彗星
彗星 机器学习平台与您现有的基础设施和工具集成在一起，因此您可以管理、可视化和优化模型——从训练运行到生产监控。
在本指南中，我们将演示如何使用彗星跟踪您的Langchain实验、评估指标和LLM会话。
示例项目： LangChain中的彗星
安装彗星和依赖项
%pip install --upgrade --quiet comet_ml langchain langchain-openai google-search-results spacy textstat pandas
!{sys.executable} -m spacy download en_core_web_sm
初始化彗星并设置您的凭据
您可以在此处获取彗星API密钥，或在初始化彗星后单击链接。
import comet_ml
comet_ml.init(project_name="comet-example-langchain")
设置OpenAI和SerpAPI凭据
您需要一个OpenAI API密钥和一个SerpAPI API密钥来运行以下示例。
import os
os.environ["OPENAI_API_KEY"] = "..."
# os.environ["OPENAI_ORGANIZATION"] = "..."
os.environ["SERPAPI_API_KEY"] = "..."
场景1：仅使用LLM
from langchain_community.callbacks import CometCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain_openai import OpenAI
comet_callback = CometCallbackHandler(
project_name="comet-example-langchain",
complexity_metrics=True,
stream_logs=True,
tags=["llm"],
visualizations=["dep"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)
llm_result = llm.generate(["Tell me a joke", "Tell me a poem", "Tell me a fact"] * 3)
print("LLM result", llm_result)
comet_callback.flush_tracker(llm, finish=True)
场景2：在链中使用LLM
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
comet_callback = CometCallbackHandler(
complexity_metrics=True,
project_name="comet-example-langchain",
stream_logs=True,
tags=["synopsis-chain"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
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)
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：使用带工具的代理
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
comet_callback = CometCallbackHandler(
project_name="comet-example-langchain",
complexity_metrics=True,
stream_logs=True,
tags=["agent"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
tools,
llm,
agent="zero-shot-react-description",
callbacks=callbacks,
verbose=True,
)
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
CometCallbackManager还允许您定义和使用自定义评估指标来评估模型生成的输出。让我们看看这是如何工作的。
在下面的代码片段中，我们将使用 ROUGE 指标来评估生成摘要的质量。
%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
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)
回调追踪器
还有另一个与 Comet 集成的功能：
查看 示例。
from langchain_community.callbacks.tracers.comet import CometTracer