From 1ff5de111b2331d2237ff3322eea3fa8885aa4e7 Mon Sep 17 00:00:00 2001 From: xupei Date: Fri, 3 Jan 2025 14:02:38 +0800 Subject: [PATCH] =?UTF-8?q?BUGFIX:chat=20robot=E6=B7=BB=E5=8A=A0internet?= =?UTF-8?q?=5Fsearch?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../chat_robot/script/service/CallQWen.py | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/app/service/chat_robot/script/service/CallQWen.py b/app/service/chat_robot/script/service/CallQWen.py index 33dcd04..781ba22 100644 --- a/app/service/chat_robot/script/service/CallQWen.py +++ b/app/service/chat_robot/script/service/CallQWen.py @@ -1,4 +1,5 @@ import json +import logging from dashscope import Generation from retry import retry @@ -159,10 +160,11 @@ def search_from_internet(message): model='qwen-turbo', api_key=QWEN_API_KEY, messages=message, - tools=tools, + prompt='The output must be in English.Keep the final result under 200 words.' + # tools=tools, # seed=random.randint(1, 10000), # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 - result_format='message', # 将输出设置为message形式 - enable_search='True' + # result_format='message', # 将输出设置为message形式 + # enable_search='True' ) return response @@ -198,14 +200,9 @@ def get_response(messages): def call_with_messages(message, gender): + global tool_info user_input = message print('\n') - # messages = [ - # { - # "content": input('请输入：'), # 提问示例："现在几点了？" "一个小时后几点" "北京天气如何？" - # "role": "user" - # } - # ] messages = [ { @@ -223,14 +220,10 @@ def call_with_messages(message, gender): } ] - # 模型的第一轮调用 - # first_response = get_response(messages) - # assistant_output = first_response.output.choices[0].message - # print(f"\n大模型第一轮输出信息：{first_response}\n") - # messages.append(assistant_output) flag = True count = 1 - result_content = "我是一个时尚AI助手，请问有什么可以帮您" + # result_content = "我是一个时尚AI助手，请问有什么可以帮您" + result_content = "I am a fashion AI assistant, how can I help you?" response_type = "chat" while flag and count <= 3: @@ -244,11 +237,15 @@ def call_with_messages(message, gender): print(f"最终答案：{assistant_output.content}") # 此处直接返回模型的回复，您可以根据您的业务，选择当无需调用工具时最终回复的内容 result_content = assistant_output.content break - # 如果模型选择的工具是search_from_internet - # elif assistant_output.tool_calls[0]['function']['name'] == 'search_from_internet': - # tool_info = {"name": "search_from_internet", "role": "tool"} - # user_input = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['user_input'] - # tool_info['content'] = search_from_internet(user_input) + # 如果模型选择的工具是internet_search + elif assistant_output.tool_calls[0]['function']['name'] == 'internet_search': + tool_info = {"name": "search_from_internet", "role": "tool"} + message = [ + {'role': 'assistant', 'content': user_input} + ] + tool_info['content'] = search_from_internet(message) + flag = False + result_content = tool_info['content'].output.text # 如果模型选择的工具是get_database_table elif assistant_output.tool_calls[0]['function']['name'] == 'get_database_table': tool_info = {"name": "get_database_table", "role": "tool", 'content': get_database_table()} @@ -275,13 +272,16 @@ def call_with_messages(message, gender): flag = False result_content = tool_info['content'] response_type = "image" + else : + tool_info = {"name": assistant_output.tool_calls[0]['function']['name'], 'content': 'null'} + logging.info(assistant_output.tool_calls[0]['function']['name'] + "(unknown tools)") + flag = False print(f"工具输出信息：{tool_info['content']}\n") messages.append(tool_info) count += 1 - final_output = {"output": result_content} - final_output["response_type"] = response_type + final_output = {"output": result_content, "response_type": response_type} QWenCallbackHandler.on_chain_end(qwen, final_output) # 模型的第二轮调用，对工具的输出进行总结