✨ feat(llm): 增强 LLM 管理功能，支持纯文本列表输出，优化模型能力识别并新增提供商

- 【LLM 管理器】为 `llm list` 命令添加 `--text` 选项，支持以纯文本格式输出模型列表。
- 【LLM 配置】新增 `OpenRouter` LLM 提供商的默认配置。
- 【模型能力】增强 `get_model_capabilities` 函数的查找逻辑，支持模型名称分段匹配和更灵活的通配符匹配。
- 【模型能力】为 `Gemini` 模型能力注册表使用更通用的通配符模式。
- 【模型能力】新增 `GPT` 系列模型的详细能力定义，包括多模态输入输出和工具调用支持。

zhenxun/builtin_plugins/llm_manager/__init__.py

@@ -1,3 +1,5 @@
+from collections import defaultdict
+
 from nonebot.permission import SUPERUSER
 from nonebot.plugin import PluginMetadata
 from nonebot_plugin_alconna import (
@@ -58,7 +60,12 @@ __plugin_meta__ = PluginMetadata(
 llm_cmd = on_alconna(
     Alconna(
         "llm",
-        Subcommand("list", alias=["ls"], help_text="查看模型列表"),
+        Subcommand(
+            "list",
+            Option("--text", action=store_true, help_text="以纯文本格式输出模型列表"),
+            alias=["ls"],
+            help_text="查看模型列表",
+        ),
         Subcommand("info", Args["model_name", str], help_text="查看模型详情"),
         Subcommand("default", Args["model_name?", str], help_text="查看或设置默认模型"),
         Subcommand(
@@ -80,13 +87,36 @@ llm_cmd = on_alconna(
 
 
 @llm_cmd.assign("list")
-async def handle_list(arp: Arparma, show_all: Query[bool] = Query("all")):
+async def handle_list(
+    arp: Arparma,
+    show_all: Query[bool] = Query("all"),
+    text_mode: Query[bool] = Query("list.text.value", False),
+):
     """处理 'llm list' 命令"""
     logger.info("获取LLM模型列表", command="LLM Manage", session=arp.header_result)
     models = await DataSource.get_model_list(show_all=show_all.result)
 
-    image = await Presenters.format_model_list_as_image(models, show_all.result)
-    await llm_cmd.finish(MessageUtils.build_message(image))
+    if text_mode.result:
+        if not models:
+            await llm_cmd.finish("当前没有配置任何LLM模型。")
+
+        grouped_models = defaultdict(list)
+        for model in models:
+            grouped_models[model["provider_name"]].append(model)
+
+        response_parts = ["可用的LLM模型列表:"]
+        for provider, model_list in grouped_models.items():
+            response_parts.append(f"\n{provider}:")
+            for model in model_list:
+                response_parts.append(
+                    f"  {model['provider_name']}/{model['model_name']}"
+                )
+
+        response_text = "\n".join(response_parts)
+        await llm_cmd.finish(response_text)
+    else:
+        image = await Presenters.format_model_list_as_image(models, show_all.result)
+        await llm_cmd.finish(MessageUtils.build_message(image))
 
 
 @llm_cmd.assign("info")
@@ -114,7 +144,7 @@ async def handle_default(arp: Arparma, model_name: Match[str]):
             command="LLM Manage",
             session=arp.header_result,
         )
-        success, message = await DataSource.set_default_model(model_name.result)
+        _success, message = await DataSource.set_default_model(model_name.result)
         await llm_cmd.finish(message)
     else:
         logger.info("查看默认模型", command="LLM Manage", session=arp.header_result)
@@ -132,7 +162,7 @@ async def handle_test(arp: Arparma, model_name: Match[str]):
     )
     await llm_cmd.send(f"正在测试模型 '{model_name.result}'，请稍候...")
 
-    success, message = await DataSource.test_model_connectivity(model_name.result)
+    _success, message = await DataSource.test_model_connectivity(model_name.result)
     await llm_cmd.finish(message)
 
 
@@ -167,5 +197,5 @@ async def handle_reset_key(
     )
     logger.info(log_msg, command="LLM Manage", session=arp.header_result)
 
-    success, message = await DataSource.reset_key(provider_name.result, key_to_reset)
+    _success, message = await DataSource.reset_key(provider_name.result, key_to_reset)
     await llm_cmd.finish(message)

zhenxun/services/llm/config/providers.py

@@ -192,10 +192,20 @@ def get_default_providers() -> list[dict[str, Any]]:
             "api_base": "https://generativelanguage.googleapis.com",
             "api_type": "gemini",
             "models": [
-                {"model_name": "gemini-2.0-flash"},
                 {"model_name": "gemini-2.5-flash"},
                 {"model_name": "gemini-2.5-pro"},
-                {"model_name": "gemini-2.5-flash-lite-preview-06-17"},
+                {"model_name": "gemini-2.5-flash-lite"},
+            ],
+        },
+        {
+            "name": "OpenRouter",
+            "api_key": "YOUR_OPENROUTER_API_KEY",
+            "api_base": "https://openrouter.ai/api",
+            "api_type": "openrouter",
+            "models": [
+                {"model_name": "google/gemini-2.5-pro"},
+                {"model_name": "google/gemini-2.5-flash"},
+                {"model_name": "x-ai/grok-4"},
             ],
         },
     ]

zhenxun/services/llm/types/capabilities.py

@@ -9,6 +9,8 @@ import fnmatch
 
 from pydantic import BaseModel, Field
 
+from zhenxun.services.log import logger
+
 
 class ModelModality(str, Enum):
     TEXT = "text"
@@ -50,6 +52,46 @@ GEMINI_IMAGE_GEN_CAPABILITIES = ModelCapabilities(
     supports_tool_calling=True,
 )
 
+GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES = ModelCapabilities(
+    input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
+    output_modalities={ModelModality.TEXT},
+    supports_tool_calling=True,
+)
+
+GPT_MULTIMODAL_IO_CAPABILITIES = ModelCapabilities(
+    input_modalities={ModelModality.TEXT, ModelModality.AUDIO, ModelModality.IMAGE},
+    output_modalities={ModelModality.TEXT, ModelModality.AUDIO},
+    supports_tool_calling=True,
+)
+
+GPT_IMAGE_GENERATION_CAPABILITIES = ModelCapabilities(
+    input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
+    output_modalities={ModelModality.IMAGE},
+    supports_tool_calling=True,
+)
+
+GPT_VIDEO_GENERATION_CAPABILITIES = ModelCapabilities(
+    input_modalities={ModelModality.TEXT, ModelModality.IMAGE, ModelModality.VIDEO},
+    output_modalities={ModelModality.VIDEO},
+    supports_tool_calling=True,
+)
+
+DEFAULT_PERMISSIVE_CAPABILITIES = ModelCapabilities(
+    input_modalities={
+        ModelModality.TEXT,
+        ModelModality.IMAGE,
+        ModelModality.AUDIO,
+        ModelModality.VIDEO,
+    },
+    output_modalities={
+        ModelModality.TEXT,
+        ModelModality.IMAGE,
+        ModelModality.AUDIO,
+        ModelModality.VIDEO,
+    },
+    supports_tool_calling=True,
+)
+
 
 DOUBAO_ADVANCED_MULTIMODAL_CAPABILITIES = ModelCapabilities(
     input_modalities={ModelModality.TEXT, ModelModality.IMAGE, ModelModality.VIDEO},
@@ -91,11 +133,8 @@ MODEL_CAPABILITIES_REGISTRY: dict[str, ModelCapabilities] = {
         is_embedding_model=True,
     ),
     "*gemini-*-image-preview*": GEMINI_IMAGE_GEN_CAPABILITIES,
-    "gemini-2.5-pro*": GEMINI_CAPABILITIES,
-    "gemini-1.5-pro*": GEMINI_CAPABILITIES,
-    "gemini-2.5-flash*": GEMINI_CAPABILITIES,
-    "gemini-2.0-flash*": GEMINI_CAPABILITIES,
-    "gemini-1.5-flash*": GEMINI_CAPABILITIES,
+    "gemini-*-pro*": GEMINI_CAPABILITIES,
+    "gemini-*-flash*": GEMINI_CAPABILITIES,
     "GLM-4V-Flash": ModelCapabilities(
         input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
         output_modalities={ModelModality.TEXT},
@@ -112,6 +151,13 @@ MODEL_CAPABILITIES_REGISTRY: dict[str, ModelCapabilities] = {
     "doubao-1-5-thinking-vision-pro": DOUBAO_ADVANCED_MULTIMODAL_CAPABILITIES,
     "deepseek-chat": STANDARD_TEXT_TOOL_CAPABILITIES,
     "deepseek-reasoner": STANDARD_TEXT_TOOL_CAPABILITIES,
+    "gpt-5*": GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES,
+    "gpt-4.1*": GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES,
+    "gpt-4o*": GPT_MULTIMODAL_IO_CAPABILITIES,
+    "o3*": GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES,
+    "o4-mini*": GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES,
+    "gpt image*": GPT_IMAGE_GENERATION_CAPABILITIES,
+    "sora*": GPT_VIDEO_GENERATION_CAPABILITIES,
 }
 
 
@@ -126,11 +172,25 @@ def get_model_capabilities(model_name: str) -> ModelCapabilities:
             canonical_name = c_name
             break
 
-    if canonical_name in MODEL_CAPABILITIES_REGISTRY:
-        return MODEL_CAPABILITIES_REGISTRY[canonical_name]
+    parts = canonical_name.split("/")
+    names_to_check = ["/".join(parts[i:]) for i in range(len(parts))]
 
-    for pattern, capabilities in MODEL_CAPABILITIES_REGISTRY.items():
-        if "*" in pattern and fnmatch.fnmatch(model_name, pattern):
-            return capabilities
+    logger.trace(f"为 '{model_name}' 生成的检查列表: {names_to_check}")
 
-    return ModelCapabilities()
+    for name in names_to_check:
+        if name in MODEL_CAPABILITIES_REGISTRY:
+            logger.debug(f"模型 '{model_name}' 通过精确匹配 '{name}' 找到能力定义。")
+            return MODEL_CAPABILITIES_REGISTRY[name]
+
+        for pattern, capabilities in MODEL_CAPABILITIES_REGISTRY.items():
+            if "*" in pattern and fnmatch.fnmatch(name, pattern):
+                logger.debug(
+                    f"模型 '{model_name}' 通过通配符匹配 '{name}'(pattern: '{pattern}')"
+                    f"找到能力定义。"
+                )
+                return capabilities
+
+    logger.warning(
+        f"模型 '{model_name}' 的能力定义未在注册表中找到，将使用默认的'全功能'回退配置"
+    )
+    return DEFAULT_PERMISSIVE_CAPABILITIES