Shopify Gisting技术深度解析：LLM提示词压缩的工程实践

import torch
from torch import nn
from transformers import AutoModelForCausalLM, AutoTokenizer

class GistingTrainer:
    """
    Gisting训练器 - 学习将长系统提示词压缩为gist token
    """
    def __init__(self, model_name="meta-llama/Llama-3-70B"):
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.num_gist_tokens = 150  # 目标gist token数量
        
    def train_gist(self, system_prompt: str, training_examples: list):
        """
        训练gist token使其能够重建系统提示词的行为
        
        Args:
            system_prompt: 原始系统提示词（如6000 token的GraphQL schema）
            training_examples: 训练数据 - (用户输入, 期望输出)对
        """
        # Step 1: 初始化可学习的gist token嵌入
        gist_embeddings = nn.Parameter(
            torch.randn(self.num_gist_tokens, self.model.config.hidden_size)
        )
        
        # Step 2: 冻结模型主体参数（可选 - 也可解冻顶层）
        for param in self.model.parameters():
            param.requires_grad = False
        
        # Step 3: 训练gist embedding
        optimizer = torch.optim.Adam([gist_embeddings], lr=1e-4)
        
        for epoch in range(num_epochs):
            for user_input, expected_output in training_examples:
                # 用gist token替代原始系统提示词
                gist_sequence = self._build_gist_sequence(
                    gist_embeddings, user_input
                )
                
                # 前向传播
                logits = self.model(inputs_embeds=gist_sequence)
                
                # 计算损失 - 让gist token的输出与原始提示词一致
                loss = self._compute_loss(logits, expected_output)
                
                loss.backward()
                optimizer.step()
        
        return gist_embeddings.detach()
    
    def _build_gist_sequence(self, gist_embeddings, user_input):
        """
        构建输入序列: [gist_tokens] + [user_input_tokens]
        替代: [system_prompt_tokens] + [user_input_tokens]
        """
        user_embeddings = self.model.get_input_embeddings()(
            self.tokenizer(user_input, return_tensors="pt").input_ids
        )
        # 拼接gist embeddings和用户输入
        return torch.cat([gist_embeddings, user_embeddings], dim=1)

class GistingPipeline:
    """
    Gisting端到端部署流水线
    """
    
    def __init__(self):
        self.phase = "initialization"
    
    def run(self, system_prompt: str, training_data: list):
        # Phase 1: 提示词分析
        analysis = self.analyze_prompt(system_prompt)
        
        # Phase 2: 训练数据准备
        prepared_data = self.prepare_training_data(
            system_prompt, training_data
        )
        
        # Phase 3: Gist训练
        gist_tokens = self.train_gist(system_prompt, prepared_data)
        
        # Phase 4: 质量评估
        quality = self.evaluate_quality(
            system_prompt, gist_tokens, test_data
        )
        
        if quality.score < 0.95:  # 质量阈值
            return self.retry_with_adjustments(analysis)
        
        # Phase 5: 部署上线
        self.deploy(gist_tokens)
        
        # Phase 6: 持续监控
        self.setup_monitoring(gist_tokens, system_prompt)

def analyze_system_prompt(prompt: str) -> dict:
    """
    分析系统提示词的结构，确定gist训练策略
    """
    analysis = {
        "total_tokens": count_tokens(prompt),
        "content_type": classify_content(prompt),
        "static_ratio": measure_static_content(prompt),
        "dynamic_ratio": measure_dynamic_content(prompt),
        "redundancy": detect_redundancy(prompt),
    }
    
    # 估算目标gist token数量
    # 经验法则: 目标token数 ≈ 原始token数 / 4
    analysis["target_gist_tokens"] = analysis["total_tokens"] // 4
    
    # 评估压缩可行性
    if analysis["dynamic_ratio"] > 0.3:
        analysis["feasibility"] = "low"
        analysis["reason"] = "动态内容比例过高，不适合gist压缩"
    elif analysis["static_ratio"] > 0.7:
        analysis["feasibility"] = "high"
        analysis["reason"] = "静态内容占比高，适合gist压缩"
    else:
        analysis["feasibility"] = "medium"
        analysis["reason"] = "部分内容可压缩，需要分离静态和动态部分"
    
    return analysis

class GistingQualityEvaluator:
    """
    Gisting质量评估器
    """
    def evaluate(self, original_prompt, gist_tokens, test_cases):
        """
        对比原始提示词和gist token的输出质量
        """
        results = {
            "semantic_similarity": [],
            "behavioral_consistency": [],
            "task_accuracy": [],
            "safety_compliance": []
        }
        
        for test_case in test_cases:
            # 用原始提示词生成基准输出
            original_output = self.generate(
                prompt=original_prompt,
                user_input=test_case.input
            )
            
            # 用gist token生成测试输出
            gist_output = self.generate(
                gist_tokens=gist_tokens,
                user_input=test_case.input
            )
            
            # 对比评估
            results["semantic_similarity"].append(
                self.compute_similarity(original_output, gist_output)
            )
            results["behavioral_consistency"].append(
                self.check_behavior(original_output, gist_output)
            )
            results["task_accuracy"].append(
                self.evaluate_task(test_case, gist_output)
            )
            results["safety_compliance"].append(
                self.check_safety(gist_output)
            )
        
        return {
            "overall_score": self.compute_overall(results),
            "details": results,
            "recommendation": self.recommend(results)
        }

class UniversalDistillationPlatform:
    """
    Shopify UDP - 通用蒸馏平台
    """
    def distill(self, task_config):
        # Step 1: 教师模型生成训练数据
        teacher_model = "claude-opus-4"  # 或 GPT-5+
        training_data = self.generate_with_teacher(
            teacher_model, task_config
        )
        
        # Step 2: 在学生模型上微调
        student_model = "Qwen/Qwen2.5-72B"  # 开源模型
        fine_tuned = self.finetune(
            student_model, training_data
        )
        
        # Step 3: 内置评估
        evaluation = self.evaluate(fine_tuned, task_config)
        
        # Step 4: 部署（蒸馏约需1天完成）
        if evaluation.passes_threshold:
            return self.deploy(fine_tuned)
        
        return self.retry_with_adjustments(task_config)

def should_use_gisting(system_prompt, request_volume, prompt_change_frequency):
    """
    决策矩阵：是否应该采用Gisting
    """
    token_count = count_tokens(system_prompt)
    
    # 条件1: 提示词足够长才有压缩价值
    if token_count < 1000:
        return False, "提示词太短，压缩收益有限"
    
    # 条件2: 请求量足够大才能摊销训练成本
    if request_volume < 100_000:  # 月请求量
        return False, "请求量不足以摊销gist训练成本"
    
    # 条件3: 提示词变化频率低
    if prompt_change_frequency > 1:  # 每周变化超过1次
        return False, "提示词变化频繁，需频繁重新训练"
    
    # 条件4: 内容以静态结构化信息为主
    static_ratio = measure_static_content(system_prompt)
    if static_ratio < 0.6:
        return False, "动态内容比例过高"
    
    return True, "满足Gisting适用条件"