update pay promo
This commit is contained in:
112
mcp_server.py
112
mcp_server.py
@@ -2495,7 +2495,13 @@ class MCPAgentIntegrated:
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"""
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主流程（流式输出）- 使用原生 OpenAI Tool Calling API
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支持 vLLM 的 --enable-auto-tool-choice --tool-call-parser qwen3_coder
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支持 vLLM 的 --enable-auto-tool-choice --tool-call-parser minimax_m2
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MiniMax-M2.1 工具调用格式:
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<minimax:tool_call>
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<invoke name="function_name">
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<parameter name="param_key">param_value</parameter>
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</invoke>
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</minimax:tool_call>
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"""
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logger.info(f"[Agent Stream] 处理查询: {user_query}")
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if chat_history:
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@@ -2885,11 +2891,27 @@ A股交易时间: 上午 9:30-11:30，下午 13:00-15:00
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"execution_time": execution_time,
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})
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# 将工具结果添加到消息历史（简化格式，因为模型可能不支持标准 tool 消息）
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# 将工具结果添加到消息历史
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# 使用模拟的 tool_calls + tool 消息结构，保持与原生工具调用一致
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result_str = json.dumps(result, ensure_ascii=False) if isinstance(result, (dict, list)) else str(result)
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# 先添加一个包含 tool_calls 的 assistant 消息
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messages.append({
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"role": "user",
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"content": f"[工具调用结果] {tool_name}: {result_str[:3000]}"
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": tool_call_id,
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"type": "function",
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"function": {
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"name": tool_name,
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"arguments": json.dumps(arguments, ensure_ascii=False)
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}
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}]
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})
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# 然后添加 tool 结果
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"content": result_str[:3000]
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})
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logger.info(f"[Tool Call] 执行成功，耗时 {execution_time:.2f}s")
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@@ -2916,9 +2938,23 @@ A股交易时间: 上午 9:30-11:30，下午 13:00-15:00
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"execution_time": execution_time,
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})
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# 将错误信息添加到消息历史（使用与成功情况一致的格式）
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messages.append({
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"role": "user",
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"content": f"[工具调用失败] {tool_name}: {error_msg}"
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": tool_call_id,
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"type": "function",
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"function": {
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"name": tool_name,
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"arguments": json.dumps(arguments, ensure_ascii=False)
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}
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}]
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})
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"content": f"工具执行失败: {error_msg}"
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})
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logger.error(f"[Tool Call] 执行失败: {error_msg}")
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@@ -3088,16 +3124,21 @@ A股交易时间: 上午 9:30-11:30，下午 13:00-15:00
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else:
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# 处理剩余的 buffer
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if buffer:
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# 过滤掉可能残留的工具调用标签
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# 过滤掉可能残留的工具调用标签（包括不完整标签）
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buffer_filtered = self._filter_tool_call_tags(buffer)
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if parse_state == "thinking":
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if buffer_filtered:
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thinking_content += buffer_filtered
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yield self._format_sse("thinking_chunk", {"content": buffer_filtered})
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# 确保 thinking 状态正确结束
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yield self._format_sse("thinking_end", {"content": thinking_content})
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else:
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final_summary += buffer_filtered
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if buffer_filtered:
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final_summary += buffer_filtered
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yield self._format_sse("summary_chunk", {"content": buffer_filtered})
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elif parse_state == "thinking":
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# buffer 为空但仍在 thinking 状态，确保正确结束
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yield self._format_sse("thinking_end", {"content": thinking_content})
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logger.info(f"[Summary] 流式总结完成，思考内容长度: {len(thinking_content)}")
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@@ -3191,10 +3232,26 @@ A股交易时间: 上午 9:30-11:30，下午 13:00-15:00
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})
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# 将工具结果添加到消息历史
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# 使用模拟的 tool_calls + tool 消息结构，保持与原生工具调用一致
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result_str = json.dumps(result, ensure_ascii=False) if isinstance(result, (dict, list)) else str(result)
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# 先添加一个包含 tool_calls 的 assistant 消息
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messages.append({
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"role": "user",
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"content": f"[工具调用结果] {tool_name}: {result_str[:3000]}"
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": tool_call_id,
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"type": "function",
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"function": {
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"name": tool_name,
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"arguments": json.dumps(arguments, ensure_ascii=False)
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}
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}]
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})
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# 然后添加 tool 结果
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"content": result_str[:3000]
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})
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logger.info(f"[Tool Call] 执行成功，耗时 {execution_time:.2f}s")
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@@ -3221,9 +3278,23 @@ A股交易时间: 上午 9:30-11:30，下午 13:00-15:00
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"execution_time": execution_time,
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})
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# 将错误信息添加到消息历史（使用与成功情况一致的格式）
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messages.append({
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"role": "user",
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"content": f"[工具调用失败] {tool_name}: {error_msg}"
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": tool_call_id,
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"type": "function",
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"function": {
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"name": tool_name,
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"arguments": json.dumps(arguments, ensure_ascii=False)
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}
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}]
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})
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"content": f"工具执行失败: {error_msg}"
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})
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logger.error(f"[Tool Call] 执行失败: {error_msg}")
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@@ -3322,12 +3393,25 @@ A股交易时间: 上午 9:30-11:30，下午 13:00-15:00
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def _filter_tool_call_tags(self, text: str) -> str:
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"""过滤掉文本中的工具调用标签（总结阶段不应包含工具调用）"""
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import re
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# 过滤 MiniMax 格式
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# 过滤完整的 MiniMax 格式
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text = re.sub(r'<minimax:tool_call>.*?</minimax:tool_call>', '', text, flags=re.DOTALL)
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# 过滤其他格式
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# 过滤其他完整格式
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text = re.sub(r'<tool_call>.*?</tool_call>', '', text, flags=re.DOTALL)
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text = re.sub(r'```tool_call.*?```', '', text, flags=re.DOTALL)
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text = re.sub(r'<[｜\|]DSML[｜\|]function_calls>.*?</[｜\|]DSML[｜\|]function_calls>', '', text, flags=re.DOTALL)
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# 过滤不完整的标签（流结束时可能残留）
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# MiniMax 不完整标签: <minimax:tool_call>... 没有闭合
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text = re.sub(r'<minimax:tool_call>.*$', '', text, flags=re.DOTALL)
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# 不完整的开始标签: <minimax:, <minimax:t, <minimax:tool, 等
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text = re.sub(r'<minimax:?[a-z_]*$', '', text)
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# 不完整的 think 标签
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text = re.sub(r'<think>.*$', '', text, flags=re.DOTALL)
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text = re.sub(r'</?thi?n?k?>?$', '', text)
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# 其他不完整标签
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text = re.sub(r'<tool_call>.*$', '', text, flags=re.DOTALL)
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text = re.sub(r'<[｜\|]DSML[｜\|]function_calls>.*$', '', text, flags=re.DOTALL)
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return text.strip()
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def _parse_text_tool_calls(self, content: str) -> List[Dict[str, Any]]:
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