本文来介绍另一款生产级AI应用维护平台：LangFuse，它是开源的，是LangSmith 的平替，并且它可集成 LangChain，同时也可直接对接 OpenAI API。
官方网站：https://langfuse.com/
项目地址：https://github.com/langfuse
0. 环境准备
（1）先注册，登录，官网地址在上面
（2）创建Project
（3）生成私钥和公钥
（4）本地安装 langfuse
!pip install langfuse openai
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# 设置OpenAI KEY环境变量
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
OpenAI API Key:··········
1. 开始使用
LangFuse有两种集成方式：
OpenAI API集成
LangChain集成
在运行之前，先将你的公钥和私钥放到环境变量中。例如.env文件中加入：
# 把如下内容加入到.env文件中，也可以使用如下方式添加环境变量
LANGFUSE_SECRET_KEY = "sk-lf-xxxxx"
LANGFUSE_PUBLIC_KEY = "pk-lf-xxxxx"
# 添加私钥
os.environ['LANGFUSE_SECRET_KEY'] = getpass.getpass('LANGFUSE_SECRET_KEY:')
LANGFUSE_SECRET_KEY:··········
# 添加公钥
os.environ['LANGFUSE_PUBLIC_KEY'] = getpass.getpass('LANGFUSE_PUBLIC_KEY')
LANGFUSE_PUBLIC_KEY··········
这样才能使你的程序与你在LangFuse官网上建立的跟踪项目链接起来。
1.1 OpenAI API集成方式
集成步骤：
（1）引入langfuse中的openai：from langfuse.openai import openai，用这个才能集成langfuse
（2）使用Langfuse实例的trace函数，传入一些个人和项目信息
（3）openai接口调用，多了一个trace_id参数
from datetime import datetime
from langfuse.openai import openai ## 1. 引入langfuse中的openai
from langfuse import Langfuse
import os
## 2. 使用Langfuse实例的trace函数，传入一些个人和项目信息
trace = Langfuse().trace(
name = "hello-world",
user_id = "Arron",
release = "v0.0.1"
)
completion = openai.chat.completions.create(
name="hello-world",
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": "对我说'Hello, World!'"}
],
temperature=0,
trace_id=trace.id, ## 3. openai接口调用，多了一个trace_id参数
)
print(completion.choices[0].message.content)
## 输出：Hello, World!
Hello, World!
运行之后,看下LangFuse平台的调用情况
1.2 LangChain集成方式
通过 LangChain 的回调集成。
集成步骤：
（1）从langfuse中引入CallbackHandler：from langfuse.callback import CallbackHandler
（2）在CallbackHandler中设置个人和项目信息
（3）正常创建LangChain应用和流程
（4）invoke时，将CallbackHandler填入config参数中：config={“callbacks”:[handler]}
!pip install langchain langchain_openai
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from langfuse.callback import CallbackHandler
handler = CallbackHandler(
trace_name="SayHello",
user_id="Arron",
)
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema import HumanMessage
from langchain.prompts.chat import HumanMessagePromptTemplate
from langchain.prompts import ChatPromptTemplate
model = ChatOpenAI(model="gpt-3.5-turbo-0613")
prompt_template = """
我的名字叫【{name}】，我的个人介绍是【{description}】。
请根据我的名字和介绍，帮我想一段有吸引力的自我介绍的句子，以此来吸引读者关注和点赞我的账号。
"""
prompt = ChatPromptTemplate.from_messages([
HumanMessagePromptTemplate.from_template(prompt_template)
])
# 定义输出解析器
parser = StrOutputParser()
chain = (
{"name":RunnablePassthrough(), "description":RunnablePassthrough() } ## 这里是给prompt的输入，两个参数
| prompt
| model
| parser
)
## invoke的第一个参数，传入json格式的参数，key与prompt中的参数名一致
response = chain.invoke({'name': 'Arron', 'description': '热爱AI，持续学习，持续干货输出'}, config={"callbacks":[handler]})
print(response)
/usr/local/lib/python3.10/dist-packages/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: The function `loads` is in beta. It is actively being worked on, so the API may change.
warn_beta(
大家好，我是Arron，一个热爱AI的学习者，不仅如此，我还持续输出干货。无论是深入探讨AI技术的最新进展，还是分享实用的学习方法，我都努力为大家提供有价值的内容。如果你也对AI充满热情，想要不断学习和成长，那么请关注我的账号，我们一起探索AI的无限可能！
运行之后看下LangFuse平台，应该能看到你的项目和调用了。
2. 详细信息查看 - Traces页面
使用Traces页面，可以看到你每次程序运行的详细过程，包括每一步的输入、输出、耗时、token数等。
（1）进入Traces，可以看到你所有的运行目录。
（2）点击上方的任一ID，可以进入到本次运行的详细跟踪页面。该页面包含了本次运行的过程：详细执行步骤（右侧）。点击任一步骤，可以在左侧看到本步骤的输入和输出，以及耗时和token数（图中的145->133表示输入145token，输出133token）。
有了这个页面，你可以轻易的跟踪输入输出是否有问题，哪一步有问题，从而更好的调优你的程序。这个比自己打日志的信息更详细和更好用多了。
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