结构化工具聊天代理

结构化工具聊天代理是专为与符合任意对象模式的输入数据的工具配合使用设计的,相比其他仅支持接受单个字符串作为输入的代理，它们具有更高的灵活性。

这使得更容易创建和使用需要多个输入值的工具 - 而不是提示输入字符串化的对象或逗号分隔列表,可以指定具有多个键的对象。 这里有一个使用DynamicStructuredTool的示例:：

import { z } from "zod";
import { ChatOpenAI } from "langchain/chat_models/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { Calculator } from "langchain/tools/calculator";
import { DynamicStructuredTool } from "langchain/tools";

export const run = async () => {
  const model = new ChatOpenAI({ temperature: 0 });
  const tools = [
    new Calculator(), // Older existing single input tools will still work
    new DynamicStructuredTool({
      name: "random-number-generator",
      description: "generates a random number between two input numbers",
      schema: z.object({
        low: z.number().describe("The lower bound of the generated number"),
        high: z.number().describe("The upper bound of the generated number"),
      }),
      func: async ({ low, high }) =>
        (Math.random() * (high - low) + low).toString(), // Outputs still must be strings
    }),
  ];

  const executor = await initializeAgentExecutorWithOptions(tools, model, {
    agentType: "structured-chat-zero-shot-react-description",
    verbose: true,
  });
  console.log("Loaded agent.");

  const input = `What is a random number between 5 and 10 raised to the second power?`;

  console.log(`Executing with input "${input}"...`);

  const result = await executor.call({ input });

  console.log({ result });

  /*
    {
      "output": "67.95299776074"
    }
  */
};

添加记忆

您可以像这样为该代理添加记忆::

import { ChatOpenAI } from "langchain/chat_models/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { Calculator } from "langchain/tools/calculator";
import { MessagesPlaceholder } from "langchain/prompts";
import { BufferMemory } from "langchain/memory";

export const run = async () => {
  const model = new ChatOpenAI({ temperature: 0 });
  const tools = [new Calculator()];

  const executor = await initializeAgentExecutorWithOptions(tools, model, {
    agentType: "structured-chat-zero-shot-react-description",
    verbose: true,
    memory: new BufferMemory({
      memoryKey: "chat_history",
      returnMessages: true,
    }),
    agentArgs: {
      inputVariables: ["input", "agent_scratchpad", "chat_history"],
      memoryPrompts: [new MessagesPlaceholder("chat_history")],
    },
  });

  const result = await executor.call({ input: `what is 9 to the 2nd power?` });

  console.log(result);

  /*
    {
      "output": "81"
    }
  */

  const result2 = await executor.call({
    input: `what is that number squared?`,
  });

  console.log(result2);

  /*
    {
      "output": "6561"
    }
  */
};