看懂Arch-Router意图识别：AI智能分流的底层逻辑，终于讲通透了！

1、轻量模型上岗：低成本搞定基础分流

2、标准化训练：给AI立好规矩、学好话术

3、兜底容错：应对所有奇葩、无解的用户提问

import jsonfrom typing import Any, Dict, Listfrom modelscope import AutoModelForCausalLM, AutoTokenizermodel_name = "katanemo/Arch-Router-1.5B"model = AutoModelForCausalLM.from_pretrained(    model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained(model_name)# Please use our provided prompt for best performanceTASK_INSTRUCTION = """You are a helpful assistant designed to find the best suited route.You are provided with route description within <routes></routes> XML tags:<routes>{routes}</routes><conversation>{conversation}</conversation>"""FORMAT_PROMPT = """Your task is to decide which route is best suit with user intent on the conversation in <conversation></conversation> XML tags.  Follow the instruction:1. If the latest intent from user is irrelevant or user intent is full filled, response with other route {"route": "other"}.2. You must analyze the route descriptions and find the best match route for user latest intent. 3. You only response the name of the route that best matches the user's request, use the exact name in the <routes></routes>.Based on your analysis, provide your response in the following JSON formats if you decide to match any route:{"route": "route_name"} """# Define route configroute_config = [    {        "name": "code_generation",        "description": "Generating new code snippets, functions, or boilerplate based on user prompts or requirements",    },    {        "name": "bug_fixing",        "description": "Identifying and fixing errors or bugs in the provided code across different programming languages",    },    {        "name": "performance_optimization",        "description": "Suggesting improvements to make code more efficient, readable, or scalable",    },    {        "name": "api_help",        "description": "Assisting with understanding or integrating external APIs and libraries",    },    {        "name": "programming",        "description": "Answering general programming questions, theory, or best practices",    },]# Helper function to create the system prompt for our modeldef format_prompt(    route_config: List[Dict[str, Any]], conversation: List[Dict[str, Any]]):    return (        TASK_INSTRUCTION.format(            routes=json.dumps(route_config), conversation=json.dumps(conversation)        )        + FORMAT_PROMPT    )# Define conversationsconversation = [    {        "role": "user",        "content": "fix this module 'torch.utils._pytree' has no attribute 'register_pytree_node'. did you mean: '_register_pytree_node'?",    }]route_prompt = format_prompt(route_config, conversation)messages = [    {"role": "user", "content": route_prompt},]input_ids = tokenizer.apply_chat_template(    messages, add_generation_prompt=True, return_tensors="pt").to(model.device)# 2. Generategenerated_ids = model.generate(    input_ids=input_ids,  # or just positional: model.generate(input_ids, …)    max_new_tokens=32768,)# 3. Strip the prompt from each sequenceprompt_lengths = input_ids.shape[1]  # same length for every row heregenerated_only = [    output_ids[prompt_lengths:]  # slice off the prompt tokens    for output_ids in generated_ids]# 4. Decode if you want textresponse = tokenizer.batch_decode(generated_only, skip_special_tokens=True)[0]print(response)