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Langfuse 🪢

什么是 Langfuse？ Langfuse 是一个开源 LLM 工程平台，可帮助团队跟踪 API 调用、监控性能并调试其 AI 应用程序中的问题。

追踪 LangChain

Langfuse Tracing 使用 Langchain 回调（Python、JS）与 Langchain 集成。因此，Langfuse SDK 会自动为您的 Langchain 应用程序的每次运行创建一个嵌套跟踪。这允许您记录、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。通过在 cloud.langfuse.com 或自托管 Langfuse 上注册来获取您的 Langfuse 凭证。

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]})

要了解如何将此集成与其他 Langfuse 功能一起使用，请查看此端到端示例。

追踪 LangGraph

本部分演示了 Langfuse 如何使用 LangChain 集成帮助调试、分析和迭代 LangGraph 应用程序。

初始化 Langfuse

注意：您至少需要运行 Python 3.11 （GitHub Issue）。

使用 Langfuse UI 中项目设置中的 API 密钥初始化 Langfuse 客户端，并将其添加到您的环境中。

%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"] = "***"

使用 LangGraph 的简单聊天应用程序

我们将在本节中做什么：

  • 在 LangGraph 中构建一个可以回答常见问题的支持聊天机器人
  • 使用 Langfuse 跟踪聊天机器人的输入和输出

我们将在下一节中从一个基本的聊天机器人开始，构建一个更高级的多代理设置，并在此过程中介绍 LangGraph 的关键概念。

创建代理

首先创建一个StateGraph.一个StateGraphobject 将我们的聊天机器人的结构定义为状态机。我们将添加节点来表示聊天机器人可以调用的 LLM 和函数，并添加 edges 来指定机器人如何在这些函数之间转换。

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

现在，我们将为 LangChain 添加 Langfuse 回调处理程序，以跟踪我们应用程序的步骤：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 中查看跟踪

Langfuse 中的跟踪示例：https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/d109e148-d188-4d6e-823f-aac0864afbab

Trace view of chat app in Langfuse