSTRUCT = (\n        \"LLM-Research/Meta-Llama-3.1-8B-Instruct\"\n    )\n    MODELSCOPE_LLAMA_3_1_70B_INSTRUCT = (\n        \"LLM-Research/Meta-Llama-3.1-70B-Instruct\"\n    )\n    MODELSCOPE_LLAMA_3_1_405B_INSTRUCT = (\n        \"LLM-Research/Meta-Llama-3.1-405B-Instruct\"\n    )\n    MODELSCOPE_LLAMA_3_3_70B_INSTRUCT = \"LLM-Research/Llama-3.3-70B-Instruct\"\n    MODELSCOPE_MINISTRAL_8B_INSTRUCT = \"mistralai/Ministral-8B-Instruct-2410\"\n    MODELSCOPE_DEEPSEEK_V3_0324 = \"deepseek-ai/DeepSeek-V3-0324\"\n\n    # WatsonX models\n    WATSONX_GRANITE_3_8B_INSTRUCT = \"ibm/granite-3-8b-instruct\"\n    WATSONX_LLAMA_3_3_70B_INSTRUCT = \"meta-llama/llama-3-3-70b-instruct\"\n    WATSONX_LLAMA_3_2_1B_INSTRUCT = \"meta-llama/llama-3-2-1b-instruct\"\n    WATSONX_LLAMA_3_2_3B_INSTRUCT = \"meta-llama/llama-3-2-3b-instruct\"\n    WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT = (\n        \"meta-llama/llama-3-2-11b-vision-instruct\"\n    )\n    WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT = (\n        \"meta-llama/llama-3-2-90b-vision-instruct\"\n    )\n    WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT = (\n        \"meta-llama/llama-guard-3-11b-vision-instruct\"\n    )\n    WATSONX_MISTRAL_LARGE = \"mistralai/mistral-large\"\n\n    # Qianfan models\n    ERNIE_X1_TURBO_32K = \"ernie-x1-turbo-32k\"\n    ERNIE_X1_32K = \"ernie-x1-32k\"\n    ERNIE_X1_32K_PREVIEW = \"ernie-x1-32k-preview\"\n    ERNIE_4_5_TURBO_128K = \"ernie-4.5-turbo-128k\"\n    ERNIE_4_5_TURBO_32K = \"ernie-4.5-turbo-32k\"\n    DEEPSEEK_V3 = \"deepseek-v3\"\n    DEEPSEEK_R1 = \"deepseek-r1\"\n    QWEN3_235B_A22B = \"qwen3-235b-a22b\"\n\n    # Crynux models\n    CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B = (\n        \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n    )\n    CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B = (\n        \"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\"\n    )\n    CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B = (\n        \"deepseek-ai/DeepSeek-R1-Distill-Llama-8B\"\n    )\n\n    CRYNUX_QWEN_3_4_B = \"Qwen/Qwen3-4B\"\n    CRYNUX_QWEN_3_8_B = \"Qwen/Qwen3-8B\"\n    CRYNUX_QWEN_2_5_7B = \"Qwen/Qwen2.5-7B\"\n    CRYNUX_QWEN_2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n\n    CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B = \"NousResearch/Hermes-3-Llama-3.1-8B\"\n    CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B = \"NousResearch/Hermes-3-Llama-3.2-3B\"\n\n    def __str__(self):\n        return self.value\n\n    def __repr__(self):\n        return self.value\n\n    def __new__(cls, value: Union[\"ModelType\", str]) -> \"ModelType\":\n        return cast(\"ModelType\", UnifiedModelType.__new__(cls, value))\n\n    @classmethod\n    def from_name(cls, name: str) -> \"ModelType\":\n        r\"\"\"Returns the ModelType enum value from a string.\"\"\"\n        for model_type in cls:\n            if model_type.value == name:\n                return model_type\n        raise ValueError(f\"Unknown ModelType name: {name}\")\n\n    @property\n    def value_for_tiktoken(self) -> str:\n        if self.is_openai:\n            return self.value\n        return \"gpt-4o-mini\"\n\n    @property\n    def support_native_structured_output(self) -> bool:\n        return any(\n            [\n                self.is_openai,\n            ]\n        )\n\n    @property\n    def support_native_tool_calling(self) -> bool:\n        return any(\n            [\n                self.is_openai,\n                self.is_gemini,\n                self.is_mistral,\n                self.is_qwen,\n                self.is_deepseek,\n                self.is_ppio,\n                self.is_cohere,\n                self.is_internlm,\n                self.is_together,\n                self.is_sambanova,\n                self.is_groq,\n                self.is_openrouter,\n                self.is_lmstudio,\n                self.is_sglang,\n                self.is_moonshot,\n                self.is_siliconflow,\n                self.is_modelscope,\n                self.is_zhipuai,\n                self.is_aiml,\n                self.is_azure_openai,\n                self.is_novita,\n            ]\n        )\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this type of models is an OpenAI-released model.\"\"\"\n        return self in {\n            ModelType.GPT_3_5_TURBO,\n            ModelType.GPT_4,\n            ModelType.GPT_4_TURBO,\n            ModelType.GPT_4O,\n            ModelType.GPT_4O_MINI,\n            ModelType.O1,\n            ModelType.O1_PREVIEW,\n            ModelType.O1_MINI,\n            ModelType.O3_PRO,\n            ModelType.O3_MINI,\n            ModelType.GPT_4_5_PREVIEW,\n            ModelType.GPT_4_1,\n            ModelType.GPT_4_1_MINI,\n            ModelType.GPT_4_1_NANO,\n            ModelType.GPT_5,\n            ModelType.GPT_5_MINI,\n            ModelType.GPT_5_NANO,\n            ModelType.O4_MINI,\n            ModelType.O3,\n        }\n\n    @property\n    def is_amd(self) -> bool:\n        r\"\"\"Returns whether this type of models is a AMD model.\"\"\"\n        return self in {\n            ModelType.AMD_GPT4,\n        }\n\n    @property\n    def is_aws_bedrock(self) -> bool:\n        r\"\"\"Returns whether this type of models is an AWS Bedrock model.\"\"\"\n        return self in {\n            ModelType.AWS_CLAUDE_3_7_SONNET,\n            ModelType.AWS_CLAUDE_3_5_SONNET,\n            ModelType.AWS_CLAUDE_3_HAIKU,\n            ModelType.AWS_CLAUDE_3_SONNET,\n            ModelType.AWS_DEEPSEEK_R1,\n            ModelType.AWS_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.AWS_LLAMA_3_2_90B_INSTRUCT,\n            ModelType.AWS_LLAMA_3_2_11B_INSTRUCT,\n            ModelType.AWS_CLAUDE_SONNET_4,\n            ModelType.AWS_CLAUDE_OPUS_4,\n            ModelType.AWS_CLAUDE_OPUS_4_1,\n        }\n\n    @property\n    def is_azure_openai(self) -> bool:\n        r\"\"\"Returns whether this type of models is an OpenAI-released model\n        from Azure.\n        \"\"\"\n        return self in {\n            ModelType.GPT_3_5_TURBO,\n            ModelType.GPT_4,\n            ModelType.GPT_4_TURBO,\n            ModelType.GPT_4O,\n            ModelType.GPT_4O_MINI,\n            ModelType.O1,\n            ModelType.O1_PREVIEW,\n            ModelType.O1_MINI,\n            ModelType.O3_MINI,\n            ModelType.O3_PRO,\n            ModelType.GPT_4_5_PREVIEW,\n            ModelType.GPT_4_1,\n            ModelType.GPT_4_1_MINI,\n            ModelType.GPT_4_1_NANO,\n            ModelType.GPT_5,\n            ModelType.O4_MINI,\n            ModelType.O3,\n        }\n\n    @property\n    def is_zhipuai(self) -> bool:\n        r\"\"\"Returns whether this type of models is an ZhipuAI model.\"\"\"\n        return self in {\n            ModelType.GLM_3_TURBO,\n            ModelType.GLM_4,\n            ModelType.GLM_4V,\n            ModelType.GLM_4V_FLASH,\n            ModelType.GLM_4V_PLUS_0111,\n            ModelType.GLM_4_PLUS,\n            ModelType.GLM_4_AIR,\n            ModelType.GLM_4_AIR_0111,\n            ModelType.GLM_4_AIRX,\n            ModelType.GLM_4_LONG,\n            ModelType.GLM_4_FLASHX,\n            ModelType.GLM_4_FLASH,\n            ModelType.GLM_ZERO_PREVIEW,\n        }\n\n    @property\n    def is_anthropic(self) -> bool:\n        r\"\"\"Returns whether this type of models is Anthropic-released model.\n\n        Returns:\n            bool: Whether this type of models is anthropic.\n        \"\"\"\n        return self in {\n            ModelType.CLAUDE_INSTANT_1_2,\n            ModelType.CLAUDE_2_0,\n            ModelType.CLAUDE_2_1,\n            ModelType.CLAUDE_3_OPUS,\n            ModelType.CLAUDE_3_SONNET,\n            ModelType.CLAUDE_3_HAIKU,\n            ModelType.CLAUDE_3_5_SONNET,\n            ModelType.CLAUDE_3_5_HAIKU,\n            ModelType.CLAUDE_3_7_SONNET,\n            ModelType.CLAUDE_SONNET_4,\n            ModelType.CLAUDE_OPUS_4,\n            ModelType.CLAUDE_OPUS_4_1,\n        }\n\n    @property\n    def is_groq(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by Groq.\"\"\"\n        return self in {\n            ModelType.GROQ_LLAMA_3_1_8B,\n            ModelType.GROQ_LLAMA_3_3_70B,\n            ModelType.GROQ_LLAMA_3_3_70B_PREVIEW,\n            ModelType.GROQ_LLAMA_3_8B,\n            ModelType.GROQ_LLAMA_3_70B,\n            ModelType.GROQ_MIXTRAL_8_7B,\n            ModelType.GROQ_GEMMA_2_9B_IT,\n        }\n\n    @property\n    def is_nebius(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by Nebius AI\n        Studio.\"\"\"\n        return self in {\n            ModelType.NEBIUS_GPT_OSS_120B,\n            ModelType.NEBIUS_GPT_OSS_20B,\n            ModelType.NEBIUS_GLM_4_5,\n            ModelType.NEBIUS_DEEPSEEK_V3,\n            ModelType.NEBIUS_DEEPSEEK_R1,\n            ModelType.NEBIUS_LLAMA_3_1_70B,\n            ModelType.NEBIUS_MISTRAL_7B_INSTRUCT,\n        }\n\n    @property\n    def is_cometapi(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by CometAPI.\"\"\"\n        return self in {\n            ModelType.COMETAPI_GPT_5_CHAT_LATEST,\n            ModelType.COMETAPI_CHATGPT_4O_LATEST,\n            ModelType.COMETAPI_GPT_5_MINI,\n            ModelType.COMETAPI_GPT_5_NANO,\n            ModelType.COMETAPI_GPT_5,\n            ModelType.COMETAPI_GPT_4_1,\n            ModelType.COMETAPI_GPT_4O_MINI,\n            ModelType.COMETAPI_O4_MINI_2025_04_16,\n            ModelType.COMETAPI_O3_PRO_2025_06_10,\n            ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805,\n            ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING,\n            ModelType.COMETAPI_CLAUDE_SONNET_4_20250514,\n            ModelType.COMETAPI_CLAUDE_SONNET_4_20250514_THINKING,\n            ModelType.COMETAPI_CLAUDE_3_7_SONNET_LATEST,\n            ModelType.COMETAPI_CLAUDE_3_5_HAIKU_LATEST,\n            ModelType.COMETAPI_GEMINI_2_5_PRO,\n            ModelType.COMETAPI_GEMINI_2_5_FLASH,\n            ModelType.COMETAPI_GEMINI_2_5_FLASH_LITE,\n            ModelType.COMETAPI_GEMINI_2_0_FLASH,\n            ModelType.COMETAPI_GROK_4_0709,\n            ModelType.COMETAPI_GROK_3,\n            ModelType.COMETAPI_GROK_3_MINI,\n            ModelType.COMETAPI_GROK_2_IMAGE_1212,\n            ModelType.COMETAPI_DEEPSEEK_V3_1,\n            ModelType.COMETAPI_DEEPSEEK_V3,\n            ModelType.COMETAPI_DEEPSEEK_R1_0528,\n            ModelType.COMETAPI_DEEPSEEK_CHAT,\n            ModelType.COMETAPI_DEEPSEEK_REASONER,\n            ModelType.COMETAPI_QWEN3_30B_A3B,\n            ModelType.COMETAPI_QWEN3_CODER_PLUS_2025_07_22,\n        }\n\n    @property\n    def is_openrouter(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by OpenRouter.\"\"\"\n        return self in {\n            ModelType.OPENROUTER_LLAMA_3_1_405B,\n            ModelType.OPENROUTER_LLAMA_3_1_70B,\n            ModelType.OPENROUTER_LLAMA_4_MAVERICK,\n            ModelType.OPENROUTER_LLAMA_4_MAVERICK_FREE,\n            ModelType.OPENROUTER_LLAMA_4_SCOUT,\n            ModelType.OPENROUTER_LLAMA_4_SCOUT_FREE,\n            ModelType.OPENROUTER_OLYMPICODER_7B,\n            ModelType.OPENROUTER_HORIZON_ALPHA,\n            ModelType.OPENROUTER_GROK_4_FAST,\n            ModelType.OPENROUTER_GEMINI_2_5_FLASH,\n            ModelType.OPENROUTER_GPT_4O_MINI,\n            ModelType.OPENROUTER_QWEN_PLUS,\n            ModelType.OPENROUTER_QWEN_VL_MAX,\n            ModelType.OPENROUTER_o3,\n            ModelType.OPENROUTER_CLAUDE_SONNET_4_5\n        }\n\n    @property\n    def is_lmstudio(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by LMStudio.\"\"\"\n        return self in {\n            ModelType.LMSTUDIO_GEMMA_3_1B,\n            ModelType.LMSTUDIO_GEMMA_3_4B,\n            ModelType.LMSTUDIO_GEMMA_3_12B,\n            ModelType.LMSTUDIO_GEMMA_3_27B,\n        }\n\n    @property\n    def is_together(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by Together AI.\"\"\"\n        return self in {\n            ModelType.TOGETHER_LLAMA_3_1_405B,\n            ModelType.TOGETHER_LLAMA_3_1_70B,\n            ModelType.TOGETHER_LLAMA_3_3_70B,\n            ModelType.TOGETHER_LLAMA_3_3_70B,\n            ModelType.TOGETHER_MISTRAL_7B,\n            ModelType.TOGETHER_MIXTRAL_8_7B,\n        }\n\n    @property\n    def is_sambanova(self) -> bool:\n        r\"\"\"Returns whether this type of model is served by SambaNova AI.\"\"\"\n        return self in {\n            ModelType.SAMBA_LLAMA_3_1_8B,\n            ModelType.SAMBA_LLAMA_3_1_70B,\n            ModelType.SAMBA_LLAMA_3_1_405B,\n        }\n\n    @property\n    def is_mistral(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by Mistral.\"\"\"\n        return self in {\n            ModelType.MISTRAL_LARGE,\n            ModelType.MISTRAL_NEMO,\n            ModelType.MISTRAL_CODESTRAL,\n            ModelType.MISTRAL_7B,\n            ModelType.MISTRAL_MIXTRAL_8x7B,\n            ModelType.MISTRAL_MIXTRAL_8x22B,\n            ModelType.MISTRAL_CODESTRAL_MAMBA,\n            ModelType.MISTRAL_PIXTRAL_12B,\n            ModelType.MISTRAL_8B,\n            ModelType.MISTRAL_3B,\n            ModelType.MISTRAL_MEDIUM_3_1,\n            ModelType.MISTRAL_SMALL_3_2,\n            ModelType.MAGISTRAL_SMALL_1_2,\n            ModelType.MAGISTRAL_MEDIUM_1_2,\n        }\n\n    @property\n    def is_nvidia(self) -> bool:\n        r\"\"\"Returns whether this type of models is a NVIDIA model.\"\"\"\n        return self in {\n            ModelType.NVIDIA_NEMOTRON_340B_INSTRUCT,\n            ModelType.NVIDIA_NEMOTRON_340B_REWARD,\n            ModelType.NVIDIA_YI_LARGE,\n            ModelType.NVIDIA_MISTRAL_LARGE,\n            ModelType.NVIDIA_LLAMA3_70B,\n            ModelType.NVIDIA_MIXTRAL_8X7B,\n            ModelType.NVIDIA_LLAMA3_1_8B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_1_70B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_1_405B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_2_1B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_2_3B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_3_70B_INSTRUCT,\n        }\n\n    @property\n    def is_gemini(self) -> bool:\n        r\"\"\"Returns whether this type of models is Gemini model.\n\n        Returns:\n            bool: Whether this type of models is gemini.\n        \"\"\"\n        return self in {\n            ModelType.GEMINI_2_5_FLASH,\n            ModelType.GEMINI_2_5_PRO,\n            ModelType.GEMINI_2_0_FLASH,\n            ModelType.GEMINI_2_0_FLASH_EXP,\n            ModelType.GEMINI_2_0_FLASH_THINKING,\n            ModelType.GEMINI_2_0_PRO_EXP,\n            ModelType.GEMINI_2_0_FLASH_LITE,\n            ModelType.GEMINI_2_0_FLASH_LITE_PREVIEW,\n            ModelType.GEMINI_1_5_FLASH,\n            ModelType.GEMINI_1_5_PRO,\n        }\n\n    @property\n    def is_reka(self) -> bool:\n        r\"\"\"Returns whether this type of models is Reka model.\n\n        Returns:\n            bool: Whether this type of models is Reka.\n        \"\"\"\n        return self in {\n            ModelType.REKA_CORE,\n            ModelType.REKA_EDGE,\n            ModelType.REKA_FLASH,\n        }\n\n    @property\n    def is_cohere(self) -> bool:\n        r\"\"\"Returns whether this type of models is a Cohere model.\n\n        Returns:\n            bool: Whether this type of models is Cohere.\n        \"\"\"\n        return self in {\n            ModelType.COHERE_COMMAND_R_PLUS,\n            ModelType.COHERE_COMMAND_R,\n            ModelType.COHERE_COMMAND_LIGHT,\n            ModelType.COHERE_COMMAND,\n            ModelType.COHERE_COMMAND_NIGHTLY,\n        }\n\n    @property\n    def is_yi(self) -> bool:\n        r\"\"\"Returns whether this type of models is Yi model.\n\n        Returns:\n            bool: Whether this type of models is Yi.\n        \"\"\"\n        return self in {\n            ModelType.YI_LIGHTNING,\n            ModelType.YI_LARGE,\n            ModelType.YI_MEDIUM,\n            ModelType.YI_LARGE_TURBO,\n            ModelType.YI_VISION,\n            ModelType.YI_MEDIUM_200K,\n            ModelType.YI_SPARK,\n            ModelType.YI_LARGE_RAG,\n            ModelType.YI_LARGE_FC,\n        }\n\n    @property\n    def is_qwen(self) -> bool:\n        return self in {\n            ModelType.QWEN_MAX,\n            ModelType.QWEN_PLUS,\n            ModelType.QWEN_TURBO,\n            ModelType.QWEN_LONG,\n            ModelType.QWEN_VL_MAX,\n            ModelType.QWEN_VL_PLUS,\n            ModelType.QWEN_MATH_PLUS,\n            ModelType.QWEN_MATH_TURBO,\n            ModelType.QWEN_CODER_TURBO,\n            ModelType.QWEN_2_5_CODER_32B,\n            ModelType.QWEN_2_5_VL_72B,\n            ModelType.QWEN_2_5_72B,\n            ModelType.QWEN_2_5_32B,\n            ModelType.QWEN_2_5_14B,\n            ModelType.QWEN_QWQ_32B,\n            ModelType.QWEN_QVQ_72B,\n            ModelType.QWEN_QWQ_PLUS,\n            ModelType.QWEN_PLUS_LATEST,\n            ModelType.QWEN_PLUS_2025_04_28,\n            ModelType.QWEN_TURBO_LATEST,\n            ModelType.QWEN_TURBO_2025_04_28,\n            ModelType.QWEN_3_CODER_PLUS,\n        }\n\n    @property\n    def is_deepseek(self) -> bool:\n        return self in {\n            ModelType.DEEPSEEK_CHAT,\n            ModelType.DEEPSEEK_REASONER,\n        }\n\n    @property\n    def is_netmind(self) -> bool:\n        return self in {\n            ModelType.NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT,\n            ModelType.NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT,\n            ModelType.NETMIND_DEEPSEEK_R1,\n            ModelType.NETMIND_DEEPSEEK_V3,\n            ModelType.NETMIND_DOUBAO_1_5_PRO,\n            ModelType.NETMIND_QWQ_32B,\n        }\n\n    @property\n    def is_ppio(self) -> bool:\n        return self in {\n            ModelType.PPIO_DEEPSEEK_PROVER_V2_671B,\n            ModelType.PPIO_DEEPSEEK_R1_TURBO,\n            ModelType.PPIO_DEEPSEEK_V3_TURBO,\n            ModelType.PPIO_DEEPSEEK_R1_COMMUNITY,\n            ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,\n            ModelType.PPIO_DEEPSEEK_R1,\n            ModelType.PPIO_DEEPSEEK_V3,\n            ModelType.PPIO_QWEN_2_5_72B,\n            ModelType.PPIO_BAICHUAN_2_13B_CHAT,\n            ModelType.PPIO_LLAMA_3_3_70B,\n            ModelType.PPIO_LLAMA_3_1_70B,\n            ModelType.PPIO_YI_1_5_34B_CHAT,\n        }\n\n    @property\n    def is_internlm(self) -> bool:\n        return self in {\n            ModelType.INTERNLM3_LATEST,\n            ModelType.INTERNLM3_8B_INSTRUCT,\n            ModelType.INTERNLM2_5_LATEST,\n            ModelType.INTERNLM2_PRO_CHAT,\n        }\n\n    @property\n    def is_modelscope(self) -> bool:\n        return self in {\n            ModelType.MODELSCOPE_QWEN_2_5_7B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_14B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_32B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_72B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_3_235B_A22B,\n            ModelType.MODELSCOPE_QWEN_3_32B,\n            ModelType.MODELSCOPE_QWQ_32B,\n            ModelType.MODELSCOPE_QWQ_32B_PREVIEW,\n            ModelType.MODELSCOPE_LLAMA_3_1_8B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_1_70B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_1_405B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.MODELSCOPE_MINISTRAL_8B_INSTRUCT,\n            ModelType.MODELSCOPE_DEEPSEEK_V3_0324,\n        }\n\n    @property\n    def is_moonshot(self) -> bool:\n        return self in {\n            ModelType.MOONSHOT_V1_8K,\n            ModelType.MOONSHOT_V1_32K,\n            ModelType.MOONSHOT_V1_128K,\n            ModelType.MOONSHOT_KIMI_K2,\n        }\n\n    @property\n    def is_sglang(self) -> bool:\n        return self in {\n            ModelType.SGLANG_LLAMA_3_1_8B,\n            ModelType.SGLANG_LLAMA_3_1_70B,\n            ModelType.SGLANG_LLAMA_3_1_405B,\n            ModelType.SGLANG_LLAMA_3_2_1B,\n            ModelType.SGLANG_MIXTRAL_NEMO,\n            ModelType.SGLANG_MISTRAL_7B,\n            ModelType.SGLANG_QWEN_2_5_7B,\n            ModelType.SGLANG_QWEN_2_5_32B,\n            ModelType.SGLANG_QWEN_2_5_72B,\n        }\n\n    @property\n    def is_siliconflow(self) -> bool:\n        return self in {\n            ModelType.SILICONFLOW_DEEPSEEK_V2_5,\n            ModelType.SILICONFLOW_DEEPSEEK_V3,\n            ModelType.SILICONFLOW_INTERN_LM2_5_20B_CHAT,\n            ModelType.SILICONFLOW_INTERN_LM2_5_7B_CHAT,\n            ModelType.SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT,\n            ModelType.SILICONFLOW_QWEN2_5_72B_INSTRUCT,\n            ModelType.SILICONFLOW_QWEN2_5_32B_INSTRUCT,\n            ModelType.SILICONFLOW_QWEN2_5_14B_INSTRUCT,\n            ModelType.SILICONFLOW_QWEN2_5_7B_INSTRUCT,\n            ModelType.SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT,\n            ModelType.SILICONFLOW_THUDM_GLM_4_9B_CHAT,\n            ModelType.SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT,\n        }\n\n    @property\n    def is_watsonx(self) -> bool:\n        return self in {\n            ModelType.WATSONX_GRANITE_3_8B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_1B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_3B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT,\n            ModelType.WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT,\n            ModelType.WATSONX_MISTRAL_LARGE,\n        }\n\n    @property\n    def is_qianfan(self) -> bool:\n        return self in {\n            ModelType.ERNIE_X1_TURBO_32K,\n            ModelType.ERNIE_X1_32K,\n            ModelType.ERNIE_X1_32K_PREVIEW,\n            ModelType.ERNIE_4_5_TURBO_128K,\n            ModelType.ERNIE_4_5_TURBO_32K,\n            ModelType.DEEPSEEK_V3,\n            ModelType.DEEPSEEK_R1,\n            ModelType.QWEN3_235B_A22B,\n        }\n\n    @property\n    def is_novita(self) -> bool:\n        return self in {\n            ModelType.NOVITA_LLAMA_4_MAVERICK_17B,\n            ModelType.NOVITA_LLAMA_4_SCOUT_17B,\n            ModelType.NOVITA_DEEPSEEK_V3_0324,\n            ModelType.NOVITA_QWEN_2_5_V1_72B,\n            ModelType.NOVITA_DEEPSEEK_V3_TURBO,\n            ModelType.NOVITA_DEEPSEEK_R1_TURBO,\n            ModelType.NOVITA_GEMMA_3_27B_IT,\n            ModelType.NOVITA_QWEN_32B,\n            ModelType.NOVITA_L3_8B_STHENO_V3_2,\n            ModelType.NOVITA_MYTHOMAX_L2_13B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n            ModelType.NOVITA_DEEPSEEK_V3,\n            ModelType.NOVITA_LLAMA_3_1_8B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B,\n            ModelType.NOVITA_LLAMA_3_3_70B,\n            ModelType.NOVITA_QWEN_2_5_72B,\n            ModelType.NOVITA_MISTRAL_NEMO,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B,\n            ModelType.NOVITA_LLAMA_3_8B,\n            ModelType.NOVITA_WIZARDLM_2_8X22B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B,\n            ModelType.NOVITA_LLAMA_3_1_70B,\n            ModelType.NOVITA_GEMMA_2_9B_IT,\n            ModelType.NOVITA_MISTRAL_7B,\n            ModelType.NOVITA_LLAMA_3_70B,\n            ModelType.NOVITA_DEEPSEEK_R1,\n            ModelType.NOVITA_HERMES_2_PRO_LLAMA_3_8B,\n            ModelType.NOVITA_L3_70B_EURYALE_V2_1,\n            ModelType.NOVITA_DOLPHIN_MIXTRAL_8X22B,\n            ModelType.NOVITA_AIROBOROS_L2_70B,\n            ModelType.NOVITA_MIDNIGHT_ROSE_70B,\n            ModelType.NOVITA_L3_8B_LUNARIS,\n            ModelType.NOVITA_GLM_4_9B_0414,\n            ModelType.NOVITA_GLM_Z1_9B_0414,\n            ModelType.NOVITA_GLM_Z1_32B_0414,\n            ModelType.NOVITA_GLM_4_32B_0414,\n            ModelType.NOVITA_GLM_Z1_RUMINATION_32B_0414,\n            ModelType.NOVITA_QWEN_2_5_7B,\n            ModelType.NOVITA_LLAMA_3_2_1B,\n            ModelType.NOVITA_LLAMA_3_2_11B_VISION,\n            ModelType.NOVITA_LLAMA_3_2_3B,\n            ModelType.NOVITA_LLAMA_3_1_8B_BF16,\n            ModelType.NOVITA_L31_70B_EURYALE_V2_2,\n        }\n\n    @property\n    def is_crynux(self) -> bool:\n        return self in {\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n            ModelType.CRYNUX_QWEN_3_4_B,\n            ModelType.CRYNUX_QWEN_3_8_B,\n            ModelType.CRYNUX_QWEN_2_5_7B,\n            ModelType.CRYNUX_QWEN_2_5_7B_INSTRUCT,\n            ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B,\n            ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B,\n        }\n\n    @property\n    def is_aiml(self) -> bool:\n        return self in {\n            ModelType.AIML_MIXTRAL_8X7B,\n            ModelType.AIML_MISTRAL_7B_INSTRUCT,\n        }\n\n    @property\n    def token_limit(self) -> int:\n        r\"\"\"Returns the maximum token limit for a given model.\n\n        Returns:\n            int: The maximum token limit for the given model.\n        \"\"\"\n        if self is ModelType.GLM_4V:\n            return 1024\n        elif self in {\n            ModelType.STUB,\n            ModelType.REKA_CORE,\n            ModelType.REKA_EDGE,\n            ModelType.REKA_FLASH,\n            ModelType.QWEN_MATH_PLUS,\n            ModelType.QWEN_MATH_TURBO,\n            ModelType.COHERE_COMMAND,\n            ModelType.COHERE_COMMAND_LIGHT,\n            ModelType.NVIDIA_NEMOTRON_340B_INSTRUCT,\n            ModelType.NVIDIA_NEMOTRON_340B_REWARD,\n            ModelType.NOVITA_MYTHOMAX_L2_13B,\n            ModelType.NOVITA_AIROBOROS_L2_70B,\n            ModelType.NOVITA_MIDNIGHT_ROSE_70B,\n        }:\n            return 4_096\n        elif self in {\n            ModelType.GPT_4,\n            ModelType.GROQ_LLAMA_3_8B,\n            ModelType.GROQ_LLAMA_3_70B,\n            ModelType.GROQ_LLAMA_3_3_70B_PREVIEW,\n            ModelType.GROQ_GEMMA_2_9B_IT,\n            ModelType.GLM_3_TURBO,\n            ModelType.GLM_4,\n            ModelType.QWEN_VL_PLUS,\n            ModelType.NVIDIA_LLAMA3_70B,\n            ModelType.TOGETHER_MISTRAL_7B,\n            ModelType.MOONSHOT_V1_8K,\n            ModelType.GLM_4V_FLASH,\n            ModelType.GLM_4_AIRX,\n            ModelType.OPENROUTER_OLYMPICODER_7B,\n            ModelType.LMSTUDIO_GEMMA_3_1B,\n            ModelType.LMSTUDIO_GEMMA_3_4B,\n            ModelType.LMSTUDIO_GEMMA_3_12B,\n            ModelType.LMSTUDIO_GEMMA_3_27B,\n            ModelType.WATSONX_GRANITE_3_8B_INSTRUCT,\n            ModelType.NOVITA_L3_8B_STHENO_V3_2,\n            ModelType.NOVITA_LLAMA_3_8B,\n            ModelType.NOVITA_GEMMA_2_9B_IT,\n            ModelType.NOVITA_LLAMA_3_70B,\n            ModelType.NOVITA_HERMES_2_PRO_LLAMA_3_8B,\n            ModelType.NOVITA_L3_70B_EURYALE_V2_1,\n            ModelType.NOVITA_L3_8B_LUNARIS,\n            ModelType.NOVITA_LLAMA_3_1_8B_BF16,\n            ModelType.NOVITA_L31_70B_EURYALE_V2_2,\n        }:\n            return 8_192\n        elif self in {\n            ModelType.PPIO_BAICHUAN_2_13B_CHAT,\n        }:\n            return 14_336\n        elif self in {\n            ModelType.PPIO_DEEPSEEK_PROVER_V2_671B,\n            ModelType.NOVITA_DOLPHIN_MIXTRAL_8X22B,\n        }:\n            return 16_000\n        elif self in {\n            ModelType.GPT_3_5_TURBO,\n            ModelType.YI_LIGHTNING,\n            ModelType.YI_MEDIUM,\n            ModelType.YI_LARGE_TURBO,\n            ModelType.YI_VISION,\n            ModelType.YI_SPARK,\n            ModelType.YI_LARGE_RAG,\n            ModelType.SAMBA_LLAMA_3_1_8B,\n            ModelType.SAMBA_LLAMA_3_1_405B,\n            ModelType.GLM_4V_PLUS_0111,\n            ModelType.GLM_ZERO_PREVIEW,\n            ModelType.PPIO_YI_1_5_34B_CHAT,\n            ModelType.NOVITA_LLAMA_3_1_8B,\n        }:\n            return 16_384\n        elif self in {\n            ModelType.NETMIND_DOUBAO_1_5_PRO,\n            ModelType.NOVITA_GEMMA_3_27B_IT,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n            ModelType.NOVITA_QWEN_2_5_72B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B,\n            ModelType.NOVITA_GLM_4_9B_0414,\n            ModelType.NOVITA_GLM_Z1_9B_0414,\n            ModelType.NOVITA_GLM_Z1_32B_0414,\n            ModelType.NOVITA_GLM_4_32B_0414,\n            ModelType.NOVITA_GLM_Z1_RUMINATION_32B_0414,\n            ModelType.NOVITA_QWEN_2_5_7B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n            ModelType.CRYNUX_QWEN_3_4_B,\n            ModelType.CRYNUX_QWEN_3_8_B,\n            ModelType.CRYNUX_QWEN_2_5_7B,\n            ModelType.CRYNUX_QWEN_2_5_7B_INSTRUCT,\n            ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B,\n            ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B,\n            ModelType.ERNIE_X1_TURBO_32K,\n            ModelType.ERNIE_X1_32K,\n            ModelType.ERNIE_X1_32K_PREVIEW,\n            ModelType.ERNIE_4_5_TURBO_32K,\n            ModelType.QWEN3_235B_A22B,\n        }:\n            return 32_000\n        elif self in {\n            ModelType.MISTRAL_CODESTRAL,\n            ModelType.MISTRAL_7B,\n            ModelType.MISTRAL_MIXTRAL_8x7B,\n            ModelType.GROQ_MIXTRAL_8_7B,\n            ModelType.YI_LARGE,\n            ModelType.YI_LARGE_FC,\n            ModelType.QWEN_MAX,\n            ModelType.QWEN_VL_MAX,\n            ModelType.NVIDIA_YI_LARGE,\n            ModelType.NVIDIA_MISTRAL_LARGE,\n            ModelType.NVIDIA_MIXTRAL_8X7B,\n            ModelType.QWEN_QWQ_32B,\n            ModelType.QWEN_QWQ_PLUS,\n            ModelType.QWEN_QVQ_72B,\n            ModelType.INTERNLM3_8B_INSTRUCT,\n            ModelType.INTERNLM3_LATEST,\n            ModelType.INTERNLM2_5_LATEST,\n            ModelType.INTERNLM2_PRO_CHAT,\n            ModelType.TOGETHER_MIXTRAL_8_7B,\n            ModelType.SGLANG_MISTRAL_7B,\n            ModelType.MOONSHOT_V1_32K,\n            ModelType.AIML_MIXTRAL_8X7B,\n            ModelType.AIML_MISTRAL_7B_INSTRUCT,\n            ModelType.PPIO_QWEN_2_5_72B,\n            ModelType.PPIO_LLAMA_3_1_70B,\n            ModelType.MODELSCOPE_QWEN_2_5_7B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_14B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_32B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_72B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_3_235B_A22B,\n            ModelType.MODELSCOPE_QWEN_3_32B,\n            ModelType.MODELSCOPE_QWQ_32B,\n            ModelType.MODELSCOPE_QWQ_32B_PREVIEW,\n            ModelType.MODELSCOPE_LLAMA_3_1_8B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_1_70B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_1_405B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.MODELSCOPE_MINISTRAL_8B_INSTRUCT,\n            ModelType.MODELSCOPE_DEEPSEEK_V3_0324,\n            ModelType.OPENROUTER_LLAMA_3_1_405B,\n            ModelType.WATSONX_MISTRAL_LARGE,\n            ModelType.NOVITA_QWEN_32B,\n            ModelType.NOVITA_LLAMA_3_1_70B,\n            ModelType.NOVITA_MISTRAL_7B,\n            ModelType.NOVITA_LLAMA_3_2_11B_VISION,\n            ModelType.NOVITA_LLAMA_3_2_3B,\n            ModelType.NEBIUS_MISTRAL_7B_INSTRUCT,\n        }:\n            return 32_768\n        elif self in {\n            ModelType.MISTRAL_MIXTRAL_8x22B,\n            ModelType.DEEPSEEK_CHAT,\n            ModelType.DEEPSEEK_REASONER,\n            ModelType.PPIO_DEEPSEEK_R1_TURBO,\n            ModelType.PPIO_DEEPSEEK_V3_TURBO,\n            ModelType.PPIO_DEEPSEEK_R1_COMMUNITY,\n            ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,\n            ModelType.PPIO_DEEPSEEK_R1,\n            ModelType.PPIO_DEEPSEEK_V3,\n            ModelType.AWS_DEEPSEEK_R1,\n            ModelType.NETMIND_QWQ_32B,\n            ModelType.NOVITA_DEEPSEEK_V3_TURBO,\n            ModelType.NOVITA_DEEPSEEK_R1_TURBO,\n            ModelType.NOVITA_DEEPSEEK_V3,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B,\n            ModelType.NOVITA_DEEPSEEK_R1,\n        }:\n            return 64_000\n        elif self in {\n            ModelType.NOVITA_WIZARDLM_2_8X22B,\n        }:\n            return 65_535\n        elif self in {\n            ModelType.NOVITA_QWEN_2_5_V1_72B,\n            ModelType.DEEPSEEK_R1,\n        }:\n            return 96_000\n        elif self in {\n            ModelType.CLAUDE_2_0,\n            ModelType.CLAUDE_INSTANT_1_2,\n        }:\n            return 100_000\n        elif self in {\n            ModelType.GPT_4O,\n            ModelType.GPT_4O_MINI,\n            ModelType.GPT_4_TURBO,\n            ModelType.O1_PREVIEW,\n            ModelType.O1_MINI,\n            ModelType.GPT_4_5_PREVIEW,\n            ModelType.GPT_5,\n            ModelType.GPT_5_NANO,\n            ModelType.GPT_5_MINI,\n            ModelType.MISTRAL_LARGE,\n            ModelType.MISTRAL_NEMO,\n            ModelType.MISTRAL_PIXTRAL_12B,\n            ModelType.MISTRAL_8B,\n            ModelType.MISTRAL_3B,\n            ModelType.MISTRAL_SMALL_3_2,\n            ModelType.MAGISTRAL_SMALL_1_2,\n            ModelType.QWEN_2_5_CODER_32B,\n            ModelType.QWEN_2_5_VL_72B,\n            ModelType.QWEN_2_5_72B,\n            ModelType.QWEN_2_5_32B,\n            ModelType.QWEN_2_5_14B,\n            ModelType.COHERE_COMMAND_R,\n            ModelType.COHERE_COMMAND_R_PLUS,\n            ModelType.COHERE_COMMAND_NIGHTLY,\n            ModelType.NVIDIA_LLAMA3_1_8B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_1_70B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_1_405B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_2_1B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_2_3B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_3_70B_INSTRUCT,\n            ModelType.GROQ_LLAMA_3_3_70B,\n            ModelType.SAMBA_LLAMA_3_1_70B,\n            ModelType.SGLANG_LLAMA_3_1_8B,\n            ModelType.SGLANG_LLAMA_3_1_70B,\n            ModelType.SGLANG_LLAMA_3_1_405B,\n            ModelType.SGLANG_LLAMA_3_2_1B,\n            ModelType.SGLANG_MIXTRAL_NEMO,\n            ModelType.MOONSHOT_V1_128K,\n            ModelType.GLM_4_PLUS,\n            ModelType.GLM_4_AIR,\n            ModelType.GLM_4_AIR_0111,\n            ModelType.GLM_4_FLASHX,\n            ModelType.GLM_4_FLASH,\n            ModelType.AWS_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.AWS_LLAMA_3_2_90B_INSTRUCT,\n            ModelType.AWS_LLAMA_3_2_11B_INSTRUCT,\n            ModelType.NETMIND_DEEPSEEK_R1,\n            ModelType.NETMIND_DEEPSEEK_V3,\n            ModelType.NOVITA_DEEPSEEK_V3_0324,\n            ModelType.MISTRAL_MEDIUM_3_1,\n            ModelType.MAGISTRAL_MEDIUM_1_2,\n            ModelType.ERNIE_4_5_TURBO_128K,\n            ModelType.DEEPSEEK_V3,\n            ModelType.MOONSHOT_KIMI_K2,\n            ModelType.NEBIUS_GLM_4_5,\n            ModelType.NEBIUS_DEEPSEEK_V3,\n            ModelType.NEBIUS_DEEPSEEK_R1,\n            ModelType.NEBIUS_GPT_OSS_120B,\n            ModelType.NEBIUS_GPT_OSS_20B,\n            ModelType.COMETAPI_GPT_5_CHAT_LATEST,\n            ModelType.COMETAPI_CHATGPT_4O_LATEST,\n            ModelType.COMETAPI_GPT_5_MINI,\n            ModelType.COMETAPI_GPT_5_NANO,\n            ModelType.COMETAPI_GPT_5,\n            ModelType.COMETAPI_GPT_4_1,\n            ModelType.COMETAPI_GPT_4O_MINI,\n            ModelType.COMETAPI_O4_MINI_2025_04_16,\n            ModelType.COMETAPI_O3_PRO_2025_06_10,\n            ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805,\n            ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING,\n            ModelType.COMETAPI_CLAUDE_SONNET_4_20250514,\n            ModelType.COMETAPI_CLAUDE_SONNET_4_20250514_THINKING,\n            ModelType.COMETAPI_CLAUDE_3_7_SONNET_LATEST,\n            ModelType.COMETAPI_CLAUDE_3_5_HAIKU_LATEST,\n            ModelType.COMETAPI_GEMINI_2_5_PRO,\n            ModelType.COMETAPI_GEMINI_2_5_FLASH,\n            ModelType.COMETAPI_GEMINI_2_5_FLASH_LITE,\n            ModelType.COMETAPI_GEMINI_2_0_FLASH,\n            ModelType.COMETAPI_GROK_4_0709,\n            ModelType.COMETAPI_GROK_3,\n            ModelType.COMETAPI_GROK_3_MINI,\n            ModelType.COMETAPI_GROK_2_IMAGE_1212,\n            ModelType.COMETAPI_DEEPSEEK_V3_1,\n            ModelType.COMETAPI_DEEPSEEK_V3,\n            ModelType.COMETAPI_DEEPSEEK_R1_0528,\n            ModelType.COMETAPI_DEEPSEEK_CHAT,\n            ModelType.COMETAPI_DEEPSEEK_REASONER,\n            ModelType.COMETAPI_QWEN3_30B_A3B,\n            ModelType.COMETAPI_QWEN3_CODER_PLUS_2025_07_22,\n        }:\n            return 128_000\n        elif self in {\n            ModelType.NOVITA_LLAMA_3_2_1B,\n        }:\n            return 131_000\n        elif self in {\n            ModelType.GROQ_LLAMA_3_1_8B,\n            ModelType.QWEN_PLUS,\n            ModelType.QWEN_TURBO,\n            ModelType.QWEN_CODER_TURBO,\n            ModelType.QWEN_PLUS_LATEST,\n            ModelType.QWEN_PLUS_2025_04_28,\n            ModelType.QWEN_TURBO_LATEST,\n            ModelType.QWEN_TURBO_2025_04_28,\n            ModelType.TOGETHER_LLAMA_3_1_8B,\n            ModelType.TOGETHER_LLAMA_3_1_70B,\n            ModelType.TOGETHER_LLAMA_3_1_405B,\n            ModelType.TOGETHER_LLAMA_3_3_70B,\n            ModelType.SGLANG_QWEN_2_5_7B,\n            ModelType.SGLANG_QWEN_2_5_32B,\n            ModelType.SGLANG_QWEN_2_5_72B,\n            ModelType.OPENROUTER_LLAMA_3_1_70B,\n            ModelType.PPIO_LLAMA_3_3_70B,\n            ModelType.OPENROUTER_LLAMA_4_SCOUT,\n            ModelType.WATSONX_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_1B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_3B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT,\n            ModelType.WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT,\n            ModelType.NOVITA_LLAMA_4_SCOUT_17B,\n            ModelType.NOVITA_LLAMA_3_3_70B,\n            ModelType.NOVITA_MISTRAL_NEMO,\n            ModelType.NEBIUS_LLAMA_3_1_70B,\n        }:\n            return 131_072\n        elif self in {\n            ModelType.O1,\n            ModelType.O3_MINI,\n            ModelType.O3_PRO,\n            ModelType.CLAUDE_2_1,\n            ModelType.CLAUDE_3_OPUS,\n            ModelType.CLAUDE_3_SONNET,\n            ModelType.CLAUDE_3_HAIKU,\n            ModelType.CLAUDE_3_5_SONNET,\n            ModelType.CLAUDE_3_5_HAIKU,\n            ModelType.CLAUDE_3_7_SONNET,\n            ModelType.CLAUDE_SONNET_4,\n            ModelType.CLAUDE_OPUS_4,\n            ModelType.CLAUDE_OPUS_4_1,\n            ModelType.YI_MEDIUM_200K,\n            ModelType.AWS_CLAUDE_3_5_SONNET,\n            ModelType.AWS_CLAUDE_3_HAIKU,\n            ModelType.AWS_CLAUDE_3_SONNET,\n            ModelType.AWS_CLAUDE_3_7_SONNET,\n            ModelType.AWS_CLAUDE_SONNET_4,\n            ModelType.AWS_CLAUDE_OPUS_4,\n            ModelType.AWS_CLAUDE_OPUS_4_1,\n            ModelType.O4_MINI,\n            ModelType.O3,\n        }:\n            return 200_000\n        elif self in {\n            ModelType.MISTRAL_CODESTRAL_MAMBA,\n            ModelType.OPENROUTER_LLAMA_4_MAVERICK_FREE,\n            ModelType.OPENROUTER_HORIZON_ALPHA,\n        }:\n            return 256_000\n\n        elif self in {\n            ModelType.NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT,\n        }:\n            return 320_000\n        elif self in {\n            ModelType.OPENROUTER_LLAMA_4_SCOUT_FREE,\n            ModelType.NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT,\n        }:\n            return 512_000\n        elif self in {\n            ModelType.GEMINI_2_5_FLASH,\n            ModelType.GEMINI_2_5_PRO,\n            ModelType.GEMINI_2_0_FLASH,\n            ModelType.GEMINI_2_0_FLASH_EXP,\n            ModelType.GEMINI_2_0_FLASH_THINKING,\n            ModelType.GEMINI_2_0_FLASH_LITE,\n            ModelType.GEMINI_2_0_FLASH_LITE_PREVIEW,\n            ModelType.GEMINI_1_5_FLASH,\n            ModelType.GEMINI_1_5_PRO,\n            ModelType.GEMINI_2_0_PRO_EXP,  # Not given in doc, assume the same\n            ModelType.GLM_4_LONG,\n            ModelType.TOGETHER_LLAMA_4_MAVERICK,\n            ModelType.OPENROUTER_LLAMA_4_MAVERICK,\n            ModelType.AMD_GPT4,\n            ModelType.GPT_4_1,\n            ModelType.GPT_4_1_MINI,\n            ModelType.GPT_4_1_NANO,\n            ModelType.NOVITA_LLAMA_4_MAVERICK_17B,\n        }:\n            return 1_048_576\n        elif self in {\n            ModelType.QWEN_3_CODER_PLUS,\n        }:\n            return 1_000_000\n        elif self in {\n            ModelType.QWEN_LONG,\n            ModelType.TOGETHER_LLAMA_4_SCOUT,\n        }:\n            return 10_000_000\n\n        else:\n            logger.warning(\n                f\"Unknown model type {self}, set maximum token limit \"\n                f\"to 999_999_999\"\n            )\n            return 999_999_999\n\n\nclass EmbeddingModelType(Enum):\n    TEXT_EMBEDDING_ADA_2 = \"text-embedding-ada-002\"\n    TEXT_EMBEDDING_3_SMALL = \"text-embedding-3-small\"\n    TEXT_EMBEDDING_3_LARGE = \"text-embedding-3-large\"\n\n    JINA_EMBEDDINGS_V3 = \"jina-embeddings-v3\"\n    JINA_CLIP_V2 = \"jina-clip-v2\"\n    JINA_COLBERT_V2 = \"jina-colbert-v2\"\n    JINA_EMBEDDINGS_V2_BASE_CODE = \"jina-embeddings-v2-base-code\"\n\n    MISTRAL_EMBED = \"mistral-embed\"\n\n    GEMINI_EMBEDDING_EXP = \"gemini-embedding-exp-03-07\"\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this type of models is an OpenAI-released model.\"\"\"\n        return self in {\n            EmbeddingModelType.TEXT_EMBEDDING_ADA_2,\n            EmbeddingModelType.TEXT_EMBEDDING_3_SMALL,\n            EmbeddingModelType.TEXT_EMBEDDING_3_LARGE,\n        }\n\n    @property\n    def is_jina(self) -> bool:\n        r\"\"\"Returns whether this type of models is an Jina model.\"\"\"\n        return self in {\n            EmbeddingModelType.JINA_EMBEDDINGS_V3,\n            EmbeddingModelType.JINA_CLIP_V2,\n            EmbeddingModelType.JINA_COLBERT_V2,\n            EmbeddingModelType.JINA_EMBEDDINGS_V2_BASE_CODE,\n        }\n\n    @property\n    def is_mistral(self) -> bool:\n        r\"\"\"Returns whether this type of models is an Mistral-released\n        model.\n        \"\"\"\n        return self in {\n            EmbeddingModelType.MISTRAL_EMBED,\n        }\n\n    @property\n    def is_gemini(self) -> bool:\n        r\"\"\"Returns whether this type of models is an Gemini-released model.\"\"\"\n        return self in {\n            EmbeddingModelType.GEMINI_EMBEDDING_EXP,\n        }\n\n    @property\n    def output_dim(self) -> int:\n        if self in {\n            EmbeddingModelType.JINA_COLBERT_V2,\n        }:\n            return 128\n        elif self in {\n            EmbeddingModelType.JINA_EMBEDDINGS_V2_BASE_CODE,\n        }:\n            return 768\n        elif self in {\n            EmbeddingModelType.JINA_EMBEDDINGS_V3,\n            EmbeddingModelType.JINA_CLIP_V2,\n        }:\n            return 1024\n        elif self is EmbeddingModelType.TEXT_EMBEDDING_ADA_2:\n            return 1536\n        elif self is EmbeddingModelType.TEXT_EMBEDDING_3_SMALL:\n            return 1536\n        elif self is EmbeddingModelType.TEXT_EMBEDDING_3_LARGE:\n            return 3072\n        elif self is EmbeddingModelType.MISTRAL_EMBED:\n            return 1024\n        elif self is EmbeddingModelType.GEMINI_EMBEDDING_EXP:\n            return 3072\n        else:\n            raise ValueError(f\"Unknown model type {self}.\")\n\n\nclass GeminiEmbeddingTaskType(str, Enum):\n    r\"\"\"Task types for Gemini embedding models.\n\n    For more information, please refer to:\n    https://ai.google.dev/gemini-api/docs/embeddings#task-types\n    \"\"\"\n\n    SEMANTIC_SIMILARITY = \"SEMANTIC_SIMILARITY\"\n    CLASSIFICATION = \"CLASSIFICATION\"\n    CLUSTERING = \"CLUSTERING\"\n    RETRIEVAL_DOCUMENT = \"RETRIEVAL_DOCUMENT\"\n    RETRIEVAL_QUERY = \"RETRIEVAL_QUERY\"\n    QUESTION_ANSWERING = \"QUESTION_ANSWERING\"\n    FACT_VERIFICATION = \"FACT_VERIFICATION\"\n    CODE_RETRIEVAL_QUERY = \"CODE_RETRIEVAL_QUERY\"\n\n\nclass TaskType(Enum):\n    AI_SOCIETY = \"ai_society\"\n    CODE = \"code\"\n    MISALIGNMENT = \"misalignment\"\n    TRANSLATION = \"translation\"\n    EVALUATION = \"evaluation\"\n    SOLUTION_EXTRACTION = \"solution_extraction\"\n    ROLE_DESCRIPTION = \"role_description\"\n    GENERATE_TEXT_EMBEDDING_DATA = \"generate_text_embedding_data\"\n    OBJECT_RECOGNITION = \"object_recognition\"\n    IMAGE_CRAFT = \"image_craft\"\n    MULTI_CONDITION_IMAGE_CRAFT = \"multi_condition_image_craft\"\n    DEFAULT = \"default\"\n    VIDEO_DESCRIPTION = \"video_description\"\n\n\nclass VectorDistance(Enum):\n    r\"\"\"Distance metrics used in a vector database.\"\"\"\n\n    DOT = \"dot\"\n    r\"\"\"Dot product. https://en.wikipedia.org/wiki/Dot_product\"\"\"\n\n    COSINE = \"cosine\"\n    r\"\"\"Cosine similarity. https://en.wikipedia.org/wiki/Cosine_similarity\"\"\"\n\n    EUCLIDEAN = \"euclidean\"\n    r\"\"\"Euclidean distance. https://en.wikipedia.org/wiki/Euclidean_distance\"\"\"\n\n\nclass OpenAIBackendRole(Enum):\n    ASSISTANT = \"assistant\"\n    SYSTEM = \"system\"\n    DEVELOPER = \"developer\"\n    USER = \"user\"\n    FUNCTION = \"function\"\n    TOOL = \"tool\"\n\n\nclass TerminationMode(Enum):\n    ANY = \"any\"\n    ALL = \"all\"\n\n\nclass OpenAIImageTypeMeta(EnumMeta):\n    def __contains__(cls, image_type: object) -> bool:\n        try:\n            cls(image_type)\n        except ValueError:\n            return False\n        return True\n\n\nclass OpenAIImageType(Enum, metaclass=OpenAIImageTypeMeta):\n    r\"\"\"Image types supported by OpenAI vision model.\"\"\"\n\n    # https://platform.openai.com/docs/guides/vision\n    PNG = \"png\"\n    JPEG = \"jpeg\"\n    JPG = \"jpg\"\n    WEBP = \"webp\"\n    GIF = \"gif\"\n\n\nclass OpenAIVisionDetailType(Enum):\n    AUTO = \"auto\"\n    LOW = \"low\"\n    HIGH = \"high\"\n\n\nclass StorageType(Enum):\n    MILVUS = \"milvus\"\n    QDRANT = \"qdrant\"\n    TIDB = \"tidb\"\n\n\nclass OpenAPIName(Enum):\n    COURSERA = \"coursera\"\n    KLARNA = \"klarna\"\n    SPEAK = \"speak\"\n    NASA_APOD = \"nasa_apod\"\n    BIZTOC = \"biztoc\"\n    CREATE_QR_CODE = \"create_qr_code\"\n    OUTSCHOOL = \"outschool\"\n    WEB_SCRAPER = \"web_scraper\"\n\n\nclass ModelPlatformType(Enum):\n    DEFAULT = os.getenv(\"DEFAULT_MODEL_PLATFORM_TYPE\", \"openai\")\n\n    OPENAI = \"openai\"\n    AWS_BEDROCK = \"aws-bedrock\"\n    AZURE = \"azure\"\n    ANTHROPIC = \"anthropic\"\n    GROQ = \"groq\"\n    NEBIUS = \"nebius\"\n    COMETAPI = \"cometapi\"\n    OPENROUTER = \"openrouter\"\n    OLLAMA = \"ollama\"\n    LITELLM = \"litellm\"\n    LMSTUDIO = \"lmstudio\"\n    ZHIPU = \"zhipuai\"\n    GEMINI = \"gemini\"\n    VLLM = \"vllm\"\n    MISTRAL = \"mistral\"\n    REKA = \"reka\"\n    TOGETHER = \"together\"\n    STUB = \"stub\"\n    OPENAI_COMPATIBLE_MODEL = \"openai-compatible-model\"\n    SAMBA = \"samba-nova\"\n    COHERE = \"cohere\"\n    YI = \"lingyiwanwu\"\n    QWEN = \"tongyi-qianwen\"\n    AMD = \"amd\"\n    NVIDIA = \"nvidia\"\n    DEEPSEEK = \"deepseek\"\n    PPIO = \"ppio\"\n    SGLANG = \"sglang\"\n    INTERNLM = \"internlm\"\n    MOONSHOT = \"moonshot\"\n    MODELSCOPE = \"modelscope\"\n    SILICONFLOW = \"siliconflow\"\n    AIML = \"aiml\"\n    VOLCANO = \"volcano\"\n    NETMIND = \"netmind\"\n    NOVITA = \"novita\"\n    WATSONX = \"watsonx\"\n    QIANFAN = \"qianfan\"\n    CRYNUX = \"crynux\"\n\n    @classmethod\n    def from_name(cls, name):\n        r\"\"\"Returns the ModelPlatformType enum value from a string.\"\"\"\n        for model_platfrom_type in cls:\n            if model_platfrom_type.value == name:\n                return model_platfrom_type\n        raise ValueError(f\"Unknown ModelPlatformType name: {name}\")\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this platform is openai.\"\"\"\n        return self is ModelPlatformType.OPENAI\n\n    @property\n    def is_aws_bedrock(self) -> bool:\n        r\"\"\"Returns whether this platform is aws-bedrock.\"\"\"\n        return self is ModelPlatformType.AWS_BEDROCK\n\n    @property\n    def is_azure(self) -> bool:\n        r\"\"\"Returns whether this platform is azure.\"\"\"\n        return self is ModelPlatformType.AZURE\n\n    @property\n    def is_anthropic(self) -> bool:\n        r\"\"\"Returns whether this platform is anthropic.\"\"\"\n        return self is ModelPlatformType.ANTHROPIC\n\n    @property\n    def is_groq(self) -> bool:\n        r\"\"\"Returns whether this platform is groq.\"\"\"\n        return self is ModelPlatformType.GROQ\n\n    @property\n    def is_openrouter(self) -> bool:\n        r\"\"\"Returns whether this platform is openrouter.\"\"\"\n        return self is ModelPlatformType.OPENROUTER\n\n    @property\n    def is_lmstudio(self) -> bool:\n        r\"\"\"Returns whether this platform is lmstudio.\"\"\"\n        return self is ModelPlatformType.LMSTUDIO\n\n    @property\n    def is_ollama(self) -> bool:\n        r\"\"\"Returns whether this platform is ollama.\"\"\"\n        return self is ModelPlatformType.OLLAMA\n\n    @property\n    def is_vllm(self) -> bool:\n        r\"\"\"Returns whether this platform is vllm.\"\"\"\n        return self is ModelPlatformType.VLLM\n\n    @property\n    def is_sglang(self) -> bool:\n        r\"\"\"Returns whether this platform is sglang.\"\"\"\n        return self is ModelPlatformType.SGLANG\n\n    @property\n    def is_together(self) -> bool:\n        r\"\"\"Returns whether this platform is together.\"\"\"\n        return self is ModelPlatformType.TOGETHER\n\n    @property\n    def is_litellm(self) -> bool:\n        r\"\"\"Returns whether this platform is litellm.\"\"\"\n        return self is ModelPlatformType.LITELLM\n\n    @property\n    def is_zhipuai(self) -> bool:\n        r\"\"\"Returns whether this platform is zhipu.\"\"\"\n        return self is ModelPlatformType.ZHIPU\n\n    @property\n    def is_mistral(self) -> bool:\n        r\"\"\"Returns whether this platform is mistral.\"\"\"\n        return self is ModelPlatformType.MISTRAL\n\n    @property\n    def is_openai_compatible_model(self) -> bool:\n        r\"\"\"Returns whether this is a platform supporting openai\n        compatibility\"\"\"\n        return self is ModelPlatformType.OPENAI_COMPATIBLE_MODEL\n\n    @property\n    def is_gemini(self) -> bool:\n        r\"\"\"Returns whether this platform is Gemini.\"\"\"\n        return self is ModelPlatformType.GEMINI\n\n    @property\n    def is_reka(self) -> bool:\n        r\"\"\"Returns whether this platform is Reka.\"\"\"\n        return self is ModelPlatformType.REKA\n\n    @property\n    def is_samba(self) -> bool:\n        r\"\"\"Returns whether this platform is Samba Nova.\"\"\"\n        return self is ModelPlatformType.SAMBA\n\n    @property\n    def is_cohere(self) -> bool:\n        r\"\"\"Returns whether this platform is Cohere.\"\"\"\n        return self is ModelPlatformType.COHERE\n\n    @property\n    def is_yi(self) -> bool:\n        r\"\"\"Returns whether this platform is Yi.\"\"\"\n        return self is ModelPlatformType.YI\n\n    @property\n    def is_qwen(self) -> bool:\n        r\"\"\"Returns whether this platform is Qwen.\"\"\"\n        return self is ModelPlatformType.QWEN\n\n    @property\n    def is_nvidia(self) -> bool:\n        r\"\"\"Returns whether this platform is Nvidia.\"\"\"\n        return self is ModelPlatformType.NVIDIA\n\n    @property\n    def is_deepseek(self) -> bool:\n        r\"\"\"Returns whether this platform is DeepSeek.\"\"\"\n        return self is ModelPlatformType.DEEPSEEK\n\n    @property\n    def is_netmind(self) -> bool:\n        r\"\"\"Returns whether this platform is Netmind.\"\"\"\n        return self is ModelPlatformType.NETMIND\n\n    @property\n    def is_ppio(self) -> bool:\n        r\"\"\"Returns whether this platform is PPIO.\"\"\"\n        return self is ModelPlatformType.PPIO\n\n    @property\n    def is_internlm(self) -> bool:\n        r\"\"\"Returns whether this platform is InternLM.\"\"\"\n        return self is ModelPlatformType.INTERNLM\n\n    @property\n    def is_moonshot(self) -> bool:\n        r\"\"\"Returns whether this platform is Moonshot model.\"\"\"\n        return self is ModelPlatformType.MOONSHOT\n\n    @property\n    def is_modelscope(self) -> bool:\n        r\"\"\"Returns whether this platform is ModelScope model.\"\"\"\n        return self is ModelPlatformType.MODELSCOPE\n\n    @property\n    def is_siliconflow(self) -> bool:\n        r\"\"\"Returns whether this platform is SiliconFlow.\"\"\"\n        return self is ModelPlatformType.SILICONFLOW\n\n    @property\n    def is_aiml(self) -> bool:\n        r\"\"\"Returns whether this platform is AIML.\"\"\"\n        return self is ModelPlatformType.AIML\n\n    @property\n    def is_volcano(self) -> bool:\n        r\"\"\"Returns whether this platform is volcano.\"\"\"\n        return self is ModelPlatformType.VOLCANO\n\n    @property\n    def is_novita(self) -> bool:\n        r\"\"\"Returns whether this platform is Novita.\"\"\"\n        return self is ModelPlatformType.NOVITA\n\n    @property\n    def is_watsonx(self) -> bool:\n        r\"\"\"Returns whether this platform is WatsonX.\"\"\"\n        return self is ModelPlatformType.WATSONX\n\n    @property\n    def is_crynux(self) -> bool:\n        r\"\"\"Returns whether this platform is Crynux.\"\"\"\n        return self is ModelPlatformType.CRYNUX\n\n\nclass AudioModelType(Enum):\n    TTS_1 = \"tts-1\"\n    TTS_1_HD = \"tts-1-hd\"\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this type of audio models is an OpenAI-released\n        model.\"\"\"\n        return self in {\n            AudioModelType.TTS_1,\n            AudioModelType.TTS_1_HD,\n        }\n\n\nclass VoiceType(Enum):\n    ALLOY = \"alloy\"\n    ECHO = \"echo\"\n    FABLE = \"fable\"\n    ONYX = \"onyx\"\n    NOVA = \"nova\"\n    SHIMMER = \"shimmer\"\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this type of voice is an OpenAI-released voice.\"\"\"\n        return self in {\n            VoiceType.ALLOY,\n            VoiceType.ECHO,\n            VoiceType.FABLE,\n            VoiceType.ONYX,\n            VoiceType.NOVA,\n            VoiceType.SHIMMER,\n        }\n\n\nclass JinaReturnFormat(Enum):\n    DEFAULT = None\n    MARKDOWN = \"markdown\"\n    HTML = \"html\"\n    TEXT = \"text\"\n\n\nclass HuggingFaceRepoType(str, Enum):\n    DATASET = \"dataset\"\n    MODEL = \"model\"\n    SPACE = \"space\""}, "ecd0ca309215d137e33bd278abaa8d73d250957b": {"t": 1785846918.0701218, "n": 70958, "h": "31041e19c42e5c5b", "c": "# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========\nimport os\nfrom enum import Enum, EnumMeta\nfrom typing import Union, cast\n\nfrom camel.logger import get_logger\nfrom camel.types.unified_model_type import UnifiedModelType\n\nlogger = get_logger(__name__)\n\n\nclass RoleType(Enum):\n    ASSISTANT = \"assistant\"\n    USER = \"user\"\n    CRITIC = \"critic\"\n    EMBODIMENT = \"embodiment\"\n    DEFAULT = \"default\"\n\n\nclass ModelType(UnifiedModelType, Enum):\n    DEFAULT = os.getenv(\"DEFAULT_MODEL_TYPE\", \"gpt-5-mini\")\n\n    GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n    GPT_4 = \"gpt-4\"\n    GPT_4_TURBO = \"gpt-4-turbo\"\n    GPT_4O = \"gpt-4o\"\n    GPT_4O_MINI = \"gpt-4o-mini\"\n    GPT_4_5_PREVIEW = \"gpt-4.5-preview\"\n    O1 = \"o1\"\n    O1_PREVIEW = \"o1-preview\"\n    O1_MINI = \"o1-mini\"\n    O3_MINI = \"o3-mini\"\n    GPT_4_1 = \"gpt-4.1-2025-04-14\"\n    GPT_4_1_MINI = \"gpt-4.1-mini-2025-04-14\"\n    GPT_4_1_NANO = \"gpt-4.1-nano-2025-04-14\"\n    O4_MINI = \"o4-mini\"\n    O3 = \"o3\"\n    O3_PRO = \"o3-pro\"\n    GPT_5 = \"gpt-5\"\n    GPT_5_MINI = \"gpt-5-mini\"\n    GPT_5_NANO = \"gpt-5-nano\"\n\n    AWS_CLAUDE_3_7_SONNET = \"anthropic.claude-3-7-sonnet-20250219-v1:0\"\n    AWS_CLAUDE_3_5_SONNET = \"anthropic.claude-3-5-sonnet-20241022-v2:0\"\n    AWS_CLAUDE_3_HAIKU = \"anthropic.claude-3-haiku-20240307-v1:0\"\n    AWS_CLAUDE_3_SONNET = \"anthropic.claude-3-sonnet-20240229-v1:0\"\n    AWS_DEEPSEEK_R1 = \"us.deepseek.r1-v1:0\"\n    AWS_LLAMA_3_3_70B_INSTRUCT = \"us.meta.llama3-3-70b-instruct-v1:0\"\n    AWS_LLAMA_3_2_90B_INSTRUCT = \"us.meta.llama3-2-90b-instruct-v1:0\"\n    AWS_LLAMA_3_2_11B_INSTRUCT = \"us.meta.llama3-2-11b-instruct-v1:0\"\n    AWS_CLAUDE_SONNET_4 = \"anthropic.claude-sonnet-4-20250514-v1:0\"\n    AWS_CLAUDE_OPUS_4 = \"anthropic.claude-opus-4-20250514-v1:0\"\n    AWS_CLAUDE_OPUS_4_1 = \"anthropic.claude-opus-4-1-20250805-v1:0\"\n\n    AMD_GPT4 = \"dvue-aoai-001-gpt-4.1\"\n\n    GLM_4 = \"glm-4\"\n    GLM_4V = \"glm-4v\"\n    GLM_4V_FLASH = \"glm-4v-flash\"\n    GLM_4V_PLUS_0111 = \"glm-4v-plus-0111\"\n    GLM_4_PLUS = \"glm-4-plus\"\n    GLM_4_AIR = \"glm-4-air\"\n    GLM_4_AIR_0111 = \"glm-4-air-0111\"\n    GLM_4_AIRX = \"glm-4-airx\"\n    GLM_4_LONG = \"glm-4-long\"\n    GLM_4_FLASHX = \"glm-4-flashx\"\n    GLM_4_FLASH = \"glm-4-flash\"\n    GLM_ZERO_PREVIEW = \"glm-zero-preview\"\n    GLM_3_TURBO = \"glm-3-turbo\"\n\n    # Groq platform models\n    GROQ_LLAMA_3_1_8B = \"llama-3.1-8b-instant\"\n    GROQ_LLAMA_3_3_70B = \"llama-3.3-70b-versatile\"\n    GROQ_LLAMA_3_3_70B_PREVIEW = \"llama-3.3-70b-specdec\"\n    GROQ_LLAMA_3_8B = \"llama3-8b-8192\"\n    GROQ_LLAMA_3_70B = \"llama3-70b-8192\"\n    GROQ_MIXTRAL_8_7B = \"mixtral-8x7b-32768\"\n    GROQ_GEMMA_2_9B_IT = \"gemma2-9b-it\"\n\n    # Nebius AI Studio platform models\n    NEBIUS_GPT_OSS_120B = \"gpt-oss-120b\"\n    NEBIUS_GPT_OSS_20B = \"gpt-oss-20b\"\n    NEBIUS_GLM_4_5 = \"GLM-4.5\"\n    NEBIUS_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3\"\n    NEBIUS_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n    NEBIUS_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n    NEBIUS_MISTRAL_7B_INSTRUCT = \"mistralai/Mistral-7B-Instruct-v0.3\"\n\n    # CometAPI platform models\n    COMETAPI_GPT_5_CHAT_LATEST = \"gpt-5-chat-latest\"\n    COMETAPI_CHATGPT_4O_LATEST = \"chatgpt-4o-latest\"\n    COMETAPI_GPT_5_MINI = \"gpt-5-mini\"\n    COMETAPI_GPT_5_NANO = \"gpt-5-nano\"\n    COMETAPI_GPT_5 = \"gpt-5\"\n    COMETAPI_GPT_4_1 = \"gpt-4.1\"\n    COMETAPI_GPT_4O_MINI = \"gpt-4o-mini\"\n    COMETAPI_O4_MINI_2025_04_16 = \"o4-mini-2025-04-16\"\n    COMETAPI_O3_PRO_2025_06_10 = \"o3-pro-2025-06-10\"\n    COMETAPI_CLAUDE_OPUS_4_1_20250805 = \"claude-opus-4-1-20250805\"\n    COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING = (\n        \"claude-opus-4-1-20250805-thinking\"\n    )\n    COMETAPI_CLAUDE_SONNET_4_20250514 = \"claude-sonnet-4-20250514\"\n    COMETAPI_CLAUDE_SONNET_4_20250514_THINKING = (\n        \"claude-sonnet-4-20250514-thinking\"\n    )\n    COMETAPI_CLAUDE_3_7_SONNET_LATEST = \"claude-3-7-sonnet-latest\"\n    COMETAPI_CLAUDE_3_5_HAIKU_LATEST = \"claude-3-5-haiku-latest\"\n    COMETAPI_GEMINI_2_5_PRO = \"gemini-2.5-pro\"\n    COMETAPI_GEMINI_2_5_FLASH = \"gemini-2.5-flash\"\n    COMETAPI_GEMINI_2_5_FLASH_LITE = \"gemini-2.5-flash-lite\"\n    COMETAPI_GEMINI_2_0_FLASH = \"gemini-2.0-flash\"\n    COMETAPI_GROK_4_0709 = \"grok-4-0709\"\n    COMETAPI_GROK_3 = \"grok-3\"\n    COMETAPI_GROK_3_MINI = \"grok-3-mini\"\n    COMETAPI_GROK_2_IMAGE_1212 = \"grok-2-image-1212\"\n    COMETAPI_DEEPSEEK_V3_1 = \"deepseek-v3.1\"\n    COMETAPI_DEEPSEEK_V3 = \"deepseek-v3\"\n    COMETAPI_DEEPSEEK_R1_0528 = \"deepseek-r1-0528\"\n    COMETAPI_DEEPSEEK_CHAT = \"deepseek-chat\"\n    COMETAPI_DEEPSEEK_REASONER = \"deepseek-reasoner\"\n    COMETAPI_QWEN3_30B_A3B = \"qwen3-30b-a3b\"\n    COMETAPI_QWEN3_CODER_PLUS_2025_07_22 = \"qwen3-coder-plus-2025-07-22\"\n\n    # OpenRouter models\n    OPENROUTER_LLAMA_3_1_405B = \"meta-llama/llama-3.1-405b-instruct\"\n    OPENROUTER_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n    OPENROUTER_LLAMA_4_MAVERICK = \"meta-llama/llama-4-maverick\"\n    OPENROUTER_LLAMA_4_MAVERICK_FREE = \"meta-llama/llama-4-maverick:free\"\n    OPENROUTER_LLAMA_4_SCOUT = \"meta-llama/llama-4-scout\"\n    OPENROUTER_LLAMA_4_SCOUT_FREE = \"meta-llama/llama-4-scout:free\"\n    OPENROUTER_OLYMPICODER_7B = \"open-r1/olympiccoder-7b:free\"\n    OPENROUTER_HORIZON_ALPHA = \"openrouter/horizon-alpha\"\n    OPENROUTER_GROK_4_FAST = \"x-ai/grok-4-fast\"\n    OPENROUTER_GEMINI_2_5_FLASH = \"google/gemini-2.5-flash\"\n    OPENROUTER_GPT_4O_MINI = 'openai/gpt-4o-mini'\n    OPENROUTER_QWEN_PLUS = 'qwen/qwen-plus'\n    OPENROUTER_QWEN_VL_MAX = 'qwen/qwen-vl-max'\n    OPENROUTER_o3 = 'openai/o3'\n    OPENROUTER_CLAUDE_SONNET_4_5 = 'anthropic/claude-sonnet-4.5'\n    \n    # LMStudio models\n    LMSTUDIO_GEMMA_3_1B = \"gemma-3-1b\"\n    LMSTUDIO_GEMMA_3_4B = \"gemma-3-4b\"\n    LMSTUDIO_GEMMA_3_12B = \"gemma-3-12b\"\n    LMSTUDIO_GEMMA_3_27B = \"gemma-3-27b\"\n\n    # TogetherAI platform models support tool calling\n    TOGETHER_LLAMA_3_1_8B = \"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\"\n    TOGETHER_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\"\n    TOGETHER_LLAMA_3_1_405B = \"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\"\n    TOGETHER_LLAMA_3_3_70B = \"meta-llama/Llama-3.3-70B-Instruct-Turbo\"\n    TOGETHER_MIXTRAL_8_7B = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n    TOGETHER_MISTRAL_7B = \"mistralai/Mistral-7B-Instruct-v0.1\"\n    TOGETHER_LLAMA_4_MAVERICK = (\n        \"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\"\n    )\n    TOGETHER_LLAMA_4_SCOUT = \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n\n    # PPIO platform models support tool calling\n    PPIO_DEEPSEEK_PROVER_V2_671B = \"deepseek/deepseek-prover-v2-671b\"\n    PPIO_DEEPSEEK_R1_TURBO = \"deepseek/deepseek-r1-turbo\"\n    PPIO_DEEPSEEK_V3_TURBO = \"deepseek/deepseek-v3-turbo\"\n    PPIO_DEEPSEEK_R1_COMMUNITY = \"deepseek/deepseek-r1/community\"\n    PPIO_DEEPSEEK_V3_COMMUNITY = \"deepseek/deepseek-v3/community\"\n    PPIO_DEEPSEEK_R1 = \"deepseek/deepseek-r1\"\n    PPIO_DEEPSEEK_V3 = \"deepseek/deepseek-v3\"\n    PPIO_QWEN_2_5_72B = \"qwen/qwen-2.5-72b-instruct\"\n    PPIO_BAICHUAN_2_13B_CHAT = \"baichuan/baichuan2-13b-chat\"\n    PPIO_LLAMA_3_3_70B = \"meta-llama/llama-3.3-70b-instruct\"\n    PPIO_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n    PPIO_YI_1_5_34B_CHAT = \"01-ai/yi-1.5-34b-chat\"\n\n    # SambaNova Cloud platform models support tool calling\n    SAMBA_LLAMA_3_1_8B = \"Meta-Llama-3.1-8B-Instruct\"\n    SAMBA_LLAMA_3_1_70B = \"Meta-Llama-3.1-70B-Instruct\"\n    SAMBA_LLAMA_3_1_405B = \"Meta-Llama-3.1-405B-Instruct\"\n\n    # SGLang models support tool calling\n    SGLANG_LLAMA_3_1_8B = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n    SGLANG_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n    SGLANG_LLAMA_3_1_405B = \"meta-llama/Meta-Llama-3.1-405B-Instruct\"\n    SGLANG_LLAMA_3_2_1B = \"meta-llama/Llama-3.2-1B-Instruct\"\n    SGLANG_MIXTRAL_NEMO = \"mistralai/Mistral-Nemo-Instruct-2407\"\n    SGLANG_MISTRAL_7B = \"mistralai/Mistral-7B-Instruct-v0.3\"\n    SGLANG_QWEN_2_5_7B = \"Qwen/Qwen2.5-7B-Instruct\"\n    SGLANG_QWEN_2_5_32B = \"Qwen/Qwen2.5-32B-Instruct\"\n    SGLANG_QWEN_2_5_72B = \"Qwen/Qwen2.5-72B-Instruct\"\n\n    STUB = \"stub\"\n\n    # Legacy anthropic models\n    # NOTE: anthropic legacy models only Claude 2.1 has system prompt support\n    CLAUDE_2_1 = \"claude-2.1\"\n    CLAUDE_2_0 = \"claude-2.0\"\n    CLAUDE_INSTANT_1_2 = \"claude-instant-1.2\"\n\n    # Claude models\n    CLAUDE_3_OPUS = \"claude-3-opus-latest\"\n    CLAUDE_3_SONNET = \"claude-3-sonnet-20240229\"\n    CLAUDE_3_HAIKU = \"claude-3-haiku-20240307\"\n    CLAUDE_3_5_SONNET = \"claude-3-5-sonnet-latest\"\n    CLAUDE_3_5_HAIKU = \"claude-3-5-haiku-latest\"\n    CLAUDE_3_7_SONNET = \"claude-3-7-sonnet-latest\"\n    CLAUDE_SONNET_4 = \"claude-sonnet-4-20250514\"\n    CLAUDE_OPUS_4 = \"claude-opus-4-20250514\"\n    CLAUDE_OPUS_4_1 = \"claude-opus-4-1-20250805\"\n\n    # Netmind models\n    NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT = (\n        \"meta-llama/Llama-4-Maverick-17B-128E-Instruct\"\n    )\n    NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT = (\n        \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n    )\n    NETMIND_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n    NETMIND_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3-0324\"\n    NETMIND_DOUBAO_1_5_PRO = \"doubao/Doubao-1.5-pro\"\n    NETMIND_QWQ_32B = \"Qwen/QwQ-32B\"\n\n    # Nvidia models\n    NVIDIA_NEMOTRON_340B_INSTRUCT = \"nvidia/nemotron-4-340b-instruct\"\n    NVIDIA_NEMOTRON_340B_REWARD = \"nvidia/nemotron-4-340b-reward\"\n    NVIDIA_YI_LARGE = \"01-ai/yi-large\"\n    NVIDIA_MISTRAL_LARGE = \"mistralai/mistral-large\"\n    NVIDIA_MIXTRAL_8X7B = \"mistralai/mixtral-8x7b-instruct\"\n    NVIDIA_LLAMA3_70B = \"meta/llama3-70b\"\n    NVIDIA_LLAMA3_1_8B_INSTRUCT = \"meta/llama-3.1-8b-instruct\"\n    NVIDIA_LLAMA3_1_70B_INSTRUCT = \"meta/llama-3.1-70b-instruct\"\n    NVIDIA_LLAMA3_1_405B_INSTRUCT = \"meta/llama-3.1-405b-instruct\"\n    NVIDIA_LLAMA3_2_1B_INSTRUCT = \"meta/llama-3.2-1b-instruct\"\n    NVIDIA_LLAMA3_2_3B_INSTRUCT = \"meta/llama-3.2-3b-instruct\"\n    NVIDIA_LLAMA3_3_70B_INSTRUCT = \"meta/llama-3.3-70b-instruct\"\n\n    # Gemini models\n    GEMINI_2_5_FLASH = \"gemini-2.5-flash\"\n    GEMINI_2_5_PRO = \"gemini-2.5-pro\"\n    GEMINI_2_0_FLASH = \"gemini-2.0-flash\"\n    GEMINI_2_0_FLASH_EXP = \"gemini-2.0-flash-exp\"\n    GEMINI_2_0_FLASH_THINKING = \"gemini-2.0-flash-thinking-exp\"\n    GEMINI_2_0_PRO_EXP = \"gemini-2.0-pro-exp-02-05\"\n    GEMINI_2_0_FLASH_LITE = \"gemini-2.0-flash-lite\"\n    GEMINI_2_0_FLASH_LITE_PREVIEW = \"gemini-2.0-flash-lite-preview-02-05\"\n    GEMINI_1_5_FLASH = \"gemini-1.5-flash\"\n    GEMINI_1_5_PRO = \"gemini-1.5-pro\"\n\n    # Mistral AI models\n    MISTRAL_3B = \"ministral-3b-latest\"\n    MISTRAL_7B = \"open-mistral-7b\"\n    MISTRAL_8B = \"ministral-8b-latest\"\n    MISTRAL_CODESTRAL = \"codestral-latest\"\n    MISTRAL_CODESTRAL_MAMBA = \"open-codestral-mamba\"\n    MISTRAL_LARGE = \"mistral-large-latest\"\n    MISTRAL_MIXTRAL_8x7B = \"open-mixtral-8x7b\"\n    MISTRAL_MIXTRAL_8x22B = \"open-mixtral-8x22b\"\n    MISTRAL_NEMO = \"open-mistral-nemo\"\n    MISTRAL_PIXTRAL_12B = \"pixtral-12b-2409\"\n    MISTRAL_MEDIUM_3_1 = \"mistral-medium-2508\"\n    MISTRAL_SMALL_3_2 = \"mistral-small-2506\"\n    MAGISTRAL_SMALL_1_2 = \"magistral-small-1.2\"\n    MAGISTRAL_MEDIUM_1_2 = \"magistral-medium-1.2\"\n\n    # Reka models\n    REKA_CORE = \"reka-core\"\n    REKA_FLASH = \"reka-flash\"\n    REKA_EDGE = \"reka-edge\"\n\n    # Cohere models\n    COHERE_COMMAND_R_PLUS = \"command-r-plus\"\n    COHERE_COMMAND_R = \"command-r\"\n    COHERE_COMMAND_LIGHT = \"command-light\"\n    COHERE_COMMAND = \"command\"\n    COHERE_COMMAND_NIGHTLY = \"command-nightly\"\n\n    # Qwen models (Aliyun)\n    QWEN_MAX = \"qwen-max\"\n    QWEN_PLUS = \"qwen-plus\"\n    QWEN_TURBO = \"qwen-turbo\"\n    QWEN_PLUS_LATEST = \"qwen-plus-latest\"\n    QWEN_PLUS_2025_04_28 = \"qwen-plus-2025-04-28\"\n    QWEN_TURBO_LATEST = \"qwen-turbo-latest\"\n    QWEN_TURBO_2025_04_28 = \"qwen-turbo-2025-04-28\"\n    QWEN_LONG = \"qwen-long\"\n    QWEN_VL_MAX = \"qwen-vl-max\"\n    QWEN_VL_PLUS = \"qwen-vl-plus\"\n    QWEN_MATH_PLUS = \"qwen-math-plus\"\n    QWEN_MATH_TURBO = \"qwen-math-turbo\"\n    QWEN_CODER_TURBO = \"qwen-coder-turbo\"\n    QWEN_2_5_CODER_32B = \"qwen2.5-coder-32b-instruct\"\n    QWEN_2_5_VL_72B = \"qwen2.5-vl-72b-instruct\"\n    QWEN_2_5_72B = \"qwen2.5-72b-instruct\"\n    QWEN_2_5_32B = \"qwen2.5-32b-instruct\"\n    QWEN_2_5_14B = \"qwen2.5-14b-instruct\"\n    QWEN_QWQ_32B = \"qwq-32b-preview\"\n    QWEN_QVQ_72B = \"qvq-72b-preview\"\n    QWEN_QWQ_PLUS = \"qwq-plus\"\n    QWEN_3_CODER_PLUS = \"qwen3-coder-plus\"\n\n    # Yi models (01-ai)\n    YI_LIGHTNING = \"yi-lightning\"\n    YI_LARGE = \"yi-large\"\n    YI_MEDIUM = \"yi-medium\"\n    YI_LARGE_TURBO = \"yi-large-turbo\"\n    YI_VISION = \"yi-vision\"\n    YI_MEDIUM_200K = \"yi-medium-200k\"\n    YI_SPARK = \"yi-spark\"\n    YI_LARGE_RAG = \"yi-large-rag\"\n    YI_LARGE_FC = \"yi-large-fc\"\n\n    # DeepSeek models\n    DEEPSEEK_CHAT = \"deepseek-chat\"\n    DEEPSEEK_REASONER = \"deepseek-reasoner\"\n    # InternLM models\n    INTERNLM3_LATEST = \"internlm3-latest\"\n    INTERNLM3_8B_INSTRUCT = \"internlm3-8b-instruct\"\n    INTERNLM2_5_LATEST = \"internlm2.5-latest\"\n    INTERNLM2_PRO_CHAT = \"internlm2-pro-chat\"\n\n    # Moonshot models\n    MOONSHOT_V1_8K = \"moonshot-v1-8k\"\n    MOONSHOT_V1_32K = \"moonshot-v1-32k\"\n    MOONSHOT_V1_128K = \"moonshot-v1-128k\"\n    MOONSHOT_KIMI_K2 = \"kimi-k2-0711-preview\"\n\n    # SiliconFlow models support tool calling\n    SILICONFLOW_DEEPSEEK_V2_5 = \"deepseek-ai/DeepSeek-V2.5\"\n    SILICONFLOW_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3\"\n    SILICONFLOW_INTERN_LM2_5_20B_CHAT = \"internlm/internlm2_5-20b-chat\"\n    SILICONFLOW_INTERN_LM2_5_7B_CHAT = \"internlm/internlm2_5-7b-chat\"\n    SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT = \"Pro/internlm/internlm2_5-7b-chat\"\n    SILICONFLOW_QWEN2_5_72B_INSTRUCT = \"Qwen/Qwen2.5-72B-Instruct\"\n    SILICONFLOW_QWEN2_5_32B_INSTRUCT = \"Qwen/Qwen2.5-32B-Instruct\"\n    SILICONFLOW_QWEN2_5_14B_INSTRUCT = \"Qwen/Qwen2.5-14B-Instruct\"\n    SILICONFLOW_QWEN2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n    SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT = \"Pro/Qwen/Qwen2.5-7B-Instruct\"\n    SILICONFLOW_THUDM_GLM_4_9B_CHAT = \"THUDM/glm-4-9b-chat\"\n    SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT = \"Pro/THUDM/glm-4-9b-chat\"\n\n    # AIML models support tool calling\n    AIML_MIXTRAL_8X7B = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n    AIML_MISTRAL_7B_INSTRUCT = \"mistralai/Mistral-7B-Instruct-v0.1\"\n\n    # Novita platform models support tool calling\n    NOVITA_LLAMA_4_MAVERICK_17B = (\n        \"meta-llama/llama-4-maverick-17b-128e-instruct-fp8\"\n    )\n    NOVITA_LLAMA_4_SCOUT_17B = \"meta-llama/llama-4-scout-17b-16e-instruct\"\n    NOVITA_DEEPSEEK_V3_0324 = \"deepseek/deepseek-v3-0324\"\n    NOVITA_QWEN_2_5_V1_72B = \"qwen/qwen2.5-vl-72b-instruct\"\n    NOVITA_DEEPSEEK_V3_TURBO = \"deepseek/deepseek-v3-turbo\"\n    NOVITA_DEEPSEEK_R1_TURBO = \"deepseek/deepseek-r1-turbo\"\n    NOVITA_GEMMA_3_27B_IT = \"google/gemma-3-27b-it\"\n    NOVITA_QWEN_32B = \"qwen/qwq-32b\"\n    NOVITA_L3_8B_STHENO_V3_2 = \"Sao10K/L3-8B-Stheno-v3.2\"\n    NOVITA_MYTHOMAX_L2_13B = \"gryphe/mythomax-l2-13b\"\n    NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B = (\n        \"deepseek/deepseek-r1-distill-llama-8b\"\n    )\n    NOVITA_DEEPSEEK_V3 = \"deepseek/deepseek_v3\"\n    NOVITA_LLAMA_3_1_8B = \"meta-llama/llama-3.1-8b-instruct\"\n    NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B = (\n        \"deepseek/deepseek-r1-distill-qwen-14b\"\n    )\n    NOVITA_LLAMA_3_3_70B = \"meta-llama/llama-3.3-70b-instruct\"\n    NOVITA_QWEN_2_5_72B = \"qwen/qwen-2.5-72b-instruct\"\n    NOVITA_MISTRAL_NEMO = \"mistralai/mistral-nemo\"\n    NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B = (\n        \"deepseek/deepseek-r1-distill-qwen-32b\"\n    )\n    NOVITA_LLAMA_3_8B = \"meta-llama/llama-3-8b-instruct\"\n    NOVITA_WIZARDLM_2_8X22B = \"microsoft/wizardlm-2-8x22b\"\n    NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B = (\n        \"deepseek/deepseek-r1-distill-llama-70b\"\n    )\n    NOVITA_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n    NOVITA_GEMMA_2_9B_IT = \"google/gemma-2-9b-it\"\n    NOVITA_MISTRAL_7B = \"mistralai/mistral-7b-instruct\"\n    NOVITA_LLAMA_3_70B = \"meta-llama/llama-3-70b-instruct\"\n    NOVITA_DEEPSEEK_R1 = \"deepseek/deepseek-r1\"\n    NOVITA_HERMES_2_PRO_LLAMA_3_8B = \"nousresearch/hermes-2-pro-llama-3-8b\"\n    NOVITA_L3_70B_EURYALE_V2_1 = \"sao10k/l3-70b-euryale-v2.1\"\n    NOVITA_DOLPHIN_MIXTRAL_8X22B = (\n        \"cognitivecomputations/dolphin-mixtral-8x22b\"\n    )\n    NOVITA_AIROBOROS_L2_70B = \"jondurbin/airoboros-l2-70b\"\n    NOVITA_MIDNIGHT_ROSE_70B = \"sophosympatheia/midnight-rose-70b\"\n    NOVITA_L3_8B_LUNARIS = \"sao10k/l3-8b-lunaris\"\n    NOVITA_GLM_4_9B_0414 = \"thudm/glm-4-9b-0414\"\n    NOVITA_GLM_Z1_9B_0414 = \"thudm/glm-z1-9b-0414\"\n    NOVITA_GLM_Z1_32B_0414 = \"thudm/glm-z1-32b-0414\"\n    NOVITA_GLM_4_32B_0414 = \"thudm/glm-4-32b-0414\"\n    NOVITA_GLM_Z1_RUMINATION_32B_0414 = \"thudm/glm-z1-rumination-32b-0414\"\n    NOVITA_QWEN_2_5_7B = \"qwen/qwen2.5-7b-instruct\"\n    NOVITA_LLAMA_3_2_1B = \"meta-llama/llama-3.2-1b-instruct\"\n    NOVITA_LLAMA_3_2_11B_VISION = \"meta-llama/llama-3.2-11b-vision-instruct\"\n    NOVITA_LLAMA_3_2_3B = \"meta-llama/llama-3.2-3b-instruct\"\n    NOVITA_LLAMA_3_1_8B_BF16 = \"meta-llama/llama-3.1-8b-instruct-bf16\"\n    NOVITA_L31_70B_EURYALE_V2_2 = \"sao10k/l31-70b-euryale-v2.2\"\n\n    # ModelScope models support tool calling\n    MODELSCOPE_QWEN_2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n    MODELSCOPE_QWEN_2_5_14B_INSTRUCT = \"Qwen/Qwen2.5-14B-Instruct\"\n    MODELSCOPE_QWEN_2_5_32B_INSTRUCT = \"Qwen/Qwen2.5-32B-Instruct\"\n    MODELSCOPE_QWEN_2_5_72B_INSTRUCT = \"Qwen/Qwen2.5-72B-Instruct\"\n    MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT = \"Qwen/Qwen2.5-Coder-7B-Instruct\"\n    MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT = \"Qwen/Qwen2.5-Coder-14B-Instruct\"\n    MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT = \"Qwen/Qwen2.5-Coder-32B-Instruct\"\n    MODELSCOPE_QWEN_3_235B_A22B = \"Qwen/Qwen3-235B-A22B\"\n    MODELSCOPE_QWEN_3_32B = \"Qwen/Qwen3-32B\"\n    MODELSCOPE_QWQ_32B = \"Qwen/QwQ-32B\"\n    MODELSCOPE_QWQ_32B_PREVIEW = \"Qwen/QwQ-32B-Preview\"\n    MODELSCOPE_LLAMA_3_1_8B_INSTRUCT = (\n        \"LLM-Research/Meta-Llama-3.1-8B-Instruct\"\n    )\n    MODELSCOPE_LLAMA_3_1_70B_INSTRUCT = (\n        \"LLM-Research/Meta-Llama-3.1-70B-Instruct\"\n    )\n    MODELSCOPE_LLAMA_3_1_405B_INSTRUCT = (\n        \"LLM-Research/Meta-Llama-3.1-405B-Instruct\"\n    )\n    MODELSCOPE_LLAMA_3_3_70B_INSTRUCT = \"LLM-Research/Llama-3.3-70B-Instruct\"\n    MODELSCOPE_MINISTRAL_8B_INSTRUCT = \"mistralai/Ministral-8B-Instruct-2410\"\n    MODELSCOPE_DEEPSEEK_V3_0324 = \"deepseek-ai/DeepSeek-V3-0324\"\n\n    # WatsonX models\n    WATSONX_GRANITE_3_8B_INSTRUCT = \"ibm/granite-3-8b-instruct\"\n    WATSONX_LLAMA_3_3_70B_INSTRUCT = \"meta-llama/llama-3-3-70b-instruct\"\n    WATSONX_LLAMA_3_2_1B_INSTRUCT = \"meta-llama/llama-3-2-1b-instruct\"\n    WATSONX_LLAMA_3_2_3B_INSTRUCT = \"meta-llama/llama-3-2-3b-instruct\"\n    WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT = (\n        \"meta-llama/llama-3-2-11b-vision-instruct\"\n    )\n    WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT = (\n        \"meta-llama/llama-3-2-90b-vision-instruct\"\n    )\n    WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT = (\n        \"meta-llama/llama-guard-3-11b-vision-instruct\"\n    )\n    WATSONX_MISTRAL_LARGE = \"mistralai/mistral-large\"\n\n    # Qianfan models\n    ERNIE_X1_TURBO_32K = \"ernie-x1-turbo-32k\"\n    ERNIE_X1_32K = \"ernie-x1-32k\"\n    ERNIE_X1_32K_PREVIEW = \"ernie-x1-32k-preview\"\n    ERNIE_4_5_TURBO_128K = \"ernie-4.5-turbo-128k\"\n    ERNIE_4_5_TURBO_32K = \"ernie-4.5-turbo-32k\"\n    DEEPSEEK_V3 = \"deepseek-v3\"\n    DEEPSEEK_R1 = \"deepseek-r1\"\n    QWEN3_235B_A22B = \"qwen3-235b-a22b\"\n\n    # Crynux models\n    CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B = (\n        \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n    )\n    CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B = (\n        \"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\"\n    )\n    CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B = (\n        \"deepseek-ai/DeepSeek-R1-Distill-Llama-8B\"\n    )\n\n    CRYNUX_QWEN_3_4_B = \"Qwen/Qwen3-4B\"\n    CRYNUX_QWEN_3_8_B = \"Qwen/Qwen3-8B\"\n    CRYNUX_QWEN_2_5_7B = \"Qwen/Qwen2.5-7B\"\n    CRYNUX_QWEN_2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n\n    CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B = \"NousResearch/Hermes-3-Llama-3.1-8B\"\n    CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B = \"NousResearch/Hermes-3-Llama-3.2-3B\"\n\n    def __str__(self):\n        return self.value\n\n    def __repr__(self):\n        return self.value\n\n    def __new__(cls, value: Union[\"ModelType\", str]) -> \"ModelType\":\n        return cast(\"ModelType\", UnifiedModelType.__new__(cls, value))\n\n    @classmethod\n    def from_name(cls, name: str) -> \"ModelType\":\n        r\"\"\"Returns the ModelType enum value from a string.\"\"\"\n        for model_type in cls:\n            if model_type.value == name:\n                return model_type\n        raise ValueError(f\"Unknown ModelType name: {name}\")\n\n    @property\n    def value_for_tiktoken(self) -> str:\n        if self.is_openai:\n            return self.value\n        return \"gpt-4o-mini\"\n\n    @property\n    def support_native_structured_output(self) -> bool:\n        return any(\n            [\n                self.is_openai,\n            ]\n        )\n\n    @property\n    def support_native_tool_calling(self) -> bool:\n        return any(\n            [\n                self.is_openai,\n                self.is_gemini,\n                self.is_mistral,\n                self.is_qwen,\n                self.is_deepseek,\n                self.is_ppio,\n                self.is_cohere,\n                self.is_internlm,\n                self.is_together,\n                self.is_sambanova,\n                self.is_groq,\n                self.is_openrouter,\n                self.is_lmstudio,\n                self.is_sglang,\n                self.is_moonshot,\n                self.is_siliconflow,\n                self.is_modelscope,\n                self.is_zhipuai,\n                self.is_aiml,\n                self.is_azure_openai,\n                self.is_novita,\n            ]\n        )\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this type of models is an OpenAI-released model.\"\"\"\n        return self in {\n            ModelType.GPT_3_5_TURBO,\n            ModelType.GPT_4,\n            ModelType.GPT_4_TURBO,\n            ModelType.GPT_4O,\n            ModelType.GPT_4O_MINI,\n            ModelType.O1,\n            ModelType.O1_PREVIEW,\n            ModelType.O1_MINI,\n            ModelType.O3_PRO,\n            ModelType.O3_MINI,\n            ModelType.GPT_4_5_PREVIEW,\n            ModelType.GPT_4_1,\n            ModelType.GPT_4_1_MINI,\n            ModelType.GPT_4_1_NANO,\n            ModelType.GPT_5,\n            ModelType.GPT_5_MINI,\n            ModelType.GPT_5_NANO,\n            ModelType.O4_MINI,\n            ModelType.O3,\n        }\n\n    @property\n    def is_amd(self) -> bool:\n        r\"\"\"Returns whether this type of models is a AMD model.\"\"\"\n        return self in {\n            ModelType.AMD_GPT4,\n        }\n\n    @property\n    def is_aws_bedrock(self) -> bool:\n        r\"\"\"Returns whether this type of models is an AWS Bedrock model.\"\"\"\n        return self in {\n            ModelType.AWS_CLAUDE_3_7_SONNET,\n            ModelType.AWS_CLAUDE_3_5_SONNET,\n            ModelType.AWS_CLAUDE_3_HAIKU,\n            ModelType.AWS_CLAUDE_3_SONNET,\n            ModelType.AWS_DEEPSEEK_R1,\n            ModelType.AWS_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.AWS_LLAMA_3_2_90B_INSTRUCT,\n            ModelType.AWS_LLAMA_3_2_11B_INSTRUCT,\n            ModelType.AWS_CLAUDE_SONNET_4,\n            ModelType.AWS_CLAUDE_OPUS_4,\n            ModelType.AWS_CLAUDE_OPUS_4_1,\n        }\n\n    @property\n    def is_azure_openai(self) -> bool:\n        r\"\"\"Returns whether this type of models is an OpenAI-released model\n        from Azure.\n        \"\"\"\n        return self in {\n            ModelType.GPT_3_5_TURBO,\n            ModelType.GPT_4,\n            ModelType.GPT_4_TURBO,\n            ModelType.GPT_4O,\n            ModelType.GPT_4O_MINI,\n            ModelType.O1,\n            ModelType.O1_PREVIEW,\n            ModelType.O1_MINI,\n            ModelType.O3_MINI,\n            ModelType.O3_PRO,\n            ModelType.GPT_4_5_PREVIEW,\n            ModelType.GPT_4_1,\n            ModelType.GPT_4_1_MINI,\n            ModelType.GPT_4_1_NANO,\n            ModelType.GPT_5,\n            ModelType.O4_MINI,\n            ModelType.O3,\n        }\n\n    @property\n    def is_zhipuai(self) -> bool:\n        r\"\"\"Returns whether this type of models is an ZhipuAI model.\"\"\"\n        return self in {\n            ModelType.GLM_3_TURBO,\n            ModelType.GLM_4,\n            ModelType.GLM_4V,\n            ModelType.GLM_4V_FLASH,\n            ModelType.GLM_4V_PLUS_0111,\n            ModelType.GLM_4_PLUS,\n            ModelType.GLM_4_AIR,\n            ModelType.GLM_4_AIR_0111,\n            ModelType.GLM_4_AIRX,\n            ModelType.GLM_4_LONG,\n            ModelType.GLM_4_FLASHX,\n            ModelType.GLM_4_FLASH,\n            ModelType.GLM_ZERO_PREVIEW,\n        }\n\n    @property\n    def is_anthropic(self) -> bool:\n        r\"\"\"Returns whether this type of models is Anthropic-released model.\n\n        Returns:\n            bool: Whether this type of models is anthropic.\n        \"\"\"\n        return self in {\n            ModelType.CLAUDE_INSTANT_1_2,\n            ModelType.CLAUDE_2_0,\n            ModelType.CLAUDE_2_1,\n            ModelType.CLAUDE_3_OPUS,\n            ModelType.CLAUDE_3_SONNET,\n            ModelType.CLAUDE_3_HAIKU,\n            ModelType.CLAUDE_3_5_SONNET,\n            ModelType.CLAUDE_3_5_HAIKU,\n            ModelType.CLAUDE_3_7_SONNET,\n            ModelType.CLAUDE_SONNET_4,\n            ModelType.CLAUDE_OPUS_4,\n            ModelType.CLAUDE_OPUS_4_1,\n        }\n\n    @property\n    def is_groq(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by Groq.\"\"\"\n        return self in {\n            ModelType.GROQ_LLAMA_3_1_8B,\n            ModelType.GROQ_LLAMA_3_3_70B,\n            ModelType.GROQ_LLAMA_3_3_70B_PREVIEW,\n            ModelType.GROQ_LLAMA_3_8B,\n            ModelType.GROQ_LLAMA_3_70B,\n            ModelType.GROQ_MIXTRAL_8_7B,\n            ModelType.GROQ_GEMMA_2_9B_IT,\n        }\n\n    @property\n    def is_nebius(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by Nebius AI\n        Studio.\"\"\"\n        return self in {\n            ModelType.NEBIUS_GPT_OSS_120B,\n            ModelType.NEBIUS_GPT_OSS_20B,\n            ModelType.NEBIUS_GLM_4_5,\n            ModelType.NEBIUS_DEEPSEEK_V3,\n            ModelType.NEBIUS_DEEPSEEK_R1,\n            ModelType.NEBIUS_LLAMA_3_1_70B,\n            ModelType.NEBIUS_MISTRAL_7B_INSTRUCT,\n        }\n\n    @property\n    def is_cometapi(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by CometAPI.\"\"\"\n        return self in {\n            ModelType.COMETAPI_GPT_5_CHAT_LATEST,\n            ModelType.COMETAPI_CHATGPT_4O_LATEST,\n            ModelType.COMETAPI_GPT_5_MINI,\n            ModelType.COMETAPI_GPT_5_NANO,\n            ModelType.COMETAPI_GPT_5,\n            ModelType.COMETAPI_GPT_4_1,\n            ModelType.COMETAPI_GPT_4O_MINI,\n            ModelType.COMETAPI_O4_MINI_2025_04_16,\n            ModelType.COMETAPI_O3_PRO_2025_06_10,\n            ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805,\n            ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING,\n            ModelType.COMETAPI_CLAUDE_SONNET_4_20250514,\n            ModelType.COMETAPI_CLAUDE_SONNET_4_20250514_THINKING,\n            ModelType.COMETAPI_CLAUDE_3_7_SONNET_LATEST,\n            ModelType.COMETAPI_CLAUDE_3_5_HAIKU_LATEST,\n            ModelType.COMETAPI_GEMINI_2_5_PRO,\n            ModelType.COMETAPI_GEMINI_2_5_FLASH,\n            ModelType.COMETAPI_GEMINI_2_5_FLASH_LITE,\n            ModelType.COMETAPI_GEMINI_2_0_FLASH,\n            ModelType.COMETAPI_GROK_4_0709,\n            ModelType.COMETAPI_GROK_3,\n            ModelType.COMETAPI_GROK_3_MINI,\n            ModelType.COMETAPI_GROK_2_IMAGE_1212,\n            ModelType.COMETAPI_DEEPSEEK_V3_1,\n            ModelType.COMETAPI_DEEPSEEK_V3,\n            ModelType.COMETAPI_DEEPSEEK_R1_0528,\n            ModelType.COMETAPI_DEEPSEEK_CHAT,\n            ModelType.COMETAPI_DEEPSEEK_REASONER,\n            ModelType.COMETAPI_QWEN3_30B_A3B,\n            ModelType.COMETAPI_QWEN3_CODER_PLUS_2025_07_22,\n        }\n\n    @property\n    def is_openrouter(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by OpenRouter.\"\"\"\n        return self in {\n            ModelType.OPENROUTER_LLAMA_3_1_405B,\n            ModelType.OPENROUTER_LLAMA_3_1_70B,\n            ModelType.OPENROUTER_LLAMA_4_MAVERICK,\n            ModelType.OPENROUTER_LLAMA_4_MAVERICK_FREE,\n            ModelType.OPENROUTER_LLAMA_4_SCOUT,\n            ModelType.OPENROUTER_LLAMA_4_SCOUT_FREE,\n            ModelType.OPENROUTER_OLYMPICODER_7B,\n            ModelType.OPENROUTER_HORIZON_ALPHA,\n            ModelType.OPENROUTER_GROK_4_FAST,\n            ModelType.OPENROUTER_GEMINI_2_5_FLASH,\n            ModelType.OPENROUTER_GPT_4O_MINI,\n            ModelType.OPENROUTER_QWEN_PLUS,\n            ModelType.OPENROUTER_QWEN_VL_MAX,\n            ModelType.OPENROUTER_o3,\n            ModelType.OPENROUTER_CLAUDE_SONNET_4_5\n        }\n\n    @property\n    def is_lmstudio(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by LMStudio.\"\"\"\n        return self in {\n            ModelType.LMSTUDIO_GEMMA_3_1B,\n            ModelType.LMSTUDIO_GEMMA_3_4B,\n            ModelType.LMSTUDIO_GEMMA_3_12B,\n            ModelType.LMSTUDIO_GEMMA_3_27B,\n        }\n\n    @property\n    def is_together(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by Together AI.\"\"\"\n        return self in {\n            ModelType.TOGETHER_LLAMA_3_1_405B,\n            ModelType.TOGETHER_LLAMA_3_1_70B,\n            ModelType.TOGETHER_LLAMA_3_3_70B,\n            ModelType.TOGETHER_LLAMA_3_3_70B,\n            ModelType.TOGETHER_MISTRAL_7B,\n            ModelType.TOGETHER_MIXTRAL_8_7B,\n        }\n\n    @property\n    def is_sambanova(self) -> bool:\n        r\"\"\"Returns whether this type of model is served by SambaNova AI.\"\"\"\n        return self in {\n            ModelType.SAMBA_LLAMA_3_1_8B,\n            ModelType.SAMBA_LLAMA_3_1_70B,\n            ModelType.SAMBA_LLAMA_3_1_405B,\n        }\n\n    @property\n    def is_mistral(self) -> bool:\n        r\"\"\"Returns whether this type of models is served by Mistral.\"\"\"\n        return self in {\n            ModelType.MISTRAL_LARGE,\n            ModelType.MISTRAL_NEMO,\n            ModelType.MISTRAL_CODESTRAL,\n            ModelType.MISTRAL_7B,\n            ModelType.MISTRAL_MIXTRAL_8x7B,\n            ModelType.MISTRAL_MIXTRAL_8x22B,\n            ModelType.MISTRAL_CODESTRAL_MAMBA,\n            ModelType.MISTRAL_PIXTRAL_12B,\n            ModelType.MISTRAL_8B,\n            ModelType.MISTRAL_3B,\n            ModelType.MISTRAL_MEDIUM_3_1,\n            ModelType.MISTRAL_SMALL_3_2,\n            ModelType.MAGISTRAL_SMALL_1_2,\n            ModelType.MAGISTRAL_MEDIUM_1_2,\n        }\n\n    @property\n    def is_nvidia(self) -> bool:\n        r\"\"\"Returns whether this type of models is a NVIDIA model.\"\"\"\n        return self in {\n            ModelType.NVIDIA_NEMOTRON_340B_INSTRUCT,\n            ModelType.NVIDIA_NEMOTRON_340B_REWARD,\n            ModelType.NVIDIA_YI_LARGE,\n            ModelType.NVIDIA_MISTRAL_LARGE,\n            ModelType.NVIDIA_LLAMA3_70B,\n            ModelType.NVIDIA_MIXTRAL_8X7B,\n            ModelType.NVIDIA_LLAMA3_1_8B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_1_70B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_1_405B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_2_1B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_2_3B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_3_70B_INSTRUCT,\n        }\n\n    @property\n    def is_gemini(self) -> bool:\n        r\"\"\"Returns whether this type of models is Gemini model.\n\n        Returns:\n            bool: Whether this type of models is gemini.\n        \"\"\"\n        return self in {\n            ModelType.GEMINI_2_5_FLASH,\n            ModelType.GEMINI_2_5_PRO,\n            ModelType.GEMINI_2_0_FLASH,\n            ModelType.GEMINI_2_0_FLASH_EXP,\n            ModelType.GEMINI_2_0_FLASH_THINKING,\n            ModelType.GEMINI_2_0_PRO_EXP,\n            ModelType.GEMINI_2_0_FLASH_LITE,\n            ModelType.GEMINI_2_0_FLASH_LITE_PREVIEW,\n            ModelType.GEMINI_1_5_FLASH,\n            ModelType.GEMINI_1_5_PRO,\n        }\n\n    @property\n    def is_reka(self) -> bool:\n        r\"\"\"Returns whether this type of models is Reka model.\n\n        Returns:\n            bool: Whether this type of models is Reka.\n        \"\"\"\n        return self in {\n            ModelType.REKA_CORE,\n            ModelType.REKA_EDGE,\n            ModelType.REKA_FLASH,\n        }\n\n    @property\n    def is_cohere(self) -> bool:\n        r\"\"\"Returns whether this type of models is a Cohere model.\n\n        Returns:\n            bool: Whether this type of models is Cohere.\n        \"\"\"\n        return self in {\n            ModelType.COHERE_COMMAND_R_PLUS,\n            ModelType.COHERE_COMMAND_R,\n            ModelType.COHERE_COMMAND_LIGHT,\n            ModelType.COHERE_COMMAND,\n            ModelType.COHERE_COMMAND_NIGHTLY,\n        }\n\n    @property\n    def is_yi(self) -> bool:\n        r\"\"\"Returns whether this type of models is Yi model.\n\n        Returns:\n            bool: Whether this type of models is Yi.\n        \"\"\"\n        return self in {\n            ModelType.YI_LIGHTNING,\n            ModelType.YI_LARGE,\n            ModelType.YI_MEDIUM,\n            ModelType.YI_LARGE_TURBO,\n            ModelType.YI_VISION,\n            ModelType.YI_MEDIUM_200K,\n            ModelType.YI_SPARK,\n            ModelType.YI_LARGE_RAG,\n            ModelType.YI_LARGE_FC,\n        }\n\n    @property\n    def is_qwen(self) -> bool:\n        return self in {\n            ModelType.QWEN_MAX,\n            ModelType.QWEN_PLUS,\n            ModelType.QWEN_TURBO,\n            ModelType.QWEN_LONG,\n            ModelType.QWEN_VL_MAX,\n            ModelType.QWEN_VL_PLUS,\n            ModelType.QWEN_MATH_PLUS,\n            ModelType.QWEN_MATH_TURBO,\n            ModelType.QWEN_CODER_TURBO,\n            ModelType.QWEN_2_5_CODER_32B,\n            ModelType.QWEN_2_5_VL_72B,\n            ModelType.QWEN_2_5_72B,\n            ModelType.QWEN_2_5_32B,\n            ModelType.QWEN_2_5_14B,\n            ModelType.QWEN_QWQ_32B,\n            ModelType.QWEN_QVQ_72B,\n            ModelType.QWEN_QWQ_PLUS,\n            ModelType.QWEN_PLUS_LATEST,\n            ModelType.QWEN_PLUS_2025_04_28,\n            ModelType.QWEN_TURBO_LATEST,\n            ModelType.QWEN_TURBO_2025_04_28,\n            ModelType.QWEN_3_CODER_PLUS,\n        }\n\n    @property\n    def is_deepseek(self) -> bool:\n        return self in {\n            ModelType.DEEPSEEK_CHAT,\n            ModelType.DEEPSEEK_REASONER,\n        }\n\n    @property\n    def is_netmind(self) -> bool:\n        return self in {\n            ModelType.NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT,\n            ModelType.NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT,\n            ModelType.NETMIND_DEEPSEEK_R1,\n            ModelType.NETMIND_DEEPSEEK_V3,\n            ModelType.NETMIND_DOUBAO_1_5_PRO,\n            ModelType.NETMIND_QWQ_32B,\n        }\n\n    @property\n    def is_ppio(self) -> bool:\n        return self in {\n            ModelType.PPIO_DEEPSEEK_PROVER_V2_671B,\n            ModelType.PPIO_DEEPSEEK_R1_TURBO,\n            ModelType.PPIO_DEEPSEEK_V3_TURBO,\n            ModelType.PPIO_DEEPSEEK_R1_COMMUNITY,\n            ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,\n            ModelType.PPIO_DEEPSEEK_R1,\n            ModelType.PPIO_DEEPSEEK_V3,\n            ModelType.PPIO_QWEN_2_5_72B,\n            ModelType.PPIO_BAICHUAN_2_13B_CHAT,\n            ModelType.PPIO_LLAMA_3_3_70B,\n            ModelType.PPIO_LLAMA_3_1_70B,\n            ModelType.PPIO_YI_1_5_34B_CHAT,\n        }\n\n    @property\n    def is_internlm(self) -> bool:\n        return self in {\n            ModelType.INTERNLM3_LATEST,\n            ModelType.INTERNLM3_8B_INSTRUCT,\n            ModelType.INTERNLM2_5_LATEST,\n            ModelType.INTERNLM2_PRO_CHAT,\n        }\n\n    @property\n    def is_modelscope(self) -> bool:\n        return self in {\n            ModelType.MODELSCOPE_QWEN_2_5_7B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_14B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_32B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_72B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_3_235B_A22B,\n            ModelType.MODELSCOPE_QWEN_3_32B,\n            ModelType.MODELSCOPE_QWQ_32B,\n            ModelType.MODELSCOPE_QWQ_32B_PREVIEW,\n            ModelType.MODELSCOPE_LLAMA_3_1_8B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_1_70B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_1_405B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.MODELSCOPE_MINISTRAL_8B_INSTRUCT,\n            ModelType.MODELSCOPE_DEEPSEEK_V3_0324,\n        }\n\n    @property\n    def is_moonshot(self) -> bool:\n        return self in {\n            ModelType.MOONSHOT_V1_8K,\n            ModelType.MOONSHOT_V1_32K,\n            ModelType.MOONSHOT_V1_128K,\n            ModelType.MOONSHOT_KIMI_K2,\n        }\n\n    @property\n    def is_sglang(self) -> bool:\n        return self in {\n            ModelType.SGLANG_LLAMA_3_1_8B,\n            ModelType.SGLANG_LLAMA_3_1_70B,\n            ModelType.SGLANG_LLAMA_3_1_405B,\n            ModelType.SGLANG_LLAMA_3_2_1B,\n            ModelType.SGLANG_MIXTRAL_NEMO,\n            ModelType.SGLANG_MISTRAL_7B,\n            ModelType.SGLANG_QWEN_2_5_7B,\n            ModelType.SGLANG_QWEN_2_5_32B,\n            ModelType.SGLANG_QWEN_2_5_72B,\n        }\n\n    @property\n    def is_siliconflow(self) -> bool:\n        return self in {\n            ModelType.SILICONFLOW_DEEPSEEK_V2_5,\n            ModelType.SILICONFLOW_DEEPSEEK_V3,\n            ModelType.SILICONFLOW_INTERN_LM2_5_20B_CHAT,\n            ModelType.SILICONFLOW_INTERN_LM2_5_7B_CHAT,\n            ModelType.SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT,\n            ModelType.SILICONFLOW_QWEN2_5_72B_INSTRUCT,\n            ModelType.SILICONFLOW_QWEN2_5_32B_INSTRUCT,\n            ModelType.SILICONFLOW_QWEN2_5_14B_INSTRUCT,\n            ModelType.SILICONFLOW_QWEN2_5_7B_INSTRUCT,\n            ModelType.SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT,\n            ModelType.SILICONFLOW_THUDM_GLM_4_9B_CHAT,\n            ModelType.SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT,\n        }\n\n    @property\n    def is_watsonx(self) -> bool:\n        return self in {\n            ModelType.WATSONX_GRANITE_3_8B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_1B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_3B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT,\n            ModelType.WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT,\n            ModelType.WATSONX_MISTRAL_LARGE,\n        }\n\n    @property\n    def is_qianfan(self) -> bool:\n        return self in {\n            ModelType.ERNIE_X1_TURBO_32K,\n            ModelType.ERNIE_X1_32K,\n            ModelType.ERNIE_X1_32K_PREVIEW,\n            ModelType.ERNIE_4_5_TURBO_128K,\n            ModelType.ERNIE_4_5_TURBO_32K,\n            ModelType.DEEPSEEK_V3,\n            ModelType.DEEPSEEK_R1,\n            ModelType.QWEN3_235B_A22B,\n        }\n\n    @property\n    def is_novita(self) -> bool:\n        return self in {\n            ModelType.NOVITA_LLAMA_4_MAVERICK_17B,\n            ModelType.NOVITA_LLAMA_4_SCOUT_17B,\n            ModelType.NOVITA_DEEPSEEK_V3_0324,\n            ModelType.NOVITA_QWEN_2_5_V1_72B,\n            ModelType.NOVITA_DEEPSEEK_V3_TURBO,\n            ModelType.NOVITA_DEEPSEEK_R1_TURBO,\n            ModelType.NOVITA_GEMMA_3_27B_IT,\n            ModelType.NOVITA_QWEN_32B,\n            ModelType.NOVITA_L3_8B_STHENO_V3_2,\n            ModelType.NOVITA_MYTHOMAX_L2_13B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n            ModelType.NOVITA_DEEPSEEK_V3,\n            ModelType.NOVITA_LLAMA_3_1_8B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B,\n            ModelType.NOVITA_LLAMA_3_3_70B,\n            ModelType.NOVITA_QWEN_2_5_72B,\n            ModelType.NOVITA_MISTRAL_NEMO,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B,\n            ModelType.NOVITA_LLAMA_3_8B,\n            ModelType.NOVITA_WIZARDLM_2_8X22B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B,\n            ModelType.NOVITA_LLAMA_3_1_70B,\n            ModelType.NOVITA_GEMMA_2_9B_IT,\n            ModelType.NOVITA_MISTRAL_7B,\n            ModelType.NOVITA_LLAMA_3_70B,\n            ModelType.NOVITA_DEEPSEEK_R1,\n            ModelType.NOVITA_HERMES_2_PRO_LLAMA_3_8B,\n            ModelType.NOVITA_L3_70B_EURYALE_V2_1,\n            ModelType.NOVITA_DOLPHIN_MIXTRAL_8X22B,\n            ModelType.NOVITA_AIROBOROS_L2_70B,\n            ModelType.NOVITA_MIDNIGHT_ROSE_70B,\n            ModelType.NOVITA_L3_8B_LUNARIS,\n            ModelType.NOVITA_GLM_4_9B_0414,\n            ModelType.NOVITA_GLM_Z1_9B_0414,\n            ModelType.NOVITA_GLM_Z1_32B_0414,\n            ModelType.NOVITA_GLM_4_32B_0414,\n            ModelType.NOVITA_GLM_Z1_RUMINATION_32B_0414,\n            ModelType.NOVITA_QWEN_2_5_7B,\n            ModelType.NOVITA_LLAMA_3_2_1B,\n            ModelType.NOVITA_LLAMA_3_2_11B_VISION,\n            ModelType.NOVITA_LLAMA_3_2_3B,\n            ModelType.NOVITA_LLAMA_3_1_8B_BF16,\n            ModelType.NOVITA_L31_70B_EURYALE_V2_2,\n        }\n\n    @property\n    def is_crynux(self) -> bool:\n        return self in {\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n            ModelType.CRYNUX_QWEN_3_4_B,\n            ModelType.CRYNUX_QWEN_3_8_B,\n            ModelType.CRYNUX_QWEN_2_5_7B,\n            ModelType.CRYNUX_QWEN_2_5_7B_INSTRUCT,\n            ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B,\n            ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B,\n        }\n\n    @property\n    def is_aiml(self) -> bool:\n        return self in {\n            ModelType.AIML_MIXTRAL_8X7B,\n            ModelType.AIML_MISTRAL_7B_INSTRUCT,\n        }\n\n    @property\n    def token_limit(self) -> int:\n        r\"\"\"Returns the maximum token limit for a given model.\n\n        Returns:\n            int: The maximum token limit for the given model.\n        \"\"\"\n        if self is ModelType.GLM_4V:\n            return 1024\n        elif self in {\n            ModelType.STUB,\n            ModelType.REKA_CORE,\n            ModelType.REKA_EDGE,\n            ModelType.REKA_FLASH,\n            ModelType.QWEN_MATH_PLUS,\n            ModelType.QWEN_MATH_TURBO,\n            ModelType.COHERE_COMMAND,\n            ModelType.COHERE_COMMAND_LIGHT,\n            ModelType.NVIDIA_NEMOTRON_340B_INSTRUCT,\n            ModelType.NVIDIA_NEMOTRON_340B_REWARD,\n            ModelType.NOVITA_MYTHOMAX_L2_13B,\n            ModelType.NOVITA_AIROBOROS_L2_70B,\n            ModelType.NOVITA_MIDNIGHT_ROSE_70B,\n        }:\n            return 4_096\n        elif self in {\n            ModelType.GPT_4,\n            ModelType.GROQ_LLAMA_3_8B,\n            ModelType.GROQ_LLAMA_3_70B,\n            ModelType.GROQ_LLAMA_3_3_70B_PREVIEW,\n            ModelType.GROQ_GEMMA_2_9B_IT,\n            ModelType.GLM_3_TURBO,\n            ModelType.GLM_4,\n            ModelType.QWEN_VL_PLUS,\n            ModelType.NVIDIA_LLAMA3_70B,\n            ModelType.TOGETHER_MISTRAL_7B,\n            ModelType.MOONSHOT_V1_8K,\n            ModelType.GLM_4V_FLASH,\n            ModelType.GLM_4_AIRX,\n            ModelType.OPENROUTER_OLYMPICODER_7B,\n            ModelType.LMSTUDIO_GEMMA_3_1B,\n            ModelType.LMSTUDIO_GEMMA_3_4B,\n            ModelType.LMSTUDIO_GEMMA_3_12B,\n            ModelType.LMSTUDIO_GEMMA_3_27B,\n            ModelType.WATSONX_GRANITE_3_8B_INSTRUCT,\n            ModelType.NOVITA_L3_8B_STHENO_V3_2,\n            ModelType.NOVITA_LLAMA_3_8B,\n            ModelType.NOVITA_GEMMA_2_9B_IT,\n            ModelType.NOVITA_LLAMA_3_70B,\n            ModelType.NOVITA_HERMES_2_PRO_LLAMA_3_8B,\n            ModelType.NOVITA_L3_70B_EURYALE_V2_1,\n            ModelType.NOVITA_L3_8B_LUNARIS,\n            ModelType.NOVITA_LLAMA_3_1_8B_BF16,\n            ModelType.NOVITA_L31_70B_EURYALE_V2_2,\n        }:\n            return 8_192\n        elif self in {\n            ModelType.PPIO_BAICHUAN_2_13B_CHAT,\n        }:\n            return 14_336\n        elif self in {\n            ModelType.PPIO_DEEPSEEK_PROVER_V2_671B,\n            ModelType.NOVITA_DOLPHIN_MIXTRAL_8X22B,\n        }:\n            return 16_000\n        elif self in {\n            ModelType.GPT_3_5_TURBO,\n            ModelType.YI_LIGHTNING,\n            ModelType.YI_MEDIUM,\n            ModelType.YI_LARGE_TURBO,\n            ModelType.YI_VISION,\n            ModelType.YI_SPARK,\n            ModelType.YI_LARGE_RAG,\n            ModelType.SAMBA_LLAMA_3_1_8B,\n            ModelType.SAMBA_LLAMA_3_1_405B,\n            ModelType.GLM_4V_PLUS_0111,\n            ModelType.GLM_ZERO_PREVIEW,\n            ModelType.PPIO_YI_1_5_34B_CHAT,\n            ModelType.NOVITA_LLAMA_3_1_8B,\n        }:\n            return 16_384\n        elif self in {\n            ModelType.NETMIND_DOUBAO_1_5_PRO,\n            ModelType.NOVITA_GEMMA_3_27B_IT,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n            ModelType.NOVITA_QWEN_2_5_72B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B,\n            ModelType.NOVITA_GLM_4_9B_0414,\n            ModelType.NOVITA_GLM_Z1_9B_0414,\n            ModelType.NOVITA_GLM_Z1_32B_0414,\n            ModelType.NOVITA_GLM_4_32B_0414,\n            ModelType.NOVITA_GLM_Z1_RUMINATION_32B_0414,\n            ModelType.NOVITA_QWEN_2_5_7B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B,\n            ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n            ModelType.CRYNUX_QWEN_3_4_B,\n            ModelType.CRYNUX_QWEN_3_8_B,\n            ModelType.CRYNUX_QWEN_2_5_7B,\n            ModelType.CRYNUX_QWEN_2_5_7B_INSTRUCT,\n            ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B,\n            ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B,\n            ModelType.ERNIE_X1_TURBO_32K,\n            ModelType.ERNIE_X1_32K,\n            ModelType.ERNIE_X1_32K_PREVIEW,\n            ModelType.ERNIE_4_5_TURBO_32K,\n            ModelType.QWEN3_235B_A22B,\n        }:\n            return 32_000\n        elif self in {\n            ModelType.MISTRAL_CODESTRAL,\n            ModelType.MISTRAL_7B,\n            ModelType.MISTRAL_MIXTRAL_8x7B,\n            ModelType.GROQ_MIXTRAL_8_7B,\n            ModelType.YI_LARGE,\n            ModelType.YI_LARGE_FC,\n            ModelType.QWEN_MAX,\n            ModelType.QWEN_VL_MAX,\n            ModelType.NVIDIA_YI_LARGE,\n            ModelType.NVIDIA_MISTRAL_LARGE,\n            ModelType.NVIDIA_MIXTRAL_8X7B,\n            ModelType.QWEN_QWQ_32B,\n            ModelType.QWEN_QWQ_PLUS,\n            ModelType.QWEN_QVQ_72B,\n            ModelType.INTERNLM3_8B_INSTRUCT,\n            ModelType.INTERNLM3_LATEST,\n            ModelType.INTERNLM2_5_LATEST,\n            ModelType.INTERNLM2_PRO_CHAT,\n            ModelType.TOGETHER_MIXTRAL_8_7B,\n            ModelType.SGLANG_MISTRAL_7B,\n            ModelType.MOONSHOT_V1_32K,\n            ModelType.AIML_MIXTRAL_8X7B,\n            ModelType.AIML_MISTRAL_7B_INSTRUCT,\n            ModelType.PPIO_QWEN_2_5_72B,\n            ModelType.PPIO_LLAMA_3_1_70B,\n            ModelType.MODELSCOPE_QWEN_2_5_7B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_14B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_32B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_72B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT,\n            ModelType.MODELSCOPE_QWEN_3_235B_A22B,\n            ModelType.MODELSCOPE_QWEN_3_32B,\n            ModelType.MODELSCOPE_QWQ_32B,\n            ModelType.MODELSCOPE_QWQ_32B_PREVIEW,\n            ModelType.MODELSCOPE_LLAMA_3_1_8B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_1_70B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_1_405B_INSTRUCT,\n            ModelType.MODELSCOPE_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.MODELSCOPE_MINISTRAL_8B_INSTRUCT,\n            ModelType.MODELSCOPE_DEEPSEEK_V3_0324,\n            ModelType.OPENROUTER_LLAMA_3_1_405B,\n            ModelType.WATSONX_MISTRAL_LARGE,\n            ModelType.NOVITA_QWEN_32B,\n            ModelType.NOVITA_LLAMA_3_1_70B,\n            ModelType.NOVITA_MISTRAL_7B,\n            ModelType.NOVITA_LLAMA_3_2_11B_VISION,\n            ModelType.NOVITA_LLAMA_3_2_3B,\n            ModelType.NEBIUS_MISTRAL_7B_INSTRUCT,\n        }:\n            return 32_768\n        elif self in {\n            ModelType.MISTRAL_MIXTRAL_8x22B,\n            ModelType.DEEPSEEK_CHAT,\n            ModelType.DEEPSEEK_REASONER,\n            ModelType.PPIO_DEEPSEEK_R1_TURBO,\n            ModelType.PPIO_DEEPSEEK_V3_TURBO,\n            ModelType.PPIO_DEEPSEEK_R1_COMMUNITY,\n            ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,\n            ModelType.PPIO_DEEPSEEK_R1,\n            ModelType.PPIO_DEEPSEEK_V3,\n            ModelType.AWS_DEEPSEEK_R1,\n            ModelType.NETMIND_QWQ_32B,\n            ModelType.NOVITA_DEEPSEEK_V3_TURBO,\n            ModelType.NOVITA_DEEPSEEK_R1_TURBO,\n            ModelType.NOVITA_DEEPSEEK_V3,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B,\n            ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B,\n            ModelType.NOVITA_DEEPSEEK_R1,\n        }:\n            return 64_000\n        elif self in {\n            ModelType.NOVITA_WIZARDLM_2_8X22B,\n        }:\n            return 65_535\n        elif self in {\n            ModelType.NOVITA_QWEN_2_5_V1_72B,\n            ModelType.DEEPSEEK_R1,\n        }:\n            return 96_000\n        elif self in {\n            ModelType.CLAUDE_2_0,\n            ModelType.CLAUDE_INSTANT_1_2,\n        }:\n            return 100_000\n        elif self in {\n            ModelType.GPT_4O,\n            ModelType.GPT_4O_MINI,\n            ModelType.GPT_4_TURBO,\n            ModelType.O1_PREVIEW,\n            ModelType.O1_MINI,\n            ModelType.GPT_4_5_PREVIEW,\n            ModelType.GPT_5,\n            ModelType.GPT_5_NANO,\n            ModelType.GPT_5_MINI,\n            ModelType.MISTRAL_LARGE,\n            ModelType.MISTRAL_NEMO,\n            ModelType.MISTRAL_PIXTRAL_12B,\n            ModelType.MISTRAL_8B,\n            ModelType.MISTRAL_3B,\n            ModelType.MISTRAL_SMALL_3_2,\n            ModelType.MAGISTRAL_SMALL_1_2,\n            ModelType.QWEN_2_5_CODER_32B,\n            ModelType.QWEN_2_5_VL_72B,\n            ModelType.QWEN_2_5_72B,\n            ModelType.QWEN_2_5_32B,\n            ModelType.QWEN_2_5_14B,\n            ModelType.COHERE_COMMAND_R,\n            ModelType.COHERE_COMMAND_R_PLUS,\n            ModelType.COHERE_COMMAND_NIGHTLY,\n            ModelType.NVIDIA_LLAMA3_1_8B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_1_70B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_1_405B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_2_1B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_2_3B_INSTRUCT,\n            ModelType.NVIDIA_LLAMA3_3_70B_INSTRUCT,\n            ModelType.GROQ_LLAMA_3_3_70B,\n            ModelType.SAMBA_LLAMA_3_1_70B,\n            ModelType.SGLANG_LLAMA_3_1_8B,\n            ModelType.SGLANG_LLAMA_3_1_70B,\n            ModelType.SGLANG_LLAMA_3_1_405B,\n            ModelType.SGLANG_LLAMA_3_2_1B,\n            ModelType.SGLANG_MIXTRAL_NEMO,\n            ModelType.MOONSHOT_V1_128K,\n            ModelType.GLM_4_PLUS,\n            ModelType.GLM_4_AIR,\n            ModelType.GLM_4_AIR_0111,\n            ModelType.GLM_4_FLASHX,\n            ModelType.GLM_4_FLASH,\n            ModelType.AWS_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.AWS_LLAMA_3_2_90B_INSTRUCT,\n            ModelType.AWS_LLAMA_3_2_11B_INSTRUCT,\n            ModelType.NETMIND_DEEPSEEK_R1,\n            ModelType.NETMIND_DEEPSEEK_V3,\n            ModelType.NOVITA_DEEPSEEK_V3_0324,\n            ModelType.MISTRAL_MEDIUM_3_1,\n            ModelType.MAGISTRAL_MEDIUM_1_2,\n            ModelType.ERNIE_4_5_TURBO_128K,\n            ModelType.DEEPSEEK_V3,\n            ModelType.MOONSHOT_KIMI_K2,\n            ModelType.NEBIUS_GLM_4_5,\n            ModelType.NEBIUS_DEEPSEEK_V3,\n            ModelType.NEBIUS_DEEPSEEK_R1,\n            ModelType.NEBIUS_GPT_OSS_120B,\n            ModelType.NEBIUS_GPT_OSS_20B,\n            ModelType.COMETAPI_GPT_5_CHAT_LATEST,\n            ModelType.COMETAPI_CHATGPT_4O_LATEST,\n            ModelType.COMETAPI_GPT_5_MINI,\n            ModelType.COMETAPI_GPT_5_NANO,\n            ModelType.COMETAPI_GPT_5,\n            ModelType.COMETAPI_GPT_4_1,\n            ModelType.COMETAPI_GPT_4O_MINI,\n            ModelType.COMETAPI_O4_MINI_2025_04_16,\n            ModelType.COMETAPI_O3_PRO_2025_06_10,\n            ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805,\n            ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING,\n            ModelType.COMETAPI_CLAUDE_SONNET_4_20250514,\n            ModelType.COMETAPI_CLAUDE_SONNET_4_20250514_THINKING,\n            ModelType.COMETAPI_CLAUDE_3_7_SONNET_LATEST,\n            ModelType.COMETAPI_CLAUDE_3_5_HAIKU_LATEST,\n            ModelType.COMETAPI_GEMINI_2_5_PRO,\n            ModelType.COMETAPI_GEMINI_2_5_FLASH,\n            ModelType.COMETAPI_GEMINI_2_5_FLASH_LITE,\n            ModelType.COMETAPI_GEMINI_2_0_FLASH,\n            ModelType.COMETAPI_GROK_4_0709,\n            ModelType.COMETAPI_GROK_3,\n            ModelType.COMETAPI_GROK_3_MINI,\n            ModelType.COMETAPI_GROK_2_IMAGE_1212,\n            ModelType.COMETAPI_DEEPSEEK_V3_1,\n            ModelType.COMETAPI_DEEPSEEK_V3,\n            ModelType.COMETAPI_DEEPSEEK_R1_0528,\n            ModelType.COMETAPI_DEEPSEEK_CHAT,\n            ModelType.COMETAPI_DEEPSEEK_REASONER,\n            ModelType.COMETAPI_QWEN3_30B_A3B,\n            ModelType.COMETAPI_QWEN3_CODER_PLUS_2025_07_22,\n        }:\n            return 128_000\n        elif self in {\n            ModelType.NOVITA_LLAMA_3_2_1B,\n        }:\n            return 131_000\n        elif self in {\n            ModelType.GROQ_LLAMA_3_1_8B,\n            ModelType.QWEN_PLUS,\n            ModelType.QWEN_TURBO,\n            ModelType.QWEN_CODER_TURBO,\n            ModelType.QWEN_PLUS_LATEST,\n            ModelType.QWEN_PLUS_2025_04_28,\n            ModelType.QWEN_TURBO_LATEST,\n            ModelType.QWEN_TURBO_2025_04_28,\n            ModelType.TOGETHER_LLAMA_3_1_8B,\n            ModelType.TOGETHER_LLAMA_3_1_70B,\n            ModelType.TOGETHER_LLAMA_3_1_405B,\n            ModelType.TOGETHER_LLAMA_3_3_70B,\n            ModelType.SGLANG_QWEN_2_5_7B,\n            ModelType.SGLANG_QWEN_2_5_32B,\n            ModelType.SGLANG_QWEN_2_5_72B,\n            ModelType.OPENROUTER_LLAMA_3_1_70B,\n            ModelType.PPIO_LLAMA_3_3_70B,\n            ModelType.OPENROUTER_LLAMA_4_SCOUT,\n            ModelType.WATSONX_LLAMA_3_3_70B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_1B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_3B_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT,\n            ModelType.WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT,\n            ModelType.WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT,\n            ModelType.NOVITA_LLAMA_4_SCOUT_17B,\n            ModelType.NOVITA_LLAMA_3_3_70B,\n            ModelType.NOVITA_MISTRAL_NEMO,\n            ModelType.NEBIUS_LLAMA_3_1_70B,\n        }:\n            return 131_072\n        elif self in {\n            ModelType.O1,\n            ModelType.O3_MINI,\n            ModelType.O3_PRO,\n            ModelType.CLAUDE_2_1,\n            ModelType.CLAUDE_3_OPUS,\n            ModelType.CLAUDE_3_SONNET,\n            ModelType.CLAUDE_3_HAIKU,\n            ModelType.CLAUDE_3_5_SONNET,\n            ModelType.CLAUDE_3_5_HAIKU,\n            ModelType.CLAUDE_3_7_SONNET,\n            ModelType.CLAUDE_SONNET_4,\n            ModelType.CLAUDE_OPUS_4,\n            ModelType.CLAUDE_OPUS_4_1,\n            ModelType.YI_MEDIUM_200K,\n            ModelType.AWS_CLAUDE_3_5_SONNET,\n            ModelType.AWS_CLAUDE_3_HAIKU,\n            ModelType.AWS_CLAUDE_3_SONNET,\n            ModelType.AWS_CLAUDE_3_7_SONNET,\n            ModelType.AWS_CLAUDE_SONNET_4,\n            ModelType.AWS_CLAUDE_OPUS_4,\n            ModelType.AWS_CLAUDE_OPUS_4_1,\n            ModelType.O4_MINI,\n            ModelType.O3,\n        }:\n            return 200_000\n        elif self in {\n            ModelType.MISTRAL_CODESTRAL_MAMBA,\n            ModelType.OPENROUTER_LLAMA_4_MAVERICK_FREE,\n            ModelType.OPENROUTER_HORIZON_ALPHA,\n        }:\n            return 256_000\n\n        elif self in {\n            ModelType.NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT,\n        }:\n            return 320_000\n        elif self in {\n            ModelType.OPENROUTER_LLAMA_4_SCOUT_FREE,\n            ModelType.NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT,\n        }:\n            return 512_000\n        elif self in {\n            ModelType.GEMINI_2_5_FLASH,\n            ModelType.GEMINI_2_5_PRO,\n            ModelType.GEMINI_2_0_FLASH,\n            ModelType.GEMINI_2_0_FLASH_EXP,\n            ModelType.GEMINI_2_0_FLASH_THINKING,\n            ModelType.GEMINI_2_0_FLASH_LITE,\n            ModelType.GEMINI_2_0_FLASH_LITE_PREVIEW,\n            ModelType.GEMINI_1_5_FLASH,\n            ModelType.GEMINI_1_5_PRO,\n            ModelType.GEMINI_2_0_PRO_EXP,  # Not given in doc, assume the same\n            ModelType.GLM_4_LONG,\n            ModelType.TOGETHER_LLAMA_4_MAVERICK,\n            ModelType.OPENROUTER_LLAMA_4_MAVERICK,\n            ModelType.AMD_GPT4,\n            ModelType.GPT_4_1,\n            ModelType.GPT_4_1_MINI,\n            ModelType.GPT_4_1_NANO,\n            ModelType.NOVITA_LLAMA_4_MAVERICK_17B,\n        }:\n            return 1_048_576\n        elif self in {\n            ModelType.QWEN_3_CODER_PLUS,\n        }:\n            return 1_000_000\n        elif self in {\n            ModelType.QWEN_LONG,\n            ModelType.TOGETHER_LLAMA_4_SCOUT,\n        }:\n            return 10_000_000\n\n        else:\n            logger.warning(\n                f\"Unknown model type {self}, set maximum token limit \"\n                f\"to 999_999_999\"\n            )\n            return 999_999_999\n\n\nclass EmbeddingModelType(Enum):\n    TEXT_EMBEDDING_ADA_2 = \"text-embedding-ada-002\"\n    TEXT_EMBEDDING_3_SMALL = \"text-embedding-3-small\"\n    TEXT_EMBEDDING_3_LARGE = \"text-embedding-3-large\"\n\n    JINA_EMBEDDINGS_V3 = \"jina-embeddings-v3\"\n    JINA_CLIP_V2 = \"jina-clip-v2\"\n    JINA_COLBERT_V2 = \"jina-colbert-v2\"\n    JINA_EMBEDDINGS_V2_BASE_CODE = \"jina-embeddings-v2-base-code\"\n\n    MISTRAL_EMBED = \"mistral-embed\"\n\n    GEMINI_EMBEDDING_EXP = \"gemini-embedding-exp-03-07\"\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this type of models is an OpenAI-released model.\"\"\"\n        return self in {\n            EmbeddingModelType.TEXT_EMBEDDING_ADA_2,\n            EmbeddingModelType.TEXT_EMBEDDING_3_SMALL,\n            EmbeddingModelType.TEXT_EMBEDDING_3_LARGE,\n        }\n\n    @property\n    def is_jina(self) -> bool:\n        r\"\"\"Returns whether this type of models is an Jina model.\"\"\"\n        return self in {\n            EmbeddingModelType.JINA_EMBEDDINGS_V3,\n            EmbeddingModelType.JINA_CLIP_V2,\n            EmbeddingModelType.JINA_COLBERT_V2,\n            EmbeddingModelType.JINA_EMBEDDINGS_V2_BASE_CODE,\n        }\n\n    @property\n    def is_mistral(self) -> bool:\n        r\"\"\"Returns whether this type of models is an Mistral-released\n        model.\n        \"\"\"\n        return self in {\n            EmbeddingModelType.MISTRAL_EMBED,\n        }\n\n    @property\n    def is_gemini(self) -> bool:\n        r\"\"\"Returns whether this type of models is an Gemini-released model.\"\"\"\n        return self in {\n            EmbeddingModelType.GEMINI_EMBEDDING_EXP,\n        }\n\n    @property\n    def output_dim(self) -> int:\n        if self in {\n            EmbeddingModelType.JINA_COLBERT_V2,\n        }:\n            return 128\n        elif self in {\n            EmbeddingModelType.JINA_EMBEDDINGS_V2_BASE_CODE,\n        }:\n            return 768\n        elif self in {\n            EmbeddingModelType.JINA_EMBEDDINGS_V3,\n            EmbeddingModelType.JINA_CLIP_V2,\n        }:\n            return 1024\n        elif self is EmbeddingModelType.TEXT_EMBEDDING_ADA_2:\n            return 1536\n        elif self is EmbeddingModelType.TEXT_EMBEDDING_3_SMALL:\n            return 1536\n        elif self is EmbeddingModelType.TEXT_EMBEDDING_3_LARGE:\n            return 3072\n        elif self is EmbeddingModelType.MISTRAL_EMBED:\n            return 1024\n        elif self is EmbeddingModelType.GEMINI_EMBEDDING_EXP:\n            return 3072\n        else:\n            raise ValueError(f\"Unknown model type {self}.\")\n\n\nclass GeminiEmbeddingTaskType(str, Enum):\n    r\"\"\"Task types for Gemini embedding models.\n\n    For more information, please refer to:\n    https://ai.google.dev/gemini-api/docs/embeddings#task-types\n    \"\"\"\n\n    SEMANTIC_SIMILARITY = \"SEMANTIC_SIMILARITY\"\n    CLASSIFICATION = \"CLASSIFICATION\"\n    CLUSTERING = \"CLUSTERING\"\n    RETRIEVAL_DOCUMENT = \"RETRIEVAL_DOCUMENT\"\n    RETRIEVAL_QUERY = \"RETRIEVAL_QUERY\"\n    QUESTION_ANSWERING = \"QUESTION_ANSWERING\"\n    FACT_VERIFICATION = \"FACT_VERIFICATION\"\n    CODE_RETRIEVAL_QUERY = \"CODE_RETRIEVAL_QUERY\"\n\n\nclass TaskType(Enum):\n    AI_SOCIETY = \"ai_society\"\n    CODE = \"code\"\n    MISALIGNMENT = \"misalignment\"\n    TRANSLATION = \"translation\"\n    EVALUATION = \"evaluation\"\n    SOLUTION_EXTRACTION = \"solution_extraction\"\n    ROLE_DESCRIPTION = \"role_description\"\n    GENERATE_TEXT_EMBEDDING_DATA = \"generate_text_embedding_data\"\n    OBJECT_RECOGNITION = \"object_recognition\"\n    IMAGE_CRAFT = \"image_craft\"\n    MULTI_CONDITION_IMAGE_CRAFT = \"multi_condition_image_craft\"\n    DEFAULT = \"default\"\n    VIDEO_DESCRIPTION = \"video_description\"\n\n\nclass VectorDistance(Enum):\n    r\"\"\"Distance metrics used in a vector database.\"\"\"\n\n    DOT = \"dot\"\n    r\"\"\"Dot product. https://en.wikipedia.org/wiki/Dot_product\"\"\"\n\n    COSINE = \"cosine\"\n    r\"\"\"Cosine similarity. https://en.wikipedia.org/wiki/Cosine_similarity\"\"\"\n\n    EUCLIDEAN = \"euclidean\"\n    r\"\"\"Euclidean distance. https://en.wikipedia.org/wiki/Euclidean_distance\"\"\"\n\n\nclass OpenAIBackendRole(Enum):\n    ASSISTANT = \"assistant\"\n    SYSTEM = \"system\"\n    DEVELOPER = \"developer\"\n    USER = \"user\"\n    FUNCTION = \"function\"\n    TOOL = \"tool\"\n\n\nclass TerminationMode(Enum):\n    ANY = \"any\"\n    ALL = \"all\"\n\n\nclass OpenAIImageTypeMeta(EnumMeta):\n    def __contains__(cls, image_type: object) -> bool:\n        try:\n            cls(image_type)\n        except ValueError:\n            return False\n        return True\n\n\nclass OpenAIImageType(Enum, metaclass=OpenAIImageTypeMeta):\n    r\"\"\"Image types supported by OpenAI vision model.\"\"\"\n\n    # https://platform.openai.com/docs/guides/vision\n    PNG = \"png\"\n    JPEG = \"jpeg\"\n    JPG = \"jpg\"\n    WEBP = \"webp\"\n    GIF = \"gif\"\n\n\nclass OpenAIVisionDetailType(Enum):\n    AUTO = \"auto\"\n    LOW = \"low\"\n    HIGH = \"high\"\n\n\nclass StorageType(Enum):\n    MILVUS = \"milvus\"\n    QDRANT = \"qdrant\"\n    TIDB = \"tidb\"\n\n\nclass OpenAPIName(Enum):\n    COURSERA = \"coursera\"\n    KLARNA = \"klarna\"\n    SPEAK = \"speak\"\n    NASA_APOD = \"nasa_apod\"\n    BIZTOC = \"biztoc\"\n    CREATE_QR_CODE = \"create_qr_code\"\n    OUTSCHOOL = \"outschool\"\n    WEB_SCRAPER = \"web_scraper\"\n\n\nclass ModelPlatformType(Enum):\n    DEFAULT = os.getenv(\"DEFAULT_MODEL_PLATFORM_TYPE\", \"openai\")\n\n    OPENAI = \"openai\"\n    AWS_BEDROCK = \"aws-bedrock\"\n    AZURE = \"azure\"\n    ANTHROPIC = \"anthropic\"\n    GROQ = \"groq\"\n    NEBIUS = \"nebius\"\n    COMETAPI = \"cometapi\"\n    OPENROUTER = \"openrouter\"\n    OLLAMA = \"ollama\"\n    LITELLM = \"litellm\"\n    LMSTUDIO = \"lmstudio\"\n    ZHIPU = \"zhipuai\"\n    GEMINI = \"gemini\"\n    VLLM = \"vllm\"\n    MISTRAL = \"mistral\"\n    REKA = \"reka\"\n    TOGETHER = \"together\"\n    STUB = \"stub\"\n    OPENAI_COMPATIBLE_MODEL = \"openai-compatible-model\"\n    SAMBA = \"samba-nova\"\n    COHERE = \"cohere\"\n    YI = \"lingyiwanwu\"\n    QWEN = \"tongyi-qianwen\"\n    AMD = \"amd\"\n    NVIDIA = \"nvidia\"\n    DEEPSEEK = \"deepseek\"\n    PPIO = \"ppio\"\n    SGLANG = \"sglang\"\n    INTERNLM = \"internlm\"\n    MOONSHOT = \"moonshot\"\n    MODELSCOPE = \"modelscope\"\n    SILICONFLOW = \"siliconflow\"\n    AIML = \"aiml\"\n    VOLCANO = \"volcano\"\n    NETMIND = \"netmind\"\n    NOVITA = \"novita\"\n    WATSONX = \"watsonx\"\n    QIANFAN = \"qianfan\"\n    CRYNUX = \"crynux\"\n\n    @classmethod\n    def from_name(cls, name):\n        r\"\"\"Returns the ModelPlatformType enum value from a string.\"\"\"\n        for model_platfrom_type in cls:\n            if model_platfrom_type.value == name:\n                return model_platfrom_type\n        raise ValueError(f\"Unknown ModelPlatformType name: {name}\")\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this platform is openai.\"\"\"\n        return self is ModelPlatformType.OPENAI\n\n    @property\n    def is_aws_bedrock(self) -> bool:\n        r\"\"\"Returns whether this platform is aws-bedrock.\"\"\"\n        return self is ModelPlatformType.AWS_BEDROCK\n\n    @property\n    def is_azure(self) -> bool:\n        r\"\"\"Returns whether this platform is azure.\"\"\"\n        return self is ModelPlatformType.AZURE\n\n    @property\n    def is_anthropic(self) -> bool:\n        r\"\"\"Returns whether this platform is anthropic.\"\"\"\n        return self is ModelPlatformType.ANTHROPIC\n\n    @property\n    def is_groq(self) -> bool:\n        r\"\"\"Returns whether this platform is groq.\"\"\"\n        return self is ModelPlatformType.GROQ\n\n    @property\n    def is_openrouter(self) -> bool:\n        r\"\"\"Returns whether this platform is openrouter.\"\"\"\n        return self is ModelPlatformType.OPENROUTER\n\n    @property\n    def is_lmstudio(self) -> bool:\n        r\"\"\"Returns whether this platform is lmstudio.\"\"\"\n        return self is ModelPlatformType.LMSTUDIO\n\n    @property\n    def is_ollama(self) -> bool:\n        r\"\"\"Returns whether this platform is ollama.\"\"\"\n        return self is ModelPlatformType.OLLAMA\n\n    @property\n    def is_vllm(self) -> bool:\n        r\"\"\"Returns whether this platform is vllm.\"\"\"\n        return self is ModelPlatformType.VLLM\n\n    @property\n    def is_sglang(self) -> bool:\n        r\"\"\"Returns whether this platform is sglang.\"\"\"\n        return self is ModelPlatformType.SGLANG\n\n    @property\n    def is_together(self) -> bool:\n        r\"\"\"Returns whether this platform is together.\"\"\"\n        return self is ModelPlatformType.TOGETHER\n\n    @property\n    def is_litellm(self) -> bool:\n        r\"\"\"Returns whether this platform is litellm.\"\"\"\n        return self is ModelPlatformType.LITELLM\n\n    @property\n    def is_zhipuai(self) -> bool:\n        r\"\"\"Returns whether this platform is zhipu.\"\"\"\n        return self is ModelPlatformType.ZHIPU\n\n    @property\n    def is_mistral(self) -> bool:\n        r\"\"\"Returns whether this platform is mistral.\"\"\"\n        return self is ModelPlatformType.MISTRAL\n\n    @property\n    def is_openai_compatible_model(self) -> bool:\n        r\"\"\"Returns whether this is a platform supporting openai\n        compatibility\"\"\"\n        return self is ModelPlatformType.OPENAI_COMPATIBLE_MODEL\n\n    @property\n    def is_gemini(self) -> bool:\n        r\"\"\"Returns whether this platform is Gemini.\"\"\"\n        return self is ModelPlatformType.GEMINI\n\n    @property\n    def is_reka(self) -> bool:\n        r\"\"\"Returns whether this platform is Reka.\"\"\"\n        return self is ModelPlatformType.REKA\n\n    @property\n    def is_samba(self) -> bool:\n        r\"\"\"Returns whether this platform is Samba Nova.\"\"\"\n        return self is ModelPlatformType.SAMBA\n\n    @property\n    def is_cohere(self) -> bool:\n        r\"\"\"Returns whether this platform is Cohere.\"\"\"\n        return self is ModelPlatformType.COHERE\n\n    @property\n    def is_yi(self) -> bool:\n        r\"\"\"Returns whether this platform is Yi.\"\"\"\n        return self is ModelPlatformType.YI\n\n    @property\n    def is_qwen(self) -> bool:\n        r\"\"\"Returns whether this platform is Qwen.\"\"\"\n        return self is ModelPlatformType.QWEN\n\n    @property\n    def is_nvidia(self) -> bool:\n        r\"\"\"Returns whether this platform is Nvidia.\"\"\"\n        return self is ModelPlatformType.NVIDIA\n\n    @property\n    def is_deepseek(self) -> bool:\n        r\"\"\"Returns whether this platform is DeepSeek.\"\"\"\n        return self is ModelPlatformType.DEEPSEEK\n\n    @property\n    def is_netmind(self) -> bool:\n        r\"\"\"Returns whether this platform is Netmind.\"\"\"\n        return self is ModelPlatformType.NETMIND\n\n    @property\n    def is_ppio(self) -> bool:\n        r\"\"\"Returns whether this platform is PPIO.\"\"\"\n        return self is ModelPlatformType.PPIO\n\n    @property\n    def is_internlm(self) -> bool:\n        r\"\"\"Returns whether this platform is InternLM.\"\"\"\n        return self is ModelPlatformType.INTERNLM\n\n    @property\n    def is_moonshot(self) -> bool:\n        r\"\"\"Returns whether this platform is Moonshot model.\"\"\"\n        return self is ModelPlatformType.MOONSHOT\n\n    @property\n    def is_modelscope(self) -> bool:\n        r\"\"\"Returns whether this platform is ModelScope model.\"\"\"\n        return self is ModelPlatformType.MODELSCOPE\n\n    @property\n    def is_siliconflow(self) -> bool:\n        r\"\"\"Returns whether this platform is SiliconFlow.\"\"\"\n        return self is ModelPlatformType.SILICONFLOW\n\n    @property\n    def is_aiml(self) -> bool:\n        r\"\"\"Returns whether this platform is AIML.\"\"\"\n        return self is ModelPlatformType.AIML\n\n    @property\n    def is_volcano(self) -> bool:\n        r\"\"\"Returns whether this platform is volcano.\"\"\"\n        return self is ModelPlatformType.VOLCANO\n\n    @property\n    def is_novita(self) -> bool:\n        r\"\"\"Returns whether this platform is Novita.\"\"\"\n        return self is ModelPlatformType.NOVITA\n\n    @property\n    def is_watsonx(self) -> bool:\n        r\"\"\"Returns whether this platform is WatsonX.\"\"\"\n        return self is ModelPlatformType.WATSONX\n\n    @property\n    def is_crynux(self) -> bool:\n        r\"\"\"Returns whether this platform is Crynux.\"\"\"\n        return self is ModelPlatformType.CRYNUX\n\n\nclass AudioModelType(Enum):\n    TTS_1 = \"tts-1\"\n    TTS_1_HD = \"tts-1-hd\"\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this type of audio models is an OpenAI-released\n        model.\"\"\"\n        return self in {\n            AudioModelType.TTS_1,\n            AudioModelType.TTS_1_HD,\n        }\n\n\nclass VoiceType(Enum):\n    ALLOY = \"alloy\"\n    ECHO = \"echo\"\n    FABLE = \"fable\"\n    ONYX = \"onyx\"\n    NOVA = \"nova\"\n    SHIMMER = \"shimmer\"\n\n    @property\n    def is_openai(self) -> bool:\n        r\"\"\"Returns whether this type of voice is an OpenAI-released voice.\"\"\"\n        return self in {\n            VoiceType.ALLOY,\n            VoiceType.ECHO,\n            VoiceType.FABLE,\n            VoiceType.ONYX,\n            VoiceType.NOVA,\n            VoiceType.SHIMMER,\n        }\n\n\nclass JinaReturnFormat(Enum):\n    DEFAULT = None\n    MARKDOWN = \"markdown\"\n    HTML = \"html\"\n    TEXT = \"text\"\n\n\nclass HuggingFaceRepoType(str, Enum):\n    DATASET = \"dataset\"\n    MODEL = \"model\"\n    SPACE = \"space\""}}}}