refactor: switch llm_entities to plugin sdk

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)