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指南

使用正则表达式和文法控制 LLM 输出，保证有效的 JSON/XML/代码生成，强制执行结构化格式，并利用 Guidance（微软研究院的约束生成框架）构建多步骤工作流

技能元数据​

来源可选 — 使用 hermes skills install official/mlops/guidance 安装
路径optional-skills/mlops/guidance
版本1.0.0
作者Orchestra Research
许可证MIT
依赖项guidance, transformers
标签Prompt Engineering, Guidance, Constrained Generation, Structured Output, JSON Validation, Grammar, Microsoft Research, Format Enforcement, Multi-Step Workflows

参考：完整 SKILL.md​

信息

以下是 Hermes 在触发此技能时加载的完整技能定义。这是技能激活时代理看到的指令。

Guidance：约束性 LLM 生成

何时使用此技能​

当您需要执行以下操作时，请使用 Guidance：

  • 使用正则表达式或文法控制 LLM 输出语法
  • 保证生成有效的 JSON/XML/代码
  • 降低延迟（相较于传统提示方法）
  • 强制执行结构化格式（日期、电子邮件、ID 等）
  • 使用 Pythonic 控制流构建多步骤工作流
  • 通过文法约束防止无效输出

GitHub Stars: 18,000+ | 来源: Microsoft Research

安装​

# Base installation
pip install guidance

# With specific backends
pip install guidance[transformers]  # Hugging Face models
pip install guidance[llama_cpp]     # llama.cpp models

快速入门​

基本示例：结构化生成​

from guidance import models, gen

# Load model (supports OpenAI, Transformers, llama.cpp)
lm = models.OpenAI("gpt-4")

# Generate with constraints
result = lm + "The capital of France is " + gen("capital", max_tokens=5)

print(result["capital"])  # "Paris"

配合 Anthropic Claude 使用​

from guidance import models, gen, system, user, assistant

# Configure Claude
lm = models.Anthropic("claude-sonnet-4-5-20250929")

# Use context managers for chat format
with system():
    lm += "You are a helpful assistant."

with user():
    lm += "What is the capital of France?"

with assistant():
    lm += gen(max_tokens=20)

核心概念​

1. 上下文管理器​

Guidance 使用 Pythonic 上下文管理器进行聊天式交互。

from guidance import system, user, assistant, gen

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# System message
with system():
    lm += "You are a JSON generation expert."

# User message
with user():
    lm += "Generate a person object with name and age."

# Assistant response
with assistant():
    lm += gen("response", max_tokens=100)

print(lm["response"])

优势：

  • 自然的聊天流程
  • 清晰的角色分离
  • 易于阅读和维护

2. 约束生成​

Guidance 确保输出符合使用正则表达式或文法指定的模式。

正则表达式约束​

from guidance import models, gen

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# Constrain to valid email format
lm += "Email: " + gen("email", regex=r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")

# Constrain to date format (YYYY-MM-DD)
lm += "Date: " + gen("date", regex=r"\d{4}-\d{2}-\d{2}")

# Constrain to phone number
lm += "Phone: " + gen("phone", regex=r"\d{3}-\d{3}-\d{4}")

print(lm["email"])  # Guaranteed valid email
print(lm["date"])   # Guaranteed YYYY-MM-DD format

工作原理：

  • 正则表达式在 token 级别转换为文法
  • 在生成过程中过滤无效 token
  • 模型只能生成匹配的输出

选择约束​

from guidance import models, gen, select

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# Constrain to specific choices
lm += "Sentiment: " + select(["positive", "negative", "neutral"], name="sentiment")

# Multiple-choice selection
lm += "Best answer: " + select(
    ["A) Paris", "B) London", "C) Berlin", "D) Madrid"],
    name="answer"
)

print(lm["sentiment"])  # One of: positive, negative, neutral
print(lm["answer"])     # One of: A, B, C, or D

3. Token 修复 (Token Healing)​

Guidance 自动“修复”提示词和生成内容之间的 token 边界。

问题： Tokenization 会产生不自然的边界。

# Without token healing
prompt = "The capital of France is "
# Last token: " is "
# First generated token might be " Par" (with leading space)
# Result: "The capital of France is  Paris" (double space!)

解决方案： Guidance 回退一个 token 并重新生成。

from guidance import models, gen

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# Token healing enabled by default
lm += "The capital of France is " + gen("capital", max_tokens=5)
# Result: "The capital of France is Paris" (correct spacing)

优势：

  • 自然的文本边界
  • 无尴尬的空格问题
  • 更好的模型性能（看到自然的 token 序列）

4. 基于文法的生成​

使用上下文无关文法定义复杂结构。

from guidance import models, gen

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# JSON grammar (simplified)
json_grammar = """
{
    "name": <gen name regex="[A-Za-z ]+" max_tokens=20>,
    "age": <gen age regex="[0-9]+" max_tokens=3>,
    "email": <gen email regex="[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}" max_tokens=50>
}
"""

# Generate valid JSON
lm += gen("person", grammar=json_grammar)

print(lm["person"])  # Guaranteed valid JSON structure

用例：

  • 复杂结构化输出
  • 嵌套数据结构
  • 编程语言语法
  • 领域特定语言

5. Guidance 函数​

使用 @guidance 装饰器创建可重用的生成模式。

from guidance import guidance, gen, models

@guidance
def generate_person(lm):
    """Generate a person with name and age."""
    lm += "Name: " + gen("name", max_tokens=20, stop="\n")
    lm += "\nAge: " + gen("age", regex=r"[0-9]+", max_tokens=3)
    return lm

# Use the function
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = generate_person(lm)

print(lm["name"])
print(lm["age"])

有状态函数：

@guidance(stateless=False)
def react_agent(lm, question, tools, max_rounds=5):
    """ReAct agent with tool use."""
    lm += f"Question: {question}\n\n"

    for i in range(max_rounds):
        # Thought
        lm += f"Thought {i+1}: " + gen("thought", stop="\n")

        # Action
        lm += "\nAction: " + select(list(tools.keys()), name="action")

        # Execute tool
        tool_result = tools[lm["action"]]()
        lm += f"\nObservation: {tool_result}\n\n"

        # Check if done
        lm += "Done? " + select(["Yes", "No"], name="done")
        if lm["done"] == "Yes":
            break

    # Final answer
    lm += "\nFinal Answer: " + gen("answer", max_tokens=100)
    return lm

后端配置​

Anthropic Claude​

from guidance import models

lm = models.Anthropic(
    model="claude-sonnet-4-5-20250929",
    api_key="your-api-key"  # Or set ANTHROPIC_API_KEY env var
)

OpenAI​

lm = models.OpenAI(
    model="gpt-4o-mini",
    api_key="your-api-key"  # Or set OPENAI_API_KEY env var
)

本地模型 (Transformers)​

from guidance.models import Transformers

lm = Transformers(
    "microsoft/Phi-4-mini-instruct",
    device="cuda"  # Or "cpu"
)

本地模型 (llama.cpp)​

from guidance.models import LlamaCpp

lm = LlamaCpp(
    model_path="/path/to/model.gguf",
    n_ctx=4096,
    n_gpu_layers=35
)

常见模式​

模式 1：JSON 生成​

from guidance import models, gen, system, user, assistant

lm = models.Anthropic("claude-sonnet-4-5-20250929")

with system():
    lm += "You generate valid JSON."

with user():
    lm += "Generate a user profile with name, age, and email."

with assistant():
    lm += """{
    "name": """ + gen("name", regex=r'"[A-Za-z ]+"', max_tokens=30) + """,
    "age": """ + gen("age", regex=r"[0-9]+", max_tokens=3) + """,
    "email": """ + gen("email", regex=r'"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"', max_tokens=50) + """
}"""

print(lm)  # Valid JSON guaranteed

模式 2：分类​

from guidance import models, gen, select

lm = models.Anthropic("claude-sonnet-4-5-20250929")

text = "This product is amazing! I love it."

lm += f"Text: {text}\n"
lm += "Sentiment: " + select(["positive", "negative", "neutral"], name="sentiment")
lm += "\nConfidence: " + gen("confidence", regex=r"[0-9]+", max_tokens=3) + "%"

print(f"Sentiment: {lm['sentiment']}")
print(f"Confidence: {lm['confidence']}%")

模式 3：多步推理​

from guidance import models, gen, guidance

@guidance
def chain_of_thought(lm, question):
    """Generate answer with step-by-step reasoning."""
    lm += f"Question: {question}\n\n"

    # Generate multiple reasoning steps
    for i in range(3):
        lm += f"Step {i+1}: " + gen(f"step_{i+1}", stop="\n", max_tokens=100) + "\n"

    # Final answer
    lm += "\nTherefore, the answer is: " + gen("answer", max_tokens=50)

    return lm

lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = chain_of_thought(lm, "What is 15% of 200?")

print(lm["answer"])

模式 4：ReAct Agent​

from guidance import models, gen, select, guidance

@guidance(stateless=False)
def react_agent(lm, question):
    """ReAct agent with tool use."""
    tools = {
        "calculator": lambda expr: eval(expr),
        "search": lambda query: f"Search results for: {query}",
    }

    lm += f"Question: {question}\n\n"

    for round in range(5):
        # Thought
        lm += f"Thought: " + gen("thought", stop="\n") + "\n"

        # Action selection
        lm += "Action: " + select(["calculator", "search", "answer"], name="action")

        if lm["action"] == "answer":
            lm += "\nFinal Answer: " + gen("answer", max_tokens=100)
            break

        # Action input
        lm += "\nAction Input: " + gen("action_input", stop="\n") + "\n"

        # Execute tool
        if lm["action"] in tools:
            result = tools[lm["action"]](lm["action_input"])
            lm += f"Observation: {result}\n\n"

    return lm

lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = react_agent(lm, "What is 25 * 4 + 10?")
print(lm["answer"])

模式 5：数据提取​

from guidance import models, gen, guidance

@guidance
def extract_entities(lm, text):
    """Extract structured entities from text."""
    lm += f"Text: {text}\n\n"

    # Extract person
    lm += "Person: " + gen("person", stop="\n", max_tokens=30) + "\n"

    # Extract organization
    lm += "Organization: " + gen("organization", stop="\n", max_tokens=30) + "\n"

    # Extract date
    lm += "Date: " + gen("date", regex=r"\d{4}-\d{2}-\d{2}", max_tokens=10) + "\n"

    # Extract location
    lm += "Location: " + gen("location", stop="\n", max_tokens=30) + "\n"

    return lm

text = "Tim Cook announced at Apple Park on 2024-09-15 in Cupertino."

lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = extract_entities(lm, text)

print(f"Person: {lm['person']}")
print(f"Organization: {lm['organization']}")
print(f"Date: {lm['date']}")
print(f"Location: {lm['location']}")

最佳实践​

1. 使用正则表达式进行格式验证​

# ✅ Good: Regex ensures valid format
lm += "Email: " + gen("email", regex=r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")

# ❌ Bad: Free generation may produce invalid emails
lm += "Email: " + gen("email", max_tokens=50)

2. 使用 select() 处理固定类别​

# ✅ Good: Guaranteed valid category
lm += "Status: " + select(["pending", "approved", "rejected"], name="status")

# ❌ Bad: May generate typos or invalid values
lm += "Status: " + gen("status", max_tokens=20)

3. 利用 Token 修复​

# Token healing is enabled by default
# No special action needed - just concatenate naturally
lm += "The capital is " + gen("capital")  # Automatic healing

4. 使用 stop 序列​

# ✅ Good: Stop at newline for single-line outputs
lm += "Name: " + gen("name", stop="\n")

# ❌ Bad: May generate multiple lines
lm += "Name: " + gen("name", max_tokens=50)

5. 创建可重用函数​

# ✅ Good: Reusable pattern
@guidance
def generate_person(lm):
    lm += "Name: " + gen("name", stop="\n")
    lm += "\nAge: " + gen("age", regex=r"[0-9]+")
    return lm

# Use multiple times
lm = generate_person(lm)
lm += "\n\n"
lm = generate_person(lm)

6. 平衡约束​

# ✅ Good: Reasonable constraints
lm += gen("name", regex=r"[A-Za-z ]+", max_tokens=30)

# ❌ Too strict: May fail or be very slow
lm += gen("name", regex=r"^(John|Jane)$", max_tokens=10)

与替代方案比较​

特性GuidanceInstructorOutlinesLMQL
正则表达式约束✅ 是❌ 否✅ 是✅ 是
文法支持✅ CFG❌ 否✅ CFG✅ CFG
Pydantic 验证❌ 否✅ 是✅ 是❌ 否
Token 修复✅ 是❌ 否✅ 是❌ 否
本地模型✅ 是⚠️ 有限✅ 是✅ 是
API 模型✅ 是✅ 是⚠️ 有限✅ 是
Pythonic 语法✅ 是✅ 是✅ 是❌ 类 SQL
学习曲线低低中高

何时选择 Guidance：

  • 需要正则表达式/文法约束
  • 想要 token 修复功能
  • 构建带有控制流的复杂工作流
  • 使用本地模型（Transformers, llama.cpp）
  • 偏好 Pythonic 语法

何时选择替代方案：

  • Instructor：需要带有自动重试功能的 Pydantic 验证
  • Outlines：需要 JSON Schema 验证
  • LMQL：偏好声明式查询语法

性能特征​

延迟降低：

  • 对于受限输出，比传统提示快 30-50%
  • Token 修复减少不必要的重新生成
  • 语法约束防止生成无效 token

内存使用：

  • 与无约束生成相比，开销极小
  • 首次使用后缓存语法编译结果
  • 推理时高效过滤 token

Token 效率：

  • 防止在无效输出上浪费 token
  • 无需重试循环
  • 直接生成有效输出

资源​

另见​

  • references/constraints.md - 全面的正则表达式和语法模式
  • references/backends.md - 特定后端的配置
  • references/examples.md - 生产就绪示例