潍坊网站建设客服长沙百度推广开户

代码

from pocketflow import Node, Flowfrom openai import OpenAIdef call_llm(messages):client = OpenAI(api_key = "your api key",base_url="https://dashscope.aliyuncs.com/compatible-mode/v1")response = client.chat.completions.create(model="qwen-turbo",messages=messages,temperature=0.7)return response.choices[0].message.contentclass UserInputNode(Node):def prep(self, shared):# Initialize messages if this is the first runif "messages" not in shared:shared["messages"] = []print("Welcome to the Travel Advisor Chat! Type 'exit' to end the conversation.")return Nonedef exec(self, _):# Get user inputuser_input = input("\nYou: ")return user_inputdef post(self, shared, prep_res, exec_res):user_input = exec_res# Check if user wants to exitif user_input and user_input.lower() == 'exit':print("\nGoodbye! Safe travels!")return None  # End the conversation# Store user input in sharedshared["user_input"] = user_input# Move to guardrail validationreturn "validate"class GuardrailNode(Node):def prep(self, shared):# Get the user input from shared datauser_input = shared.get("user_input", "")return user_inputdef exec(self, user_input):# Basic validation checksif not user_input or user_input.strip() == "":return False, "Your query is empty. Please provide a travel-related question."if len(user_input.strip()) < 3:return False, "Your query is too short. Please provide more details about your travel question."# LLM-based validation for travel topicsprompt = f"""
Evaluate if the following user query is related to travel advice, destinations, planning, or other travel topics.
The chat should ONLY answer travel-related questions and reject any off-topic, harmful, or inappropriate queries.
User query: {user_input}
Return your evaluation in YAML format:
```yaml
valid: true/false
reason: [Explain why the query is valid or invalid]
```"""# Call LLM with the validation promptmessages = [{"role": "user", "content": prompt}]response = call_llm(messages)# Extract YAML contentyaml_content = response.split("```yaml")[1].split("```")[0].strip() if "```yaml" in response else responseimport yamlresult = yaml.safe_load(yaml_content)assert result is not None, "Error: Invalid YAML format"assert "valid" in result and "reason" in result, "Error: Invalid YAML format"is_valid = result.get("valid", False)reason = result.get("reason", "Missing reason in YAML response")return is_valid, reasondef post(self, shared, prep_res, exec_res):is_valid, message = exec_resif not is_valid:# Display error message to userprint(f"\nTravel Advisor: {message}")# Skip LLM call and go back to user inputreturn "retry"# Valid input, add to message historyshared["messages"].append({"role": "user", "content": shared["user_input"]})# Proceed to LLM processingreturn "process"class LLMNode(Node):def prep(self, shared):# Add system message if not presentif not any(msg.get("role") == "system" for msg in shared["messages"]):shared["messages"].insert(0, {"role": "system", "content": "You are a helpful travel advisor that provides information about destinations, travel planning, accommodations, transportation, activities, and other travel-related topics. Only respond to travel-related queries and keep responses informative and friendly. Your response are concise in 100 words."})# Return all messages for the LLMreturn shared["messages"]def exec(self, messages):# Call LLM with the entire conversation historyresponse = call_llm(messages)return responsedef post(self, shared, prep_res, exec_res):# Print the assistant's responseprint(f"\nTravel Advisor: {exec_res}")# Add assistant message to historyshared["messages"].append({"role": "assistant", "content": exec_res})# Loop back to continue the conversationreturn "continue"# Create the flow with nodes and connections
user_input_node = UserInputNode()
guardrail_node = GuardrailNode()
llm_node = LLMNode()# Create flow connections
user_input_node - "validate" >> guardrail_node
guardrail_node - "retry" >> user_input_node  # Loop back if input is invalid
guardrail_node - "process" >> llm_node
llm_node - "continue" >> user_input_node     # Continue conversationflow = Flow(start=user_input_node)shared = {}
flow.run(shared)

代码解释

这个示例展示了如何使用PocketFlow框架实现一个带有guardrail（安全防护）机制的旅行顾问聊天机器人。整个系统由三个核心节点组成，通过条件流控制实现智能对话管理。

1. 系统架构

用户输入 → 安全验证 → LLM处理 → 用户输入↑         ↓←─────────┘(验证失败时重试)

2. 核心组件详解

2.1 LLM调用函数

def call_llm(messages):client = OpenAI(api_key = "your api key",base_url="https://dashscope.aliyuncs.com/compatible-mode/v1")

• 使用阿里云DashScope的兼容OpenAI API接口
• 配置qwen-turbo模型，temperature=0.7保证回答的创造性

2.2 UserInputNode（用户输入节点）

class UserInputNode(Node):def prep(self, shared):if "messages" not in shared:shared["messages"] = []

功能：

• prep：初始化对话历史，首次运行时显示欢迎信息
• exec：获取用户输入
• post：检查退出条件，将输入存储到共享状态，返回"validate"进入验证流程

2.3 GuardrailNode（安全防护节点）

这是系统的核心安全组件，实现多层验证：

基础验证：

if not user_input or user_input.strip() == "":return False, "Your query is empty..."
if len(user_input.strip()) < 3:return False, "Your query is too short..."

LLM智能验证：

prompt = f"""
Evaluate if the following user query is related to travel advice...
Return your evaluation in YAML format:
```yaml
valid: true/false
reason: [Explain why the query is valid or invalid]
```"""

验证流程：

1. 基础格式检查（空输入、长度）
2. 使用LLM判断是否为旅行相关话题
3. 解析YAML格式的验证结果
4. 根据验证结果决定流向：
   • 失败：返回"retry"重新输入
   • 成功：返回"process"进入LLM处理

2.4 LLMNode（LLM处理节点）

def prep(self, shared):if not any(msg.get("role") == "system" for msg in shared["messages"]):shared["messages"].insert(0, {"role": "system", "content": "You are a helpful travel advisor..."})

功能：

• prep：确保系统提示词存在，定义AI助手角色
• exec：调用LLM生成回答
• post：显示回答，更新对话历史，返回"continue"继续对话

3. 流程控制机制

user_input_node - "validate" >> guardrail_node
guardrail_node - "retry" >> user_input_node
guardrail_node - "process" >> llm_node
llm_node - "continue" >> user_input_node

流程说明：

1. 正常流程：用户输入 → 验证通过 → LLM处理 → 继续对话
2. 安全拦截：用户输入 → 验证失败 → 重新输入
3. 退出机制：用户输入"exit" → 程序结束

示例运行