Create get_react_data.py

增加了快速构建agent微调数据的功能

agent_demo/get_react_data.py

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+import re
+from vllm import LLM, SamplingParams
+from build_react_prompt import build_input_text,TOOL_DESC,PROMPT_REACT,parse_latest_plugin_call
+import json
+import json5
+from transformers import AutoTokenizer
+#from agent_demo import *
+model_path = "/root/ld/ld_model_pretrained/Qwen2.5-72B-Instruct-GPTQ-Int4" # 教师模型地址
+save_question_json = '/root/ld/ld_project/pull_request/MiniCPM_Series_Tutorial/agent_demo/question_react.json' # 保存query的json地址
+save_react_qa_json = '/root/ld/ld_project/pull_request/MiniCPM_Series_Tutorial/agent_demo/react_qa_react.json' # 保存react的json地址
+inference_batch_size = 8 # 教师模型生成数据时的batch
+gen_datas_per_tool = 10 # 每个tool生成多少条react数据
+cpm3_path = '/root/ld/ld_model_pretrained/minicpm3' # if you  want to get react data，cpm3_path can be none
+cpm3_data_save_path = '/root/ld/ld_project/pull_request/MiniCPM_Series_Tutorial/agent_demo/cpm3_fc_train_data.json' #cpm3的数据保存json地址
+tools = [
+            {
+            'name_for_human': '图生文',
+            'name_for_model': 'image_gen_prompt',
+            'excute_function': False, # 是否可以使用这个工具进行函数调用以生成数据
+            'description_for_model': '图生文是一个可以看图生成文字描述的服务，输入一张图片的地址，将返回图片详细逼真的表述',
+            'example':'帮我看一下www.baidu.com/img/PCtm_d9c8750bed0b3c7d089fa7d55720d6cf.png这张图片上的今日股价是多少',
+            'parameters': [
+                {
+                    'name': 'image_path',
+                    'description': '需要图片描述的URL或者本地地址',
+                    'scope':None, # 这个参数的取值范围，如果不限定为None
+                    'required': True,#这个是否必须
+                    'schema': {'type': 'string'},
+                }
+
+            ]
+        },
+        {
+            'name_for_human': '知识图谱',
+            'name_for_model': 'knowledge_graph',
+            'excute_function': True,
+            'description_for_model': '知识图谱是输入武器种类获取该武器的属性，也可以输入某种属性获得所有武器的该属性',
+            'example':'帮我查一下敌方直升机的续航里程',
+            'parameters': [
+                {
+                    'name': 'weapon_query',
+                    'description': '武器名称',
+                    'scope':['直升机','坦克','反坦克导弹','直升机','火箭炮','所有武器'],#参数的取值范围
+                    'required': True,
+                    'schema': {'type': 'string'},
+                },
+                {
+                    'name': 'attribute',
+                    'description': '武器的属性',
+                    'scope':['射程','续航里程','重量','速度','承载量','适应场景','克制武器'],
+                    'required': True,
+                    'schema': {'type': 'string'},
+                }
+            ],
+        }
+        ]
+params_dict = {
+        "n": 1,
+        "best_of": 1,
+        "presence_penalty": 1,    
+        "frequency_penalty": 1.0,
+        "temperature": 0.8,
+        "top_p": 0.8,
+        "top_k": -1,
+        "use_beam_search": False,
+        "length_penalty": 1,
+        "early_stopping": False,
+        "stop": None,
+        "stop_token_ids": None,
+        "ignore_eos": False,
+        "max_tokens": 1000,
+        "logprobs": None,
+        "prompt_logprobs": None,
+        "skip_special_tokens": True,
+    }
+sampling_params = SamplingParams(**params_dict)
+
+def save_cpm3_data(cpm3_data_path,cpm3_data):
+    # 将列表转换为 JSON 格式的字符串
+    json_str = json.dumps(cpm3_data, ensure_ascii=False, indent=4)
+
+
+    # 将 JSON 字符串保存到文件
+    with open(cpm3_data_path, 'w', encoding='utf-8') as json_file:
+        json_file.write(json_str)
+def switch_cpm_tool(tools):
+    format_tool={
+        "type": "function",
+        "function": {
+            "name": "get_delivery_date",
+            "description": "Get the delivery date for a customer's order. Call this whenever you need to know the delivery date, for example when a customer asks 'Where is my package'",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "order_id": {
+                        "type": "string",
+                        "description": "The customer's order ID.",
+                    },
+                },
+                "required": ["order_id"],
+                "additionalProperties": False,
+            },
+        },
+    }
+    cpm_tools = []
+    for tool in tools:
+        format_tool['function']['name']=tool['name_for_model']
+        format_tool['function']['description']=tool['description_for_model']
+        #format_tool['function']["parameters"]['properties']=
+        required_list=[]
+        for param in tool['parameters']:
+            """param{
+                    'name': 'weapon_query',
+                    'description': '武器名称',
+                    'scope':['直升机','坦克','反坦克导弹','直升机','火箭炮','所有武器'],
+                    'required': True,
+                    'schema': {'type': 'string'},
+                }"""
+            format_tool['function']["parameters"]['properties'][param['name']]={'type':param['schema']['type'],"description":param['description']}
+            if param['required']:
+                required_list.append(param['name'])
+        format_tool['function']["parameters"]["required"] = required_list
+        format_tool['function']["parameters"]["additionalProperties"] = False
+        cpm_tools.append(format_tool)
+    return cpm_tools
+def function_call(plugin_name, plugin_args):
+    args_dict = json5.loads(plugin_args)
+    if plugin_name == 'knowledge_graph':
+        weapon_name = args_dict['weapon_query']
+        attribute = args_dict['attribute']
+        kg={'直升机':{'飞行高度':'0.3km以内','携带武器':'火箭弹','克制武器':'对空导弹','重量':'3000kg',"速度":"100km/h","射程":"2km",'适应场景':'空战','续航':'500km','满载人数':'7人','承载重量':'10000kg','续航里程':'1000km'},
+                        '反坦克导弹':{'重量':'100kg','射程':'0.5千米','克制武器':'拦截导弹','适应场景':'打击重装甲武器','速度':'200km/h'},
+                        '步兵':{'射程':'0.3km','克制武器':'无人机','适应场景':'陆地',"速度":'40km/h','重量':'60kg','承载重量':'50kg'},
+                        '无人机':{'速度':'100km/h','重量':'10kg','适应场景':'侦察和暗杀','飞行高度':'0.3km以下','克制武器':'电磁攻击','续航':'50km'},
+                        '豹2A7坦克':{'速度':'50km/h','携带武器':'激光炮','克制武器':'反坦克导弹',"射程":"5km",'重量':'10000kg','续航':'1000km','承载重量':'200000kg','满载人数':'5人' ,'适应场景' :'野战和掩护步兵'},
+                        '黑狐坦克':{'速度':'70km/h','携带武器':'主炮','克制武器':'反坦克导弹',"射程":"15km",'重量':'10000kg','承载重量':'50000kg','续航':'1000km','满载人数':'5人','适应场景' :'野战和掩护步兵'},
+                        "火箭炮":{'速度':'4500km/h','重量':'500kg','射程':'1000km','适应场景':'超远程打击','飞行高度':'万米高空','克制武器':'拦截导弹'},
+                        "雷达":{'重量':'5000kg','探测范围':'2km以上20km以下','适应场景':'探测敌军'},
+                        '装甲车':{'速度':'80km/h','携带武器':'副炮','克制武器':'穿甲弹',"射程":"0.5km",'重量':'10000kg','承载重量':'10000kg','续航':'600km','满载人数':'10人'},
+                        '狙击枪':{'射程':'1.2km','重量':'30kg','适应场景':'暗杀'}
+                        }
+        if weapon_name != '所有武器':
+            try:
+                return '{}的{}是:{}'.format(weapon_name,attribute,kg[weapon_name][attribute])
+            except:
+                if weapon_name not in kg:
+                    return '该武器不存在'
+                else:
+                    return '{}的{}属性不存在'.format(weapon_name,attribute)
+        return kg
+
+def split_react_data(react_str):
+    pattern = re.compile(r'Thought:\s*(.*?)\nAction:\s*(.*?)\nAction Input:\s*(.*?)\nObservation:\s*(.*?)\nThought:\s*(.*?)\nFinal Answer:\s*(.*)', re.DOTALL)
+
+    matches = pattern.findall(react_str)
+    try:
+        for match in matches:
+            Thought1=match[0]
+            Action=match[1]
+            Action_Input=match[2]
+            Observation=match[3]
+            Thought2=match[4]
+            Final_Answer=match[5]
+        return Thought1,Action,Action_Input,Observation,Thought2,Final_Answer
+    except:
+        return None,None,None,None,None,None
+
+
+
+def get_answer_from_output(output):
+    pattern = r'「问题开始」(.*?)「问题结束」'
+    questions = re.findall(pattern, output, re.DOTALL)
+    questions = [q.strip() for q in questions]
+    return questions
+
+
+def get_tool_description(tool):
+    tool_descp = "工具名称是{},作用是{},".format(tool['name_for_human'],tool['description_for_model'])
+    for t in tool['parameters']:
+        if t['required']:
+            if t['scope']:
+                tool_descp+='参数:{}是必须输入的，作用是{},该参数的取值范围是{}。'.format(t['name'], t['description'], t['scope'])
+            else:
+                tool_descp+='参数:{}是必须输入的，作用是{}。'.format(t['name'], t['description'])
+        elif t['scope']:
+            tool_descp+='参数:{}是可选的，作用是{},该参数的取值范围是{}。'.format(t['name'], t['description'], t['scope'])
+        else:
+            tool_descp+='参数:{}是可选的，作用是{}。'.format(t['name'], t['description'])
+    return tool_descp
+
+def get_question():
+    if llm not in locals():
+        llm = LLM(model=model_path, tensor_parallel_size=8,max_model_len=4096, dtype='bfloat16',trust_remote_code=True,enforce_eager=True,gpu_memory_utilization=0.8)
+
+    prompt_template = """你是一个智能助手，现在我请你为以下工具生成问题，要求生成的问题能够被这个工具解决。工具的详细介绍如下：\n{}\n我现在给你一个关于此工具问题的示例「问题开始」{}「问题结束」,接下来请你根据此示例和工具描述再生成{}个能够使用该工具解决的问题，并且用「问题开始」和「问题结束」将其包裹。"""
+
+
+    all_questions = []
+    all_react_prompt = []
+    questinos_dict = {}
+    for tool in tools:
+        questions = []
+        while True:
+            tool_description = get_tool_description(tool)
+            input_prompt = prompt_template.format(tool_description,tool['example'],gen_datas_per_tool)
+            input_prompt = """<|im_start|> system\n you are a helpful assistant<|im_end|>\n<|im_start|> user\n {}<|im_end|>\n""".format(input_prompt)
+            outputs = llm.generate(input_prompt, sampling_params)
+            output=outputs[0].outputs[0].text
+            questions.extend(get_answer_from_output(output))
+
+            if len(questions)>=gen_datas_per_tool:
+                all_questions.extend(questions)
+                print(questions)
+                questinos_dict[tool['name_for_model']] = questions
+                break
+    with open(save_question_json, 'w', encoding='utf-8') as f:
+        json.dump(questinos_dict, f, ensure_ascii=False, indent=4)
+        print('{}条输入指令已经保存到{}'.format(len(all_questions),save_question_json))
+
+def get_react_data():
+    if llm not in locals():
+        llm = LLM(model=model_path, tensor_parallel_size=8,max_model_len=4096, dtype='bfloat16',trust_remote_code=True,enforce_eager=True,gpu_memory_utilization=0.8)
+
+    with open(save_question_json, 'r', encoding='utf-8') as file:
+        # 将json文件内容解析为Python对象
+        all_questions = json.load(file)
+    react_question = [build_input_text([(q,'')],tools) for q in all_questions]
+    params_dict["top_k"] = 1
+    params_dict['stop'] = ['Observation:']
+
+    react_qa = []
+    sampling_params = SamplingParams(**params_dict)
+    for index in range(0, len(react_question), inference_batch_size):
+        outputs = llm.generate(react_question[index:index+inference_batch_size], sampling_params)
+        for i in range(len(outputs)):
+            output=outputs[i].outputs[0].text
+            try:
+                plugin_name, plugin_args, text = parse_latest_plugin_call(output)
+                excute_flag = True 
+                for tool in tools:
+                    if tool['name_for_model'] == plugin_name and tool['excute_function']==False:
+                        excute_flag = False
+                        second_input = react_question[index+i]+output+'Observation: '
+                        output2 = llm.generate(second_input, sampling_params)[0].outputs[0].text
+                if excute_flag:
+                    observation = function_call(plugin_name,plugin_args)
+                    second_input = react_question[index+i]+output+'Observation: {}'.format(observation)
+                output2 = llm.generate(second_input, sampling_params)[0].outputs[0].text
+                print(output2)
+                #react_qa.append({react_question[index+i]: second_input[len(react_question[index+i]):]+output2})
+                react_qa.append({'instruction':"You are a helpful assistant.",'input':react_question[index+i][75:-33],'output':second_input[len(react_question[index+i]):]+output2})
+            except:
+                pass
+
+
+    with open(save_react_qa_json, 'w', encoding='utf-8') as f:
+        json.dump(react_qa, f, ensure_ascii=False, indent=4)
+        print('{}条react qa数据已经保存到{}'.format(len(react_qa),save_react_qa_json))
+def get_cpm_function_call():
+    with open(save_react_qa_json, 'r', encoding='utf-8') as file:
+        # 将json文件内容解析为Python对象
+        react_qa = json.load(file)
+    cpm_tool = switch_cpm_tool(tools)
+    tokenizer = AutoTokenizer.from_pretrained(cpm3_path,trust_remote_code=True)
+    cpm_fc_train_data = []
+    for react in react_qa:
+        messages = [
+    {
+        "role": "system",
+        "content": "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
+    } 
+]
+        query = react['input'].split('Question: ')[-1]
+        print(query)
+        react_str = list(react.values())[-1]
+        Thought1,Action,Action_Input,Observation,Thought2,Final_Answer = split_react_data(react_str)
+        if Thought1 and Action and Action_Input and Observation and Thought2 and Final_Answer:
+            messages.append({"role": "user", "content": query})
+            prompt = tokenizer.apply_chat_template(messages, tools=cpm_tool, tokenize=False, add_generation_prompt=True)
+            cpm_thought1 = "<|thought_start|>\n{}\n<|thought_end|>".format(Thought1)
+            cpm_function_and_param = "\n<|tool_call_start|>\n```python\n{}({})\n```\n<|tool_call_end|>".format(Action,re.sub(': ','=',Action_Input))
+
+            cpm_fc_train_data.append([{"role":"system",'content':prompt.split('<|im_end|>')[0][19:]},{"role":'user','content':query},{"role":'assistant','content':cpm_thought1+cpm_function_and_param}])
+
+            cpm_response = '<|im_end|>\n<|im_start|>tool\n{}<|im_end|>\n<|im_start|>assistant\n'.format(Observation)
+            cpm_thought2 ='<|thought_start|>\n{}\n<|thought_end|>\n'.format(Thought2)
+            cpm_answer = Final_Answer
+            cpm_fc_train_data.append([{"role":"system",'content':prompt.split('<|im_end|>')[0][19:]},{'role':'user','content':query+'<|im_start|>assistant\n'+cpm_function_and_param+cpm_response},{'role':'assistant','content':cpm_thought2+cpm_answer}])
+        else:
+            print(1)
+            continue
+        #cpm_fc_train_data.append({"role":"system",'content':prompt+cpm_function_and_param+cpm_response,'role':'assistant','content':cpm_thought2+cpm_answer})
+    save_cpm3_data(cpm3_data_save_path,cpm_fc_train_data)
+    print('{}条cpm3 function call数据已经保存到{}'.format(len(cpm_fc_train_data),cpm3_data_save_path))
+
+
+if __name__ == "__main__":
+    get_question()
+    get_react_data()
+    get_cpm_function_call()