Argilla

Argilla 是一个开源数据管理平台，专为 LLMs 设计。 使用 Argilla，任何人都可以通过更快的数据管理，结合人类和机器反馈，构建强大的语言模型。我们在 MLOps 生命周期的每个步骤中提供支持，从数据标注到模型监控。

在本指南中，我们将演示如何使用 ArgillaCallbackHandler 跟踪您的 LLM 的输入和响应，以便在 Argilla 中生成数据集。

跟踪 LLM 的输入和输出以生成未来微调的数据集是非常有用的。特别是在您使用 LLM 为特定任务生成数据时，例如问答、摘要或翻译。

安装和设置

%pip install --upgrade --quiet  langchain langchain-openai argilla

获取 API 凭证

要获取 Argilla API 凭证，请按照以下步骤操作：

1. 进入您的 Argilla 用户界面。
2. 点击您的头像，然后转到“我的设置”。
3. 然后复制 API 密钥。

在 Argilla 中，API URL 将与您的 Argilla 用户界面的 URL 相同。

要获取 OpenAI API 凭证，请访问 https://platform.openai.com/account/api-keys

import os

os.environ["ARGILLA_API_URL"] = "..."
os.environ["ARGILLA_API_KEY"] = "..."

os.environ["OPENAI_API_KEY"] = "..."

设置 Argilla

要使用 ArgillaCallbackHandler，我们需要在 Argilla 中创建一个新的 FeedbackDataset 以跟踪您的 LLM 实验。为此，请使用以下代码：

import argilla as rg

from packaging.version import parse as parse_version

if parse_version(rg.__version__) < parse_version("1.8.0"):
    raise RuntimeError(
        "`FeedbackDataset` 仅在 Argilla v1.8.0 或更高版本中可用，请 "
        "升级 `argilla`，方法是 `pip install argilla --upgrade`。"
    )

dataset = rg.FeedbackDataset(
    fields=[
        rg.TextField(name="prompt"),
        rg.TextField(name="response"),
    ],
    questions=[
        rg.RatingQuestion(
            name="response-rating",
            description="您如何评价该响应的质量？",
            values=[1, 2, 3, 4, 5],
            required=True,
        ),
        rg.TextQuestion(
            name="response-feedback",
            description="您对该响应有什么反馈？",
            required=False,
        ),
    ],
    guidelines="您被要求评价响应的质量并提供反馈。",
)

rg.init(
    api_url=os.environ["ARGILLA_API_URL"],
    api_key=os.environ["ARGILLA_API_KEY"],
)

dataset.push_to_argilla("langchain-dataset")

📌 注意：目前，仅支持 prompt-response 对作为 FeedbackDataset.fields，因此 ArgillaCallbackHandler 只会跟踪 prompt，即 LLM 输入，以及 response，即 LLM 输出。

跟踪

要使用 ArgillaCallbackHandler，您可以使用以下代码，或者仅重现以下部分中提供的示例。

from langchain_community.callbacks.argilla_callback import ArgillaCallbackHandler

argilla_callback = ArgillaCallbackHandler(
    dataset_name="langchain-dataset",
    api_url=os.environ["ARGILLA_API_URL"],
    api_key=os.environ["ARGILLA_API_KEY"],
)

场景 1：跟踪 LLM

首先，让我们运行一个 LLM 几次，并在 Argilla 中捕获生成的提示-响应对。

from langchain_core.callbacks.stdout import StdOutCallbackHandler
from langchain_openai import OpenAI

argilla_callback = ArgillaCallbackHandler(
    dataset_name="langchain-dataset",
    api_url=os.environ["ARGILLA_API_URL"],
    api_key=os.environ["ARGILLA_API_KEY"],
)
callbacks = [StdOutCallbackHandler(), argilla_callback]

llm = OpenAI(temperature=0.9, callbacks=callbacks)
llm.generate(["Tell me a joke", "Tell me a poem"] * 3)

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'})

场景 2：在链中跟踪 LLM

然后我们可以使用提示模板创建一个链，并在 Argilla 中跟踪初始提示和最终响应。

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

argilla_callback = ArgillaCallbackHandler(
    dataset_name="langchain-dataset",
    api_url=os.environ["ARGILLA_API_URL"],
    api_key=os.environ["ARGILLA_API_KEY"],
)
callbacks = [StdOutCallbackHandler(), argilla_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"}]
synopsis_chain.apply(test_prompts)

> Entering new LLMChain chain...
Prompt after formatting:
You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: Documentary about Bigfoot in Paris
Playwright: This is a synopsis for the above play:

> Finished chain.

[{'text': "\n\nDocumentary about Bigfoot in Paris focuses on the story of a documentary filmmaker and their search for evidence of the legendary Bigfoot creature in the city of Paris. The play follows the filmmaker as they explore the city, meeting people from all walks of life who have had encounters with the mysterious creature. Through their conversations, the filmmaker unravels the story of Bigfoot and finds out the truth about the creature's presence in Paris. As the story progresses, the filmmaker learns more and more about the mysterious creature, as well as the different perspectives of the people living in the city, and what they think of the creature. In the end, the filmmaker's findings lead them to some surprising and heartwarming conclusions about the creature's existence and the importance it holds in the lives of the people in Paris."}]

场景 3：使用带工具的代理

最后，作为一种更高级的工作流程，您可以创建一个使用某些工具的代理。因此，ArgillaCallbackHandler 将跟踪输入和输出，但不跟踪中间步骤/思路，因此在给定提示时，我们记录原始提示和对该提示的最终响应。

请注意，在此场景中，我们将使用 Google Search API（Serp API），因此您需要安装 google-search-results，命令为 pip install google-search-results，并将 Serp API 密钥设置为 os.environ["SERPAPI_API_KEY"] = "..."（您可以在 https://serpapi.com/dashboard 找到它），否则下面的示例将无法工作。

from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_core.callbacks.stdout import StdOutCallbackHandler
from langchain_openai import OpenAI

argilla_callback = ArgillaCallbackHandler(
    dataset_name="langchain-dataset",
    api_url=os.environ["ARGILLA_API_URL"],
    api_key=os.environ["ARGILLA_API_KEY"],
)
callbacks = [StdOutCallbackHandler(), argilla_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)

tools = load_tools(["serpapi"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    callbacks=callbacks,
)
agent.run("Who was the first president of the United States of America?")

> Entering new AgentExecutor chain...
 I need to answer a historical question
Action: Search
Action Input: "who was the first president of the United States of America" 
Observation: George Washington
Thought: George Washington was the first president
Final Answer: George Washington was the first president of the United States of America.

> Finished chain.

'George Washington was the first president of the United States of America.'