Prompt Engineering Guidance 使用示例

# 生产级示例 使用 Guidance 进行结构化生成、智能体和工作流的真实世界示例。 ## 目录 - JSON 生成 - 数据提取 - 分类系统 - 智能体系统 - 多步工作流 - 代码生成 - 生产建议 ## JSON 生成 ### 基础 JSON python from guidance import models, gen, guidance @guidance def generate_user(lm): """生成有效的用户 JSON。""" lm += "{n" lm += ' "name": ' + gen("name", regex=r'"[A-Za-z ]+"') + ",n" lm += ' "age": ' + gen("age", regex=r"[0-9]+") + ",n" lm += ' "email": ' + gen( "email", regex=r'"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}"' ) + "n" lm += "}" return lm # 使用它 lm = models.Anthropic("claude-sonnet-4-5-20250929") lm += "生成用户资料：n" lm = generate_user(lm) print(lm) # 输出：保证有效的 JSON ### 嵌套 JSON python @guidance def generate_order(lm): """生成嵌套的订单 JSON。""" lm += "{n" # 客户信息 lm += ' "customer": {n' lm += ' "name": ' + gen("customer_name", regex=r'"[A-Za-z ]+"') + ",n" lm += ' "email": ' + gen( "customer_email", regex=r'"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}"' ) + "n" lm += ' },n' # 订单详情 lm += ' "order": {n' lm += ' "id": ' + gen("order_id", regex=r'"ORD-[0-9]{6}"') + ",n" lm += ' "date": ' + gen("order_date", regex=r'"d{4}-d{2}-d{2}"') + ",n" lm += ' "total": ' + gen("order_total", regex=r"[0-9]+.[0-9]{2}") + "n" lm += ' },n' # 状态 lm += ' "status": ' + gen( "status", regex=r'"(pending|processing|shipped|delivered)"' ) + "n" lm += "}" return lm lm = models.Anthropic("claude-sonnet-4-5-20250929") lm = generate_order(lm) ### JSON 数组 python @guidance def generate_user_list(lm, count=3): """生成用户 JSON 数组。""" lm += "[n" for i in range(count): lm += " {n" lm += ' "id": ' + gen(f"id_{i}", regex=r"[0-9]+") + ",n" lm += ' "name": ' + gen(f"name_{i}", regex=r'"[A-Za-z ]+"') + ",n" lm += ' "active": ' + gen(f"active_{i}", regex=r"(true|false)") + "n" lm += " }" if i < count - 1: lm += "," lm += "n" lm += "]" return lm lm = models.Anthropic("claude-sonnet-4-5-20250929") lm = generate_user_list(lm, count=5) ### 动态 JSON 模式