跳至主要内容

如何添加聊天历史记录

本教程之前使用 RunnableWithMessageHistory 构建了一个聊天机器人。您可以在 v0.2 文档 中访问本教程的此版本。

LangGraph 实现提供了许多优于 RunnableWithMessageHistory 的优势，包括能够持久化应用程序状态的任意组件（而不仅仅是消息）。

在许多问答应用程序中，我们希望允许用户进行来回对话，这意味着应用程序需要某种形式的“记忆”来存储过去的问题和答案，以及将这些答案纳入当前思考的一些逻辑。

在本指南中，我们重点关注 **添加用于合并历史消息的逻辑。**

这在很大程度上是 对话 RAG 教程 的浓缩版本。

我们将介绍两种方法

  1. 链，其中我们始终执行检索步骤；
  2. 代理，其中我们赋予 LLM 决定是否以及如何执行检索步骤（或多个步骤）的自由。

对于外部知识来源，我们将使用来自 RAG 教程 的 Lilian Weng 所写的 LLM 驱动的自主代理 博客文章。

设置​

依赖项​

在本演练中，我们将使用 OpenAI 聊天模型和嵌入以及 Memory 向量存储，但此处显示的所有内容都适用于任何 ChatModel 或 LLM、嵌入 以及 VectorStore 或 检索器。

我们将使用以下包

npm install --save langchain @langchain/openai langchain cheerio uuid

我们需要设置环境变量 OPENAI_API_KEY

export OPENAI_API_KEY=YOUR_KEY

LangSmith​

您使用 LangChain 构建的许多应用程序将包含多个步骤，并多次调用 LLM。随着这些应用程序变得越来越复杂，能够检查链或代理内部究竟发生了什么变得至关重要。使用 LangSmith 是执行此操作的最佳方法。

请注意，LangSmith 不是必需的，但它很有帮助。如果您确实想使用 LangSmith，在您从上面的链接注册后，请确保设置您的环境变量以开始记录跟踪

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=YOUR_KEY

# Reduce tracing latency if you are not in a serverless environment
# export LANGCHAIN_CALLBACKS_BACKGROUND=true

链​

在对话式 RAG 应用程序中，发给检索器的查询应以对话的上下文为依据。LangChain 提供了一个 createHistoryAwareRetriever 构造函数来简化此过程。它构建一个链，该链接受键 input 和 chat_history 作为输入，并具有与检索器相同的输出模式。createHistoryAwareRetriever 需要以下输入

  1. LLM；
  2. 检索器；
  3. 提示。

首先，我们获取这些对象

LLM​

我们可以使用任何支持的聊天模型

选择您的聊天模型

安装依赖项

提示

参见 本节了解有关安装集成包的一般说明.

yarn add @langchain/openai

添加环境变量

OPENAI_API_KEY=your-api-key

实例化模型

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

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  temperature: 0
});

初始设置​

import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";

const loader = new CheerioWebBaseLoader(
  "https://lilianweng.github.io/posts/2023-06-23-agent/"
);

const docs = await loader.load();

const textSplitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});
const splits = await textSplitter.splitDocuments(docs);
const vectorStore = await MemoryVectorStore.fromDocuments(
  splits,
  new OpenAIEmbeddings()
);

// Retrieve and generate using the relevant snippets of the blog.
const retriever = vectorStore.asRetriever();

提示​

我们将使用包含名为“chat_history”的`MessagesPlaceholder`变量的提示。这使我们能够使用“chat_history”输入键将消息列表传递给提示，并且这些消息将在系统消息之后以及包含最新问题的用户消息之前插入。

import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";

const contextualizeQSystemPrompt =
  "Given a chat history and the latest user question " +
  "which might reference context in the chat history, " +
  "formulate a standalone question which can be understood " +
  "without the chat history. Do NOT answer the question, " +
  "just reformulate it if needed and otherwise return it as is.";

const contextualizeQPrompt = ChatPromptTemplate.fromMessages([
  ["system", contextualizeQSystemPrompt],
  new MessagesPlaceholder("chat_history"),
  ["human", "{input}"],
]);

组装链​

然后，我们可以实例化具有历史记录感知的检索器

import { createHistoryAwareRetriever } from "langchain/chains/history_aware_retriever";

const historyAwareRetriever = await createHistoryAwareRetriever({
  llm,
  retriever,
  rephrasePrompt: contextualizeQPrompt,
});

此链将输入查询的改述附加到我们的检索器，以便检索包含对话的上下文。

现在，我们可以构建完整的问答链。

与RAG 教程一样，我们将使用createStuffDocumentsChain 生成一个`questionAnswerChain`，输入键为`context`、`chat_history`和`input` - 它接受检索到的上下文以及对话历史记录和查询以生成答案。

我们使用createRetrievalChain 构建最终的`ragChain`。此链按顺序应用`historyAwareRetriever` 和 `questionAnswerChain`，保留中间输出，例如检索到的上下文以方便起见。它具有输入键`input` 和 `chat_history`，并在其输出中包含`input`、`chat_history`、`context` 和 `answer`。

import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { createRetrievalChain } from "langchain/chains/retrieval";

const systemPrompt =
  "You are an assistant for question-answering tasks. " +
  "Use the following pieces of retrieved context to answer " +
  "the question. If you don't know the answer, say that you " +
  "don't know. Use three sentences maximum and keep the " +
  "answer concise." +
  "\n\n" +
  "{context}";

const qaPrompt = ChatPromptTemplate.fromMessages([
  ["system", systemPrompt],
  new MessagesPlaceholder("chat_history"),
  ["human", "{input}"],
]);

const questionAnswerChain = await createStuffDocumentsChain({
  llm,
  prompt: qaPrompt,
});

const ragChain = await createRetrievalChain({
  retriever: historyAwareRetriever,
  combineDocsChain: questionAnswerChain,
});

对话历史记录的状态管理​

我们添加了用于合并对话历史记录的应用程序逻辑，但我们仍然通过应用程序手动将其连接起来。在生产中，我们通常将问答应用程序中的对话历史记录持久化到数据库中，并能够适当地读取和更新它。

LangGraph 实现了一个内置的持久化层，使其成为支持多个对话轮次的聊天应用程序的理想选择。

将我们的聊天模型包装在一个最小的 LangGraph 应用程序中，使我们能够自动持久化消息历史记录，从而简化多轮应用程序的开发。

LangGraph 带有一个简单的内存检查点，我们在下面使用它。有关详细信息，请参阅其文档，包括如何使用不同的持久化后端（例如，SQLite 或 Postgres）。

有关如何管理消息历史记录的详细演练，请访问如何添加消息历史记录（内存）指南。

import { AIMessage, BaseMessage, HumanMessage } from "@langchain/core/messages";
import {
  StateGraph,
  START,
  END,
  MemorySaver,
  messagesStateReducer,
  Annotation,
} from "@langchain/langgraph";

// Define the State interface
const GraphAnnotation = Annotation.Root({
  input: Annotation<string>(),
  chat_history: Annotation<BaseMessage[]>({
    reducer: messagesStateReducer,
    default: () => [],
  }),
  context: Annotation<string>(),
  answer: Annotation<string>(),
});

// Define the call_model function
async function callModel(state: typeof GraphAnnotation.State) {
  const response = await ragChain.invoke(state);
  return {
    chat_history: [
      new HumanMessage(state.input),
      new AIMessage(response.answer),
    ],
    context: response.context,
    answer: response.answer,
  };
}

// Create the workflow
const workflow = new StateGraph(GraphAnnotation)
  .addNode("model", callModel)
  .addEdge(START, "model")
  .addEdge("model", END);

// Compile the graph with a checkpointer object
const memory = new MemorySaver();
const app = workflow.compile({ checkpointer: memory });
import { v4 as uuidv4 } from "uuid";

const threadId = uuidv4();
const config = { configurable: { thread_id: threadId } };

const result = await app.invoke(
  { input: "What is Task Decomposition?" },
  config
);
console.log(result.answer);
Task Decomposition is the process of breaking down a complicated task into smaller, simpler, and more manageable steps. Techniques like Chain of Thought (CoT) and Tree of Thoughts expand on this by enabling agents to think step by step or explore multiple reasoning possibilities at each step. This allows for a more structured and interpretable approach to handling complex tasks.
const result2 = await app.invoke(
  { input: "What is one way of doing it?" },
  config
);
console.log(result2.answer);
One way of doing task decomposition is by using an LLM with simple prompting, such as asking "Steps for XYZ.\n1." or "What are the subgoals for achieving XYZ?" This method leverages direct prompts to guide the model in breaking down tasks.

可以通过应用程序的状态检查对话历史记录

const chatHistory = (await app.getState(config)).values.chat_history;
for (const message of chatHistory) {
  console.log(message);
}
HumanMessage {
  "content": "What is Task Decomposition?",
  "additional_kwargs": {},
  "response_metadata": {}
}
AIMessage {
  "content": "Task Decomposition is the process of breaking down a complicated task into smaller, simpler, and more manageable steps. Techniques like Chain of Thought (CoT) and Tree of Thoughts expand on this by enabling agents to think step by step or explore multiple reasoning possibilities at each step. This allows for a more structured and interpretable approach to handling complex tasks.",
  "additional_kwargs": {},
  "response_metadata": {},
  "tool_calls": [],
  "invalid_tool_calls": []
}
HumanMessage {
  "content": "What is one way of doing it?",
  "additional_kwargs": {},
  "response_metadata": {}
}
AIMessage {
  "content": "One way of doing task decomposition is by using an LLM with simple prompting, such as asking \"Steps for XYZ.\\n1.\" or \"What are the subgoals for achieving XYZ?\" This method leverages direct prompts to guide the model in breaking down tasks.",
  "additional_kwargs": {},
  "response_metadata": {},
  "tool_calls": [],
  "invalid_tool_calls": []
}

将它们联系在一起​

为了方便起见，我们将所有必要的步骤在一个代码单元格中组合在一起

import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { createHistoryAwareRetriever } from "langchain/chains/history_aware_retriever";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { createRetrievalChain } from "langchain/chains/retrieval";
import { AIMessage, BaseMessage, HumanMessage } from "@langchain/core/messages";
import {
  StateGraph,
  START,
  END,
  MemorySaver,
  messagesStateReducer,
  Annotation,
} from "@langchain/langgraph";
import { v4 as uuidv4 } from "uuid";

const llm2 = new ChatOpenAI({ model: "gpt-4o" });

const loader2 = new CheerioWebBaseLoader(
  "https://lilianweng.github.io/posts/2023-06-23-agent/"
);

const docs2 = await loader2.load();

const textSplitter2 = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});
const splits2 = await textSplitter2.splitDocuments(docs2);
const vectorStore2 = await MemoryVectorStore.fromDocuments(
  splits2,
  new OpenAIEmbeddings()
);

// Retrieve and generate using the relevant snippets of the blog.
const retriever2 = vectorStore2.asRetriever();

const contextualizeQSystemPrompt2 =
  "Given a chat history and the latest user question " +
  "which might reference context in the chat history, " +
  "formulate a standalone question which can be understood " +
  "without the chat history. Do NOT answer the question, " +
  "just reformulate it if needed and otherwise return it as is.";

const contextualizeQPrompt2 = ChatPromptTemplate.fromMessages([
  ["system", contextualizeQSystemPrompt2],
  new MessagesPlaceholder("chat_history"),
  ["human", "{input}"],
]);

const historyAwareRetriever2 = await createHistoryAwareRetriever({
  llm: llm2,
  retriever: retriever2,
  rephrasePrompt: contextualizeQPrompt2,
});

const systemPrompt2 =
  "You are an assistant for question-answering tasks. " +
  "Use the following pieces of retrieved context to answer " +
  "the question. If you don't know the answer, say that you " +
  "don't know. Use three sentences maximum and keep the " +
  "answer concise." +
  "\n\n" +
  "{context}";

const qaPrompt2 = ChatPromptTemplate.fromMessages([
  ["system", systemPrompt2],
  new MessagesPlaceholder("chat_history"),
  ["human", "{input}"],
]);

const questionAnswerChain2 = await createStuffDocumentsChain({
  llm: llm2,
  prompt: qaPrompt2,
});

const ragChain2 = await createRetrievalChain({
  retriever: historyAwareRetriever2,
  combineDocsChain: questionAnswerChain2,
});

// Define the State interface
const GraphAnnotation2 = Annotation.Root({
  input: Annotation<string>(),
  chat_history: Annotation<BaseMessage[]>({
    reducer: messagesStateReducer,
    default: () => [],
  }),
  context: Annotation<string>(),
  answer: Annotation<string>(),
});

// Define the call_model function
async function callModel2(state: typeof GraphAnnotation2.State) {
  const response = await ragChain2.invoke(state);
  return {
    chat_history: [
      new HumanMessage(state.input),
      new AIMessage(response.answer),
    ],
    context: response.context,
    answer: response.answer,
  };
}

// Create the workflow
const workflow2 = new StateGraph(GraphAnnotation2)
  .addNode("model", callModel2)
  .addEdge(START, "model")
  .addEdge("model", END);

// Compile the graph with a checkpointer object
const memory2 = new MemorySaver();
const app2 = workflow2.compile({ checkpointer: memory2 });

const threadId2 = uuidv4();
const config2 = { configurable: { thread_id: threadId2 } };

const result3 = await app2.invoke(
  { input: "What is Task Decomposition?" },
  config2
);
console.log(result3.answer);

const result4 = await app2.invoke(
  { input: "What is one way of doing it?" },
  config2
);
console.log(result4.answer);
Task Decomposition is the process of breaking a complicated task into smaller, simpler steps to enhance model performance on complex tasks. Techniques like Chain of Thought (CoT) and Tree of Thoughts (ToT) are used for this, with CoT focusing on step-by-step thinking and ToT exploring multiple reasoning possibilities at each step. Decomposition can be carried out by the LLM itself, using task-specific instructions, or through human inputs.
One way of doing task decomposition is by prompting the LLM with simple instructions such as "Steps for XYZ.\n1." or "What are the subgoals for achieving XYZ?" This encourages the model to break down the task into smaller, manageable steps on its own.

代理​

代理利用 LLM 的推理能力在执行期间做出决策。使用代理可以让你在检索过程中卸下一些自由裁量权。虽然它们的行为不像链条那样可预测，但在这种情况下它们提供了一些优势：- 代理直接生成检索器的输入，而不必像上面那样明确地构建上下文；- 代理可以执行多个检索步骤来服务于查询，或者完全避免执行检索步骤（例如，响应用户的通用问候语）。

检索工具​

代理可以访问“工具”并管理其执行。在这种情况下，我们将把我们的检索器转换为 LangChain 工具，供代理使用

import { createRetrieverTool } from "langchain/tools/retriever";

const tool = createRetrieverTool(retriever, {
  name: "blog_post_retriever",
  description:
    "Searches and returns excerpts from the Autonomous Agents blog post.",
});
const tools = [tool];

代理构造函数​

现在我们已经定义了工具和 LLM，我们可以创建代理。我们将使用LangGraph 来构建代理。目前，我们正在使用高级接口来构建代理，但 LangGraph 的好处是，此高级接口由低级、高度可控的 API 支持，以防你想要修改代理逻辑。

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

const agentExecutor = createReactAgent({ llm, tools });

现在我们可以尝试一下。请注意，到目前为止它还没有状态（我们仍然需要添加内存）

const query = "What is Task Decomposition?";

for await (const s of await agentExecutor.stream({
  messages: [{ role: "user", content: query }],
})) {
  console.log(s);
  console.log("----");
}
{
  agent: {
    messages: [
      AIMessage {
        "id": "chatcmpl-AB7xlcJBGSKSp1GvgDY9FP8KvXxwB",
        "content": "",
        "additional_kwargs": {
          "tool_calls": [
            {
              "id": "call_Ev0nA6nzGwOeMC5upJUUxTuw",
              "type": "function",
              "function": "[Object]"
            }
          ]
        },
        "response_metadata": {
          "tokenUsage": {
            "completionTokens": 19,
            "promptTokens": 66,
            "totalTokens": 85
          },
          "finish_reason": "tool_calls",
          "system_fingerprint": "fp_52a7f40b0b"
        },
        "tool_calls": [
          {
            "name": "blog_post_retriever",
            "args": {
              "query": "Task Decomposition"
            },
            "type": "tool_call",
            "id": "call_Ev0nA6nzGwOeMC5upJUUxTuw"
          }
        ],
        "invalid_tool_calls": [],
        "usage_metadata": {
          "input_tokens": 66,
          "output_tokens": 19,
          "total_tokens": 85
        }
      }
    ]
  }
}
----
{
  tools: {
    messages: [
      ToolMessage {
        "content": "Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\n\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\nAnother quite distinct approach, LLM+P (Liu et al. 2023), involves relying on an external classical planner to do long-horizon planning. This approach utilizes the Planning Domain Definition Language (PDDL) as an intermediate interface to describe the planning problem. In this process, LLM (1) translates the problem into “Problem PDDL”, then (2) requests a classical planner to generate a PDDL plan based on an existing “Domain PDDL”, and finally (3) translates the PDDL plan back into natural language. Essentially, the planning step is outsourced to an external tool, assuming the availability of domain-specific PDDL and a suitable planner which is common in certain robotic setups but not in many other domains.\nSelf-Reflection#\n\nAgent System Overview\n                \n                    Component One: Planning\n                        \n                \n                    Task Decomposition\n                \n                    Self-Reflection\n                \n                \n                    Component Two: Memory\n                        \n                \n                    Types of Memory\n                \n                    Maximum Inner Product Search (MIPS)\n                \n                \n                    Component Three: Tool Use\n                \n                    Case Studies\n                        \n                \n                    Scientific Discovery Agent\n                \n                    Generative Agents Simulation\n                \n                    Proof-of-Concept Examples\n                \n                \n                    Challenges\n                \n                    Citation\n                \n                    References\n\n(3) Task execution: Expert models execute on the specific tasks and log results.\nInstruction:\n\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.",
        "name": "blog_post_retriever",
        "additional_kwargs": {},
        "response_metadata": {},
        "tool_call_id": "call_Ev0nA6nzGwOeMC5upJUUxTuw"
      }
    ]
  }
}
----
{
  agent: {
    messages: [
      AIMessage {
        "id": "chatcmpl-AB7xmiPNPbMX2KvZKHM2oPfcoFMnY",
        "content": "**Task Decomposition** involves breaking down a complicated or large task into smaller, more manageable subtasks. Here are some insights based on current techniques and research:\n\n1. **Chain of Thought (CoT)**:\n   - Introduced by Wei et al. (2022), this technique prompts the model to \"think step by step\".\n   - It helps decompose hard tasks into several simpler steps.\n   - Enhances the interpretability of the model's thought process.\n\n2. **Tree of Thoughts (ToT)**:\n   - An extension of CoT by Yao et al. (2023).\n   - Decomposes problems into multiple thought steps and generates several possibilities at each step.\n   - Utilizes tree structures through BFS (Breadth-First Search) or DFS (Depth-First Search) with evaluation by a classifier or majority vote.\n\n3. **Methods of Task Decomposition**:\n   - **Simple Prompting**: Asking the model directly, e.g., \"Steps for XYZ.\\n1.\" or \"What are the subgoals for achieving XYZ?\".\n   - **Task-Specific Instructions**: Tailoring instructions to the task, such as \"Write a story outline\" for writing a novel.\n   - **Human Inputs**: Receiving inputs from humans to refine the process.\n\n4. **LLM+P Approach**:\n   - Suggested by Liu et al. (2023), combines language models with an external classical planner.\n   - Uses Planning Domain Definition Language (PDDL) for long-horizon planning:\n     1. Translates the problem into a PDDL problem.\n     2. Requests an external planner to generate a PDDL plan.\n     3. Translates the PDDL plan back into natural language.\n   - This method offloads the planning complexity to a specialized tool, especially relevant for domains utilizing robotic setups.\n\nTask Decomposition is a fundamental component of planning in autonomous agent systems, aiding in the efficient accomplishment of complex tasks by breaking them into smaller, actionable steps.",
        "additional_kwargs": {},
        "response_metadata": {
          "tokenUsage": {
            "completionTokens": 411,
            "promptTokens": 732,
            "totalTokens": 1143
          },
          "finish_reason": "stop",
          "system_fingerprint": "fp_e375328146"
        },
        "tool_calls": [],
        "invalid_tool_calls": [],
        "usage_metadata": {
          "input_tokens": 732,
          "output_tokens": 411,
          "total_tokens": 1143
        }
      }
    ]
  }
}
----

LangGraph 带有内置持久性，因此我们不需要使用 `ChatMessageHistory`！相反，我们可以将检查点直接传递给我们的 LangGraph 代理。

通过在配置对象中为对话线程指定一个键来管理不同的对话，如下所示。

import { MemorySaver } from "@langchain/langgraph";

const memory3 = new MemorySaver();

const agentExecutor2 = createReactAgent({
  llm,
  tools,
  checkpointSaver: memory3,
});

这就是构建对话式 RAG 代理所需的一切。

让我们观察一下它的行为。请注意，如果我们输入一个不需要检索步骤的查询，代理就不会执行检索步骤

const threadId3 = uuidv4();
const config3 = { configurable: { thread_id: threadId3 } };

for await (const s of await agentExecutor2.stream(
  { messages: [{ role: "user", content: "Hi! I'm bob" }] },
  config3
)) {
  console.log(s);
  console.log("----");
}
{
  agent: {
    messages: [
      AIMessage {
        "id": "chatcmpl-AB7y8P8AGHkxOwKpwMc3qj6r0skYr",
        "content": "Hello, Bob! How can I assist you today?",
        "additional_kwargs": {},
        "response_metadata": {
          "tokenUsage": {
            "completionTokens": 12,
            "promptTokens": 64,
            "totalTokens": 76
          },
          "finish_reason": "stop",
          "system_fingerprint": "fp_e375328146"
        },
        "tool_calls": [],
        "invalid_tool_calls": [],
        "usage_metadata": {
          "input_tokens": 64,
          "output_tokens": 12,
          "total_tokens": 76
        }
      }
    ]
  }
}
----

此外，如果我们输入一个需要检索步骤的查询，代理将生成工具的输入

const query2 = "What is Task Decomposition?";

for await (const s of await agentExecutor2.stream(
  { messages: [{ role: "user", content: query2 }] },
  config3
)) {
  console.log(s);
  console.log("----");
}
{
  agent: {
    messages: [
      AIMessage {
        "id": "chatcmpl-AB7y8Do2IHJ2rnUvvMU3pTggmuZud",
        "content": "",
        "additional_kwargs": {
          "tool_calls": [
            {
              "id": "call_3tSaOZ3xdKY4miIJdvBMR80V",
              "type": "function",
              "function": "[Object]"
            }
          ]
        },
        "response_metadata": {
          "tokenUsage": {
            "completionTokens": 19,
            "promptTokens": 89,
            "totalTokens": 108
          },
          "finish_reason": "tool_calls",
          "system_fingerprint": "fp_e375328146"
        },
        "tool_calls": [
          {
            "name": "blog_post_retriever",
            "args": {
              "query": "Task Decomposition"
            },
            "type": "tool_call",
            "id": "call_3tSaOZ3xdKY4miIJdvBMR80V"
          }
        ],
        "invalid_tool_calls": [],
        "usage_metadata": {
          "input_tokens": 89,
          "output_tokens": 19,
          "total_tokens": 108
        }
      }
    ]
  }
}
----
{
  tools: {
    messages: [
      ToolMessage {
        "content": "Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\n\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\nAnother quite distinct approach, LLM+P (Liu et al. 2023), involves relying on an external classical planner to do long-horizon planning. This approach utilizes the Planning Domain Definition Language (PDDL) as an intermediate interface to describe the planning problem. In this process, LLM (1) translates the problem into “Problem PDDL”, then (2) requests a classical planner to generate a PDDL plan based on an existing “Domain PDDL”, and finally (3) translates the PDDL plan back into natural language. Essentially, the planning step is outsourced to an external tool, assuming the availability of domain-specific PDDL and a suitable planner which is common in certain robotic setups but not in many other domains.\nSelf-Reflection#\n\nAgent System Overview\n                \n                    Component One: Planning\n                        \n                \n                    Task Decomposition\n                \n                    Self-Reflection\n                \n                \n                    Component Two: Memory\n                        \n                \n                    Types of Memory\n                \n                    Maximum Inner Product Search (MIPS)\n                \n                \n                    Component Three: Tool Use\n                \n                    Case Studies\n                        \n                \n                    Scientific Discovery Agent\n                \n                    Generative Agents Simulation\n                \n                    Proof-of-Concept Examples\n                \n                \n                    Challenges\n                \n                    Citation\n                \n                    References\n\n(3) Task execution: Expert models execute on the specific tasks and log results.\nInstruction:\n\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.",
        "name": "blog_post_retriever",
        "additional_kwargs": {},
        "response_metadata": {},
        "tool_call_id": "call_3tSaOZ3xdKY4miIJdvBMR80V"
      }
    ]
  }
}
----
{
  agent: {
    messages: [
      AIMessage {
        "id": "chatcmpl-AB7y9tpoTvM3lsrhoxCWkkerk9fb2",
        "content": "Task decomposition is a methodology used to break down complex tasks into smaller, more manageable steps. Here’s an overview of various approaches to task decomposition:\n\n1. **Chain of Thought (CoT)**: This technique prompts a model to \"think step by step,\" which aids in transforming big tasks into multiple smaller tasks. This method enhances the model’s performance on complex tasks by making the problem more manageable and interpretable.\n\n2. **Tree of Thoughts (ToT)**: An extension of Chain of Thought, this approach explores multiple reasoning possibilities at each step, effectively creating a tree structure. The search process can be carried out using Breadth-First Search (BFS) or Depth-First Search (DFS), with each state evaluated by either a classifier or a majority vote.\n\n3. **Simple Prompting**: Involves straightforward instructions to decompose a task, such as starting with \"Steps for XYZ. 1.\" or asking \"What are the subgoals for achieving XYZ?\". This can also include task-specific instructions like \"Write a story outline\" for writing a novel.\n\n4. **LLM+P**: Combines Large Language Models (LLMs) with an external classical planner. The problem is translated into a Planning Domain Definition Language (PDDL) format, an external planner generates a plan, and then the plan is translated back into natural language. This approach highlights a synergy between modern AI techniques and traditional planning strategies.\n\nThese approaches allow complex problems to be approached and solved more efficiently by focusing on manageable sub-tasks.",
        "additional_kwargs": {},
        "response_metadata": {
          "tokenUsage": {
            "completionTokens": 311,
            "promptTokens": 755,
            "totalTokens": 1066
          },
          "finish_reason": "stop",
          "system_fingerprint": "fp_52a7f40b0b"
        },
        "tool_calls": [],
        "invalid_tool_calls": [],
        "usage_metadata": {
          "input_tokens": 755,
          "output_tokens": 311,
          "total_tokens": 1066
        }
      }
    ]
  }
}
----

上面，代理没有将我们的查询逐字插入工具，而是删除了不必要的单词，例如“what”和“is”。

同样的原则使代理能够在必要时使用对话的上下文

const query3 =
  "What according to the blog post are common ways of doing it? redo the search";

for await (const s of await agentExecutor2.stream(
  { messages: [{ role: "user", content: query3 }] },
  config3
)) {
  console.log(s);
  console.log("----");
}
{
  agent: {
    messages: [
      AIMessage {
        "id": "chatcmpl-AB7yDE4rCOXTPZ3595GknUgVzASmt",
        "content": "",
        "additional_kwargs": {
          "tool_calls": [
            {
              "id": "call_cWnDZq2aloVtMB4KjZlTxHmZ",
              "type": "function",
              "function": "[Object]"
            }
          ]
        },
        "response_metadata": {
          "tokenUsage": {
            "completionTokens": 21,
            "promptTokens": 1089,
            "totalTokens": 1110
          },
          "finish_reason": "tool_calls",
          "system_fingerprint": "fp_52a7f40b0b"
        },
        "tool_calls": [
          {
            "name": "blog_post_retriever",
            "args": {
              "query": "common ways of task decomposition"
            },
            "type": "tool_call",
            "id": "call_cWnDZq2aloVtMB4KjZlTxHmZ"
          }
        ],
        "invalid_tool_calls": [],
        "usage_metadata": {
          "input_tokens": 1089,
          "output_tokens": 21,
          "total_tokens": 1110
        }
      }
    ]
  }
}
----
{
  tools: {
    messages: [
      ToolMessage {
        "content": "Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\n\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\nAnother quite distinct approach, LLM+P (Liu et al. 2023), involves relying on an external classical planner to do long-horizon planning. This approach utilizes the Planning Domain Definition Language (PDDL) as an intermediate interface to describe the planning problem. In this process, LLM (1) translates the problem into “Problem PDDL”, then (2) requests a classical planner to generate a PDDL plan based on an existing “Domain PDDL”, and finally (3) translates the PDDL plan back into natural language. Essentially, the planning step is outsourced to an external tool, assuming the availability of domain-specific PDDL and a suitable planner which is common in certain robotic setups but not in many other domains.\nSelf-Reflection#\n\nAgent System Overview\n                \n                    Component One: Planning\n                        \n                \n                    Task Decomposition\n                \n                    Self-Reflection\n                \n                \n                    Component Two: Memory\n                        \n                \n                    Types of Memory\n                \n                    Maximum Inner Product Search (MIPS)\n                \n                \n                    Component Three: Tool Use\n                \n                    Case Studies\n                        \n                \n                    Scientific Discovery Agent\n                \n                    Generative Agents Simulation\n                \n                    Proof-of-Concept Examples\n                \n                \n                    Challenges\n                \n                    Citation\n                \n                    References\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.",
        "name": "blog_post_retriever",
        "additional_kwargs": {},
        "response_metadata": {},
        "tool_call_id": "call_cWnDZq2aloVtMB4KjZlTxHmZ"
      }
    ]
  }
}
----
{
  agent: {
    messages: [
      AIMessage {
        "id": "chatcmpl-AB7yGASxz0Z0g2jiCxwx4gYHYJTi4",
        "content": "According to the blog post, there are several common methods of task decomposition:\n\n1. **Simple Prompting by LLMs**: This involves straightforward instructions to decompose a task. Examples include:\n   - \"Steps for XYZ. 1.\"\n   - \"What are the subgoals for achieving XYZ?\"\n   - Task-specific instructions like \"Write a story outline\" for writing a novel.\n\n2. **Human Inputs**: Decomposition can be guided by human insights and instructions.\n\n3. **Chain of Thought (CoT)**: This technique prompts a model to think step-by-step, enabling it to break down complex tasks into smaller, more manageable tasks. CoT has become a standard method to enhance model performance on intricate tasks.\n\n4. **Tree of Thoughts (ToT)**: An extension of CoT, this approach decomposes the problem into multiple thought steps and generates several thoughts per step, forming a tree structure. The search process can be performed using Breadth-First Search (BFS) or Depth-First Search (DFS), with each state evaluated by a classifier or through a majority vote.\n\n5. **LLM+P (Large Language Model plus Planner)**: This method integrates LLMs with an external classical planner. It involves:\n   - Translating the problem into “Problem PDDL” (Planning Domain Definition Language).\n   - Using an external planner to generate a PDDL plan based on an existing “Domain PDDL”.\n   - Translating the PDDL plan back into natural language.\n  \nBy utilizing these methods, tasks can be effectively decomposed into more manageable parts, allowing for more efficient problem-solving and planning.",
        "additional_kwargs": {},
        "response_metadata": {
          "tokenUsage": {
            "completionTokens": 334,
            "promptTokens": 1746,
            "totalTokens": 2080
          },
          "finish_reason": "stop",
          "system_fingerprint": "fp_52a7f40b0b"
        },
        "tool_calls": [],
        "invalid_tool_calls": [],
        "usage_metadata": {
          "input_tokens": 1746,
          "output_tokens": 334,
          "total_tokens": 2080
        }
      }
    ]
  }
}
----

请注意，代理能够推断出我们查询中的“it”指的是“任务分解”，并因此生成了一个合理的搜索查询 - 在这种情况下，是“常见的任务分解方式”。

将它们联系在一起​

为了方便起见，我们将所有必要的步骤在一个代码单元格中组合在一起

import { createRetrieverTool } from "langchain/tools/retriever";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { MemorySaver } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";

const llm3 = new ChatOpenAI({ model: "gpt-4o" });

const loader3 = new CheerioWebBaseLoader(
  "https://lilianweng.github.io/posts/2023-06-23-agent/"
);

const docs3 = await loader3.load();

const textSplitter3 = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});
const splits3 = await textSplitter3.splitDocuments(docs3);
const vectorStore3 = await MemoryVectorStore.fromDocuments(
  splits3,
  new OpenAIEmbeddings()
);

// Retrieve and generate using the relevant snippets of the blog.
const retriever3 = vectorStore3.asRetriever();

const tool2 = createRetrieverTool(retriever3, {
  name: "blog_post_retriever",
  description:
    "Searches and returns excerpts from the Autonomous Agents blog post.",
});
const tools2 = [tool2];
const memory4 = new MemorySaver();

const agentExecutor3 = createReactAgent({
  llm: llm3,
  tools: tools2,
  checkpointSaver: memory4,
});

下一步​

我们已经涵盖了构建基本对话式问答应用程序的步骤

  • 我们使用链构建了一个可预测的应用程序，该应用程序为每个用户输入生成搜索查询；
  • 我们使用代理构建了一个应用程序，该应用程序“决定”何时以及如何生成搜索查询。

要探索不同类型的检索器和检索策略，请访问操作指南中的检索器 部分。

有关 LangChain 的对话内存抽象的详细演练，请访问 LCEL 页面上的如何添加消息历史记录（内存）。

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您也可以留下详细的反馈 在 GitHub 上.