# encoding:utf-8 import json from models.bot import Bot from models.session_manager import SessionManager from bridge.context import ContextType from bridge.reply import Reply, ReplyType from common.log import logger from config import conf, load_config from .dashscope_session import DashscopeSession import os import dashscope from http import HTTPStatus dashscope_models = { "qwen-turbo": dashscope.Generation.Models.qwen_turbo, "qwen-plus": dashscope.Generation.Models.qwen_plus, "qwen-max": dashscope.Generation.Models.qwen_max, "qwen-bailian-v1": dashscope.Generation.Models.bailian_v1, # Qwen3 series models - use string directly as model name "qwen3-max": "qwen3-max", "qwen3-plus": "qwen3-plus", "qwen3-turbo": "qwen3-turbo", # Other new models "qwen-long": "qwen-long", "qwq-32b-preview": "qwq-32b-preview", "qvq-72b-preview": "qvq-72b-preview" } # ZhipuAI对话模型API class DashscopeBot(Bot): def __init__(self): super().__init__() self.sessions = SessionManager(DashscopeSession, model=conf().get("model") or "qwen-plus") self.model_name = conf().get("model") or "qwen-plus" self.api_key = conf().get("dashscope_api_key") if self.api_key: os.environ["DASHSCOPE_API_KEY"] = self.api_key self.client = dashscope.Generation def reply(self, query, context=None): # acquire reply content if context.type == ContextType.TEXT: logger.info("[DASHSCOPE] query={}".format(query)) session_id = context["session_id"] reply = None clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"]) if query in clear_memory_commands: self.sessions.clear_session(session_id) reply = Reply(ReplyType.INFO, "记忆已清除") elif query == "#清除所有": self.sessions.clear_all_session() reply = Reply(ReplyType.INFO, "所有人记忆已清除") elif query == "#更新配置": load_config() reply = Reply(ReplyType.INFO, "配置已更新") if reply: return reply session = self.sessions.session_query(query, session_id) logger.debug("[DASHSCOPE] session query={}".format(session.messages)) reply_content = self.reply_text(session) logger.debug( "[DASHSCOPE] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format( session.messages, session_id, reply_content["content"], reply_content["completion_tokens"], ) ) if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0: reply = Reply(ReplyType.ERROR, reply_content["content"]) elif reply_content["completion_tokens"] > 0: self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"]) reply = Reply(ReplyType.TEXT, reply_content["content"]) else: reply = Reply(ReplyType.ERROR, reply_content["content"]) logger.debug("[DASHSCOPE] reply {} used 0 tokens.".format(reply_content)) return reply else: reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type)) return reply def reply_text(self, session: DashscopeSession, retry_count=0) -> dict: """ call openai's ChatCompletion to get the answer :param session: a conversation session :param session_id: session id :param retry_count: retry count :return: {} """ try: dashscope.api_key = self.api_key response = self.client.call( dashscope_models[self.model_name], messages=session.messages, result_format="message" ) if response.status_code == HTTPStatus.OK: content = response.output.choices[0]["message"]["content"] return { "total_tokens": response.usage["total_tokens"], "completion_tokens": response.usage["output_tokens"], "content": content, } else: logger.error('Request id: %s, Status code: %s, error code: %s, error message: %s' % ( response.request_id, response.status_code, response.code, response.message )) result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"} need_retry = retry_count < 2 result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"} if need_retry: return self.reply_text(session, retry_count + 1) else: return result except Exception as e: logger.exception(e) need_retry = retry_count < 2 result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"} if need_retry: return self.reply_text(session, retry_count + 1) else: return result def call_with_tools(self, messages, tools=None, stream=False, **kwargs): """ Call DashScope API with tool support for agent integration This method handles: 1. Format conversion (Claude format → DashScope format) 2. System prompt injection 3. API calling with DashScope SDK 4. Thinking mode support (enable_thinking for Qwen3) Args: messages: List of messages (may be in Claude format from agent) tools: List of tool definitions (may be in Claude format from agent) stream: Whether to use streaming **kwargs: Additional parameters (max_tokens, temperature, system, etc.) Returns: Formatted response or generator for streaming """ try: # Convert messages from Claude format to DashScope format messages = self._convert_messages_to_dashscope_format(messages) # Convert tools from Claude format to DashScope format if tools: tools = self._convert_tools_to_dashscope_format(tools) # Handle system prompt system_prompt = kwargs.get('system') if system_prompt: # Add system message at the beginning if not already present if not messages or messages[0].get('role') != 'system': messages = [{"role": "system", "content": system_prompt}] + messages else: # Replace existing system message messages[0] = {"role": "system", "content": system_prompt} # Build request parameters model_name = kwargs.get("model", self.model_name) parameters = { "result_format": "message", # Required for tool calling "temperature": kwargs.get("temperature", conf().get("temperature", 0.85)), "top_p": kwargs.get("top_p", conf().get("top_p", 0.8)), } # Add max_tokens if specified if kwargs.get("max_tokens"): parameters["max_tokens"] = kwargs["max_tokens"] # Add tools if provided if tools: parameters["tools"] = tools # Add tool_choice if specified if kwargs.get("tool_choice"): parameters["tool_choice"] = kwargs["tool_choice"] # Add thinking parameters for Qwen3 models (disabled by default for stability) if "qwen3" in model_name.lower() or "qwq" in model_name.lower(): # Only enable thinking mode if explicitly requested enable_thinking = kwargs.get("enable_thinking", False) if enable_thinking: parameters["enable_thinking"] = True # Set thinking budget if specified if kwargs.get("thinking_budget"): parameters["thinking_budget"] = kwargs["thinking_budget"] # Qwen3 requires incremental_output=true in thinking mode if stream: parameters["incremental_output"] = True # Always use incremental_output for streaming (for better token-by-token streaming) # This is especially important for tool calling to avoid incomplete responses if stream: parameters["incremental_output"] = True # Make API call with DashScope SDK if stream: return self._handle_stream_response(model_name, messages, parameters) else: return self._handle_sync_response(model_name, messages, parameters) except Exception as e: error_msg = str(e) logger.error(f"[DASHSCOPE] call_with_tools error: {error_msg}") if stream: def error_generator(): yield { "error": True, "message": error_msg, "status_code": 500 } return error_generator() else: return { "error": True, "message": error_msg, "status_code": 500 } def _handle_sync_response(self, model_name, messages, parameters): """Handle synchronous DashScope API response""" try: # Set API key before calling dashscope.api_key = self.api_key response = dashscope.Generation.call( model=dashscope_models.get(model_name, model_name), messages=messages, **parameters ) if response.status_code == HTTPStatus.OK: # Convert DashScope response to OpenAI-compatible format choice = response.output.choices[0] return { "id": response.request_id, "object": "chat.completion", "created": 0, "model": model_name, "choices": [{ "index": 0, "message": { "role": choice.message.role, "content": choice.message.content, "tool_calls": self._convert_tool_calls_to_openai_format( choice.message.get("tool_calls") ) }, "finish_reason": choice.finish_reason }], "usage": { "prompt_tokens": response.usage.input_tokens, "completion_tokens": response.usage.output_tokens, "total_tokens": response.usage.total_tokens } } else: logger.error(f"[DASHSCOPE] API error: {response.code} - {response.message}") return { "error": True, "message": response.message, "status_code": response.status_code } except Exception as e: logger.error(f"[DASHSCOPE] sync response error: {e}") return { "error": True, "message": str(e), "status_code": 500 } def _handle_stream_response(self, model_name, messages, parameters): """Handle streaming DashScope API response""" try: # Set API key before calling dashscope.api_key = self.api_key responses = dashscope.Generation.call( model=dashscope_models.get(model_name, model_name), messages=messages, stream=True, **parameters ) # Stream chunks to caller, converting to OpenAI format for response in responses: if response.status_code != HTTPStatus.OK: logger.error(f"[DASHSCOPE] Stream error: {response.code} - {response.message}") yield { "error": True, "message": response.message, "status_code": response.status_code } continue # Get choice - use try-except because DashScope raises KeyError on hasattr() try: if isinstance(response.output, dict): choice = response.output['choices'][0] else: choice = response.output.choices[0] except (KeyError, AttributeError, IndexError) as e: logger.warning(f"[DASHSCOPE] Cannot get choice: {e}") continue # Get finish_reason safely finish_reason = None try: if isinstance(choice, dict): finish_reason = choice.get('finish_reason') else: finish_reason = choice.finish_reason except (KeyError, AttributeError): pass # Convert to OpenAI-compatible format openai_chunk = { "id": response.request_id, "object": "chat.completion.chunk", "created": 0, "model": model_name, "choices": [{ "index": 0, "delta": {}, "finish_reason": finish_reason }] } # Get message safely - use try-except message = {} try: if isinstance(choice, dict): message = choice.get('message', {}) else: message = choice.message except (KeyError, AttributeError): pass # Add role if present role = None try: if isinstance(message, dict): role = message.get('role') else: role = message.role except (KeyError, AttributeError): pass if role: openai_chunk["choices"][0]["delta"]["role"] = role # Add content if present content = None try: if isinstance(message, dict): content = message.get('content') else: content = message.content except (KeyError, AttributeError): pass if content: openai_chunk["choices"][0]["delta"]["content"] = content # Add tool_calls if present # DashScope's response object raises KeyError on hasattr() if attr doesn't exist # So we use try-except instead tool_calls = None try: if isinstance(message, dict): tool_calls = message.get('tool_calls') else: tool_calls = message.tool_calls except (KeyError, AttributeError): pass if tool_calls: openai_chunk["choices"][0]["delta"]["tool_calls"] = self._convert_tool_calls_to_openai_format(tool_calls) yield openai_chunk except Exception as e: logger.error(f"[DASHSCOPE] stream response error: {e}") yield { "error": True, "message": str(e), "status_code": 500 } def _convert_tools_to_dashscope_format(self, tools): """ Convert tools from Claude format to DashScope format Claude format: {name, description, input_schema} DashScope format: {type: "function", function: {name, description, parameters}} """ if not tools: return None dashscope_tools = [] for tool in tools: # Check if already in DashScope/OpenAI format if 'type' in tool and tool['type'] == 'function': dashscope_tools.append(tool) else: # Convert from Claude format dashscope_tools.append({ "type": "function", "function": { "name": tool.get("name"), "description": tool.get("description"), "parameters": tool.get("input_schema", {}) } }) return dashscope_tools def _convert_messages_to_dashscope_format(self, messages): """ Convert messages from Claude format to DashScope format Claude uses content blocks with types like 'tool_use', 'tool_result' DashScope uses 'tool_calls' in assistant messages and 'tool' role for results """ if not messages: return [] dashscope_messages = [] for msg in messages: role = msg.get("role") content = msg.get("content") # Handle string content (already in correct format) if isinstance(content, str): dashscope_messages.append(msg) continue # Handle list content (Claude format with content blocks) if isinstance(content, list): # Check if this is a tool result message (user role with tool_result blocks) if role == "user" and any(block.get("type") == "tool_result" for block in content): # Convert each tool_result block to a separate tool message for block in content: if block.get("type") == "tool_result": dashscope_messages.append({ "role": "tool", "content": block.get("content", ""), "tool_call_id": block.get("tool_use_id") # DashScope uses 'tool_call_id' }) # Check if this is an assistant message with tool_use blocks elif role == "assistant": # Separate text content and tool_use blocks text_parts = [] tool_calls = [] for block in content: if block.get("type") == "text": text_parts.append(block.get("text", "")) elif block.get("type") == "tool_use": tool_calls.append({ "id": block.get("id"), "type": "function", "function": { "name": block.get("name"), "arguments": json.dumps(block.get("input", {})) } }) # Build DashScope format assistant message dashscope_msg = { "role": "assistant" } # Add content only if there is actual text # DashScope API: when tool_calls exist, content should be None or omitted if empty if text_parts: dashscope_msg["content"] = " ".join(text_parts) elif not tool_calls: # If no tool_calls and no text, set empty string (rare case) dashscope_msg["content"] = "" # If there are tool_calls but no text, don't set content field at all if tool_calls: dashscope_msg["tool_calls"] = tool_calls dashscope_messages.append(dashscope_msg) else: # Other list content, keep as is dashscope_messages.append(msg) else: # Other formats, keep as is dashscope_messages.append(msg) return dashscope_messages def _convert_tool_calls_to_openai_format(self, tool_calls): """Convert DashScope tool_calls to OpenAI format""" if not tool_calls: return None openai_tool_calls = [] for tool_call in tool_calls: # DashScope format is already similar to OpenAI if isinstance(tool_call, dict): openai_tool_calls.append(tool_call) else: # Handle object format openai_tool_calls.append({ "id": getattr(tool_call, 'id', None), "type": "function", "function": { "name": tool_call.function.name, "arguments": tool_call.function.arguments } }) return openai_tool_calls