fix:The handling of the streaming tool calls has been fixed, but there are still bugs in the model's reply messages with thoughtfulness.

pkg/api/http/controller/groups/pipelines/webchat.py

@@ -13,6 +13,7 @@ class WebChatDebugRouterGroup(group.RouterGroup):
             """Send a message to the pipeline for debugging"""
 
             async def stream_generator(generator):
+                yield 'data: {"type": "start"}\n\n'
                 async for message in generator:
                     yield f'data: {json.dumps({"message": message})}\n\n'
                 yield 'data: {"type": "end"}\n\n'
@@ -38,8 +39,14 @@ class WebChatDebugRouterGroup(group.RouterGroup):
                     generator = webchat_adapter.send_webchat_message(
                         pipeline_uuid, session_type, message_chain_obj, is_stream
                     )
-
+                    # 设置正确的响应头
-                    return quart.Response(stream_generator(generator), mimetype='text/event-stream')
+                    headers = {
+                        'Content-Type': 'text/event-stream',
+                        'Transfer-Encoding': 'chunked',
+                        'Cache-Control': 'no-cache',
+                        'Connection': 'keep-alive'
+                    }
+                    return quart.Response(stream_generator(generator), mimetype='text/event-stream',headers=headers)
 
                 else:
                     # result = await webchat_adapter.send_webchat_message(pipeline_uuid, session_type, message_chain_obj)

pkg/pipeline/pipelinemgr.py

@@ -93,12 +93,21 @@ class RuntimePipeline:
                 query.message_event, platform_events.GroupMessage
             ):
                 result.user_notice.insert(0, platform_message.At(query.message_event.sender.id))
-
+            if await query.adapter.is_stream_output_supported():
-            await query.adapter.reply_message(
+                print(query.resp_messages[-1])
-                message_source=query.message_event,
+                await query.adapter.reply_message_chunk(
-                message=result.user_notice,
+                    message_source=query.message_event,
-                quote_origin=query.pipeline_config['output']['misc']['quote-origin'],
+                    message_id=str(query.resp_messages[-1].resp_message_id),
-            )
+                    message=result.user_notice,
+                    quote_origin=query.pipeline_config['output']['misc']['quote-origin'],
+                    is_final=[msg.is_final for msg in query.resp_messages][0]
+                )
+            else:
+                await query.adapter.reply_message(
+                    message_source=query.message_event,
+                    message=result.user_notice,
+                    quote_origin=query.pipeline_config['output']['misc']['quote-origin'],
+                )
         if result.debug_notice:
             self.ap.logger.debug(result.debug_notice)
         if result.console_notice:

pkg/pipeline/process/handlers/chat.py

@@ -72,9 +72,9 @@ class ChatMessageHandler(handler.MessageHandler):
                     raise ValueError(f'未找到请求运行器: {query.pipeline_config["ai"]["runner"]["runner"]}')
                 if is_stream:
                     resp_message_id = uuid.uuid4()
-                    await query.adapter.create_message_card(resp_message_id, query.message_event)
+                    await query.adapter.create_message_card(str(resp_message_id), query.message_event)
                     async for result in runner.run(query):
-                        result.resp_message_id = resp_message_id
+                        result.resp_message_id = str(resp_message_id)
                         if query.resp_messages:
                             query.resp_messages.pop()
                         if query.resp_message_chain:

pkg/pipeline/respback/respback.py

@@ -41,6 +41,7 @@ class SendResponseBackStage(stage.PipelineStage):
         # TODO 命令与流式的兼容性问题
         if await query.adapter.is_stream_output_supported():
             is_final = [msg.is_final for msg in query.resp_messages][0]
+            print(query.resp_messages[-1])
             await query.adapter.reply_message_chunk(
                 message_source=query.message_event,
                 message_id=query.resp_messages[-1].resp_message_id,

pkg/provider/modelmgr/requesters/anthropicmsgs.py

@@ -273,13 +273,11 @@ class AnthropicMessages(requester.ProviderAPIRequester):
                 del msg_dict['tool_calls']
 
             req_messages.append(msg_dict)
-        if args["thinking"]:
+        if args.get("thinking", False):
             args['thinking'] = {
                 "type": "enabled",
                 "budget_tokens": 10000
             }
-        else:
-            args.pop('thinking')
 
         args['messages'] = req_messages
 
@@ -296,15 +294,32 @@ class AnthropicMessages(requester.ProviderAPIRequester):
             think_ended = False
             finish_reason = False
             content = ''
+            tool_name = ''
+            tool_id = ''
+            tool_calls = []
             async for chunk in await self.client.messages.create(**args):
-                # print(chunk)
+                print(chunk)
+                tool_call = {"id":None, 'function': {"name": None, "arguments": None},'type':'function'}
                 if isinstance(chunk, anthropic.types.raw_content_block_start_event.RawContentBlockStartEvent):  # 记录开始
+                    if chunk.content_block.type == 'tool_use':
+
+                        if chunk.content_block.name is not None:
+                            tool_name = chunk.content_block.name
+                        if chunk.content_block.id is not None:
+                            tool_id = chunk.content_block.id
+
+
+                        tool_call['function']['name'] = tool_name
+                        tool_call['function']['arguments'] = ''
+                        tool_call['id'] = tool_id
+
+                        print(chunk.content_block)
                     if not remove_think:
-                        if chunk.content_block.type == 'thinking':
+                        if chunk.content_block.type == 'thinking' and not remove_think:
                             think_started = True
-                        elif chunk.content_block.type == 'text':
+                        elif chunk.content_block.type == 'text' and chunk.index != 0 and not remove_think:
                             think_ended = True
-                    continue
+                        continue
                 elif isinstance(chunk, anthropic.types.raw_content_block_delta_event.RawContentBlockDeltaEvent):
                     if chunk.delta.type == "thinking_delta":
                         if think_started:
@@ -320,6 +335,10 @@ class AnthropicMessages(requester.ProviderAPIRequester):
                             content = '\n</think>\n' + chunk.delta.text
                         else:
                             content = chunk.delta.text
+                    elif chunk.delta.type == "input_json_delta":
+                        tool_call['function']["arguments"] = chunk.delta.partial_json
+                        tool_call['function']['name'] = tool_name
+                        tool_call['id'] = tool_id
                 elif isinstance(chunk, anthropic.types.raw_content_block_stop_event.RawContentBlockStopEvent):
                     continue  # 记录raw_content_block结束的
 
@@ -333,10 +352,12 @@ class AnthropicMessages(requester.ProviderAPIRequester):
                     continue
 
 
+
                 args = {
                     'content': content,
                     'role': role,
-                    "is_final": finish_reason
+                    "is_final": finish_reason,
+                    'tool_calls': None if tool_call['id'] is None else [tool_call],
                 }
                 # if chunk_idx == 0:
                 #     chunk_idx += 1

pkg/provider/modelmgr/requesters/chatcmpl.py

@@ -160,18 +160,21 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
         thinking_started = False
         thinking_ended = False
         role = 'assistant'  # 默认角色
+        tool_id = ""
+        tool_name = ''
         # 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']
@@ -208,41 +211,29 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
             #     delta_content = re.sub(r'<think>.*?</think>', '', delta_content, flags=re.DOTALL)
 
             # 处理工具调用增量
-            delta_tool_calls = None
+            # delta_tool_calls = None
             if delta.get('tool_calls'):
-                delta_tool_calls = []
                 for tool_call in delta['tool_calls']:
-                    tc_id = tool_call.get('id')
+                    if tool_call['id'] and tool_call['function']['name']:
-                    if tc_id:
+                        tool_id = tool_call['id']
-                        if tc_id not in tool_calls_map:
+                        tool_name = tool_call['function']['name']
-                            # 新的工具调用
+                    else:
-                            tool_calls_map[tc_id] = llm_entities.ToolCall(
+                        tool_call['id'] = tool_id
-                                id=tc_id,
+                        tool_call['function']['name'] = tool_name
-                                type=tool_call.get('type', 'function'),
+                    if tool_call['type'] is None:
-                                function=llm_entities.FunctionCall(
+                        tool_call['type'] = 'function'
-                                    name=tool_call.get('function', {}).get('name', ''),
+
-                                    arguments=tool_call.get('function', {}).get('arguments', ''),
+
-                                ),
-                            )
-                            delta_tool_calls.append(tool_calls_map[tc_id])
-                        else:
-                            # 追加函数参数
-                            func_args = tool_call.get('function', {}).get('arguments', '')
-                            if func_args:
-                                tool_calls_map[tc_id].function.arguments += func_args
-                                # 返回更新后的完整工具调用
-                                delta_tool_calls.append(tool_calls_map[tc_id])
 
             # 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_tool_calls if delta_tool_calls else None,
+                'tool_calls': delta.get('tool_calls'),
                 'is_final': bool(finish_reason),
             }
 
@@ -289,7 +280,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
         # 发送请求
 
         resp = await self._req(args, extra_body=extra_args)
-        print(resp)
         # 处理请求结果
         message = await self._make_msg(resp, remove_think)
 

pkg/provider/modelmgr/requesters/modelscopechatcmpl.py

@@ -289,30 +289,16 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
             #     delta_content = re.sub(r'<think>.*?</think>', '', delta_content, flags=re.DOTALL)
 
             # 处理工具调用增量
-            delta_tool_calls = None
             if delta.get('tool_calls'):
-                delta_tool_calls = []
                 for tool_call in delta['tool_calls']:
-                    tc_id = tool_call.get('id')
+                    if tool_call['id'] and tool_call['function']['name']:
-                    if tc_id:
+                        tool_id = tool_call['id']
-                        if tc_id not in tool_calls_map:
+                        tool_name = tool_call['function']['name']
-                            # 新的工具调用
+                    else:
-                            tool_calls_map[tc_id] = llm_entities.ToolCall(
+                        tool_call['id'] = tool_id
-                                id=tc_id,
+                        tool_call['function']['name'] = tool_name
-                                type=tool_call.get('type', 'function'),
+                    if tool_call['type'] is None:
-                                function=llm_entities.FunctionCall(
+                        tool_call['type'] = 'function'
-                                    name=tool_call.get('function', {}).get('name', ''),
-                                    arguments=tool_call.get('function', {}).get('arguments', ''),
-                                ),
-                            )
-                            delta_tool_calls.append(tool_calls_map[tc_id])
-                        else:
-                            # 追加函数参数
-                            func_args = tool_call.get('function', {}).get('arguments', '')
-                            if func_args:
-                                tool_calls_map[tc_id].function.arguments += func_args
-                                # 返回更新后的完整工具调用
-                                delta_tool_calls.append(tool_calls_map[tc_id])
 
             # 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'):
@@ -323,7 +309,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
             chunk_data = {
                 'role': role,
                 'content': delta_content if delta_content else None,
-                'tool_calls': delta_tool_calls if delta_tool_calls else None,
+                'tool_calls': delta.get('tool_calls'),
                 'is_final': bool(finish_reason),
             }
 

pkg/provider/modelmgr/requesters/ppiochatcmpl.py

@@ -179,39 +179,30 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
 
             delta_tool_calls = None
             if delta.get('tool_calls'):
-                delta_tool_calls = []
                 for tool_call in delta['tool_calls']:
-                    tc_id = tool_call.get('id')
+                    if tool_call['id'] and tool_call['function']['name']:
-                    if tc_id:
+                        tool_id = tool_call['id']
-                        if tc_id not in tool_calls_map:
+                        tool_name = tool_call['function']['name']
-                            # 新的工具调用
+
-                            tool_calls_map[tc_id] = llm_entities.ToolCall(
+                    if tool_call['id'] is None:
-                                id=tc_id,
+                        tool_call['id'] = tool_id
-                                type=tool_call.get('type', 'function'),
+                    if tool_call['function']['name'] is None:
-                                function=llm_entities.FunctionCall(
+                        tool_call['function']['name'] = tool_name
-                                    name=tool_call.get('function', {}).get('name', ''),
+                    if tool_call['function']['arguments'] is None:
-                                    arguments=tool_call.get('function', {}).get('arguments', ''),
+                        tool_call['function']['arguments'] = ''
-                                ),
+                    if tool_call['type'] is None:
-                            )
+                        tool_call['type'] = 'function'
-                            delta_tool_calls.append(tool_calls_map[tc_id])
-                        else:
-                            # 追加函数参数
-                            func_args = tool_call.get('function', {}).get('arguments', '')
-                            if func_args:
-                                tool_calls_map[tc_id].function.arguments += func_args
-                                # 返回更新后的完整工具调用
-                                delta_tool_calls.append(tool_calls_map[tc_id])
 
             # 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 delta.get('tool_calls'):
                 chunk_idx += 1
                 continue
 
-                # 构建 MessageChunk - 只包含增量内容
+            # 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_tool_calls if delta_tool_calls else None,
+                'tool_calls': delta.get('tool_calls'),
                 'is_final': bool(finish_reason),
             }
 

pkg/provider/runners/localagent.py

@@ -148,13 +148,13 @@ class LocalAgentRunner(runner.RequestRunner):
                         if tool_call.function and tool_call.function.arguments:
                             # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
                             tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
-
+                print(list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None)
+                # continue
                 # 每8个chunk或最后一个chunk时，输出所有累积的内容
                 if msg_idx % 8 == 0 or msg.is_final:
                     yield llm_entities.MessageChunk(
                         role=last_role,
                         content=accumulated_content,  # 输出所有累积内容
-                        tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
                         is_final=msg.is_final,
                     )
 
@@ -178,12 +178,18 @@ class LocalAgentRunner(runner.RequestRunner):
                     parameters = json.loads(func.arguments)
 
                     func_ret = await self.ap.tool_mgr.execute_func_call(query, func.name, parameters)
-
+                    if is_stream:
-                    msg = llm_entities.Message(
+                        msg = llm_entities.MessageChunk(
-                        role='tool',
+                            role='tool',
-                        content=json.dumps(func_ret, ensure_ascii=False),
+                            content=json.dumps(func_ret, ensure_ascii=False),
-                        tool_call_id=tool_call.id,
+                            tool_call_id=tool_call.id,
-                    )
+                        )
+                    else:
+                        msg = llm_entities.Message(
+                            role='tool',
+                            content=json.dumps(func_ret, ensure_ascii=False),
+                            tool_call_id=tool_call.id,
+                        )
 
                     yield msg
 

web/src/app/infra/http/HttpClient.ts

@@ -443,6 +443,9 @@ class HttpClient {
                   onComplete();
                   return;
                 }
+                if (data.type === 'start') {
+                    console,log(data.type)
+                    }
 
                 if (data.message) {
                   // 处理消息数据