Confident

DeepEval 包用于 LLM 的单元测试。 使用 Confident，任何人都可以通过更快的迭代构建稳健的语言模型， 采用单元测试和集成测试。我们为每个迭代步骤提供支持， 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衡量 LLM 的性能。我们展示了如何使用我们的回调来衡量性能，以及如何定义自己的指标并将其记录到我们的仪表板中。

DeepEval 还提供：

• 如何生成合成数据
• 如何衡量性能
• 一个仪表板，用于监控和审查结果随时间的变化

安装与设置

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

获取 API 凭证

要获取 DeepEval API 凭证，请按照以下步骤操作：

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

登录时，系统还会要求您设置 implementation 名称。实现名称用于描述实现的类型。（考虑一下您想为项目命名什么。我们建议使用描述性名称。）

!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。然而，目前对指标的支持有限（将很快添加更多）。目前支持：

• 答案相关性
• 偏见
• 有毒性

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 LLM

然后您可以将其输入到您的 LLM 中，使用 OpenAI。

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)",
    ]
)

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'})

您可以通过调用 is_successful() 方法来检查指标是否成功。

answer_relevancy_metric.is_successful()
# returns True/False

一旦您运行了该代码，您应该能够看到下面的仪表板。

场景 2：在没有回调的情况下跟踪 LLM 链

要在没有回调的情况下跟踪 LLM 链，可以在链的末尾插入它。

我们可以通过下面的示例定义一个简单的链。

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(),
)

# 提供一个新的问答管道
query = "Who is the president?"
result = qa.run(query)

定义链后，您可以手动检查答案的相关性。

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

接下来是什么？

您可以在 这里 创建您自己的自定义指标。

DeepEval 还提供其他功能，例如能够 自动创建单元测试 和 幻觉测试。

如果您感兴趣，请查看我们的 GitHub 仓库 https://github.com/confident-ai/deepeval。我们欢迎任何 PR 和关于如何提高 LLM 性能的讨论。