自信

用于单元测试 LLM 的 DeepEval 软件包。使用 Confident，每个人都可以通过更快的迭代来构建强大的语言模型同时使用单元测试和集成测试。我们为迭代中的每个步骤提供支持从合成数据创建到测试。

在本指南中，我们将演示如何测试和测量 LLM 的性能。我们将展示如何使用我们的回传来衡量性能，以及如何定义自己的指标并将其记录到我们的控制面板中。

DeepEval 还提供：

如何生成合成数据
如何衡量效果
用于监控和查看一段时间内结果的仪表板

安装和设置

%pip install --upgrade --quiet  langchain langchain-openai langchain-community deepeval langchain-chroma

获取 API 凭证

要获取 DeepEval API 凭证，请执行以下步骤：

转至 https://app.confident-ai.com
点击“Organization”
复制 API 密钥。

登录时，系统还会要求您设置implementation名字。需要 implementation name 来描述实现类型。（想想你想怎么称呼你的项目。我们建议将其设为描述性。

!deepeval login

设置 DeepEval

默认情况下，您可以使用DeepEvalCallbackHandler以设置要跟踪的指标。但是，目前这对 metrics 的支持有限（即将添加更多）。它目前支持：

from deepeval.metrics.answer_relevancy import AnswerRelevancy

# Here we want to make sure the answer is minimally relevant
answer_relevancy_metric = AnswerRelevancy(minimum_score=0.5)

开始使用

要使用DeepEvalCallbackHandler，我们需要implementation_name.

from langchain_community.callbacks.confident_callback import DeepEvalCallbackHandler

deepeval_callback = DeepEvalCallbackHandler(
    implementation_name="langchainQuickstart", metrics=[answer_relevancy_metric]
)

API 参考：DeepEvalCallbackHandler

场景 1：馈入 LLM

然后，您可以使用 OpenAI 将其输入到您的 LLM 中。

from langchain_openai import OpenAI

llm = OpenAI(
    temperature=0,
    callbacks=[deepeval_callback],
    verbose=True,
    openai_api_key="<YOUR_API_KEY>",
)
output = llm.generate(
    [
        "What is the best evaluation tool out there? (no bias at all)",
    ]
)

API 参考：OpenAI

LLMResult(generations=[[Generation(text='\n\nQ: What did the fish say when he hit the wall? \nA: Dam.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nThe Moon \n\nThe moon is high in the midnight sky,\nSparkling like a star above.\nThe night so peaceful, so serene,\nFilling up the air with love.\n\nEver changing and renewing,\nA never-ending light of grace.\nThe moon remains a constant view,\nA reminder of life’s gentle pace.\n\nThrough time and space it guides us on,\nA never-fading beacon of hope.\nThe moon shines down on us all,\nAs it continues to rise and elope.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ. What did one magnet say to the other magnet?\nA. "I find you very attractive!"', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nThe world is charged with the grandeur of God.\nIt will flame out, like shining from shook foil;\nIt gathers to a greatness, like the ooze of oil\nCrushed. Why do men then now not reck his rod?\n\nGenerations have trod, have trod, have trod;\nAnd all is seared with trade; bleared, smeared with toil;\nAnd wears man's smudge and shares man's smell: the soil\nIs bare now, nor can foot feel, being shod.\n\nAnd for all this, nature is never spent;\nThere lives the dearest freshness deep down things;\nAnd though the last lights off the black West went\nOh, morning, at the brown brink eastward, springs —\n\nBecause the Holy Ghost over the bent\nWorld broods with warm breast and with ah! bright wings.\n\n~Gerard Manley Hopkins", generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nQ: What did one ocean say to the other ocean?\nA: Nothing, they just waved.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text="\n\nA poem for you\n\nOn a field of green\n\nThe sky so blue\n\nA gentle breeze, the sun above\n\nA beautiful world, for us to love\n\nLife is a journey, full of surprise\n\nFull of joy and full of surprise\n\nBe brave and take small steps\n\nThe future will be revealed with depth\n\nIn the morning, when dawn arrives\n\nA fresh start, no reason to hide\n\nSomewhere down the road, there's a heart that beats\n\nBelieve in yourself, you'll always succeed.", generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'completion_tokens': 504, 'total_tokens': 528, 'prompt_tokens': 24}, 'model_name': 'text-davinci-003'})

然后，您可以通过调用is_successful()方法。

answer_relevancy_metric.is_successful()
# returns True/False

运行该作后，您应该能够在下面看到我们的控制面板。

Dashboard

场景 2：在没有回调的情况下跟踪链中的 LLM

要在没有回调的情况下跟踪链中的 LLM，您可以在末尾插入它。

我们可以从定义一个简单的链开始，如下所示。

import requests
from langchain.chains import RetrievalQA
from langchain_chroma import Chroma
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

text_file_url = "https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt"

openai_api_key = "sk-XXX"

with open("state_of_the_union.txt", "w") as f:
    response = requests.get(text_file_url)
    f.write(response.text)

loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
docsearch = Chroma.from_documents(texts, embeddings)

qa = RetrievalQA.from_chain_type(
    llm=OpenAI(openai_api_key=openai_api_key),
    chain_type="stuff",
    retriever=docsearch.as_retriever(),
)

# Providing a new question-answering pipeline
query = "Who is the president?"
result = qa.run(query)

API 参考：RetrievalQA | 文本加载器 | OpenAI | OpenAI 嵌入 | CharacterTextSplitter（字符文本拆分器）

定义链后，您可以手动检查答案相似性。

answer_relevancy_metric.measure(result, query)
answer_relevancy_metric.is_successful()

下一步是什么？

您可以在此处创建自己的自定义指标。

DeepEval 还提供其他功能，例如能够自动创建单元测试、幻觉测试。

如果您有兴趣，请在此处查看我们的 Github 存储库 https://github.com/confident-ai/deepeval。我们欢迎任何关于如何提高 LLM 绩效的 PR 和讨论。