feat: 重构MCP功能和AI服务提供者架构
This commit is contained in:
xiamuceer-j
+28
-130
@@ -775,148 +775,46 @@ async def generate_character_stream(
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logger.info(f"🎯 开始为项目 {request.project_id} 生成角色（SSE流式）")
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try:
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# 🔧 MCP工具增强：静默检查并收集参考资料
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# 直接使用 AIService 流式生成
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ai_response = ""
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chunk_count = 0
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if user_id:
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try:
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from app.services.mcp_tool_service import mcp_tool_service
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available_tools = await mcp_tool_service.get_user_enabled_tools(
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user_id=user_id,
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db_session=db
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)
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# 只在有工具时才调用
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if available_tools:
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logger.info(f"🔍 检测到可用MCP工具，尝试收集参考资料...")
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result = await user_ai_service.generate_text_with_mcp(
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prompt=prompt,
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user_id=user_id,
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db_session=db,
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enable_mcp=True,
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max_tool_rounds=2,
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tool_choice="auto",
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provider=None,
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model=None
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)
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if isinstance(result, dict):
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ai_response = result.get('content', '')
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finish_reason = result.get('finish_reason', '')
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tool_calls_made = result.get('tool_calls_made', 0)
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# 🔧 修复：检查工具调用是否真正成功
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if tool_calls_made > 0:
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if finish_reason == 'tool_error':
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logger.warning(f"⚠️ MCP工具调用失败，降级为基础模式")
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# 工具调用失败，重新用基础模式生成
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ai_response = ""
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elif not ai_response.strip():
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logger.warning(f"⚠️ MCP工具调用后返回空响应，降级为基础模式")
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# 工具调用成功但返回空内容，重新生成
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ai_response = ""
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else:
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logger.info(f"✅ MCP工具调用成功（{tool_calls_made}次），内容长度: {len(ai_response)}")
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# MCP成功且有内容，模拟流式输出（分块发送）
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chunk_size = 50
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for i in range(0, len(ai_response), chunk_size):
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chunk = ai_response[i:i+chunk_size]
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chunk_count += 1
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yield await SSEResponse.send_chunk(chunk)
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if chunk_count % 3 == 0:
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yield await SSEResponse.send_progress(
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f"AI生成角色中... ({i+len(chunk)}/{len(ai_response)}字符)",
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10 + min(85 * (i+len(chunk)) // len(ai_response), 85)
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)
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# 跳过后续的流式生成
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ai_response = result.get('content', '')
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else:
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ai_response = result
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# 如果MCP调用失败或返回空，继续走流式生成
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if not ai_response or not ai_response.strip():
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logger.info(f"🔄 开始流式生成...")
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ai_response = ""
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async for chunk in user_ai_service.generate_text_stream(prompt=prompt):
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chunk_count += 1
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ai_response += chunk
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# 发送内容块
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yield await SSEResponse.send_chunk(chunk)
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# 定期更新进度
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if chunk_count % 5 == 0:
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yield await SSEResponse.send_progress(
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f"AI生成角色中... ({len(ai_response)}字符)",
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10 + min(chunk_count // 2, 85)
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)
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# 心跳
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if chunk_count % 20 == 0:
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yield await SSEResponse.send_heartbeat()
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else:
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logger.debug(f"用户 {user_id} 未启用MCP工具，使用流式基础模式")
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async for chunk in user_ai_service.generate_text_stream(prompt=prompt):
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chunk_count += 1
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ai_response += chunk
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# 发送内容块
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yield await SSEResponse.send_chunk(chunk)
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# 定期更新进度
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if chunk_count % 5 == 0:
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yield await SSEResponse.send_progress(
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f"AI生成角色中... ({len(ai_response)}字符)",
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10 + min(chunk_count // 2, 85)
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)
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# 心跳
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if chunk_count % 20 == 0:
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yield await SSEResponse.send_heartbeat()
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except Exception as mcp_error:
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logger.warning(f"⚠️ MCP工具调用异常，降级为流式基础模式: {str(mcp_error)}")
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ai_response = ""
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async for chunk in user_ai_service.generate_text_stream(prompt=prompt):
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chunk_count += 1
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ai_response += chunk
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# 发送内容块
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yield await SSEResponse.send_chunk(chunk)
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# 定期更新进度
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if chunk_count % 5 == 0:
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yield await SSEResponse.send_progress(
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f"AI生成角色中... ({len(ai_response)}字符)",
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10 + min(chunk_count // 2, 85)
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)
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# 心跳
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if chunk_count % 20 == 0:
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yield await SSEResponse.send_heartbeat()
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else:
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logger.debug(f"未登录用户，使用流式基础模式")
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async for chunk in user_ai_service.generate_text_stream(prompt=prompt):
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chunk_count += 1
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ai_response += chunk
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logger.info(f"🎯 开始生成角色（流式模式）...")
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yield await SSEResponse.send_progress("🎯 开始生成角色...", 15)
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async for chunk in user_ai_service.generate_text_stream(
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prompt=prompt,
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tool_choice="required",
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):
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# chunk 现在可能是 dict 或 str，提取 content 字段
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if isinstance(chunk, dict):
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content = chunk.get("content", "")
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else:
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content = chunk
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if content:
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ai_response += content
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# 发送内容块
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yield await SSEResponse.send_chunk(chunk)
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yield await SSEResponse.send_chunk(content)
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# 定期更新进度
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if chunk_count % 5 == 0:
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# 定期更新进度（每收到约500字符更新一次，避免过于频繁）
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current_len = len(ai_response)
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if current_len >= chunk_count * 500:
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chunk_count += 1
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# 使用实际字符数量计算进度，上限85%（留15%给后续解析和保存）
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# 估算最终字符数约为提示词的8倍，最少3000字符
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estimated_total = max(3000, len(prompt) * 8)
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progress = min(15 + int(current_len / estimated_total * 70), 85)
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yield await SSEResponse.send_progress(
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f"AI生成角色中... ({len(ai_response)}字符)",
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10 + min(chunk_count // 2, 85)
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f"AI生成角色中... ({current_len}字符)",
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progress
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)
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# 心跳
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if chunk_count % 20 == 0:
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yield await SSEResponse.send_heartbeat()
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except Exception as ai_error:
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logger.error(f"❌ AI服务调用异常：{str(ai_error)}")
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yield await SSEResponse.send_error(f"AI服务调用失败：{str(ai_error)}")
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