# 實戰(zhàn)代碼片段 - 延遲熱力圖生成
import logging
import langsmith

# 配置日志記錄
langsmith_logger = logging.getLogger("langsmith")
langsmith_logger.setLevel(level=logging.DEBUG)
# 性能指標(biāo)獲取 ? 可以獲取延遲相關(guān)指標(biāo)  
# ? 包括:latency_p50、latency_p99、first_token_p50、first_token_p99

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# 從 LangSmith 導(dǎo)出數(shù)據(jù)
data = client.list_runs(...)
df = pd.DataFrame(data)

# 生成熱力圖
plt.figure(figsize=(10, 6))
sns.heatmap(df[['latency', 'metadata']], cmap='YlOrRd')
plt.title('LangSmith 延遲熱力圖')
plt.show()

// 前端實現(xiàn)偽代碼 - 多層級狀態(tài)流
function renderAgentThinking(step) {
  const stages = ["?? 檢索知識庫", "?? 構(gòu)建推理鏈", "?? 調(diào)用支付API"];
  stages.slice(0, step+1).forEach((text, index) => {
    createFakeProgressBar(index, text);
  });
}

from langgraph.graph import StateGraph, START, END
from typing import TypedDict

# 定義狀態(tài)類型
class State(TypedDict):
    topic: str
    joke: str
    story: str
    combined_output: str

# 定義并行的 LLM 調(diào)用函數(shù)
def call_llm_1(state: State):
    """第一個 LLM 調(diào)用生成笑話"""
    msg = llm.invoke(f"Write a joke about {state['topic']}")
    return {"joke": msg.content}

def call_llm_2(state: State):
    """第二個 LLM 調(diào)用生成故事"""
    msg = llm.invoke(f"Write a story about {state['topic']}")
    return {"story": msg.content}

def aggregator(state: State):
    """聚合所有生成的內(nèi)容"""
    combined = f"Here's a story, joke, and poem about {state['topic']}!\n\n"
    combined += f"STORY:\n{state['story']}\n\n"
    combined += f"JOKE:\n{state['joke']}\n\n"
    return {"combined_output": combined}

# 構(gòu)建工作流
parallel_builder = StateGraph(State)

# 添加節(jié)點
parallel_builder.add_node("call_llm_1", call_llm_1)
parallel_builder.add_node("call_llm_2", call_llm_2)
parallel_builder.add_node("aggregator", aggregator)

# 添加邊連接節(jié)點
parallel_builder.add_edge(START, "call_llm_1")
parallel_builder.add_edge(START, "call_llm_2")
parallel_builder.add_edge("call_llm_1", "aggregator")
parallel_builder.add_edge("call_llm_2", "aggregator")
parallel_builder.add_edge("aggregator", END)

# 編譯工作流
parallel_workflow = parallel_builder.compile()

# 調(diào)用工作流
state = parallel_workflow.invoke({"topic": "cats"})
print(state["combined_output"])

# 預(yù)加載代碼示例
prefetch_tools(["sentiment_analysis", "geoip_lookup"])