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Langfuse 🪢

什么是 Langfuse？ Langfuse 是一个开源的 LLM 工程平台，帮助团队追踪 API 调用、监控性能并调试其 AI 应用中的问题。

追踪 LangChain​

Langfuse 追踪 使用 Langchain 回调（Python、JS）与 Langchain 集成。因此，Langfuse SDK 会为每个 Langchain 应用程序的运行自动创建嵌套追踪。这使您能够记录、分析和调试您的 LangChain 应用程序。

您可以通过 (1) 构造函数参数或 (2) 环境变量来配置集成。通过访问 cloud.langfuse.com 注册，或 自行托管 Langfuse 获取您的 Langfuse 凭据。

构造函数参数​

pip install langfuse
# Initialize Langfuse handler
from langfuse.callback import CallbackHandler
langfuse_handler = CallbackHandler(
    secret_key="sk-lf-...",
    public_key="pk-lf-...",
    host="https://cloud.langfuse.com", # 🇪🇺 EU region
  # host="https://us.cloud.langfuse.com", # 🇺🇸 US region
)
 
# Your Langchain code
 
# Add Langfuse handler as callback (classic and LCEL)
chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler]})

环境变量

LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_PUBLIC_KEY="pk-lf-..."
# 🇪🇺 EU region
LANGFUSE_HOST="https://cloud.langfuse.com"
# 🇺🇸 US region
# LANGFUSE_HOST="https://us.cloud.langfuse.com"
# Initialize Langfuse handler
from langfuse.callback import CallbackHandler
langfuse_handler = CallbackHandler()
 
# Your Langchain code
 
# Add Langfuse handler as callback (classic and LCEL)
chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler]})

要查看如何将此集成与其他 Langfuse 功能结合使用，请查看此端到端示例。

追踪 LangGraph​

本部分演示了如何使用Langfuse配合LangChain 集成来调试、分析并迭代您的 LangGraph 应用。

初始化 Langfuse​

注意： 您需要运行至少 Python 3.11（GitHub Issue）。

使用 Langfuse UI 中项目设置下的 API 密钥 初始化 Langfuse 客户端，并将它们添加到您的环境变量中。

%pip install langfuse
%pip install langchain langgraph langchain_openai langchain_community
import os

# get keys for your project from https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-***"
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-***"
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # for EU data region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # for US data region

# your openai key
os.environ["OPENAI_API_KEY"] = "***"

带有 LangGraph 的简单聊天应用​

本节我们将做什么：

  • 在 LangGraph 中构建一个支持聊天机器人，用于回答常见问题
  • 使用 Langfuse 追踪聊天机器人的输入和输出

我们将从一个基本的聊天机器人开始，并在下一节中构建一个更高级的多智能体设置，同时在此过程中介绍关键的 LangGraph 概念。

创建智能体​

首先创建一个 StateGraph。StateGraph 对象将我们的聊天机器人结构定义为状态机。我们将添加节点来表示 LLM 和聊天机器人可调用函数，并添加边来指定聊天机器人在这些函数之间的转换方式。

from typing import Annotated

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from typing_extensions import TypedDict

from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages

class State(TypedDict):
    # Messages have the type "list". The `add_messages` function in the annotation defines how this state key should be updated
    # (in this case, it appends messages to the list, rather than overwriting them)
    messages: Annotated[list, add_messages]

graph_builder = StateGraph(State)

llm = ChatOpenAI(model = "gpt-4o", temperature = 0.2)

# The chatbot node function takes the current State as input and returns an updated messages list. This is the basic pattern for all LangGraph node functions.
def chatbot(state: State):
    return {"messages": [llm.invoke(state["messages"])]}

# Add a "chatbot" node. Nodes represent units of work. They are typically regular python functions.
graph_builder.add_node("chatbot", chatbot)

# Add an entry point. This tells our graph where to start its work each time we run it.
graph_builder.set_entry_point("chatbot")

# Set a finish point. This instructs the graph "any time this node is run, you can exit."
graph_builder.set_finish_point("chatbot")

# To be able to run our graph, call "compile()" on the graph builder. This creates a "CompiledGraph" we can use invoke on our state.
graph = graph_builder.compile()

将 Langfuse 作为回调添加到调用中​

现在，我们将添加 Langfuse 回调处理器（适用于 LangChain） 来追踪我们应用的步骤：config={"callbacks": [langfuse_handler]}

from langfuse.callback import CallbackHandler

# Initialize Langfuse CallbackHandler for Langchain (tracing)
langfuse_handler = CallbackHandler()

for s in graph.stream({"messages": [HumanMessage(content = "What is Langfuse?")]},
                      config={"callbacks": [langfuse_handler]}):
    print(s)
{'chatbot': {'messages': [AIMessage(content='Langfuse is a tool designed to help developers monitor and observe the performance of their Large Language Model (LLM) applications. It provides detailed insights into how these applications are functioning, allowing for better debugging, optimization, and overall management. Langfuse offers features such as tracking key metrics, visualizing data, and identifying potential issues in real-time, making it easier for developers to maintain and improve their LLM-based solutions.', response_metadata={'token_usage': {'completion_tokens': 86, 'prompt_tokens': 13, 'total_tokens': 99}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_400f27fa1f', 'finish_reason': 'stop', 'logprobs': None}, id='run-9a0c97cb-ccfe-463e-902c-5a5900b796b4-0', usage_metadata={'input_tokens': 13, 'output_tokens': 86, 'total_tokens': 99})]}}

在 Langfuse 中查看追踪​

Langfuse 中的示例追踪：https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/d109e148-d188-4d6e-823f-aac0864afbab

Trace view of chat app in Langfuse