如何合并相同类型的连续消息

mergeMessageRuns 函数在 @langchain/core 0.2.8 及更高版本中可用。

某些模型不支持传入相同类型的连续消息（也称为相同消息类型的“runs”）。

mergeMessageRuns 实用程序可以轻松合并相同类型的连续消息。

基本用法

import {
  HumanMessage,
  SystemMessage,
  AIMessage,
  mergeMessageRuns,
} from "@langchain/core/messages";

const messages = [
  new SystemMessage("you're a good assistant."),
  new SystemMessage("you always respond with a joke."),
  new HumanMessage({
    content: [{ type: "text", text: "i wonder why it's called langchain" }],
  }),
  new HumanMessage("and who is harrison chasing anyways"),
  new AIMessage(
    'Well, I guess they thought "WordRope" and "SentenceString" just didn\'t have the same ring to it!'
  ),
  new AIMessage(
    "Why, he's probably chasing after the last cup of coffee in the office!"
  ),
];

const merged = mergeMessageRuns(messages);
console.log(
  merged
    .map((x) =>
      JSON.stringify(
        {
          role: x._getType(),
          content: x.content,
        },
        null,
        2
      )
    )
    .join("\n\n")
);

{
  "role": "system",
  "content": "you're a good assistant.\nyou always respond with a joke."
}

{
  "role": "human",
  "content": [
    {
      "type": "text",
      "text": "i wonder why it's called langchain"
    },
    {
      "type": "text",
      "text": "and who is harrison chasing anyways"
    }
  ]
}

{
  "role": "ai",
  "content": "Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn't have the same ring to it!\nWhy, he's probably chasing after the last cup of coffee in the office!"
}

请注意，如果要合并的消息之一的内容是内容块列表，则合并后的消息将具有内容块列表。 并且，如果要合并的两个消息都具有字符串内容，则会将它们与换行符连接起来。

链式调用

mergeMessageRuns 可以命令式（如上所示）或声明式使用，使其易于与链中的其他组件组合。

import { ChatAnthropic } from "@langchain/anthropic";
import { mergeMessageRuns } from "@langchain/core/messages";

const llm = new ChatAnthropic({
  model: "claude-3-sonnet-20240229",
  temperature: 0,
});
// Notice we don't pass in messages. This creates
// a RunnableLambda that takes messages as input
const merger = mergeMessageRuns();
const chain = merger.pipe(llm);
await chain.invoke(messages);

AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: [],
    additional_kwargs: {
      id: 'msg_01LsdS4bjQ3EznH7Tj4xujV1',
      type: 'message',
      role: 'assistant',
      model: 'claude-3-sonnet-20240229',
      stop_reason: 'end_turn',
      stop_sequence: null,
      usage: [Object]
    },
    tool_calls: [],
    usage_metadata: { input_tokens: 84, output_tokens: 3, total_tokens: 87 },
    invalid_tool_calls: [],
    response_metadata: {}
  },
  lc_namespace: [ 'langchain_core', 'messages' ],
  content: [],
  name: undefined,
  additional_kwargs: {
    id: 'msg_01LsdS4bjQ3EznH7Tj4xujV1',
    type: 'message',
    role: 'assistant',
    model: 'claude-3-sonnet-20240229',
    stop_reason: 'end_turn',
    stop_sequence: null,
    usage: { input_tokens: 84, output_tokens: 3 }
  },
  response_metadata: {
    id: 'msg_01LsdS4bjQ3EznH7Tj4xujV1',
    model: 'claude-3-sonnet-20240229',
    stop_reason: 'end_turn',
    stop_sequence: null,
    usage: { input_tokens: 84, output_tokens: 3 }
  },
  id: undefined,
  tool_calls: [],
  invalid_tool_calls: [],
  usage_metadata: { input_tokens: 84, output_tokens: 3, total_tokens: 87 }
}

查看 LangSmith 追踪，我们可以看到在消息传递到模型之前，它们已被合并。

仅查看 merger，我们可以看到它是一个 Runnable 对象，可以像所有 Runnables 一样调用。

await merger.invoke(messages);

[
  SystemMessage {
    lc_serializable: true,
    lc_kwargs: {
      content: "you're a good assistant.\nyou always respond with a joke.",
      name: undefined,
      additional_kwargs: {},
      response_metadata: {},
      id: undefined
    },
    lc_namespace: [ 'langchain_core', 'messages' ],
    content: "you're a good assistant.\nyou always respond with a joke.",
    name: undefined,
    additional_kwargs: {},
    response_metadata: {},
    id: undefined
  },
  HumanMessage {
    lc_serializable: true,
    lc_kwargs: {
      content: [Array],
      name: undefined,
      additional_kwargs: {},
      response_metadata: {},
      id: undefined
    },
    lc_namespace: [ 'langchain_core', 'messages' ],
    content: [ [Object], [Object] ],
    name: undefined,
    additional_kwargs: {},
    response_metadata: {},
    id: undefined
  },
  AIMessage {
    lc_serializable: true,
    lc_kwargs: {
      content: `Well, I guess they thought "WordRope" and "SentenceString" just didn't have the same ring to it!\n` +
        "Why, he's probably chasing after the last cup of coffee in the office!",
      name: undefined,
      additional_kwargs: {},
      response_metadata: {},
      id: undefined,
      tool_calls: [],
      invalid_tool_calls: [],
      usage_metadata: undefined
    },
    lc_namespace: [ 'langchain_core', 'messages' ],
    content: `Well, I guess they thought "WordRope" and "SentenceString" just didn't have the same ring to it!\n` +
      "Why, he's probably chasing after the last cup of coffee in the office!",
    name: undefined,
    additional_kwargs: {},
    response_metadata: {},
    id: undefined,
    tool_calls: [],
    invalid_tool_calls: [],
    usage_metadata: undefined
  }
]

API 参考

有关所有参数的完整说明，请访问 API 参考。