如何评估 runnable

INFO

• langchain：Python 和 JS/TS
• Runnable：Python 和 JS/TS

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

设置

我们来定义一个要评估的简单链。首先安装所有必需的包：

Python

pip install -U langsmith langchain[openai]

TypeScript

yarn add langsmith @langchain/openai

现在定义一个链：

Python

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()

TypeScript

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

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，可以直接将其传给 evaluate() / aevaluate() 方法。请注意，链的输入变量必须与示例输入的键匹配。在本例中，示例输入应采用 {"text": "..."} 的形式。

Python

import asyncio
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())

TypeScript

import { evaluate } from "langsmith/evaluation";
import { Client } from "langsmith";

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
});

该 runnable 会针对每个输出进行相应的追踪。

相关

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