掌握Ragbits中的Prompt发现模式：从基础到自定义的全流程指南
项目地址: https://gitcode.com/GitHub_Trending/ra/ragbits 引言：告别Prompt开发的混沌时代
你是否还在为GenAI应用中的Prompt工程（提示工程）而烦恼？面对复杂的业务需求，通用Prompt模板总是显得力不从心，而定制化开发又常常陷入重复劳动的困境。Ragbits项目的Prompt发现模式（Prompt Discovery Pattern）为这一痛点提供了优雅的解决方案。本文将深入解析Ragbits中Prompt发现模式的自定义功能，帮助你构建灵活、可复用且高度适配业务需求的提示模板系统。
读完本文后，你将能够：
- 理解Ragbits中Prompt发现模式的核心原理与优势
- 掌握自定义Prompt模板的完整流程与最佳实践
- 学会使用高级特性如Few-Shot学习、多模态输入优化提示效果
- 通过实际案例掌握复杂业务场景下的Prompt设计技巧
Prompt发现模式：Ragbits的核心创新
什么是Prompt发现模式？
Prompt发现模式（Prompt Discovery Pattern）是Ragbits项目提出的一种声明式提示工程方法论，它通过结构化定义输入输出格式、系统指令和示例案例，使LLM（大语言模型）能够自动"发现"并应用最佳提示策略。这种模式将传统的临时性Prompt字符串转化为可维护、可复用的代码组件，极大提升了GenAI应用的开发效率和可靠性。
核心优势解析
| 特性 | 传统Prompt方式 | Ragbits Prompt发现模式 |
|---|---|---|
| 代码组织 | 字符串拼接，难以维护 | 类定义与类型注解，结构化管理 |
| 类型安全 | 无类型检查，运行时错误 | Pydantic模型验证，编译期错误捕获 |
| 复用性 | 复制粘贴，冗余严重 | 类继承与组合，模块化复用 |
| 可测试性 | 难以单元测试 | 输入输出明确，易于编写测试用例 |
| 扩展性 | 修改需查找所有字符串 | 继承重写，开闭原则设计 |
技术架构概览
自定义Prompt的完整指南
基础实现：三步创建你的第一个自定义Prompt
步骤1：定义输入输出模型
使用Pydantic模型定义输入输出数据结构，享受类型检查和自动验证带来的安全性：
from pydantic import BaseModel
class ProductDescriptionInput(BaseModel):
"""
产品描述生成器的输入参数
"""
product_name: str
features: list[str]
target_audience: str = "general"
tone: str = "professional"
class ProductDescriptionOutput(BaseModel):
"""
产品描述生成器的输出结果
"""
short_description: str # 50字以内的简短描述
detailed_description: str # 200-300字的详细说明
key_points: list[str] # 3-5个核心卖点
步骤2：实现Prompt类
继承Prompt基类，定义系统提示和用户提示模板：
from ragbits.core.prompt import Prompt
class ProductDescriptionPrompt(Prompt[ProductDescriptionInput, ProductDescriptionOutput]):
"""
电商产品描述生成Prompt，根据产品名称和特性生成吸引人的销售文案
"""
system_prompt = """
You are an expert e-commerce copywriter specializing in product descriptions.
Your task is to create compelling product descriptions that highlight key features
while appealing to the specified target audience.
Guidelines:
1. Use language appropriate for the target audience
2. Emphasize benefits, not just features
3. Keep short_description under 50 characters
4. detailed_description should be 200-300 words
5. key_points should be 3-5 bullet points focusing on unique selling points
"""
user_prompt = """
Product Name: {{ product_name }}
Features: {{ features | join(', ') }}
Target Audience: {{ target_audience }}
Tone: {{ tone }}
"""
步骤3：使用自定义Prompt
实例化Prompt并结合LLM生成结果：
import asyncio
from ragbits.core.llms import LiteLLM
async def main():
# 配置LLM
llm = LiteLLM(
model_name="gpt-4o-2024-08-06",
use_structured_output=True
)
# 创建输入数据
input_data = ProductDescriptionInput(
product_name="Smart Fitness Tracker",
features=[
"24/7 heart rate monitoring",
"14-day battery life",
"Waterproof up to 50m",
"Sleep quality analysis",
"100+ exercise modes"
],
target_audience="fitness enthusiasts",
tone="energetic and motivational"
)
# 生成产品描述
prompt = ProductDescriptionPrompt(input_data)
result = await llm.generate(prompt)
print("Short Description:", result.short_description)
print("Detailed Description:", result.detailed_description)
print("Key Points:", result.key_points)
if __name__ == "__main__":
asyncio.run(main())
高级特性：Few-Shot学习与示例优化
Few-Shot学习原理
Few-Shot学习是一种通过提供少量高质量示例来引导模型生成特定风格或格式输出的技术。在Ragbits中，这一功能通过few_shots类属性实现，使模型能够快速"学习"新任务而无需大量标注数据。
实现带Few-Shot示例的Prompt
class MarketingSloganPrompt(Prompt[MarketingSloganInput, MarketingSloganOutput]):
"""
营销口号生成Prompt，根据产品特性和品牌调性生成吸引人的标语
"""
system_prompt = """
You are a world-class marketing copywriter specializing in catchy slogans.
Generate 5 memorable slogans that capture the essence of the product
and appeal to the target demographic.
"""
user_prompt = """
Product: {{ product_name }}
Key Features: {{ features | join(', ') }}
Brand Tone: {{ tone }}
Target Audience: {{ audience }}
"""
few_shots = [
(
MarketingSloganInput(
product_name="EcoWater Bottle",
features=["BPA-free", "Double-wall insulation", "1L capacity", "Recyclable materials"],
tone="environmentally conscious",
audience="millennial professionals"
),
MarketingSloganOutput(
slogans=[
"Hydrate sustainably, live consciously.",
"Every sip, a step towards a greener planet.",
"EcoWater: Where convenience meets conservation.",
"Your daily hydration, reimagined for the Earth.",
"Drink clean, live green with EcoWater."
]
)
),
(
MarketingSloganInput(
product_name="PowerFit Pro",
features=["24-hour battery", "Heart rate monitor", "GPS tracking", "50+ workout modes"],
tone="energetic and empowering",
audience="fitness enthusiasts"
),
MarketingSloganOutput(
slogans=[
"PowerFit Pro: Track more, achieve more.",
"Your fitness journey, powered by precision.",
"Beyond limits, beyond goals with PowerFit Pro.",
"Every beat, every step, every goal – tracked.",
"Train smarter, not harder with PowerFit Pro."
]
)
)
]
Few-Shot示例设计最佳实践
- 质量优先于数量：3-5个高质量示例比20个普通示例效果更好
- 覆盖不同场景：示例应展示不同输入组合和输出变化
- 保持一致性：所有示例应遵循相同的风格和格式
- 渐进复杂度：从简单到复杂排列示例，帮助模型逐步理解任务
- 包含边缘情况：适当包含一些特殊情况，提高模型鲁棒性
多模态Prompt：整合文本与图像输入
Ragbits支持处理图像等非文本输入，通过Attachment类实现多模态提示功能：
from ragbits.core.prompt import Attachment
class SocialMediaCaptionPrompt(Prompt[SocialMediaCaptionInput, SocialMediaCaptionOutput]):
"""
社交媒体图片配文生成Prompt，根据图片内容和平台特性生成吸引人的配文
"""
system_prompt = """
You are a social media content creator specializing in engaging captions.
Analyze the provided image and generate platform-appropriate captions
that will maximize engagement (likes, comments, shares).
"""
user_prompt = """
Platform: {{ platform }}
Theme: {{ theme }}
Tone: {{ tone }}
Hashtag Count: {{ hashtag_count }}
"""
async def format(self, input: SocialMediaCaptionInput) -> str:
"""
重写format方法，添加图像处理逻辑
"""
formatted_prompt = super().format(input)
# 添加图像描述逻辑
if input.image.url.endswith(('.jpg', '.png', '.jpeg')):
formatted_prompt += "\n\nImage Description: Analyze the visual content and incorporate key elements into the caption."
return formatted_prompt
# 使用示例
async def generate_social_media_content():
llm = LiteLLM(model_name="gpt-4o-2024-08-06", use_structured_output=True)
# 本地图像或网络图像均可
image = Attachment(
url="/path/to/sunset_beach.jpg", # 本地文件路径
# 或使用网络URL: "https://example.com/sunset_beach.jpg"
media_type="image/jpeg"
)
prompt_input = SocialMediaCaptionInput(
platform="instagram",
theme="summer vacation",
tone="relaxed and inspiring",
hashtag_count=5,
image=image
)
prompt = SocialMediaCaptionPrompt(prompt_input)
result = await llm.generate(prompt)
return result
实战案例：构建复杂业务场景的Prompt
案例一：客户服务自动回复系统
需求分析
构建一个客户服务自动回复系统，能够理解客户问题类型，提取关键信息，并生成符合品牌语调的回复。系统需要处理产品咨询、订单查询、投诉处理等多种场景。
实现方案
from enum import Enum
class InquiryType(str, Enum):
PRODUCT_QUESTION = "product_question"
ORDER_STATUS = "order_status"
COMPLAINT = "complaint"
RETURN_REQUEST = "return_request"
GENERAL_INQUIRY = "general_inquiry"
class CustomerServiceInput(BaseModel):
customer_message: str
customer_name: str
order_id: Optional[str] = None
product_id: Optional[str] = None
inquiry_type: Optional[InquiryType] = None # 可选，让模型自动分类
class CustomerServiceOutput(BaseModel):
reply: str
suggested_action: str
inquiry_type: InquiryType
confidence_score: float # 0-1之间，表示模型对分类的置信度
class CustomerServicePrompt(Prompt[CustomerServiceInput, CustomerServiceOutput]):
"""
客户服务自动回复系统Prompt，生成专业、友好的客户回复
"""
system_prompt = """
You are a senior customer service representative for TechGear, a leading electronics retailer.
Your role is to provide helpful, professional, and friendly responses to customer inquiries.
Guidelines:
1. Address the customer by name in a friendly manner
2. If order_id is provided, reference it in your response
3. For complaints, apologize sincerely and offer a clear solution
4. Keep responses concise but thorough (50-150 words)
5. Only suggest actions that are within standard company policy
6. Calculate a confidence score (0-1) for your inquiry_type classification
Company Policy Reminders:
- Returns are accepted within 30 days of delivery
- Order status can be checked at techgear.com/track with order_id
- Technical support is available 24/7 at support@techgear.com
"""
few_shots = [
(
CustomerServiceInput(
customer_message="Hi, I ordered the XPS 15 laptop last week and haven't received a shipping update yet. Can you check on this?",
customer_name="Sarah Johnson",
order_id="ORD-78291",
product_id="XPS15-2023",
inquiry_type=None
),
CustomerServiceOutput(
reply="Dear Sarah, thank you for reaching out about your XPS 15 laptop order (ORD-78291). I've checked our system and see your order was shipped yesterday via Express Post. You should receive a tracking number via email shortly. If you don't see it within the hour, please check your spam folder.",
suggested_action="Provide tracking information once available",
inquiry_type=InquiryType.ORDER_STATUS,
confidence_score=0.95
)
),
(
CustomerServiceInput(
customer_message="The screen on my new tablet has a dead pixel right in the center. I just received it yesterday. What can be done about this?",
customer_name="Michael Chen",
order_id="ORD-77542",
product_id="TAB-PRO-10",
inquiry_type=None
),
CustomerServiceOutput(
reply="Dear Michael, I'm sorry to hear about the dead pixel on your new PRO-10 tablet. This is certainly not the quality we stand for. Since your purchase is within our 30-day return period, I'd be happy to arrange a replacement. Please reply with a photo of the dead pixel and we'll send a prepaid return label immediately.",
suggested_action="Initiate replacement process for defective tablet",
inquiry_type=InquiryType.COMPLAINT,
confidence_score=0.98
)
)
]
user_prompt = """
Customer Name: {{ customer_name }}
Order ID: {{ order_id or 'Not provided' }}
Product ID: {{ product_id or 'Not provided' }}
Customer Message: {{ customer_message }}
"""
案例二：市场调研报告生成器
需求分析
创建一个能够基于产品描述和市场数据生成结构化市场调研报告的系统，帮助产品经理快速了解市场定位和竞争格局。
实现方案
class MarketResearchInput(BaseModel):
product_description: str
target_market: str
competitors: list[str]
key_features: list[str]
price_range: tuple[float, float]
class CompetitorAnalysis(BaseModel):
competitor_name: str
strengths: list[str]
weaknesses: list[str]
market_position: str
price_point: str # "lower", "similar", "higher" compared to our product
class MarketResearchOutput(BaseModel):
executive_summary: str
target_audience_analysis: str
competitive_landscape: list[CompetitorAnalysis]
market_positioning_strategy: str
key_differentiators: list[str]
potential_risks: list[str]
opportunities: list[str]
class MarketResearchPrompt(Prompt[MarketResearchInput, MarketResearchOutput]):
"""
市场调研报告生成Prompt，分析产品在市场中的定位和竞争优势
"""
system_prompt = """
You are a senior market research analyst with expertise in technology products.
Your task is to generate a comprehensive market research report based on the provided
product information and competitive landscape.
Structure your report to include all sections specified in the output format,
with each section containing insightful analysis rather than just restating facts.
For competitive analysis, focus on meaningful differences rather than trivial features.
Provide actionable recommendations for market positioning and differentiation.
"""
user_prompt = """
Product Description: {{ product_description }}
Target Market: {{ target_market }}
Key Features:
{% for feature in key_features %}
- {{ feature }}
{% endfor %}
Price Range: ${{ price_range[0] }} - ${{ price_range[1] }}
Competitors: {{ competitors | join(', ') }}
"""
# 此处可添加行业特定的few_shots示例...
最佳实践与性能优化
输入输出模型设计技巧
- 最小完备原则：只包含必要的字段，避免信息过载
- 合理使用嵌套模型：复杂结构使用嵌套Pydantic模型而非长扁平结构
- 适当使用默认值：为非关键参数提供合理默认值，减少用户输入负担
- 添加详细文档：为每个字段添加描述，既帮助用户也指导模型理解
class AdvancedProductInput(BaseModel):
"""
高级产品描述生成输入模型
包含SEO优化和多语言支持的高级参数
"""
product_name: str
"""产品名称，将作为描述的核心关键词"""
features: list[str]
"""产品特性列表，每个特性1-2个简短短语"""
target_audience: str
"""目标受众描述，如"年轻专业人士"或"家庭用户" """
seo_keywords: list[str] = Field(default_factory=list, max_items=5)
"""SEO关键词列表，最多5个，将自然融入描述中"""
language: Literal["en", "es", "fr", "de"] = "en"
"""输出语言代码，默认英语"""
tone: Literal["professional", "casual", "technical", "marketing"] = "marketing"
"""描述语气风格，默认营销风格"""
max_length: int = Field(500, ge=100, le=1000)
"""描述最大长度，100-1000字之间"""
提示模板优化策略
-
系统提示分层设计：
- 第一层：角色定义（"你是..."）
- 第二层：核心任务描述
- 第三层：详细指南和约束条件
- 第四层：格式要求和输出规范
-
用户提示简洁原则：
- 使用清晰的标题和列表
- 避免冗余信息
- 关键信息使用强调格式
- 适当使用分隔线组织内容
-
条件逻辑运用：
system_prompt = """ You are a content moderator for a social media platform. {% if content_type == 'comment' %} Comments should be reviewed for personal attacks and hate speech. {% elif content_type == 'post' %} Posts require additional checks for misinformation and copyright issues. {% else %} Apply general community guidelines to all other content types. {% endif %} {% if is_premium_user %} Premium users have slightly more leniency for controversial content. {% endif %} """
性能优化与资源管理
- 提示缓存策略：对于频繁使用相同输入的场景，缓存格式化后的提示字符串
- 动态示例选择：根据输入特征动态选择最相关的few-shot示例，减少提示长度
- 渐进式提示生成：复杂任务拆分为多步提示，而非一次性生成
- 模型选择优化：简单任务使用轻量级模型，复杂任务使用能力更强的模型
class OptimizedPrompt(Prompt[InputModel, OutputModel]):
# 实现动态示例选择
def get_relevant_few_shots(self, input: InputModel) -> list[tuple[InputModel, OutputModel]]:
"""根据输入特征选择最相关的示例"""
if input.complexity == "high":
return self.few_shots[:3] # 复杂示例
elif input.industry == "healthcare":
return [s for s in self.few_shots if "healthcare" in s[0].industry]
else:
return self.few_shots[-2:] # 基础示例
结论与未来展望
Ragbits的Prompt发现模式彻底改变了GenAI应用的开发方式，通过将Prompt工程转化为结构化的代码开发，极大提升了开发效率和系统可靠性。本文详细介绍了自定义Prompt的完整流程，从基础实现到高级特性，再到实战案例和最佳实践，为开发者提供了全面的指导。
随着大语言模型技术的不断发展，Prompt发现模式也将继续演进。未来可能的发展方向包括：
- 自动化Prompt优化：基于反馈数据自动调整提示策略
- 多模型协作：不同专业领域的Prompt模型协同工作
- 智能示例管理：自动生成、筛选和更新few-shot示例
- 跨模态理解增强：更深入地整合图像、音频等多模态输入
掌握Prompt发现模式不仅是提升当前项目开发效率的实用技能，更是未来GenAI应用开发的基础能力。通过本文介绍的方法和技巧，你可以构建出更强大、更灵活且更可靠的生成式AI系统，为用户提供卓越的智能体验。
附录：快速参考指南
Prompt类核心属性
| 属性 | 类型 | 描述 |
|---|---|---|
| system_prompt | str | 系统指令，定义模型角色和行为准则 |
| user_prompt | str | 用户提示模板，使用Jinja2语法 |
| few_shots | list[tuple[Input, Output]] | 示例案例列表，用于Few-Shot学习 |
| input_model | Type[Input] | 输入数据模型类（自动推断） |
| output_model | Type[Output] | 输出数据模型类（自动推断） |
常用Jinja2模板过滤器
| 过滤器 | 用途 | 示例 | 输出 |
|---|---|---|---|
| join | 列表转字符串 | {{ features | join(', ') }} | "特性1, 特性2, 特性3" |
| capitalize | 首字母大写 | {{ name | capitalize }} | "John" |
| default | 提供默认值 | {{ value | default('N/A') }} | 如value为None则输出"N/A" |
| length | 获取长度 | {{ items | length }} | 5 |
| tojson | 转为JSON字符串 | {{ data | tojson }} | '{"key": "value"}' |
开发与调试工具
- Prompt测试工具：使用
ragbits-cli prompt test命令测试单个Prompt - 示例生成器：
ragbits-cli prompt generate-examples自动生成few-shot示例 - 性能分析器：
ragbits-cli prompt profile分析提示长度和执行时间 - 格式验证器：
ragbits-cli prompt validate验证模板语法和模型定义
要开始使用这些工具，请运行：
uv run ragbits-cli prompt --help
创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
