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自信

DeepEval 包用于对大语言模型（LLM）进行单元测试。 使用 Confident，每个人都可以通过单元测试和集成测试的快速迭代来构建稳健的语言模型。 我们为迭代过程中的每个步骤提供支持，从合成数据生成到测试。

在本指南中，我们将演示如何测试和评估大语言模型（LLM）的性能。我们将展示如何使用我们的回调函数来测量性能，以及如何定义自己的指标并将其记录到我们的仪表板中。

DeepEval 还提供：

  • 如何生成合成数据
  • 如何衡量性能
  • 一个用于随时间监控和审查结果的仪表板

安装与设置

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

获取 API 凭据​

获取 DeepEval API 凭据，请按照以下步骤操作：

  1. 转到 https://app.confident-ai.com
  2. 点击“组织”
  3. 复制API密钥。

当您登录时，系统还会要求您设置implementation名称。必须提供实现名称以描述实现的类型。（想想您希望如何命名您的项目。我们建议使用具有描述性的名称。）

!deepeval login

设置 DeepEval​

默认情况下，您可以使用 DeepEvalCallbackHandler 来设置您想要跟踪的指标。但是目前对指标的支持有限（更多功能即将添加）。当前支持的功能包括：

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]
)

场景1：输入到LLM​

然后你可以将其输入到你的使用OpenAI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() 方法来检查该指标是否成功。

answer_relevancy_metric.is_successful()
# returns True/False

一旦你运行了该程序，你应该能够在下方看到我们的仪表板。

Dashboard

场景2：在没有回调的情况下跟踪链中的LLM​

在链中跟踪一个LLM时，如果不使用回调，可以在末尾将其接入。

我们可以通过定义一个如下所示的简单链来开始。

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)

定义链后，您可以手动检查答案的相似性。

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

下一步？​

您可以在此处创建自己的自定义指标 这里。

DeepEval 还提供其他功能，例如能够自动创建单元测试，幻觉测试。

如果您感兴趣，请在此处查看我们的 Github 仓库 https://github.com/confident-ai/deepeval。我们欢迎任何关于如何提升大语言模型性能的 Pull Request 和讨论。