Merge branch 'rc/new-plugin' into refactor/new-plugin-system

pkg/provider/modelmgr/requesters/modelscopechatcmpl.py

@@ -5,7 +5,6 @@ import typing
 
 import openai
 import openai.types.chat.chat_completion as chat_completion
-import openai.types.chat.chat_completion_message_tool_call as chat_completion_message_tool_call
 import httpx
 
 from .. import entities, errors, requester
@@ -14,7 +13,7 @@ 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):
+class ModelScopeChatCompletions(requester.ProviderAPIRequester):
     """ModelScope ChatCompletion API 请求器"""
 
     client: openai.AsyncClient
@@ -34,9 +33,11 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
 
     async def _req(
         self,
+        query: pipeline_query.Query,
         args: dict,
         extra_body: dict = {},
-    ) -> chat_completion.ChatCompletion:
+        remove_think: bool = False,
+    ) -> list[dict[str, typing.Any]]:
         args['stream'] = True
 
         chunk = None
@@ -47,73 +48,71 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
 
         resp_gen: openai.AsyncStream = await self.client.chat.completions.create(**args, extra_body=extra_body)
 
+        chunk_idx = 0
+        thinking_started = False
+        thinking_ended = False
+        tool_id = ''
+        tool_name = ''
+        message_delta = {}
         async for chunk in resp_gen:
-            # print(chunk)
             if not chunk or not chunk.id or not chunk.choices or not chunk.choices[0] or not chunk.choices[0].delta:
                 continue
 
-            if chunk.choices[0].delta.content is not None:
-                pending_content += chunk.choices[0].delta.content
+            delta = chunk.choices[0].delta.model_dump() if hasattr(chunk.choices[0], 'delta') else {}
+            reasoning_content = delta.get('reasoning_content')
+            # 处理 reasoning_content
+            if reasoning_content:
+                # accumulated_reasoning += reasoning_content
+                # 如果设置了 remove_think，跳过 reasoning_content
+                if remove_think:
+                    chunk_idx += 1
+                    continue
 
-            if chunk.choices[0].delta.tool_calls is not None:
-                for tool_call in chunk.choices[0].delta.tool_calls:
-                    if tool_call.function.arguments is None:
+                # 第一次出现 reasoning_content，添加 <think> 开始标签
+                if not thinking_started:
+                    thinking_started = True
+                    pending_content += '<think>\n' + reasoning_content
+                else:
+                    # 继续输出 reasoning_content
+                    pending_content += reasoning_content
+            elif thinking_started and not thinking_ended and delta.get('content'):
+                # reasoning_content 结束，normal content 开始，添加 </think> 结束标签
+                thinking_ended = True
+                pending_content += '\n</think>\n' + delta.get('content')
+
+            if delta.get('content') is not None:
+                pending_content += delta.get('content')
+
+            if delta.get('tool_calls') is not None:
+                for tool_call in delta.get('tool_calls'):
+                    if tool_call['id'] != '':
+                        tool_id = tool_call['id']
+                    if tool_call['function']['name'] is not None:
+                        tool_name = tool_call['function']['name']
+                    if tool_call['function']['arguments'] is None:
                         continue
+                    tool_call['id'] = tool_id
+                    tool_call['name'] = tool_name
                     for tc in tool_calls:
-                        if tc.index == tool_call.index:
-                            tc.function.arguments += tool_call.function.arguments
+                        if tc['index'] == tool_call['index']:
+                            tc['function']['arguments'] += tool_call['function']['arguments']
                             break
                     else:
                         tool_calls.append(tool_call)
 
             if chunk.choices[0].finish_reason is not None:
                 break
+        message_delta['content'] = pending_content
+        message_delta['role'] = 'assistant'
 
-        real_tool_calls = []
-
-        for tc in tool_calls:
-            function = chat_completion_message_tool_call.Function(
-                name=tc.function.name, arguments=tc.function.arguments
-            )
-            real_tool_calls.append(
-                chat_completion_message_tool_call.ChatCompletionMessageToolCall(
-                    id=tc.id, function=function, type='function'
-                )
-            )
-
-        return (
-            chat_completion.ChatCompletion(
-                id=chunk.id,
-                object='chat.completion',
-                created=chunk.created,
-                choices=[
-                    chat_completion.Choice(
-                        index=0,
-                        message=chat_completion.ChatCompletionMessage(
-                            role='assistant',
-                            content=pending_content,
-                            tool_calls=real_tool_calls if len(real_tool_calls) > 0 else None,
-                        ),
-                        finish_reason=chunk.choices[0].finish_reason
-                        if hasattr(chunk.choices[0], 'finish_reason') and chunk.choices[0].finish_reason is not None
-                        else 'stop',
-                        logprobs=chunk.choices[0].logprobs,
-                    )
-                ],
-                model=chunk.model,
-                service_tier=chunk.service_tier if hasattr(chunk, 'service_tier') else None,
-                system_fingerprint=chunk.system_fingerprint if hasattr(chunk, 'system_fingerprint') else None,
-                usage=chunk.usage if hasattr(chunk, 'usage') else None,
-            )
-            if chunk
-            else None
-        )
+        message_delta['tool_calls'] = tool_calls if tool_calls else None
+        return [message_delta]
 
     async def _make_msg(
         self,
-        chat_completion: chat_completion.ChatCompletion,
+        chat_completion: list[dict[str, typing.Any]],
     ) -> provider_message.Message:
-        chatcmpl_message = chat_completion.choices[0].message.dict()
+        chatcmpl_message = chat_completion[0]
 
         # 确保 role 字段存在且不为 None
         if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
@@ -130,6 +129,7 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
+        remove_think: bool = False,
     ) -> provider_message.Message:
         self.client.api_key = use_model.token_mgr.get_token()
 
@@ -157,13 +157,145 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
         args['messages'] = messages
 
         # 发送请求
-        resp = await self._req(args, extra_body=extra_args)
+        resp = await self._req(query, args, extra_body=extra_args, remove_think=remove_think)
 
         # 处理请求结果
         message = await self._make_msg(resp)
 
         return message
 
+    async def _req_stream(
+        self,
+        args: dict,
+        extra_body: dict = {},
+    ) -> chat_completion.ChatCompletion:
+        async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
+            yield chunk
+
+    async def _closure_stream(
+        self,
+        query: pipeline_query.Query,
+        req_messages: list[dict],
+        use_model: requester.RuntimeLLMModel,
+        use_funcs: list[resource_tool.LLMTool] = None,
+        extra_args: dict[str, typing.Any] = {},
+        remove_think: bool = False,
+    ) -> provider_message.Message | typing.AsyncGenerator[provider_message.MessageChunk, None]:
+        self.client.api_key = use_model.token_mgr.get_token()
+
+        args = {}
+        args['model'] = use_model.model_entity.name
+
+        if use_funcs:
+            tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
+
+            if tools:
+                args['tools'] = tools
+
+        # 设置此次请求中的messages
+        messages = req_messages.copy()
+
+        # 检查vision
+        for msg in messages:
+            if 'content' in msg and isinstance(msg['content'], list):
+                for me in msg['content']:
+                    if me['type'] == 'image_base64':
+                        me['image_url'] = {'url': me['image_base64']}
+                        me['type'] = 'image_url'
+                        del me['image_base64']
+
+        args['messages'] = messages
+        args['stream'] = True
+
+        # 流式处理状态
+        # tool_calls_map: dict[str, provider_message.ToolCall] = {}
+        chunk_idx = 0
+        thinking_started = False
+        thinking_ended = False
+        role = 'assistant'  # 默认角色
+        # accumulated_reasoning = ''  # 仅用于判断何时结束思维链
+
+        async for chunk in self._req_stream(args, extra_body=extra_args):
+            # 解析 chunk 数据
+            if hasattr(chunk, 'choices') and chunk.choices:
+                choice = chunk.choices[0]
+                delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
+                finish_reason = getattr(choice, 'finish_reason', None)
+            else:
+                delta = {}
+                finish_reason = None
+
+            # 从第一个 chunk 获取 role，后续使用这个 role
+            if 'role' in delta and delta['role']:
+                role = delta['role']
+
+            # 获取增量内容
+            delta_content = delta.get('content', '')
+            reasoning_content = delta.get('reasoning_content', '')
+
+            # 处理 reasoning_content
+            if reasoning_content:
+                # accumulated_reasoning += reasoning_content
+                # 如果设置了 remove_think，跳过 reasoning_content
+                if remove_think:
+                    chunk_idx += 1
+                    continue
+
+                # 第一次出现 reasoning_content，添加 <think> 开始标签
+                if not thinking_started:
+                    thinking_started = True
+                    delta_content = '<think>\n' + reasoning_content
+                else:
+                    # 继续输出 reasoning_content
+                    delta_content = reasoning_content
+            elif thinking_started and not thinking_ended and delta_content:
+                # reasoning_content 结束，normal content 开始，添加 </think> 结束标签
+                thinking_ended = True
+                delta_content = '\n</think>\n' + delta_content
+
+            # 处理 content 中已有的 <think> 标签（如果需要移除）
+            # if delta_content and remove_think and '<think>' in delta_content:
+            #     import re
+            #
+            #     # 移除 <think> 标签及其内容
+            #     delta_content = re.sub(r'<think>.*?</think>', '', delta_content, flags=re.DOTALL)
+
+            # 处理工具调用增量
+            if delta.get('tool_calls'):
+                for tool_call in delta['tool_calls']:
+                    if tool_call['id'] != '':
+                        tool_id = tool_call['id']
+                    if tool_call['function']['name'] is not None:
+                        tool_name = tool_call['function']['name']
+
+                    if tool_call['type'] is None:
+                        tool_call['type'] = 'function'
+                    tool_call['id'] = tool_id
+                    tool_call['function']['name'] = tool_name
+                    tool_call['function']['arguments'] = (
+                        '' if tool_call['function']['arguments'] is None else tool_call['function']['arguments']
+                    )
+
+            # 跳过空的第一个 chunk（只有 role 没有内容）
+            if chunk_idx == 0 and not delta_content and not reasoning_content and not delta.get('tool_calls'):
+                chunk_idx += 1
+                continue
+
+            # 构建 MessageChunk - 只包含增量内容
+            chunk_data = {
+                'role': role,
+                'content': delta_content if delta_content else None,
+                'tool_calls': delta.get('tool_calls'),
+                'is_final': bool(finish_reason),
+            }
+
+            # 移除 None 值
+            chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
+
+            yield provider_message.MessageChunk(**chunk_data)
+            chunk_idx += 1
+            # return
+
     async def invoke_llm(
         self,
         query: pipeline_query.Query,
@@ -171,6 +303,7 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
         messages: typing.List[provider_message.Message],
         funcs: typing.List[resource_tool.LLMTool] = None,
         extra_args: dict[str, typing.Any] = {},
+        remove_think: bool = False,
     ) -> 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:
@@ -185,7 +318,12 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
 
         try:
             return await self._closure(
-                query=query, req_messages=req_messages, use_model=model, use_funcs=funcs, extra_args=extra_args
+                query=query,
+                req_messages=req_messages,
+                use_model=model,
+                use_funcs=funcs,
+                extra_args=extra_args,
+                remove_think=remove_think,
             )
         except asyncio.TimeoutError:
             raise errors.RequesterError('请求超时')
@@ -202,3 +340,50 @@ class ModelScopeChatCompletions(requester.LLMAPIRequester):
             raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
         except openai.APIError as e:
             raise errors.RequesterError(f'请求错误: {e.message}')
+
+    async def invoke_llm_stream(
+        self,
+        query: pipeline_query.Query,
+        model: requester.RuntimeLLMModel,
+        messages: typing.List[provider_message.Message],
+        funcs: typing.List[resource_tool.LLMTool] = None,
+        extra_args: dict[str, typing.Any] = {},
+        remove_think: bool = False,
+    ) -> provider_message.MessageChunk:
+        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)
+            content = msg_dict.get('content')
+            if isinstance(content, list):
+                # 检查 content 列表中是否每个部分都是文本
+                if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
+                    # 将所有文本部分合并为一个字符串
+                    msg_dict['content'] = '\n'.join(part['text'] for part in content)
+            req_messages.append(msg_dict)
+
+        try:
+            async for item in self._closure_stream(
+                query=query,
+                req_messages=req_messages,
+                use_model=model,
+                use_funcs=funcs,
+                extra_args=extra_args,
+                remove_think=remove_think,
+            ):
+                yield item
+
+        except asyncio.TimeoutError:
+            raise errors.RequesterError('请求超时')
+        except openai.BadRequestError as e:
+            if 'context_length_exceeded' in e.message:
+                raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
+            else:
+                raise errors.RequesterError(f'请求参数错误: {e.message}')
+        except openai.AuthenticationError as e:
+            raise errors.RequesterError(f'无效的 api-key: {e.message}')
+        except openai.NotFoundError as e:
+            raise errors.RequesterError(f'请求路径错误: {e.message}')
+        except openai.RateLimitError as e:
+            raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
+        except openai.APIError as e:
+            raise errors.RequesterError(f'请求错误: {e.message}')