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https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-03-04 15:47:52 +08:00
feat: add qwen models tool call
This commit is contained in:
zhayujie
@@ -1,5 +1,6 @@
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# encoding:utf-8
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import json
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from models.bot import Bot
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from models.session_manager import SessionManager
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from bridge.context import ContextType
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@@ -17,7 +18,15 @@ dashscope_models = {
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"qwen-turbo": dashscope.Generation.Models.qwen_turbo,
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"qwen-plus": dashscope.Generation.Models.qwen_plus,
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"qwen-max": dashscope.Generation.Models.qwen_max,
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"qwen-bailian-v1": dashscope.Generation.Models.bailian_v1
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"qwen-bailian-v1": dashscope.Generation.Models.bailian_v1,
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# Qwen3 series models - use string directly as model name
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"qwen3-max": "qwen3-max",
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"qwen3-plus": "qwen3-plus",
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"qwen3-turbo": "qwen3-turbo",
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# Other new models
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"qwen-long": "qwen-long",
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"qwq-32b-preview": "qwq-32b-preview",
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"qvq-72b-preview": "qvq-72b-preview"
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}
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# ZhipuAI对话模型API
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class DashscopeBot(Bot):
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@@ -115,3 +124,404 @@ class DashscopeBot(Bot):
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return self.reply_text(session, retry_count + 1)
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else:
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return result
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def call_with_tools(self, messages, tools=None, stream=False, **kwargs):
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"""
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Call DashScope API with tool support for agent integration
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This method handles:
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1. Format conversion (Claude format → DashScope format)
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2. System prompt injection
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3. API calling with DashScope SDK
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4. Thinking mode support (enable_thinking for Qwen3)
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Args:
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messages: List of messages (may be in Claude format from agent)
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tools: List of tool definitions (may be in Claude format from agent)
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stream: Whether to use streaming
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**kwargs: Additional parameters (max_tokens, temperature, system, etc.)
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Returns:
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Formatted response or generator for streaming
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"""
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try:
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# Convert messages from Claude format to DashScope format
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messages = self._convert_messages_to_dashscope_format(messages)
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# Convert tools from Claude format to DashScope format
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if tools:
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tools = self._convert_tools_to_dashscope_format(tools)
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# Handle system prompt
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system_prompt = kwargs.get('system')
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if system_prompt:
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# Add system message at the beginning if not already present
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if not messages or messages[0].get('role') != 'system':
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messages = [{"role": "system", "content": system_prompt}] + messages
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else:
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# Replace existing system message
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messages[0] = {"role": "system", "content": system_prompt}
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# Build request parameters
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model_name = kwargs.get("model", self.model_name)
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parameters = {
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"result_format": "message", # Required for tool calling
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"temperature": kwargs.get("temperature", conf().get("temperature", 0.85)),
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"top_p": kwargs.get("top_p", conf().get("top_p", 0.8)),
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}
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# Add max_tokens if specified
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if kwargs.get("max_tokens"):
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parameters["max_tokens"] = kwargs["max_tokens"]
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# Add tools if provided
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if tools:
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parameters["tools"] = tools
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# Add tool_choice if specified
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if kwargs.get("tool_choice"):
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parameters["tool_choice"] = kwargs["tool_choice"]
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# Add thinking parameters for Qwen3 models (disabled by default for stability)
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if "qwen3" in model_name.lower() or "qwq" in model_name.lower():
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# Only enable thinking mode if explicitly requested
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enable_thinking = kwargs.get("enable_thinking", False)
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if enable_thinking:
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parameters["enable_thinking"] = True
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# Set thinking budget if specified
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if kwargs.get("thinking_budget"):
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parameters["thinking_budget"] = kwargs["thinking_budget"]
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# Qwen3 requires incremental_output=true in thinking mode
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if stream:
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parameters["incremental_output"] = True
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# Always use incremental_output for streaming (for better token-by-token streaming)
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# This is especially important for tool calling to avoid incomplete responses
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if stream:
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parameters["incremental_output"] = True
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# Make API call with DashScope SDK
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if stream:
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return self._handle_stream_response(model_name, messages, parameters)
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else:
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return self._handle_sync_response(model_name, messages, parameters)
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except Exception as e:
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error_msg = str(e)
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logger.error(f"[DASHSCOPE] call_with_tools error: {error_msg}")
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if stream:
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def error_generator():
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yield {
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"error": True,
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"message": error_msg,
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"status_code": 500
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}
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return error_generator()
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else:
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return {
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"error": True,
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"message": error_msg,
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"status_code": 500
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}
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def _handle_sync_response(self, model_name, messages, parameters):
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"""Handle synchronous DashScope API response"""
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try:
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# Set API key before calling
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dashscope.api_key = self.api_key
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response = dashscope.Generation.call(
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model=dashscope_models.get(model_name, model_name),
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messages=messages,
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**parameters
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)
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if response.status_code == HTTPStatus.OK:
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# Convert DashScope response to OpenAI-compatible format
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choice = response.output.choices[0]
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return {
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"id": response.request_id,
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"object": "chat.completion",
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"created": 0,
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"model": model_name,
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"choices": [{
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"index": 0,
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"message": {
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"role": choice.message.role,
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"content": choice.message.content,
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"tool_calls": self._convert_tool_calls_to_openai_format(
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choice.message.get("tool_calls")
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)
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},
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"finish_reason": choice.finish_reason
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}],
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"usage": {
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"prompt_tokens": response.usage.input_tokens,
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"completion_tokens": response.usage.output_tokens,
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"total_tokens": response.usage.total_tokens
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}
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}
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else:
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logger.error(f"[DASHSCOPE] API error: {response.code} - {response.message}")
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return {
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"error": True,
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"message": response.message,
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"status_code": response.status_code
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}
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except Exception as e:
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logger.error(f"[DASHSCOPE] sync response error: {e}")
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return {
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"error": True,
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"message": str(e),
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"status_code": 500
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}
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def _handle_stream_response(self, model_name, messages, parameters):
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"""Handle streaming DashScope API response"""
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try:
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# Set API key before calling
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dashscope.api_key = self.api_key
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responses = dashscope.Generation.call(
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model=dashscope_models.get(model_name, model_name),
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messages=messages,
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stream=True,
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**parameters
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)
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# Stream chunks to caller, converting to OpenAI format
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for response in responses:
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if response.status_code != HTTPStatus.OK:
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logger.error(f"[DASHSCOPE] Stream error: {response.code} - {response.message}")
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yield {
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"error": True,
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"message": response.message,
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"status_code": response.status_code
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}
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continue
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# Get choice - use try-except because DashScope raises KeyError on hasattr()
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try:
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if isinstance(response.output, dict):
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choice = response.output['choices'][0]
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else:
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choice = response.output.choices[0]
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except (KeyError, AttributeError, IndexError) as e:
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logger.warning(f"[DASHSCOPE] Cannot get choice: {e}")
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continue
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# Get finish_reason safely
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finish_reason = None
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try:
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if isinstance(choice, dict):
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finish_reason = choice.get('finish_reason')
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else:
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finish_reason = choice.finish_reason
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except (KeyError, AttributeError):
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pass
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# Convert to OpenAI-compatible format
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openai_chunk = {
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"id": response.request_id,
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"object": "chat.completion.chunk",
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"created": 0,
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {},
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"finish_reason": finish_reason
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}]
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}
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# Get message safely - use try-except
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message = {}
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try:
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if isinstance(choice, dict):
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message = choice.get('message', {})
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else:
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message = choice.message
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except (KeyError, AttributeError):
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pass
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# Add role if present
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role = None
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try:
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if isinstance(message, dict):
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role = message.get('role')
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else:
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role = message.role
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except (KeyError, AttributeError):
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pass
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if role:
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openai_chunk["choices"][0]["delta"]["role"] = role
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# Add content if present
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content = None
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try:
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if isinstance(message, dict):
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content = message.get('content')
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else:
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content = message.content
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except (KeyError, AttributeError):
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pass
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if content:
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openai_chunk["choices"][0]["delta"]["content"] = content
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# Add tool_calls if present
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# DashScope's response object raises KeyError on hasattr() if attr doesn't exist
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# So we use try-except instead
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tool_calls = None
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try:
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if isinstance(message, dict):
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tool_calls = message.get('tool_calls')
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else:
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tool_calls = message.tool_calls
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except (KeyError, AttributeError):
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pass
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if tool_calls:
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openai_chunk["choices"][0]["delta"]["tool_calls"] = self._convert_tool_calls_to_openai_format(tool_calls)
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yield openai_chunk
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except Exception as e:
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logger.error(f"[DASHSCOPE] stream response error: {e}")
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yield {
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"error": True,
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"message": str(e),
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"status_code": 500
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}
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def _convert_tools_to_dashscope_format(self, tools):
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"""
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Convert tools from Claude format to DashScope format
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Claude format: {name, description, input_schema}
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DashScope format: {type: "function", function: {name, description, parameters}}
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"""
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if not tools:
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return None
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dashscope_tools = []
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for tool in tools:
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# Check if already in DashScope/OpenAI format
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if 'type' in tool and tool['type'] == 'function':
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dashscope_tools.append(tool)
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else:
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# Convert from Claude format
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dashscope_tools.append({
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"type": "function",
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"function": {
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"name": tool.get("name"),
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"description": tool.get("description"),
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"parameters": tool.get("input_schema", {})
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}
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})
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return dashscope_tools
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def _convert_messages_to_dashscope_format(self, messages):
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"""
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Convert messages from Claude format to DashScope format
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Claude uses content blocks with types like 'tool_use', 'tool_result'
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DashScope uses 'tool_calls' in assistant messages and 'tool' role for results
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"""
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if not messages:
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return []
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dashscope_messages = []
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for msg in messages:
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role = msg.get("role")
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content = msg.get("content")
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# Handle string content (already in correct format)
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if isinstance(content, str):
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dashscope_messages.append(msg)
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continue
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# Handle list content (Claude format with content blocks)
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if isinstance(content, list):
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# Check if this is a tool result message (user role with tool_result blocks)
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if role == "user" and any(block.get("type") == "tool_result" for block in content):
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# Convert each tool_result block to a separate tool message
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for block in content:
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if block.get("type") == "tool_result":
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dashscope_messages.append({
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"role": "tool",
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"content": block.get("content", ""),
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"tool_call_id": block.get("tool_use_id") # DashScope uses 'tool_call_id'
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})
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# Check if this is an assistant message with tool_use blocks
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elif role == "assistant":
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# Separate text content and tool_use blocks
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text_parts = []
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tool_calls = []
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for block in content:
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if block.get("type") == "text":
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text_parts.append(block.get("text", ""))
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elif block.get("type") == "tool_use":
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tool_calls.append({
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"id": block.get("id"),
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"type": "function",
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"function": {
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"name": block.get("name"),
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"arguments": json.dumps(block.get("input", {}))
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}
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})
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# Build DashScope format assistant message
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dashscope_msg = {
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"role": "assistant"
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}
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# Add content only if there is actual text
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# DashScope API: when tool_calls exist, content should be None or omitted if empty
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if text_parts:
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dashscope_msg["content"] = " ".join(text_parts)
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elif not tool_calls:
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# If no tool_calls and no text, set empty string (rare case)
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dashscope_msg["content"] = ""
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# If there are tool_calls but no text, don't set content field at all
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if tool_calls:
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dashscope_msg["tool_calls"] = tool_calls
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dashscope_messages.append(dashscope_msg)
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else:
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# Other list content, keep as is
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dashscope_messages.append(msg)
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else:
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# Other formats, keep as is
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dashscope_messages.append(msg)
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return dashscope_messages
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def _convert_tool_calls_to_openai_format(self, tool_calls):
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"""Convert DashScope tool_calls to OpenAI format"""
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if not tool_calls:
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return None
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openai_tool_calls = []
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for tool_call in tool_calls:
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# DashScope format is already similar to OpenAI
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if isinstance(tool_call, dict):
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openai_tool_calls.append(tool_call)
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else:
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# Handle object format
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openai_tool_calls.append({
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"id": getattr(tool_call, 'id', None),
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"type": "function",
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"function": {
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"name": tool_call.function.name,
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"arguments": tool_call.function.arguments
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}
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})
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return openai_tool_calls
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