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如何让RAG应用程序返回来源

在问答（Q&A）应用中，通常向用户展示生成答案所使用的来源非常重要。最简单的方法是让链（chain）在每次生成时返回检索到的文档（Documents）。

我们将基于 RAG 教程中，Lilian Weng 撰写的关于 LLM 驱动的自主智能体博客文章中构建的问答应用进行操作。

我们将介绍两种方法

  1. 使用 RAG 教程第 1 部分中介绍的基础 RAG 链；
  2. 使用教程第 2 部分中介绍的对话式 RAG 链。

我们还将展示如何将来源结构化到模型响应中，以便模型可以报告其在生成答案时使用了哪些具体来源。

设置​

依赖项​

我们将使用以下包

%pip install --upgrade --quiet langchain langchain-community langchainhub beautifulsoup4

LangSmith​

你使用 LangChain 构建的许多应用将包含多个步骤，并多次调用 LLM。随着这些应用变得越来越复杂，能够检查链或智能体（agent）内部到底发生了什么变得至关重要。做到这一点的最佳方法是使用 LangSmith。

请注意，LangSmith并非必需，但它会很有帮助。如果您确实想使用LangSmith，在通过上述链接注册后，请务必设置您的环境变量以开始记录跟踪信息

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

组件​

我们需要从LangChain的集成套件中选择三个组件。

一个聊天模型

pip install -qU "langchain[google-genai]"
import getpass
import os

if not os.environ.get("GOOGLE_API_KEY"):
  os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter API key for Google Gemini: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("gemini-2.0-flash", model_provider="google_genai")

一个嵌入模型

pip install -qU langchain-openai
import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

和一个向量存储

pip install -qU langchain-core
from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(embeddings)

RAG 应用​

让我们重建在 RAG 教程中，Lilian Weng 撰写的关于 LLM 驱动的自主智能体博客文章中构建的、包含来源的问答应用。

首先，我们索引文档

import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from typing_extensions import List, TypedDict

# Load and chunk contents of the blog
loader = WebBaseLoader(
    web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
    bs_kwargs=dict(
        parse_only=bs4.SoupStrainer(
            class_=("post-content", "post-title", "post-header")
        )
    ),
)
docs = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)
# Index chunks
_ = vector_store.add_documents(documents=all_splits)

接下来我们构建应用

from langchain import hub
from langchain_core.documents import Document
from langgraph.graph import START, StateGraph
from typing_extensions import List, TypedDict

# Define prompt for question-answering
prompt = hub.pull("rlm/rag-prompt")


# Define state for application
class State(TypedDict):
    question: str
    context: List[Document]
    answer: str


# Define application steps
def retrieve(state: State):
    retrieved_docs = vector_store.similarity_search(state["question"])
    return {"context": retrieved_docs}


def generate(state: State):
    docs_content = "\n\n".join(doc.page_content for doc in state["context"])
    messages = prompt.invoke({"question": state["question"], "context": docs_content})
    response = llm.invoke(messages)
    return {"answer": response.content}


# Compile application and test
graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()
API 参考:hub | Document | StateGraph
from IPython.display import Image, display

display(Image(graph.get_graph().draw_mermaid_png()))

因为我们正在应用的内部状态中跟踪检索到的上下文，所以在调用应用后即可访问它

result = graph.invoke({"question": "What is Task Decomposition?"})

print(f"Context: {result['context']}\n\n")
print(f"Answer: {result['answer']}")
Context: [Document(id='c8471b37-07d8-4d51-856e-4b2c22bca88d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_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.'), Document(id='acb7eb6f-f252-4353-aec2-f459135354ba', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_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.\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.'), Document(id='4fae6668-7fec-4237-9b2d-78132f4f3f3f', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Resources:\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.'), Document(id='3c79dd86-595e-42e8-b64d-404780f9e2d9', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content="(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.")]


Answer: Task Decomposition is the process of breaking down a complex task into smaller, manageable steps to facilitate execution. This can be achieved through techniques like Chain of Thought, which encourages step-by-step reasoning, or Tree of Thoughts, which explores multiple reasoning paths for each step. It can be implemented using simple prompts, specific instructions, or human input to effectively tackle the original task.

在这里，"context" 包含 LLM 用于生成 "answer" 中响应的来源。

在模型响应中结构化来源​

到目前为止，我们只是简单地将检索步骤返回的文档传播到最终响应中。但这可能无法说明模型在生成答案时依赖了哪些信息子集。下面，我们将展示如何将来源结构化到模型响应中，允许模型报告它在答案中依赖了哪些具体的上下文。

扩展上述 LangGraph 实现非常简单。下面，我们进行一个简单的更改：我们使用模型的工具调用功能来生成结构化输出，包括答案和来源列表。响应的 Schema 在下面的 AnswerWithSources TypedDict 中表示。

from typing import List

from typing_extensions import Annotated, TypedDict


# Desired schema for response
class AnswerWithSources(TypedDict):
    """An answer to the question, with sources."""

    answer: str
    sources: Annotated[
        List[str],
        ...,
        "List of sources (author + year) used to answer the question",
    ]


class State(TypedDict):
    question: str
    context: List[Document]
    answer: AnswerWithSources


def generate(state: State):
    docs_content = "\n\n".join(doc.page_content for doc in state["context"])
    messages = prompt.invoke({"question": state["question"], "context": docs_content})
    structured_llm = llm.with_structured_output(AnswerWithSources)
    response = structured_llm.invoke(messages)
    return {"answer": response}


graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()
import json

result = graph.invoke({"question": "What is Chain of Thought?"})
print(json.dumps(result["answer"], indent=2))
{
  "answer": "Chain of Thought (CoT) is a prompting technique that enhances model performance by instructing it to think step by step, allowing the decomposition of complex tasks into smaller, manageable steps. This method not only aids in task execution but also provides insights into the model's reasoning process. CoT has become a standard approach in improving how language models handle intricate problem-solving tasks.",
  "sources": [
    "Wei et al. 2022"
  ]
}
提示

查看 LangSmith 追踪。

对话式 RAG​

RAG 教程的第 2 部分实现了一种不同的架构，其中 RAG 流程中的步骤通过连续的 message 对象表示。这利用了聊天模型的额外工具调用功能，并更自然地适应了“来回”的对话式用户体验。

在该教程中（以及下文中），我们将检索到的文档作为工具消息上的 artifacts 进行传播。这样可以轻松地提取检索到的文档。为了方便起见，我们在下面将它们作为状态中的一个额外键（key）添加。

请注意，我们将工具的响应格式定义为 "content_and_artifact"

from langchain_core.tools import tool


@tool(response_format="content_and_artifact")
def retrieve(query: str):
    """Retrieve information related to a query."""
    retrieved_docs = vector_store.similarity_search(query, k=2)
    serialized = "\n\n".join(
        (f"Source: {doc.metadata}\nContent: {doc.page_content}")
        for doc in retrieved_docs
    )
    return serialized, retrieved_docs
API 参考：tool

我们现在可以构建和编译与 RAG 教程第 2 部分中完全相同的应用，并进行两处更改

  1. 我们添加一个状态的 context 键来存储检索到的文档；
  2. 在 generate 步骤中，我们提取检索到的文档并将其填充到状态中。

这些更改在下面突出显示。

from langchain_core.messages import SystemMessage
from langgraph.graph import END, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition


class State(MessagesState):
    context: List[Document]


# Step 1: Generate an AIMessage that may include a tool-call to be sent.
def query_or_respond(state: State):
    """Generate tool call for retrieval or respond."""
    llm_with_tools = llm.bind_tools([retrieve])
    response = llm_with_tools.invoke(state["messages"])
    # MessagesState appends messages to state instead of overwriting
    return {"messages": [response]}


# Step 2: Execute the retrieval.
tools = ToolNode([retrieve])


# Step 3: Generate a response using the retrieved content.
def generate(state: MessagesState):
    """Generate answer."""
    # Get generated ToolMessages
    recent_tool_messages = []
    for message in reversed(state["messages"]):
        if message.type == "tool":
            recent_tool_messages.append(message)
        else:
            break
    tool_messages = recent_tool_messages[::-1]

    # Format into prompt
    docs_content = "\n\n".join(doc.content for doc in tool_messages)
    system_message_content = (
        "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"
        f"{docs_content}"
    )
    conversation_messages = [
        message
        for message in state["messages"]
        if message.type in ("human", "system")
        or (message.type == "ai" and not message.tool_calls)
    ]
    prompt = [SystemMessage(system_message_content)] + conversation_messages

    # Run
    response = llm.invoke(prompt)
    context = []
    for tool_message in tool_messages:
        context.extend(tool_message.artifact)
    return {"messages": [response], "context": context}

我们可以像以前一样编译应用

graph_builder = StateGraph(MessagesState)

graph_builder.add_node(query_or_respond)
graph_builder.add_node(tools)
graph_builder.add_node(generate)

graph_builder.set_entry_point("query_or_respond")
graph_builder.add_conditional_edges(
    "query_or_respond",
    tools_condition,
    {END: END, "tools": "tools"},
)
graph_builder.add_edge("tools", "generate")
graph_builder.add_edge("generate", END)

graph = graph_builder.compile()
display(Image(graph.get_graph().draw_mermaid_png()))

调用我们的应用后，我们看到检索到的 Document 对象可以从应用状态中访问。

input_message = "What is Task Decomposition?"

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

What is Task Decomposition?
================================== Ai Message ==================================
Tool Calls:
  retrieve (call_oA0XZ5hF70X0oW4ccNUFCFxX)
 Call ID: call_oA0XZ5hF70X0oW4ccNUFCFxX
  Args:
    query: Task Decomposition
================================= Tool Message =================================
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.
================================== Ai Message ==================================

Task Decomposition is the process of breaking down a complicated task into smaller, manageable steps. It often utilizes techniques like Chain of Thought (CoT) prompting, which encourages models to think step by step, enhancing performance on complex tasks. This approach helps clarify the model's reasoning and makes it easier to tackle difficult problems.
step["context"]
[Document(id='c8471b37-07d8-4d51-856e-4b2c22bca88d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_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.'),
 Document(id='acb7eb6f-f252-4353-aec2-f459135354ba', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_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.\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.')]
提示

查看 LangSmith 追踪。