langchain Runnable 对象（如聊天模型、检索器、链等）可以直接传入 evaluate() / aevaluate().

设置

让我们定义一个简单的链来评估。首先，安装所有必需的包：

pip install -U langsmith langchain[openai]

yarn add langsmith @langchain/openai

现在定义一个链：

from langchain.chat_models import init_chat_model
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

instructions = (
    "Please review the user query below and determine if it contains any form "
    "of toxic behavior, such as insults, threats, or highly negative comments. "
    "Respond with 'Toxic' if it does, and 'Not toxic' if it doesn't."
)

prompt = ChatPromptTemplate(
    [("system", instructions), ("user", "{text}")],
)

model = init_chat_model("gpt-5.5")
chain = prompt | model | StrOutputParser()

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "Please review the user query below and determine if it contains any form of toxic behavior, such as insults, threats, or highly negative comments. Respond with 'Toxic' if it does, and 'Not toxic' if it doesn't."],
  ["user", "{text}"]
]);

const chatModel = new ChatOpenAI();
const outputParser = new StringOutputParser();
const chain = prompt.pipe(chatModel).pipe(outputParser);

评估

要评估我们的链，我们可以直接将其传递给 evaluate() / aevaluate() 方法。请注意，链的输入变量必须与示例输入的键匹配。在这种情况下，示例输入应具有以下形式 {"text": "..."}.

from langsmith import Client, aevaluate

client = Client()

# Clone a dataset of texts with toxicity labels.
# Each example input has a "text" key and each output has a "label" key.
dataset = client.clone_public_dataset(
    "https://smith.langchain.com/public/3d6831e6-1680-4c88-94df-618c8e01fc55/d"
)

def correct(outputs: dict, reference_outputs: dict) -> bool:
    # Since our chain outputs a string not a dict, this string
    # gets stored under the default "output" key in the outputs dict:
    actual = outputs["output"]
    expected = reference_outputs["label"]
    return actual == expected

async def main():
    results = await aevaluate(
        chain,
        data=dataset,
        evaluators=[correct],
        experiment_prefix="gpt-5.5, baseline",
        metadata={"models": "openai:gpt-5.5"},  # optional, used to populate model/prompt/tool columns in UI
    )
    print(results)

asyncio.run(main())

const langsmith = new Client();

const dataset = await client.clonePublicDataset(
  "https://smith.langchain.com/public/3d6831e6-1680-4c88-94df-618c8e01fc55/d"
)

await evaluate(chain, {
  data: dataset.name,
  evaluators: [correct],
  experimentPrefix: "gpt-5.5, baseline",
  metadata: { models: "openai:gpt-5.5" },  // optional, used to populate model/prompt/tool columns in UI
});

可运行对象会被适当地追踪每个输出。

!可运行对象评估

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