feat: 移除fastapi 接口

backend/ai/views/chat_message.py

@@ -1,6 +1,15 @@
-from rest_framework import serializers
+import asyncio
 
+from django.http import StreamingHttpResponse
+from rest_framework import serializers, status
+from rest_framework.decorators import action
+from rest_framework.response import Response
+
+from ai.llm.enums import LLMProvider
+from ai.llm.factory import get_adapter
 from ai.models import ChatMessage
+from backend import settings
+from models.ai import MessageType
 from utils.serializers import CustomModelSerializer
 from utils.custom_model_viewSet import CustomModelViewSet
 from django_filters import rest_framework as filters
@@ -36,3 +45,119 @@ class ChatMessageViewSet(CustomModelViewSet):
     ordering_fields = ['create_time', 'id']
     ordering = ['-create_time']
 
+    @action(detail=False, methods=['post'], url_path='stream')
+    def stream(self, request):
+        """
+        流式聊天接口
+        """
+        content = request.data.get('content')
+        conversation_id = request.data.get('conversation_id')
+        platform = request.data.get('platform', 'deepseek')
+
+        # 获取平台配置
+        if platform == 'tongyi':
+            model = 'qwen-plus'
+            api_key = settings.DASHSCOPE_API_KEY
+            provider = LLMProvider.TONGYI
+        else:
+            # 默认使用 DeepSeek
+            model = 'deepseek-chat'
+            api_key = settings.DEEPSEEK_API_KEY
+            provider = LLMProvider.DEEPSEEK
+
+        # 获取当前用户
+        user_id = request.user.id
+
+        try:
+            # 获取或创建对话
+            conversation = ChatMessage.objects.filter(conversation_id=conversation_id).order_by('id')
+        except ValueError as e:
+            return Response({"error": str(e)}, status=status.HTTP_400_BAD_REQUEST)
+
+        # 添加用户消息
+        ChatMessage.objects.create(
+            conversation_id=conversation_id,
+            user_id=user_id,
+            role_id=None,
+            model=model,
+            model_id=None,
+            type=MessageType.USER,
+            reply_id=None,
+            content=content,
+            use_context=True,
+            segment_ids=None,
+        )
+        # 构建上下文
+        context = [("system", "You are a helpful assistant")]
+        history = ChatMessage.objects.filter(conversation_id=conversation_id).order_by('id')
+
+        for msg in history:
+            context.append((msg.type, msg.content))
+
+        # 获取LLM适配器
+        llm = get_adapter(provider, api_key=api_key, model=model)
+
+        # 创建流式响应
+
+        # 8. 同步生成器（包装异步LLM流，核心修复点）
+        def generate():
+            ai_reply = ""
+            loop = None
+            try:
+                # 创建新的事件循环（避免复用主线程循环）
+                loop = asyncio.new_event_loop()
+                asyncio.set_event_loop(loop)
+
+                # 异步生成器包装函数
+                async def async_stream():
+                    # 调用LLM的异步流式接口（假设 llm.stream_chat 是 async_generator）
+                    async for chunk in llm.stream_chat(context):
+                        yield chunk
+
+                # 将异步生成器转换为同步迭代
+                async_gen = async_stream()
+                while True:
+                    try:
+                        # 逐个获取异步chunk
+                        chunk = loop.run_until_complete(async_gen.__anext__())
+                    except StopAsyncIteration:
+                        break  # 流结束，退出循环
+                    except Exception as e:
+                        # 捕获LLM流异常，返回错误信息
+                        yield f"data: 错误：{str(e)}\n\n"
+                        break
+
+                    # 提取chunk内容（适配不同LLM的返回格式）
+                    if hasattr(chunk, 'content'):
+                        chunk_content = chunk.content.strip()
+                    elif isinstance(chunk, dict) and 'content' in chunk:
+                        chunk_content = chunk['content'].strip()
+                    else:
+                        chunk_content = str(chunk).strip()
+
+                    # 只返回非空内容
+                    if chunk_content:
+                        ai_reply += chunk_content
+                        # 遵循SSE格式：data: 内容\n\n（必须以\n\n结尾）
+                        yield f"data: {chunk_content}\n\n"
+
+            finally:
+                # 关闭事件循环（避免资源泄漏）
+                if loop:
+                    loop.close()
+            # 保存AI回复
+            if ai_reply.strip():
+                ChatMessage.objects.create(
+                    conversation_id=conversation_id,
+                    user_id=user_id,
+                    role_id=None,
+                    model=model,
+                    model_id=None,
+                    type=MessageType.ASSISTANT,
+                    reply_id=None,
+                    content=ai_reply,
+                    use_context=True,
+                    segment_ids=None,
+                )
+
+        return StreamingHttpResponse(generate(), content_type='text/event-stream')