如何将 Runnables 转换为工具

先决条件

本指南假设您熟悉以下概念

为方便起见，接受字符串或对象输入的 Runnables 可以使用 asTool 方法转换为工具，该方法允许指定名称、描述以及参数的其他模式信息。

在这里，我们将演示如何使用此方法将 LangChain Runnable 转换为可供代理、链或聊天模型使用的工具。

兼容性

此功能需要 @langchain/core>=0.2.16。请在此处查看升级指南。

`asTool`

与通用 Runnables 相比，工具还有一些额外的要求

它们的输入被限制为可序列化的，特别是字符串和对象；
它们包含名称和描述，指示如何以及何时应使用它们；
它们包含用于其参数的详细 schema 属性。也就是说，虽然工具（作为 Runnable）可能接受单个对象输入，但填充对象所需的特定键和类型信息应在 schema 字段中指定。

因此，asTool() 方法需要此附加信息才能从 runnable 创建工具。这是一个基本示例

import { RunnableLambda } from "@langchain/core/runnables";
import { z } from "zod";

const schema = z.object({
  a: z.number(),
  b: z.array(z.number()),
});

const runnable = RunnableLambda.from((input: z.infer<typeof schema>) => {
  return input.a * Math.max(...input.b);
});

const asTool = runnable.asTool({
  name: "My tool",
  description: "Explanation of when to use the tool.",
  schema,
});

asTool.description;

Explanation of when to use the tool.

await asTool.invoke({ a: 3, b: [1, 2] });

也支持接受字符串输入的 Runnables

const firstRunnable = RunnableLambda.from<string, string>((input) => {
  return input + "a";
});

const secondRunnable = RunnableLambda.from<string, string>((input) => {
  return input + "z";
});

const runnable = firstRunnable.pipe(secondRunnable);
const asTool = runnable.asTool({
  name: "append_letters",
  description: "Adds letters to a string.",
  schema: z.string(),
});

asTool.description;

Adds letters to a string.

await asTool.invoke("b");

baz

在代理中

下面，我们将 LangChain Runnables 作为工具整合到代理应用程序中。我们将演示

文档检索器；
一个简单的 RAG 链，允许代理将相关查询委托给它。

我们首先实例化一个支持工具调用的聊天模型

按照RAG 教程，我们首先构建一个检索器

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({ model: "gpt-3.5-turbo-0125", temperature: 0 });

import { Document } from "@langchain/core/documents";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";

const documents = [
  new Document({
    pageContent:
      "Dogs are great companions, known for their loyalty and friendliness.",
  }),
  new Document({
    pageContent: "Cats are independent pets that often enjoy their own space.",
  }),
];

const vectorstore = await MemoryVectorStore.fromDocuments(
  documents,
  new OpenAIEmbeddings()
);

const retriever = vectorstore.asRetriever({
  k: 1,
  searchType: "similarity",
});

接下来，我们创建一个预构建的 LangGraph 代理并为其提供该工具

import { createReactAgent } from "@langchain/langgraph/prebuilt";

const tools = [
  retriever.asTool({
    name: "pet_info_retriever",
    description: "Get information about pets.",
    schema: z.string(),
  }),
];

const agent = createReactAgent({ llm: llm, tools });

const stream = await agent.stream({
  messages: [["human", "What are dogs known for?"]],
});

for await (const chunk of stream) {
  // Log output from the agent or tools node
  if (chunk.agent) {
    console.log("AGENT:", chunk.agent.messages[0]);
  } else if (chunk.tools) {
    console.log("TOOLS:", chunk.tools.messages[0]);
  }
  console.log("----");
}

AGENT: AIMessage {
  "id": "chatcmpl-9m9RIN1GQVeXcrVdp0lNBTcZFVHb9",
  "content": "",
  "additional_kwargs": {
    "tool_calls": [
      {
        "id": "call_n30LPDbegmytrj5GdUxZt9xn",
        "type": "function",
        "function": "[Object]"
      }
    ]
  },
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 17,
      "promptTokens": 52,
      "totalTokens": 69
    },
    "finish_reason": "tool_calls"
  },
  "tool_calls": [
    {
      "name": "pet_info_retriever",
      "args": {
        "input": "dogs"
      },
      "type": "tool_call",
      "id": "call_n30LPDbegmytrj5GdUxZt9xn"
    }
  ],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 52,
    "output_tokens": 17,
    "total_tokens": 69
  }
}
----
TOOLS: ToolMessage {
  "content": "[{\"pageContent\":\"Dogs are great companions, known for their loyalty and friendliness.\",\"metadata\":{}}]",
  "name": "pet_info_retriever",
  "additional_kwargs": {},
  "response_metadata": {},
  "tool_call_id": "call_n30LPDbegmytrj5GdUxZt9xn"
}
----
AGENT: AIMessage {
  "id": "chatcmpl-9m9RJ3TT3ITfv6R0Tb7pcrNOUtnm8",
  "content": "Dogs are known for being great companions, known for their loyalty and friendliness.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 18,
      "promptTokens": 104,
      "totalTokens": 122
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 104,
    "output_tokens": 18,
    "total_tokens": 122
  }
}
----

此 LangSmith 追踪显示了上述运行在幕后发生的事情。

更进一步，我们甚至可以从完整的 RAG 链创建一个工具

import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";

const SYSTEM_TEMPLATE = `
You are an assistant for question-answering tasks.
Use the below context to answer the question. If
you don't know the answer, say you don't know.
Use three sentences maximum and keep the answer
concise.

Answer in the style of {answer_style}.

Context: {context}`;

const prompt = ChatPromptTemplate.fromMessages([
  ["system", SYSTEM_TEMPLATE],
  ["human", "{question}"],
]);

const ragChain = RunnableSequence.from([
  {
    context: (input, config) => retriever.invoke(input.question, config),
    question: (input) => input.question,
    answer_style: (input) => input.answer_style,
  },
  prompt,
  llm,
  new StringOutputParser(),
]);

下面我们再次调用代理。请注意，代理在其 tool_calls 中填充了必需的参数

const ragTool = ragChain.asTool({
  name: "pet_expert",
  description: "Get information about pets.",
  schema: z.object({
    context: z.string(),
    question: z.string(),
    answer_style: z.string(),
  }),
});

const agent = createReactAgent({ llm: llm, tools: [ragTool] });

const stream = await agent.stream({
  messages: [["human", "What would a pirate say dogs are known for?"]],
});

for await (const chunk of stream) {
  // Log output from the agent or tools node
  if (chunk.agent) {
    console.log("AGENT:", chunk.agent.messages[0]);
  } else if (chunk.tools) {
    console.log("TOOLS:", chunk.tools.messages[0]);
  }
  console.log("----");
}

AGENT: AIMessage {
  "id": "chatcmpl-9m9RKY2nAa8LeGoBiO7N1SR4nAoED",
  "content": "",
  "additional_kwargs": {
    "tool_calls": [
      {
        "id": "call_ukzivO4jRn1XdDpuVTI6CvtU",
        "type": "function",
        "function": "[Object]"
      }
    ]
  },
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 30,
      "promptTokens": 63,
      "totalTokens": 93
    },
    "finish_reason": "tool_calls"
  },
  "tool_calls": [
    {
      "name": "pet_expert",
      "args": {
        "context": "pirate",
        "question": "What are dogs known for?",
        "answer_style": "short"
      },
      "type": "tool_call",
      "id": "call_ukzivO4jRn1XdDpuVTI6CvtU"
    }
  ],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 63,
    "output_tokens": 30,
    "total_tokens": 93
  }
}
----
TOOLS: ToolMessage {
  "content": "Dogs are known for their loyalty, companionship, and ability to provide emotional support to their owners.",
  "name": "pet_expert",
  "additional_kwargs": {},
  "response_metadata": {},
  "tool_call_id": "call_ukzivO4jRn1XdDpuVTI6CvtU"
}
----
AGENT: AIMessage {
  "id": "chatcmpl-9m9RMwAEc14TTKtitq3CH2x9wpGik",
  "content": "A pirate would say that dogs are known for their loyalty, companionship, and ability to provide emotional support to their owners.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 26,
      "promptTokens": 123,
      "totalTokens": 149
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 123,
    "output_tokens": 26,
    "total_tokens": 149
  }
}
----

请参阅此 LangSmith 追踪，了解上述运行在内部发生了什么。

如何将 Runnables 转换为工具

`asTool`

在代理中

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asTool​

在代理中​

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您也可以留下详细的反馈 在 GitHub 上.

`asTool`

在代理中

相关

您也可以留下详细的反馈在 GitHub 上.