LLMonitor

LLMonitor is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools.

设置

在llmonitor.com上创建一个帐户，然后复制新应用的tracking id。

一旦你拥有了它，可以通过运行以下命令将其设置为环境变量：

export LLMONITOR_APP_ID="..."

如果你不想设置环境变量，可以在初始化回调处理器时直接传入密钥：

from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler

handler = LLMonitorCallbackHandler(app_id="...")

API 参考：LLMonitorCallbackHandler

与LLM/聊天模型一起使用

from langchain_openai import OpenAI
from langchain_openai import ChatOpenAI

handler = LLMonitorCallbackHandler()

llm = OpenAI(
    callbacks=[handler],
)

chat = ChatOpenAI(callbacks=[handler])

llm("Tell me a joke")

API 参考：OpenAI | ChatOpenAI

与链和代理一起使用

请确保将回调处理器传递给 run 方法，以便正确跟踪所有相关的链和 LLM 调用。

还建议在元数据中传递 agent_name，以便能够在仪表板中区分不同的代理。

Example:

from langchain_openai import ChatOpenAI
from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler
from langchain_core.messages import SystemMessage, HumanMessage
from langchain.agents import OpenAIFunctionsAgent, AgentExecutor, tool

llm = ChatOpenAI(temperature=0)

handler = LLMonitorCallbackHandler()

@tool
def get_word_length(word: str) -> int:
    """Returns the length of a word."""
    return len(word)

tools = [get_word_length]

prompt = OpenAIFunctionsAgent.create_prompt(
    system_message=SystemMessage(
        content="You are very powerful assistant, but bad at calculating lengths of words."
    )
)

agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt, verbose=True)
agent_executor = AgentExecutor(
    agent=agent, tools=tools, verbose=True, metadata={"agent_name": "WordCount"}  # <- recommended, assign a custom name
)
agent_executor.run("how many letters in the word educa?", callbacks=[handler])

API 参考：ChatOpenAI | LLMonitorCallbackHandler | SystemMessage | HumanMessage | OpenAIFunctionsAgent | AgentExecutor | 工具

另一个例子：

from langchain.agents import load_tools, initialize_agent, AgentType
from langchain_openai import OpenAI
from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler


handler = LLMonitorCallbackHandler()

llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, metadata={ "agent_name": "GirlfriendAgeFinder" })  # <- recommended, assign a custom name

agent.run(
    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
    callbacks=[handler],
)

API 参考：load_tools | initialize_agent | AgentType | OpenAI | LLMonitorCallbackHandler

用户追踪

用户追踪功能允许您识别您的用户，跟踪他们的成本、对话等信息。

from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler, identify

with identify("user-123"):
    llm.invoke("Tell me a joke")

with identify("user-456", user_props={"email": "user456@test.com"}):
    agent.run("Who is Leo DiCaprio's girlfriend?")

API 参考：LLMonitorCallbackHandler | 识别

支持

如果您在集成方面有任何问题或疑问，可以通过 Discord 或通过 电子邮件 联系 LLMonitor 团队。