使用Comet追踪LangChain实验及模型性能优化指南

技术背景介绍

Comet是一款强大的机器学习平台，可帮助开发者管理、可视化和优化模型，从训练到生产监控都能轻松处理。它可与现有的基础设施和工具集成，使得跟踪实验、评估指标和LLM（大型语言模型）会话更为便捷。在本文中，我们将演示如何通过Comet追踪LangChain实验、评估指标和LLM会话，并介绍一些具体的应用案例。

核心原理解析

Comet通过回调机制集成到LangChain中，以便实时记录模型执行过程中的复杂性指标和生成结果。这种集成方式不仅有利于模型训练和调试，也为后续的性能优化提供了有效数据支持。Comet的回调机制使得用户能够在实验过程中自动记录日志、生成可视化图表以及计算自定义评估指标。

代码实现演示

下面将通过几个实际代码示例展示如何将Comet与LangChain结合使用。

安装Comet和相关依赖

%pip install --upgrade --quiet  comet_ml langchain langchain-openai google-search-results spacy textstat pandas
!{sys.executable} -m spacy download en_core_web_sm

初始化Comet并设置凭证

首先需要初始化Comet并设置项目名称以便追踪实验过程：

import os
import comet_ml

comet_ml.init(project_name="comet-example-langchain")

同时设置OpenAI和SerpAPI的API Key：

os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["SERPAPI_API_KEY"] = "your-serpapi-api-key"

场景1: 使用纯LLM

使用CometCallbackHandler来追踪LLM的复杂性指标及生成结果：

from langchain_community.callbacks import CometCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain_openai import OpenAI

# 初始化Comet回调
comet_callback = CometCallbackHandler(
    project_name="comet-example-langchain",
    complexity_metrics=True,
    stream_logs=True,
    tags=["llm"],
    visualizations=["dep"]
)
callbacks = [StdOutCallbackHandler(), comet_callback]

# 设置LLM
llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)

# 生成示例请求
llm_result = llm.generate(["Tell me a joke", "Tell me a poem", "Tell me a fact"] * 3)
print("LLM result", llm_result)

# 刷新并结束追踪
comet_callback.flush_tracker(llm, finish=True)

场景2: 在链中使用LLM

在该场景中，我们将LLM集成到一个生成链中，并使用Comet记录生成过程：

from langchain.chains import LLMChain
from langchain_community.callbacks import CometCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

# 初始化Comet回调
comet_callback = CometCallbackHandler(
    complexity_metrics=True,
    project_name="comet-example-langchain",
    stream_logs=True,
    tags=["synopsis-chain"]
)
callbacks = [StdOutCallbackHandler(), comet_callback]

# 设置LLM和生成链
llm = OpenAI(temperature=0.9, callbacks=callbacks)
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)

# 测试生成链
test_prompts = [{"title": "Documentary about Bigfoot in Paris"}]
print(synopsis_chain.apply(test_prompts))
comet_callback.flush_tracker(synopsis_chain, finish=True)

场景3: 使用工具初始化代理

使用Comet集成到LangChain的代理工具中以实现更复杂的查询请求：

from langchain.agents import initialize_agent, load_tools
from langchain_community.callbacks import CometCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain_openai import OpenAI

# 初始化Comet回调
comet_callback = CometCallbackHandler(
    project_name="comet-example-langchain",
    complexity_metrics=True,
    stream_logs=True,
    tags=["agent"]
)
callbacks = [StdOutCallbackHandler(), comet_callback]

# 配置LLM和工具
llm = OpenAI(temperature=0.9, callbacks=callbacks)
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)

# 初始化代理
agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    callbacks=callbacks,
    verbose=True,
)

# 运行示例请求
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
comet_callback.flush_tracker(agent, finish=True)

场景4: 使用自定义评估指标

使用自定义评估指标（如ROUGE）来评价生成内容质量：

%pip install --upgrade --quiet rouge-score

from langchain.chains import LLMChain
from langchain_community.callbacks import CometCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
from rouge_score import rouge_scorer

# 自定义ROUGE评估类
class Rouge:
    def __init__(self, reference):
        self.reference = reference
        self.scorer = rouge_scorer.RougeScorer(["rougeLsum"], use_stemmer=True)

    def compute_metric(self, generation, prompt_idx, gen_idx):
        prediction = generation.text
        results = self.scorer.score(target=self.reference, prediction=prediction)
        return {
            "rougeLsum_score": results["rougeLsum"].fmeasure,
            "reference": self.reference,
        }

reference = """
The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building.
It was the first structure to reach a height of 300 metres.

It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft)
Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France.
"""
rouge_score = Rouge(reference=reference)

template = """Given the following article, it is your job to write a summary.
Article:
{article}
Summary: This is the summary for the above article:"""
prompt_template = PromptTemplate(input_variables=["article"], template=template)

comet_callback = CometCallbackHandler(
    project_name="comet-example-langchain",
    complexity_metrics=False,
    stream_logs=True,
    tags=["custom_metrics"],
    custom_metrics=rouge_score.compute_metric,
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9)

synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)

test_prompts = [
    {
        "article": """
            The tower is 324 metres (1,063 ft) tall, about the same height as
            an 81-storey building, and the tallest structure in Paris. Its base is square,
            measuring 125 metres (410 ft) on each side.
            During its construction, the Eiffel Tower surpassed the
            Washington Monument to become the tallest man-made structure in the world,
            a title it held for 41 years until the Chrysler Building
            in New York City was finished in 1930.

            It was the first structure to reach a height of 300 metres.
            Due to the addition of a broadcasting aerial at the top of the tower in 1957,
            it is now taller than the Chrysler Building by 5.2 metres (17 ft).

            Excluding transmitters, the Eiffel Tower is the second tallest
            free-standing structure in France after the Millau Viaduct.
        """
    }
]
print(synopsis_chain.apply(test_prompts, callbacks=callbacks))
comet_callback.flush_tracker(synopsis_chain, finish=True)

应用场景分析

上述示例展示了在不同场景中使用Comet结合LangChain的效果。第一种场景用于基本的LLM模型评估，第二种则将LLM嵌入生成链中以实现更复杂的文本生成任务。第三种场景基于代理工具进行复杂查询处理，而第四种场景展示了如何定义和使用自定义评估指标来改进模型质量。

实践建议

• 充分利用Comet的复杂性指标和可视化功能来细化模型训练过程。
• 在测试和生产环境中使用自定义评估指标以优化生成内容的质量。
• 将Comet与其他工具结合使用以实现更复杂的任务，并提高开发效率。

如果遇到问题欢迎在评论区交流。
—END—