Argilla

Argilla 是一个用于大型语言模型 (LLM) 的开源数据整理平台。使用 Argilla，每个人都可以通过结合人工和机器反馈，更快地整理数据，从而构建强大的语言模型。我们为 MLOps 生命周期的每个步骤提供支持，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，以在 Argilla 中生成数据集。

跟踪您的 LLM 的输入和输出对于生成用于未来微调的数据集非常有用。当您使用 LLM 为特定任务（例如问答、摘要或翻译）生成数据时，这尤其有用。

安装与设置

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

获取 API 凭证

要获取 Argilla API 凭据，请按照以下步骤操作

1. 访问您的 Argilla UI。
2. 点击您的个人资料图片，然后进入“我的设置”。
3. 然后复制 API 密钥。

在 Argilla 中，API URL 将与您的 Argilla UI 的 URL 相同。

要获取 OpenAI API 凭据，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 来跟踪您的 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` is only available in Argilla v1.8.0 or higher, please "
        "upgrade `argilla` as `pip install argilla --upgrade`."
    )

dataset = rg.FeedbackDataset(
    fields=[
        rg.TextField(name="prompt"),
        rg.TextField(name="response"),
    ],
    questions=[
        rg.RatingQuestion(
            name="response-rating",
            description="How would you rate the quality of the response?",
            values=[1, 2, 3, 4, 5],
            required=True,
        ),
        rg.TextQuestion(
            name="response-feedback",
            description="What feedback do you have for the response?",
            required=False,
        ),
    ],
    guidelines="You're asked to rate the quality of the response and provide feedback.",
)

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，FeedbackDataset.fields 只支持提示-响应对，因此 ArgillaCallbackHandler 将只跟踪提示（即 LLM 输入）和响应（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介绍的示例之一。

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"],
)

API 参考:ArgillaCallbackHandler

场景 1: 跟踪 LLM

首先，让我们多次运行一个 LLM，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)

API 参考:StdOutCallbackHandler | 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'})

场景 2: 跟踪链中的 LLM

然后我们可以使用提示模板创建一个链，然后在 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)

API 参考:LLMChain | StdOutCallbackHandler | PromptTemplate | OpenAI

> 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: 将 Agent 与工具配合使用

最后，作为一个更高级的工作流程，您可以创建一个使用某些工具的 agent。这样，ArgillaCallbackHandler 将跟踪输入和输出，但不会跟踪中间步骤/思考过程，因此对于给定的提示，我们将记录原始提示和对该提示的最终响应。

请注意，对于此场景，我们将使用 Google Search API (Serp API)，因此您需要安装 google-search-results（通过 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运行。

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?")

API 参考:AgentType | initialize_agent | load_tools | StdOutCallbackHandler | OpenAI

> 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.'