refactor: switch llm_entities to plugin sdk

pkg/plugin/events.py

@@ -1,171 +0,0 @@
-from __future__ import annotations
-
-import typing
-
-import pydantic.v1 as pydantic
-
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-import langbot_plugin.api.entities.builtin.provider.session as provider_session
-from ..provider import entities as llm_entities
-
-
-class BaseEventModel(pydantic.BaseModel):
-    """事件模型基类"""
-
-    query: typing.Union[pipeline_query.Query, None]
-    """此次请求的query对象，非请求过程的事件时为None"""
-
-    class Config:
-        arbitrary_types_allowed = True
-
-
-class PersonMessageReceived(BaseEventModel):
-    """收到任何私聊消息时"""
-
-    launcher_type: str
-    """发起对象类型(group/person)"""
-
-    launcher_id: typing.Union[int, str]
-    """发起对象ID(群号/QQ号)"""
-
-    sender_id: typing.Union[int, str]
-    """发送者ID(QQ号)"""
-
-    message_chain: platform_message.MessageChain
-
-
-class GroupMessageReceived(BaseEventModel):
-    """收到任何群聊消息时"""
-
-    launcher_type: str
-
-    launcher_id: typing.Union[int, str]
-
-    sender_id: typing.Union[int, str]
-
-    message_chain: platform_message.MessageChain
-
-
-class PersonNormalMessageReceived(BaseEventModel):
-    """判断为应该处理的私聊普通消息时触发"""
-
-    launcher_type: str
-
-    launcher_id: typing.Union[int, str]
-
-    sender_id: typing.Union[int, str]
-
-    text_message: str
-
-    alter: typing.Optional[str] = None
-    """修改后的消息文本"""
-
-    reply: typing.Optional[list] = None
-    """回复消息组件列表"""
-
-
-class PersonCommandSent(BaseEventModel):
-    """判断为应该处理的私聊命令时触发"""
-
-    launcher_type: str
-
-    launcher_id: typing.Union[int, str]
-
-    sender_id: typing.Union[int, str]
-
-    command: str
-
-    params: list[str]
-
-    text_message: str
-
-    is_admin: bool
-
-    alter: typing.Optional[str] = None
-    """修改后的完整命令文本"""
-
-    reply: typing.Optional[list] = None
-    """回复消息组件列表"""
-
-
-class GroupNormalMessageReceived(BaseEventModel):
-    """判断为应该处理的群聊普通消息时触发"""
-
-    launcher_type: str
-
-    launcher_id: typing.Union[int, str]
-
-    sender_id: typing.Union[int, str]
-
-    text_message: str
-
-    alter: typing.Optional[str] = None
-    """修改后的消息文本"""
-
-    reply: typing.Optional[list] = None
-    """回复消息组件列表"""
-
-
-class GroupCommandSent(BaseEventModel):
-    """判断为应该处理的群聊命令时触发"""
-
-    launcher_type: str
-
-    launcher_id: typing.Union[int, str]
-
-    sender_id: typing.Union[int, str]
-
-    command: str
-
-    params: list[str]
-
-    text_message: str
-
-    is_admin: bool
-
-    alter: typing.Optional[str] = None
-    """修改后的完整命令文本"""
-
-    reply: typing.Optional[list] = None
-    """回复消息组件列表"""
-
-
-class NormalMessageResponded(BaseEventModel):
-    """回复普通消息时触发"""
-
-    launcher_type: str
-
-    launcher_id: typing.Union[int, str]
-
-    sender_id: typing.Union[int, str]
-
-    session: provider_session.Session
-    """会话对象"""
-
-    prefix: str
-    """回复消息的前缀"""
-
-    response_text: str
-    """回复消息的文本"""
-
-    finish_reason: str
-    """响应结束原因"""
-
-    funcs_called: list[str]
-    """调用的函数列表"""
-
-    reply: typing.Optional[list] = None
-    """回复消息组件列表"""
-
-
-class PromptPreProcessing(BaseEventModel):
-    """会话中的Prompt预处理时触发"""
-
-    session_name: str
-
-    default_prompt: list[llm_entities.Message]
-    """此对话的情景预设，可修改"""
-
-    prompt: list[llm_entities.Message]
-    """此对话现有消息记录，可修改"""

pkg/provider/entities.py

@@ -1,135 +0,0 @@
-from __future__ import annotations
-
-import typing
-import pydantic
-
-from pkg.provider import entities
-
-
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-
-
-class FunctionCall(pydantic.BaseModel):
-    name: str
-
-    arguments: str
-
-
-class ToolCall(pydantic.BaseModel):
-    id: str
-
-    type: str
-
-    function: FunctionCall
-
-
-class ImageURLContentObject(pydantic.BaseModel):
-    url: str
-
-    def __str__(self):
-        return self.url[:128] + ('...' if len(self.url) > 128 else '')
-
-
-class ContentElement(pydantic.BaseModel):
-    type: str
-    """内容类型"""
-
-    text: typing.Optional[str] = None
-
-    image_url: typing.Optional[ImageURLContentObject] = None
-
-    image_base64: typing.Optional[str] = None
-
-    def __str__(self):
-        if self.type == 'text':
-            return self.text
-        elif self.type == 'image_url':
-            return f'[图片]({self.image_url})'
-        else:
-            return '未知内容'
-
-    @classmethod
-    def from_text(cls, text: str):
-        return cls(type='text', text=text)
-
-    @classmethod
-    def from_image_url(cls, image_url: str):
-        return cls(type='image_url', image_url=ImageURLContentObject(url=image_url))
-
-    @classmethod
-    def from_image_base64(cls, image_base64: str):
-        return cls(type='image_base64', image_base64=image_base64)
-
-
-class Message(pydantic.BaseModel):
-    """消息"""
-
-    role: str  # user, system, assistant, tool, command, plugin
-    """消息的角色"""
-
-    name: typing.Optional[str] = None
-    """名称，仅函数调用返回时设置"""
-
-    content: typing.Optional[list[ContentElement]] | typing.Optional[str] = None
-    """内容"""
-
-    tool_calls: typing.Optional[list[ToolCall]] = None
-    """工具调用"""
-
-    tool_call_id: typing.Optional[str] = None
-
-    def readable_str(self) -> str:
-        if self.content is not None:
-            return str(self.role) + ': ' + str(self.get_content_platform_message_chain())
-        elif self.tool_calls is not None:
-            return f'调用工具: {self.tool_calls[0].id}'
-        else:
-            return '未知消息'
-
-    def get_content_platform_message_chain(self, prefix_text: str = '') -> platform_message.MessageChain | None:
-        """将内容转换为平台消息 MessageChain 对象
-
-        Args:
-            prefix_text (str): 首个文字组件的前缀文本
-        """
-
-        if self.content is None:
-            return None
-        elif isinstance(self.content, str):
-            return platform_message.MessageChain([platform_message.Plain(text=(prefix_text + self.content))])
-        elif isinstance(self.content, list):
-            mc = []
-            for ce in self.content:
-                if ce.type == 'text':
-                    mc.append(platform_message.Plain(ce.text))
-                elif ce.type == 'image_url':
-                    if ce.image_url.url.startswith('http'):
-                        mc.append(platform_message.Image(url=ce.image_url.url))
-                    else:  # base64
-                        b64_str = ce.image_url.url
-
-                        if b64_str.startswith('data:'):
-                            b64_str = b64_str.split(',')[1]
-
-                        mc.append(platform_message.Image(base64=b64_str))
-
-            # 找第一个文字组件
-            if prefix_text:
-                for i, c in enumerate(mc):
-                    if isinstance(c, platform_message.Plain):
-                        mc[i] = platform_message.Plain(prefix_text + c.text)
-                        break
-                else:
-                    mc.insert(0, platform_message.Plain(prefix_text))
-
-            return platform_message.MessageChain(mc)
-
-
-class Prompt(pydantic.BaseModel):
-    """供AI使用的Prompt"""
-
-    name: str
-    """名称"""
-
-    messages: list[entities.Message]
-    """消息列表"""

pkg/provider/modelmgr/requester.py

@@ -4,11 +4,11 @@ import abc
 import typing
 
 from ...core import app
-from .. import entities as llm_entities
 from ...entity.persistence import model as persistence_model
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 from . import token
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class RuntimeLLMModel:
@@ -58,10 +58,10 @@ class LLMAPIRequester(metaclass=abc.ABCMeta):
         self,
         query: pipeline_query.Query,
         model: RuntimeLLMModel,
-        messages: typing.List[llm_entities.Message],
+        messages: typing.List[provider_message.Message],
         funcs: typing.List[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         """调用API
 
         Args:

pkg/provider/modelmgr/requesters/anthropicmsgs.py

@@ -9,10 +9,10 @@ import httpx
 
 from .. import errors, requester
 
-from ... import entities as llm_entities
 from ....utils import image
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class AnthropicMessages(requester.LLMAPIRequester):
@@ -50,10 +50,10 @@ class AnthropicMessages(requester.LLMAPIRequester):
         self,
         query: pipeline_query.Query,
         model: requester.RuntimeLLMModel,
-        messages: typing.List[llm_entities.Message],
+        messages: typing.List[provider_message.Message],
         funcs: typing.List[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         self.client.api_key = model.token_mgr.get_token()
 
         args = extra_args.copy()
@@ -73,7 +73,7 @@ class AnthropicMessages(requester.LLMAPIRequester):
         if system_role_message:
             messages.pop(i)
 
-        if isinstance(system_role_message, llm_entities.Message) and isinstance(system_role_message.content, str):
+        if isinstance(system_role_message, provider_message.Message) and isinstance(system_role_message.content, str):
             args['system'] = system_role_message.content
 
         req_messages = []
@@ -157,16 +157,16 @@ class AnthropicMessages(requester.LLMAPIRequester):
                     args['content'] += block.text
                 elif block.type == 'tool_use':
                     assert type(block) is anthropic.types.tool_use_block.ToolUseBlock
-                    tool_call = llm_entities.ToolCall(
+                    tool_call = provider_message.ToolCall(
                         id=block.id,
                         type='function',
-                        function=llm_entities.FunctionCall(name=block.name, arguments=json.dumps(block.input)),
+                        function=provider_message.FunctionCall(name=block.name, arguments=json.dumps(block.input)),
                     )
                     if 'tool_calls' not in args:
                         args['tool_calls'] = []
                     args['tool_calls'].append(tool_call)
 
-            return llm_entities.Message(**args)
+            return provider_message.Message(**args)
         except anthropic.AuthenticationError as e:
             raise errors.RequesterError(f'api-key 无效: {e.message}')
         except anthropic.BadRequestError as e:

pkg/provider/modelmgr/requesters/chatcmpl.py

@@ -8,9 +8,9 @@ import openai.types.chat.chat_completion as chat_completion
 import httpx
 
 from .. import errors, requester
-from ... import entities as llm_entities
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class OpenAIChatCompletions(requester.LLMAPIRequester):
@@ -41,7 +41,7 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
     async def _make_msg(
         self,
         chat_completion: chat_completion.ChatCompletion,
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         chatcmpl_message = chat_completion.choices[0].message.model_dump()
 
         # 确保 role 字段存在且不为 None
@@ -54,7 +54,7 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
         if reasoning_content is not None:
             chatcmpl_message['content'] = '<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
 
-        message = llm_entities.Message(**chatcmpl_message)
+        message = provider_message.Message(**chatcmpl_message)
 
         return message
 
@@ -65,7 +65,7 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         self.client.api_key = use_model.token_mgr.get_token()
 
         args = {}
@@ -103,10 +103,10 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
         self,
         query: pipeline_query.Query,
         model: requester.RuntimeLLMModel,
-        messages: typing.List[llm_entities.Message],
+        messages: typing.List[provider_message.Message],
         funcs: typing.List[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
         for m in messages:
             msg_dict = m.dict(exclude_none=True)

pkg/provider/modelmgr/requesters/deepseekchatcmpl.py

@@ -4,9 +4,9 @@ import typing
 
 from . import chatcmpl
 from .. import errors, requester
-from ... import entities as llm_entities
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
@@ -24,7 +24,7 @@ class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         self.client.api_key = use_model.token_mgr.get_token()
 
         args = {}

pkg/provider/modelmgr/requesters/giteeaichatcmpl.py

@@ -5,9 +5,9 @@ import typing
 
 from . import chatcmpl
 from .. import requester
-from ... import entities as llm_entities
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
@@ -25,7 +25,7 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         self.client.api_key = use_model.token_mgr.get_token()
 
         args = {}

pkg/provider/modelmgr/requesters/modelscopechatcmpl.py

@@ -9,9 +9,9 @@ import openai.types.chat.chat_completion_message_tool_call as chat_completion_me
 import httpx
 
 from .. import entities, errors, requester
-from ... import entities as llm_entities
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class ModelScopeChatCompletions(requester.LLMAPIRequester):
@@ -112,14 +112,14 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
     async def _make_msg(
         self,
         chat_completion: chat_completion.ChatCompletion,
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         chatcmpl_message = chat_completion.choices[0].message.dict()
 
         # 确保 role 字段存在且不为 None
         if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
             chatcmpl_message['role'] = 'assistant'
 
-        message = llm_entities.Message(**chatcmpl_message)
+        message = provider_message.Message(**chatcmpl_message)
 
         return message
 
@@ -130,7 +130,7 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         self.client.api_key = use_model.token_mgr.get_token()
 
         args = {}
@@ -168,10 +168,10 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
         self,
         query: pipeline_query.Query,
         model: entities.LLMModelInfo,
-        messages: typing.List[llm_entities.Message],
+        messages: typing.List[provider_message.Message],
         funcs: typing.List[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
         for m in messages:
             msg_dict = m.dict(exclude_none=True)

pkg/provider/modelmgr/requesters/moonshotchatcmpl.py

@@ -5,9 +5,9 @@ import typing
 
 from . import chatcmpl
 from .. import requester
-from ... import entities as llm_entities
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class MoonshotChatCompletions(chatcmpl.OpenAIChatCompletions):
@@ -25,7 +25,7 @@ class MoonshotChatCompletions(chatcmpl.OpenAIChatCompletions):
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         self.client.api_key = use_model.token_mgr.get_token()
 
         args = {}

pkg/provider/modelmgr/requesters/ollamachat.py

@@ -10,9 +10,9 @@ import json
 import ollama
 
 from .. import errors, requester
-from ... import entities as llm_entities
 import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 REQUESTER_NAME: str = 'ollama-chat'
 
@@ -44,7 +44,7 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         args = extra_args.copy()
         args['model'] = use_model.model_entity.name
 
@@ -73,27 +73,27 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
                 args['tools'] = tools
 
         resp = await self._req(args)
-        message: llm_entities.Message = await self._make_msg(resp)
+        message: provider_message.Message = await self._make_msg(resp)
         return message
 
-    async def _make_msg(self, chat_completions: ollama.ChatResponse) -> llm_entities.Message:
+    async def _make_msg(self, chat_completions: ollama.ChatResponse) -> provider_message.Message:
         message: ollama.Message = chat_completions.message
         if message is None:
             raise ValueError("chat_completions must contain a 'message' field")
 
-        ret_msg: llm_entities.Message = None
+        ret_msg: provider_message.Message = None
 
         if message.content is not None:
-            ret_msg = llm_entities.Message(role='assistant', content=message.content)
+            ret_msg = provider_message.Message(role='assistant', content=message.content)
         if message.tool_calls is not None and len(message.tool_calls) > 0:
-            tool_calls: list[llm_entities.ToolCall] = []
+            tool_calls: list[provider_message.ToolCall] = []
 
             for tool_call in message.tool_calls:
                 tool_calls.append(
-                    llm_entities.ToolCall(
+                    provider_message.ToolCall(
                         id=uuid.uuid4().hex,
                         type='function',
-                        function=llm_entities.FunctionCall(
+                        function=provider_message.FunctionCall(
                             name=tool_call.function.name,
                             arguments=json.dumps(tool_call.function.arguments),
                         ),
@@ -107,10 +107,10 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
         self,
         query: pipeline_query.Query,
         model: requester.RuntimeLLMModel,
-        messages: typing.List[llm_entities.Message],
+        messages: typing.List[provider_message.Message],
         funcs: typing.List[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
-    ) -> llm_entities.Message:
+    ) -> provider_message.Message:
         req_messages: list = []
         for m in messages:
             msg_dict: dict = m.dict(exclude_none=True)

pkg/provider/runner.py

@@ -4,7 +4,7 @@ import abc
 import typing
 
 from ..core import app
-from . import entities as llm_entities
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 
 
@@ -36,6 +36,6 @@ class RequestRunner(abc.ABC):
         self.pipeline_config = pipeline_config
 
     @abc.abstractmethod
-    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
         """运行请求"""
         pass

pkg/provider/runners/dashscopeapi.py

@@ -7,8 +7,8 @@ import dashscope
 
 from .. import runner
 from ...core import app
-from .. import entities as llm_entities
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class DashscopeAPIError(Exception):
@@ -90,7 +90,9 @@ class DashScopeAPIRunner(runner.RequestRunner):
 
         return plain_text, image_ids
 
-    async def _agent_messages(self, query: pipeline_query.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    async def _agent_messages(
+        self, query: pipeline_query.Query
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
         """Dashscope 智能体对话请求"""
 
         # 局部变量
@@ -143,14 +145,14 @@ class DashScopeAPIRunner(runner.RequestRunner):
             # 将参考资料替换到文本中
             pending_content = self._replace_references(pending_content, references_dict)
 
-        yield llm_entities.Message(
+        yield provider_message.Message(
             role='assistant',
             content=pending_content,
         )
 
     async def _workflow_messages(
         self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
         """Dashscope 工作流对话请求"""
 
         # 局部变量
@@ -208,12 +210,12 @@ class DashScopeAPIRunner(runner.RequestRunner):
             # 将参考资料替换到文本中
             pending_content = self._replace_references(pending_content, references_dict)
 
-        yield llm_entities.Message(
+        yield provider_message.Message(
             role='assistant',
             content=pending_content,
         )
 
-    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
         """运行"""
         if self.app_type == 'agent':
             async for msg in self._agent_messages(query):

pkg/provider/runners/difysvapi.py

@@ -9,7 +9,7 @@ import base64
 
 from .. import runner
 from ...core import app
-from .. import entities as llm_entities
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 from ...utils import image
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 from libs.dify_service_api.v1 import client, errors
@@ -90,7 +90,9 @@ class DifyServiceAPIRunner(runner.RequestRunner):
 
         return plain_text, image_ids
 
-    async def _chat_messages(self, query: pipeline_query.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    async def _chat_messages(
+        self, query: pipeline_query.Query
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
         """调用聊天助手"""
         cov_id = query.session.using_conversation.uuid or ''
         query.variables['conversation_id'] = cov_id
@@ -132,7 +134,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
             if mode == 'workflow':
                 if chunk['event'] == 'node_finished':
                     if chunk['data']['node_type'] == 'answer':
-                        yield llm_entities.Message(
+                        yield provider_message.Message(
                             role='assistant',
                             content=self._try_convert_thinking(chunk['data']['outputs']['answer']),
                         )
@@ -140,7 +142,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
                 if chunk['event'] == 'message':
                     basic_mode_pending_chunk += chunk['answer']
                 elif chunk['event'] == 'message_end':
-                    yield llm_entities.Message(
+                    yield provider_message.Message(
                         role='assistant',
                         content=self._try_convert_thinking(basic_mode_pending_chunk),
                     )
@@ -153,7 +155,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
 
     async def _agent_chat_messages(
         self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
         """调用聊天助手"""
         cov_id = query.session.using_conversation.uuid or ''
         query.variables['conversation_id'] = cov_id
@@ -198,7 +200,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
             else:
                 if pending_agent_message.strip() != '':
                     pending_agent_message = pending_agent_message.replace('</details>Action:', '</details>')
-                    yield llm_entities.Message(
+                    yield provider_message.Message(
                         role='assistant',
                         content=self._try_convert_thinking(pending_agent_message),
                     )
@@ -209,13 +211,13 @@ class DifyServiceAPIRunner(runner.RequestRunner):
                         continue
 
                     if chunk['tool']:
-                        msg = llm_entities.Message(
+                        msg = provider_message.Message(
                             role='assistant',
                             tool_calls=[
-                                llm_entities.ToolCall(
+                                provider_message.ToolCall(
                                     id=chunk['id'],
                                     type='function',
-                                    function=llm_entities.FunctionCall(
+                                    function=provider_message.FunctionCall(
                                         name=chunk['tool'],
                                         arguments=json.dumps({}),
                                     ),
@@ -232,9 +234,9 @@ class DifyServiceAPIRunner(runner.RequestRunner):
 
                         image_url = base_url + chunk['url']
 
-                        yield llm_entities.Message(
+                        yield provider_message.Message(
                             role='assistant',
-                            content=[llm_entities.ContentElement.from_image_url(image_url)],
+                            content=[provider_message.ContentElement.from_image_url(image_url)],
                         )
                 if chunk['event'] == 'error':
                     raise errors.DifyAPIError('dify 服务错误: ' + chunk['message'])
@@ -246,7 +248,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
 
     async def _workflow_messages(
         self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
         """调用工作流"""
 
         if not query.session.using_conversation.uuid:
@@ -290,14 +292,14 @@ class DifyServiceAPIRunner(runner.RequestRunner):
                 if chunk['data']['node_type'] == 'start' or chunk['data']['node_type'] == 'end':
                     continue
 
-                msg = llm_entities.Message(
+                msg = provider_message.Message(
                     role='assistant',
                     content=None,
                     tool_calls=[
-                        llm_entities.ToolCall(
+                        provider_message.ToolCall(
                             id=chunk['data']['node_id'],
                             type='function',
-                            function=llm_entities.FunctionCall(
+                            function=provider_message.FunctionCall(
                                 name=chunk['data']['title'],
                                 arguments=json.dumps({}),
                             ),
@@ -311,14 +313,14 @@ class DifyServiceAPIRunner(runner.RequestRunner):
                 if chunk['data']['error']:
                     raise errors.DifyAPIError(chunk['data']['error'])
 
-                msg = llm_entities.Message(
+                msg = provider_message.Message(
                     role='assistant',
                     content=chunk['data']['outputs']['summary'],
                 )
 
                 yield msg
 
-    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
         """运行请求"""
         if self.pipeline_config['ai']['dify-service-api']['app-type'] == 'chat':
             async for msg in self._chat_messages(query):

pkg/provider/runners/localagent.py

@@ -4,15 +4,15 @@ import json
 import typing
 
 from .. import runner
-from .. import entities as llm_entities
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 @runner.runner_class('local-agent')
 class LocalAgentRunner(runner.RequestRunner):
     """本地Agent请求运行器"""
 
-    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
         """运行请求"""
         pending_tool_calls = []
 
@@ -45,7 +45,7 @@ class LocalAgentRunner(runner.RequestRunner):
 
                     func_ret = await self.ap.tool_mgr.execute_func_call(func.name, parameters)
 
-                    msg = llm_entities.Message(
+                    msg = provider_message.Message(
                         role='tool',
                         content=json.dumps(func_ret, ensure_ascii=False),
                         tool_call_id=tool_call.id,
@@ -56,7 +56,7 @@ class LocalAgentRunner(runner.RequestRunner):
                     req_messages.append(msg)
                 except Exception as e:
                     # 工具调用出错，添加一个报错信息到 req_messages
-                    err_msg = llm_entities.Message(role='tool', content=f'err: {e}', tool_call_id=tool_call.id)
+                    err_msg = provider_message.Message(role='tool', content=f'err: {e}', tool_call_id=tool_call.id)
 
                     yield err_msg
 

pkg/provider/runners/n8nsvapi.py

@@ -7,8 +7,8 @@ import aiohttp
 
 from .. import runner
 from ...core import app
-from .. import entities as llm_entities
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
 class N8nAPIError(Exception):
@@ -68,7 +68,7 @@ class N8nServiceAPIRunner(runner.RequestRunner):
 
         return plain_text
 
-    async def _call_webhook(self, query: pipeline_query.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    async def _call_webhook(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
         """调用n8n webhook"""
         # 生成会话ID（如果不存在）
         if not query.session.using_conversation.uuid:
@@ -146,7 +146,7 @@ class N8nServiceAPIRunner(runner.RequestRunner):
                         output_content = json.dumps(response_data, ensure_ascii=False)
 
                     # 返回消息
-                    yield llm_entities.Message(
+                    yield provider_message.Message(
                         role='assistant',
                         content=output_content,
                     )
@@ -154,7 +154,7 @@ class N8nServiceAPIRunner(runner.RequestRunner):
             self.ap.logger.error(f'n8n webhook call exception: {str(e)}')
             raise N8nAPIError(f'n8n webhook call exception: {str(e)}')
 
-    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
         """运行请求"""
         async for msg in self._call_webhook(query):
             yield msg