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| """
聊天提示词模板
演示：如何使用ChatPromptTemplate处理多角色对话
"""
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage, AIMessage, SystemMessage
from dotenv import load_dotenv
import os
load_dotenv()
def example1_basic_chat_template() -> None:
"""示例1：基础聊天模板"""
print("=== 示例1：基础聊天模板 ===\n")
chat_template = ChatPromptTemplate.from_messages([
("system", "你是一个{role}，你的特点是{characteristic}"),
("human", "{user_input}")
])
messages = chat_template.format_messages(
role="幽默的助手",
characteristic="说话风趣，喜欢用比喻",
user_input="什么是云计算？"
)
print("生成的消息：")
for msg in messages:
print(f"{msg.__class__.__name__}: {msg.content}")
print()
def example2_chat_with_llm() -> None:
"""示例2：聊天模板配合LLM"""
print("=== 示例2：聊天模板配合LLM ===\n")
llm = ChatOpenAI(
base_url=os.getenv("ALIBABA_BASE_URL"),
api_key=os.getenv("ALIBABA_API_KEY"),
model="qwen-plus",
temperature=0.7,
)
customer_service_template = ChatPromptTemplate.from_messages([
("system", """
你是一个专业的客服人员，工作在{company}公司。
你的职责是：
1. 友好地回答客户问题
2. 提供准确的信息
3. 保持礼貌和耐心
4. 回答要简洁明了
"""),
("human", "客户问题：{question}")
])
questions = [
"你们的退货政策是什么？",
"订单什么时候能发货？",
"如何联系人工客服？"
]
for question in questions:
messages = customer_service_template.format_messages(
company="科技商城",
question=question
)
response = llm.invoke(messages)
print(f"客户：{question}")
print(f"客服：{response.content}\n")
def example3_multi_turn_conversation() -> None:
"""示例3：多轮对话模板"""
print("=== 示例3：多轮对话 ===\n")
llm = ChatOpenAI(
base_url=os.getenv("ALIBABA_BASE_URL"),
api_key=os.getenv("ALIBABA_API_KEY"),
model="qwen-plus",
temperature=0.7,
)
chat_template = ChatPromptTemplate.from_messages([
("system", "你是一个Python编程导师，擅长解释编程概念"),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{user_input}")
])
chat_history = []
user_input1 = "什么是列表推导式？"
messages = chat_template.format_messages(
chat_history=chat_history,
user_input=user_input1
)
response1 = llm.invoke(messages)
print(f"学生：{user_input1}")
print(f"导师：{response1.content}\n")
chat_history.append(HumanMessage(content=user_input1))
chat_history.append(AIMessage(content=response1.content))
user_input2 = "能给我举个例子吗？"
messages = chat_template.format_messages(
chat_history=chat_history,
user_input=user_input2
)
response2 = llm.invoke(messages)
print(f"学生：{user_input2}")
print(f"导师：{response2.content}\n")
def example4_role_based_template() -> None:
"""示例4：基于角色的模板"""
print("=== 示例4：不同角色的对话 ===\n")
llm = ChatOpenAI(
base_url=os.getenv("ALIBABA_BASE_URL"),
api_key=os.getenv("ALIBABA_API_KEY"),
model="qwen-plus",
temperature=0.9,
)
roleplay_template = ChatPromptTemplate.from_messages([
("system", "你现在扮演{character}，请完全进入角色"),
("human", "场景：{scenario}"),
("human", "{action}")
])
scenarios = [
{
"character": "一个侦探",
"scenario": "在案发现场调查",
"action": "发现了一个可疑的脚印，你会怎么做？"
},
{
"character": "一位厨师",
"scenario": "在厨房准备晚餐",
"action": "客人突然说对海鲜过敏，你会怎么调整菜单？"
}
]
for scenario in scenarios:
messages = roleplay_template.format_messages(**scenario)
response = llm.invoke(messages)
print(f"角色：{scenario['character']}")
print(f"场景：{scenario['scenario']}")
print(f"情况：{scenario['action']}")
print(f"回应：{response.content}\n")
def example5_template_composition() -> None:
"""示例5：模板组合"""
print("=== 示例5：组合多个模板 ===\n")
system_template = "你是一个{profession}，专长是{specialty}"
task_template = """
任务类型：{task_type}
具体要求：{requirements}
"""
full_template = ChatPromptTemplate.from_messages([
("system", system_template),
("human", task_template)
])
messages = full_template.format_messages(
profession="数据分析师",
specialty="用数据讲故事",
task_type="分析销售数据",
requirements="找出销售下降的原因，用简单的语言解释"
)
print("组合后的消息：")
for msg in messages:
print(f"\n{msg.__class__.__name__}:")
print(msg.content)
def main() -> None:
"""主函数"""
example1_basic_chat_template()
print("\n" + "="*50 + "\n")
example2_chat_with_llm()
print("\n" + "="*50 + "\n")
example3_multi_turn_conversation()
print("\n" + "="*50 + "\n")
example4_role_based_template()
print("\n" + "="*50 + "\n")
example5_template_composition()
if __name__ == "__main__":
main()
|