Initial commit: add LangGraph examples
This commit is contained in:
Eric Wang
23
.gitignore
vendored
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23
.gitignore
vendored
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# Environment variables and secrets
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.env
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.env.*
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!.env.example
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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.pytest_cache/
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.mypy_cache/
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.ruff_cache/
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# Virtual environments
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.venv/
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venv/
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env/
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# IDE and OS files
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.vscode/
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.idea/
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.DS_Store
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Thumbs.db
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115
basic_agent.py
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115
basic_agent.py
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"""
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Basic LangGraph Example - A simple conversational agent with state management.
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This demonstrates core LangGraph concepts:
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- State schemas
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- Nodes (functions that process state)
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- Edges (connections between nodes)
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- Conditional routing
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"""
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from typing import TypedDict, Literal, Annotated
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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# Define the state schema
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class State(TypedDict):
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"""State represents what flows through our graph."""
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messages: Annotated[list, add_messages]
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counter: int
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# Define node functions
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def greeting_node(state: State) -> dict:
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"""Process greeting messages."""
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print("Processing greeting...")
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return {"counter": state["counter"] + 1}
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def math_node(state: State) -> dict:
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"""Process math-related messages."""
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print("Processing math request...")
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return {"counter": state["counter"] + 1}
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def default_node(state: State) -> dict:
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"""Handle unknown/unrecognized messages."""
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print("Handling general message...")
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return {"counter": state["counter"] + 1}
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# Define the router function
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def route_message(state: State) -> Literal["greeting", "math", "default"]:
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"""Route to appropriate node based on message content."""
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messages = state["messages"]
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if not messages:
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return "default"
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last_message = messages[-1].lower() if isinstance(messages[-1], str) else str(messages[-1]).lower()
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if any(word in last_message for word in ["hello", "hi", "hey", "greetings"]):
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return "greeting"
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elif any(word in last_message for word in ["add", "subtract", "multiply", "divide", "calculate"]):
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return "math"
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else:
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return "default"
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# Build the graph
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def create_graph():
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"""Create and compile the LangGraph workflow."""
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# Initialize the graph builder
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builder = StateGraph(State)
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# Add nodes
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builder.add_node("greeting", greeting_node)
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builder.add_node("math", math_node)
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builder.add_node("default", default_node)
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# Add edges with conditional routing
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builder.add_conditional_edges(
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START,
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route_message,
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{
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"greeting": "greeting",
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"math": "math",
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"default": "default"
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}
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)
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# All nodes lead to END (or could chain to other nodes)
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builder.add_edge("greeting", END)
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builder.add_edge("math", END)
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builder.add_edge("default", END)
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# Compile the graph
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return builder.compile()
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def main():
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"""Run the basic LangGraph agent."""
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graph = create_graph()
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print("=== Basic LangGraph Agent ===")
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print("Try messages like: 'hello', 'add 5 and 3', or anything else")
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print()
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# Test cases
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test_messages = [
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"Hello there!",
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"Can you add 5 and 3?",
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"What's the weather like?"
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]
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for message in test_messages:
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print(f"Input: {message}")
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initial_state = {"messages": [message], "counter": 0}
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result = graph.invoke(initial_state)
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print(f"Output: Processed with counter = {result['counter']}")
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print()
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if __name__ == "__main__":
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main()
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231
chatopenai_structured.py
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chatopenai_structured.py
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"""
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LangGraph with ChatOpenAI and Structured Output Example.
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This demonstrates using LangGraph with ChatOpenAI's with_structured_output feature
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for type-safe structured JSON responses.
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"""
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from typing import TypedDict, Literal, List
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from langgraph.graph import StateGraph, START, END
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from langchain_openai import ChatOpenAI
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from pydantic import BaseModel, Field
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import os
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from dotenv import load_dotenv
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load_dotenv()
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# ============ Helper to create model ============
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def create_chat_model(model: str = "glm-4.5-air", temperature: float = 0.7):
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"""Create a ChatOpenAI model instance."""
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return ChatOpenAI(
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model=model,
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temperature=temperature,
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max_retries=2,
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://open.bigmodel.cn/api/paas/v4"),
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)
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# ============ Structured Output Schemas ============
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class SentimentAnalysis(BaseModel):
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"""Schema for sentiment analysis output."""
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sentiment: Literal["positive", "negative", "neutral"] = Field(
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description="Overall sentiment of the text"
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)
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confidence: float = Field(
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description="Confidence score between 0 and 1",
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ge=0,
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le=1
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)
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emotions: List[str] = Field(
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description="List of detected emotions"
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)
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keywords: List[str] = Field(
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description="Key words or phrases extracted"
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)
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analysis: str = Field(
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description="Detailed analysis explanation"
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)
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class EntityExtraction(BaseModel):
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"""Schema for entity extraction output."""
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persons: List[str] = Field(default=[], description="Person names mentioned")
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locations: List[str] = Field(default=[], description="Locations mentioned")
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organizations: List[str] = Field(default=[], description="Organizations mentioned")
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dates: List[str] = Field(default=[], description="Dates or time references")
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class CombinedAnalysis(BaseModel):
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"""Combined analysis result."""
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sentiment: SentimentAnalysis
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entities: EntityExtraction
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summary: str = Field(description="Brief summary of the input text")
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# ============ State Schema ============
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class State(TypedDict):
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"""State flows through the graph."""
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input_text: str
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sentiment_result: SentimentAnalysis | None
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entity_result: EntityExtraction | None
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final_summary: str
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# ============ Node Functions ============
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def analyze_sentiment(state: State) -> dict:
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"""Analyze sentiment using with_structured_output."""
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print(f"Analyzing sentiment for: '{state['input_text']}'...")
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# Create model with structured output
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model = create_chat_model().with_structured_output(SentimentAnalysis)
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prompt = f"""
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你是一个情感分析专家。请分析用户输入的情感。
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输入文本：{state['input_text']}
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请按照指定的 JSON Schema 返回分析结果，包括 sentiment, confidence, emotions, keywords 和 analysis。
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"""
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result: SentimentAnalysis = model.invoke(prompt)
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print(f" Sentiment: {result.sentiment}")
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print(f" Confidence: {result.confidence:.0%}")
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print(f" Emotions: {', '.join(result.emotions)}")
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return {"sentiment_result": result}
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def extract_entities(state: State) -> dict:
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"""Extract entities using with_structured_output."""
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print("Extracting entities...")
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# Create model with structured output
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model = create_chat_model().with_structured_output(EntityExtraction)
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prompt = f"""
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你是一个命名实体识别专家。请从文本中提取实体。
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输入文本：{state['input_text']}
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请提取 persons, locations, organizations, dates 等实体信息。
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"""
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result: EntityExtraction = model.invoke(prompt)
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print(f" Persons: {result.persons}")
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print(f" Locations: {result.locations}")
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print(f" Organizations: {result.organizations}")
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return {"entity_result": result}
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def generate_summary(state: State) -> dict:
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"""Generate a combined summary using with_structured_output."""
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print("Generating final summary...")
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sentiment = state.get("sentiment_result")
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entities = state.get("entity_result")
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# Create model with structured output
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model = create_chat_model().with_structured_output(CombinedAnalysis)
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prompt = f"""
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请对以下分析结果生成综合摘要：
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输入文本：{state['input_text']}
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情感分析结果：
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- 情感：{sentiment.sentiment if sentiment else 'N/A'}
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- 置信度：{sentiment.confidence if sentiment else 'N/A'}
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- 情绪：{sentiment.emotions if sentiment else 'N/A'}
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- 关键词：{sentiment.keywords if sentiment else 'N/A'}
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实体提取结果：
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- 人名：{entities.persons if entities else 'N/A'}
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- 地点：{entities.locations if entities else 'N/A'}
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- 组织：{entities.organizations if entities else 'N/A'}
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- 日期：{entities.dates if entities else 'N/A'}
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请生成一个简短的摘要。
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"""
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result: CombinedAnalysis = model.invoke(prompt)
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return {"final_summary": result.summary}
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# ============ Build Graph ============
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def create_analysis_graph():
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"""Create and compile the analysis workflow graph."""
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builder = StateGraph(State)
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# Add nodes
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builder.add_node("sentiment", analyze_sentiment)
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builder.add_node("entities", extract_entities)
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builder.add_node("summarize", generate_summary)
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# Add edges
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builder.add_edge(START, "sentiment")
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builder.add_edge("sentiment", "entities")
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builder.add_edge("entities", "summarize")
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builder.add_edge("summarize", END)
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return builder.compile()
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# ============ Main ============
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def main():
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"""Run the structured analysis agent."""
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graph = create_analysis_graph()
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print("=== LangGraph with ChatOpenAI Structured Output ===\n")
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# Test with a sample text
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test_text = "今天天气真好，我和朋友们一起去公园野餐，大家都很开心！"
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print(f"Input text: {test_text}\n")
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print("-" * 50)
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# Run the graph
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initial_state = {
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"input_text": test_text,
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"sentiment_result": None,
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"entity_result": None,
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"final_summary": ""
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}
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result = graph.invoke(initial_state)
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print("-" * 50)
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print("\n=== Final Summary ===")
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print(result["final_summary"])
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# Access structured pydantic models
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print("\n=== Structured Sentiment Data ===")
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sentiment: SentimentAnalysis = result["sentiment_result"]
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print(f" sentiment: {sentiment.sentiment}")
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print(f" confidence: {sentiment.confidence}")
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print(f" emotions: {sentiment.emotions}")
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print(f" keywords: {sentiment.keywords}")
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print(f" analysis: {sentiment.analysis}")
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print("\n=== Structured Entity Data ===")
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entities: EntityExtraction = result["entity_result"]
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print(f" persons: {entities.persons}")
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print(f" locations: {entities.locations}")
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print(f" organizations: {entities.organizations}")
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print(f" dates: {entities.dates}")
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if __name__ == "__main__":
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main()
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198
langgraph_chatopenai.py
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198
langgraph_chatopenai.py
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"""
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LangGraph Example with ChatOpenAI.
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A multi-turn conversational agent with memory and tool routing.
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"""
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from typing import TypedDict, List, Literal
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from langgraph.graph import StateGraph, START, END
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from langchain_openai import ChatOpenAI
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from pydantic import BaseModel, Field
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import os
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from dotenv import load_dotenv
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load_dotenv()
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# ============ Model Helper ============
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def create_chat_model(model: str = "openai/gpt-5.4", temperature: float = 0.7):
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"""Create a ChatOpenAI model instance."""
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return ChatOpenAI(
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model=model,
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temperature=temperature,
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max_retries=2,
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://api.qnaigc.com/v1"),
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)
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# ============ State Schema ============
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class Message(BaseModel):
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role: str
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content: str
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class State(TypedDict):
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messages: List[Message]
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category: str
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# ============ Router Function ============
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def categorize_message(state: State) -> Literal["greeting", "question", "fallback"]:
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"""Use LLM to categorize the user's message."""
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model = create_chat_model(temperature=0.1)
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last_message = state["messages"][-1].content if state["messages"] else ""
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prompt = f"""
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Categorize this message into ONE category: greeting, question, or fallback.
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Return ONLY the category name.
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Message: {last_message}
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"""
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response = model.invoke(prompt).content.strip().lower()
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if "greeting" in response:
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return "greeting"
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elif "question" in response:
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return "question"
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else:
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return "fallback"
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# ============ Node Functions ============
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def handle_greeting(state: State) -> dict:
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"""Handle greeting messages."""
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print(" [Node: Greeting]")
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model = create_chat_model()
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last_message = state["messages"][-1].content
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prompt = f"""
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Respond to this greeting in a friendly way. Keep it brief.
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User: {last_message}
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Assistant:
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"""
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response = model.invoke(prompt).content
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return {
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"messages": state["messages"] + [Message(role="assistant", content=response)]
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}
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def handle_question(state: State) -> dict:
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"""Handle question messages."""
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print(" [Node: Question]")
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model = create_chat_model()
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last_message = state["messages"][-1].content
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prompt = f"""
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Answer this question helpfully and concisely.
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User: {last_message}
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Assistant:
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"""
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response = model.invoke(prompt).content
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return {
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"messages": state["messages"] + [Message(role="assistant", content=response)]
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}
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def handle_fallback(state: State) -> dict:
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"""Handle unrecognized messages."""
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print(" [Node: Fallback]")
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model = create_chat_model()
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last_message = state["messages"][-1].content
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prompt = f"""
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Respond to this message in a helpful way.
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User: {last_message}
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Assistant:
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"""
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response = model.invoke(prompt).content
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return {
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"messages": state["messages"] + [Message(role="assistant", content=response)]
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}
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# ============ Build Graph ============
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def create_chat_graph():
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"""Create and compile the chat graph."""
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builder = StateGraph(State)
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# Add nodes
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builder.add_node("greeting", handle_greeting)
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builder.add_node("question", handle_question)
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builder.add_node("fallback", handle_fallback)
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||||
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||||
# Add conditional routing
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builder.add_conditional_edges(
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||||
START,
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categorize_message,
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||||
{
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||||
"greeting": "greeting",
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||||
"question": "question",
|
||||
"fallback": "fallback"
|
||||
}
|
||||
)
|
||||
|
||||
# All paths lead to END
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||||
builder.add_edge("greeting", END)
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builder.add_edge("question", END)
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builder.add_edge("fallback", END)
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||||
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return builder.compile()
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||||
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||||
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||||
# ============ Main ============
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||||
|
||||
def main():
|
||||
"""Run the chat agent."""
|
||||
graph = create_chat_graph()
|
||||
|
||||
print("=== LangGraph Chat Agent with ChatOpenAI ===\n")
|
||||
|
||||
# Test messages
|
||||
test_inputs = [
|
||||
"你好，很高兴见到你！",
|
||||
"Python 中如何读取 JSON 文件？",
|
||||
"随便聊聊吧"
|
||||
]
|
||||
|
||||
for user_input in test_inputs:
|
||||
print(f"User: {user_input}")
|
||||
|
||||
initial_state = {
|
||||
"messages": [Message(role="user", content=user_input)],
|
||||
"category": ""
|
||||
}
|
||||
|
||||
result = graph.invoke(initial_state)
|
||||
|
||||
assistant_message = result["messages"][-1].content
|
||||
print(f"Assistant: {assistant_message}\n")
|
||||
print("-" * 50 + "\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user