目标

Aim 使得可视化和调试 LangChain 执行变得非常简单。Aim 跟踪 LLMs 和工具的输入和输出，以及代理的动作。

使用 Aim，您可以轻松调试和检查单个执行：

此外，您可以选择并排比较多个执行：

Aim 是完全开源的，了解更多 关于 Aim 的信息，请访问 GitHub。

让我们继续，看看如何启用和配置 Aim 回调。

使用 Aim 跟踪 LangChain 执行

在本笔记本中，我们将探讨三种使用场景。首先，我们将安装必要的软件包并导入特定模块。随后，我们将配置两个环境变量，可以在 Python 脚本内或通过终端建立。

%pip install --upgrade --quiet  aim
%pip install --upgrade --quiet  langchain
%pip install --upgrade --quiet  langchain-openai
%pip install --upgrade --quiet  google-search-results

import os
from datetime import datetime
from langchain_community.callbacks import AimCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain_openai import OpenAI

我们的示例使用 GPT 模型作为 LLM，OpenAI 为此提供了 API。您可以从以下链接获取密钥：https://platform.openai.com/account/api-keys。

我们将使用 SerpApi 从 Google 检索搜索结果。要获取 SerpApi 密钥，请访问 https://serpapi.com/manage-api-key。

os.environ["OPENAI_API_KEY"] = "..."
os.environ["SERPAPI_API_KEY"] = "..."

AimCallbackHandler 的事件方法接受 LangChain 模块或代理作为输入，并至少记录提示和生成的结果，以及 LangChain 模块的序列化版本到指定的 Aim 运行。

session_group = datetime.now().strftime("%m.%d.%Y_%H.%M.%S")
aim_callback = AimCallbackHandler(
    repo=".",
    experiment_name="scenario 1: OpenAI LLM",
)
callbacks = [StdOutCallbackHandler(), aim_callback]
llm = OpenAI(temperature=0, callbacks=callbacks)

flush_tracker 函数用于在 Aim 上记录 LangChain 资产。默认情况下，会重置会话而不是直接终止。

场景 1

在第一个场景中，我们将使用 OpenAI LLM。

# scenario 1 - LLM
llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3)
aim_callback.flush_tracker(
    langchain_asset=llm,
    experiment_name="scenario 2: Chain with multiple SubChains on multiple generations",
)

场景 2

第二个场景涉及跨多代的多个 SubChains 进行链式操作。

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate

# scenario 2 - Chain
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)
test_prompts = [
    {"title": "documentary about good video games that push the boundary of game design"},
    {"title": "the phenomenon behind the remarkable speed of cheetahs"},
    {"title": "the best in class mlops tooling"},
]
synopsis_chain.apply(test_prompts)
aim_callback.flush_tracker(
    langchain_asset=synopsis_chain, experiment_name="scenario 3: Agent with Tools"
)

场景 3

第三个场景涉及带有工具的代理。

from langchain.agents import AgentType, initialize_agent, load_tools

# scenario 3 - Agent with Tools
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    callbacks=callbacks,
)
agent.run(
    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"
)
aim_callback.flush_tracker(langchain_asset=agent, reset=False, finish=True)

> Entering new AgentExecutor chain...
 I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.
Action: Search
Action Input: "Leo DiCaprio girlfriend"
Observation: Leonardo DiCaprio seemed to prove a long-held theory about his love life right after splitting from girlfriend Camila Morrone just months ...
Thought: I need to find out Camila Morrone's age
Action: Search
Action Input: "Camila Morrone age"
Observation: 25 years
Thought: I need to calculate 25 raised to the 0.43 power
Action: Calculator
Action Input: 25^0.43
Observation: Answer: 3.991298452658078
Thought: I now know the final answer
Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.
> Finished chain.