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2025年07月28日

使用最强大的AI开发工具 LangChain 构建RAG应用-第2部分构建Agents

<<<<接上文

聊天记录的状态管理

说明：教程的这一部分以前使用的是
RunnableWithMessageHistory 抽象类。你还可以在 v0.2文档中查看该版本的内容。从LangChainv0.3开始，建议你利用 LangGraph 的持久化机制，将记忆功能集成到新的 LangChain 应用中。

如果你的代码已经在使用
RunnableWithMessageHistory 或 BaseChatMessageHistory，不需要进行任何更改。官方目前没有计划废弃这些功能，因为它们在构建简单聊天应用时依然有效，所有使用
RunnableWithMessageHistory 的代码也能正常运行。


在生产环境中，问答类应用通常会将聊天记录持久化到数据库中，并能够适当地读取和更新这些记录。

LangGraph 实现了一个内置的持久化层，使其非常适合支持多轮对话的聊天应用。

为了管理多轮对话和多路(thread)对话，我们只需在编译应用时指定一个 checkpointer（检查点管理器）。由于Graph中的节点会将消息附加到状态State中，因此我们可以在多次调用中保留一致的聊天记录。

LangGraph 自带了一个简单的内存检查点管理器，我们在下方示例中会使用它。

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()
graph = graph_builder.compile(checkpointer=memory)

# Specify an ID for the thread
config = {"configurable": {"thread_id": "abc123"}}

我们现在可以沿用与之前类似的调用方式：

input_message = "What is Task Decomposition?"

for step in graph.stream(
    {"messages": [{"role": "user", "content": input_message}]},
    stream_mode="values",
    config=config,
):
    step["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================

What is Task Decomposition?
==================================[1m Ai Message [0m==================================
Tool Calls:
  retrieve (call_JZb6GLD812bW2mQsJ5EJQDnN)
 Call ID: call_JZb6GLD812bW2mQsJ5EJQDnN
  Args:
    query: Task Decomposition
=================================[1m Tool Message [0m=================================
Name: retrieve

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Fig. 1. Overview of a LLM-powered autonomous agent system.
Component One: Planning#
A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
Task Decomposition#
Chain 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.

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Tree 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.
Task 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.
==================================[1m Ai Message [0m==================================

Task Decomposition is a technique used to break down complicated tasks into smaller, manageable steps. It involves using methods like Chain of Thought (CoT) prompting, which encourages the model to think step by step, enhancing performance on complex tasks. This process helps to clarify the model's reasoning and makes it easier to tackle difficult problems.
input_message = "Can you look up some common ways of doing it?"

for step in graph.stream(
    {"messages": [{"role": "user", "content": input_message}]},
    stream_mode="values",
    config=config,
):
    step["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================

Can you look up some common ways of doing it?
==================================[1m Ai Message [0m==================================
Tool Calls:
  retrieve (call_kjRI4Y5cJOiB73yvd7dmb6ux)
 Call ID: call_kjRI4Y5cJOiB73yvd7dmb6ux
  Args:
    query: common methods of task decomposition
=================================[1m Tool Message [0m=================================
Name: retrieve

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Tree 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.
Task 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.

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Fig. 1. Overview of a LLM-powered autonomous agent system.
Component One: Planning#
A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
Task Decomposition#
Chain 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.
==================================[1m Ai Message [0m==================================

Common ways of performing Task Decomposition include: (1) using Large Language Models (LLMs) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?", (2) employing task-specific instructions such as "Write a story outline" for specific tasks, and (3) incorporating human inputs to guide the decomposition process.

请注意，模型在第二个问题中生成的查询语句结合了对话上下文。

在这里，LangSmith 的追踪记录尤其有帮助，因为我们可以清楚地看到，在每一个步骤中，聊天模型能够访问到哪些消息。

Agents（智能体）

Agents（智能体）利用大型语言模型（LLM）的推理能力在执行过程中做出决策。使用智能体可以将更多的检索过程决策权交由模型处理。尽管它们的行为不像上面的 “chain（链式流程）” 那样可预测，但它们能够为了完成一个查询：

  • 执行多轮检索步骤，或
  • 反复优化某一次检索。

下面我们将构建一个最简化的RAG（检索增强生成）智能体。借助 LangGraph 提供的预构建 ReAct 智能体构造器（ReAct agent constructor），我们只需一行代码即可实现。

from langgraph.prebuilt import create_react_agent

agent_executor = create_react_agent(llm, [retrieve], checkpointer=memory)
display(Image(agent_executor.get_graph().draw_mermaid_png()))

我们之前的实现中，流程在最后的生成步骤结束。而这里的关键区别在于：

工具调用（tool invocation）不会直接结束流程，而是循环回到最初的 LLM 调用。

模型随后可以选择：

  • 使用已检索到的上下文回答问题，或者
  • 生成另一个工具调用，以获取更多信息。

我们来测试一下这个机制。下面我们构造一个问题，这个问题通常需要多轮检索步骤才能得到答案：

config = {"configurable": {"thread_id": "def234"}}

input_message = (
    "What is the standard method for Task Decomposition?\n\n"
    "Once you get the answer, look up common extensions of that method."
)

for event in agent_executor.stream(
    {"messages": [{"role": "user", "content": input_message}]},
    stream_mode="values",
    config=config,
):
    event["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================

What is the standard method for Task Decomposition?

Once you get the answer, look up common extensions of that method.
==================================[1m Ai Message [0m==================================
Tool Calls:
  retrieve (call_Y3YaIzL71B83Cjqa8d2G0O8N)
 Call ID: call_Y3YaIzL71B83Cjqa8d2G0O8N
  Args:
    query: standard method for Task Decomposition
=================================[1m Tool Message [0m=================================
Name: retrieve

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Tree 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.
Task 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.

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Fig. 1. Overview of a LLM-powered autonomous agent system.
Component One: Planning#
A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
Task Decomposition#
Chain 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.
==================================[1m Ai Message [0m==================================
Tool Calls:
  retrieve (call_2JntP1x4XQMWwgVpYurE12ff)
 Call ID: call_2JntP1x4XQMWwgVpYurE12ff
  Args:
    query: common extensions of Task Decomposition methods
=================================[1m Tool Message [0m=================================
Name: retrieve

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Tree 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.
Task 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.

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Fig. 1. Overview of a LLM-powered autonomous agent system.
Component One: Planning#
A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
Task Decomposition#
Chain 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.
==================================[1m Ai Message [0m==================================

The standard method for task decomposition involves using techniques such as Chain of Thought (CoT), where a model is instructed to "think step by step" to break down complex tasks into smaller, more manageable components. This approach enhances model performance by allowing for more thorough reasoning and planning. Task decomposition can be accomplished through various means, including:

1. Simple prompting (e.g., asking for steps to achieve a goal).
2. Task-specific instructions (e.g., asking for a story outline).
3. Human inputs to guide the decomposition process.

### Common Extensions of Task Decomposition Methods:

1. **Tree of Thoughts**: This extension builds on CoT by not only decomposing the problem into thought steps but also generating multiple thoughts at each step, creating a tree structure. The search process can employ breadth-first search (BFS) or depth-first search (DFS), with each state evaluated by a classifier or through majority voting.

These extensions aim to enhance reasoning capabilities and improve the effectiveness of task decompositio
n in various contexts.

这个智能体的行为包括：

  1. 生成搜索一个task decomposition 标准方法的查询
  2. 收到回答后，又生成了第二个查询，用于查找任务标准分解方法的常归扩展；
  3. 在获取了所有必要的上下文之后，最终回答了问题。

通过 LangSmith 的追踪记录（trace），我们可以查看完整的执行步骤序列，包括延迟时间和其他元数据。

总结

通过前面3个章节和本章节的学习我们已经完成了构建一个基础对话式问答应用的全部步骤：

  • 我们使用 chain（链式流程） 构建了一个可预测的应用，每次用户输入最多生成一个查询；
  • 我们使用 agent（智能体） 构建了一个可以执行多轮查询的应用，适合更复杂的场景。

我是一名有十年以上经验的Java老码农，曾经沉迷于代码的世界，也曾在传统业务系统中摸爬滚打。但时代在变，AI 正在重塑技术格局。我不想被浪潮甩在身后，所以选择重新出发，走上 AI 学习与转型的旅程。

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标签：graphstream 

作者:aoaohe , 分类:编程文章 , 浏览:60 , 评论:0

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