如何让 RAG 应用添加引用

本指南回顾了让模型引用其在生成响应时引用的源文档部分的方法。

我们将介绍五种方法：

使用工具调用引用文档 ID；
使用工具调用引用文档 ID 并提供文本片段；
直接提示；
检索后处理（即压缩检索到的上下文以提高相关性）；
生成后处理（即发出第二次 LLM 调用，用引用注释生成的答案）。

我们通常建议使用列表中的第一项，该项适用于您的用例。也就是说，如果您的模型支持工具调用，请尝试方法 1 或 2；否则，或者如果这些方法失败，请继续列表中的下一项。

我们首先创建一个简单的 RAG 链。一开始，我们将只使用 WikipediaRetriever 从维基百科检索。我们将使用与RAG 教程相同的 LangGraph 实现。

设置

首先，我们需要安装一些依赖项：

%pip install -qU langchain-community wikipedia

首先，让我们选择一个 LLM：

Select chat model:

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")

我们现在可以加载一个retriever 并构建我们的prompt：

from langchain_community.retrievers import WikipediaRetriever
from langchain_core.prompts import ChatPromptTemplate

system_prompt = (
    "You're a helpful AI assistant. Given a user question "
    "and some Wikipedia article snippets, answer the user "
    "question. If none of the articles answer the question, "
    "just say you don't know."
    "\n\nHere are the Wikipedia articles: "
    "{context}"
)

retriever = WikipediaRetriever(top_k_results=6, doc_content_chars_max=2000)
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        ("human", "{question}"),
    ]
)
prompt.pretty_print()

API Reference:WikipediaRetriever | ChatPromptTemplate

================================[1m System Message [0m================================

You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question. If none of the articles answer the question, just say you don't know.

Here are the Wikipedia articles: [33;1m[1;3m{context}[0m

================================[1m Human Message [0m=================================

[33;1m[1;3m{question}[0m

现在我们有了模型、检索器和提示词，让我们将它们链接起来。遵循给 RAG 应用添加引文的操作指南，我们将使我们的链同时返回答案和检索到的文档。这使用了与 RAG 教程中相同的 LangGraph 实现。

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


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


# Define application steps
def retrieve(state: State):
    retrieved_docs = retriever.invoke(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 Reference:Document | StateGraph

from IPython.display import Image, display

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

result = graph.invoke({"question": "How fast are cheetahs?"})

sources = [doc.metadata["source"] for doc in result["context"]]
print(f"Sources: {sources}\n\n")
print(f'Answer: {result["answer"]}')

Sources: ['https://en.wikipedia.org/wiki/Cheetah', 'https://en.wikipedia.org/wiki/Southeast_African_cheetah', 'https://en.wikipedia.org/wiki/Footspeed', 'https://en.wikipedia.org/wiki/Fastest_animals', 'https://en.wikipedia.org/wiki/Pursuit_predation', 'https://en.wikipedia.org/wiki/Gepard-class_fast_attack_craft']

Answer: Cheetahs are capable of running at speeds between 93 to 104 km/h (58 to 65 mph).

查看 LangSmith 追踪。

工具调用

如果您的首选 LLM 实现了工具调用功能，您可以使用它让模型在生成答案时指定它所引用的文档。LangChain 的工具调用模型实现了一个 .with_structured_output 方法，该方法会强制生成遵循期望的模式（详细信息参见此处）。

引用文档

要使用标识符引用文档，我们将标识符格式化到提示中，然后使用 .with_structured_output 来强制 LLM 在其输出中引用这些标识符。

首先，我们定义一个输出模式。.with_structured_output 支持多种格式，包括 JSON schema 和 Pydantic。这里我们将使用 Pydantic：

from pydantic import BaseModel, Field


class CitedAnswer(BaseModel):
    """Answer the user question based only on the given sources, and cite the sources used."""

    answer: str = Field(
        ...,
        description="The answer to the user question, which is based only on the given sources.",
    )
    citations: List[int] = Field(
        ...,
        description="The integer IDs of the SPECIFIC sources which justify the answer.",
    )

让我们看看当我们传入我们的函数和用户输入时，模型的输出是什么样的：

structured_llm = llm.with_structured_output(CitedAnswer)

example_q = """What Brian's height?

Source: 1
Information: Suzy is 6'2"

Source: 2
Information: Jeremiah is blonde

Source: 3
Information: Brian is 3 inches shorter than Suzy"""
result = structured_llm.invoke(example_q)

result

CitedAnswer(answer='Brian is 5\'11".', citations=[1, 3])

或者作为字典：

result.dict()

{'answer': 'Brian is 5\'11".', 'citations': [1, 3]}

现在，我们将源标识符构建到提示中，以便与我们的链进行复现。我们将进行三处更改：

更新提示以包含源标识符；
使用 structured_llm（即 llm.with_structured_output(CitedAnswer)）；
在输出中返回 Pydantic 对象。

def format_docs_with_id(docs: List[Document]) -> str:
    formatted = [
        f"Source ID: {i}\nArticle Title: {doc.metadata['title']}\nArticle Snippet: {doc.page_content}"
        for i, doc in enumerate(docs)
    ]
    return "\n\n" + "\n\n".join(formatted)


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


def generate(state: State):
    formatted_docs = format_docs_with_id(state["context"])
    messages = prompt.invoke({"question": state["question"], "context": formatted_docs})
    structured_llm = llm.with_structured_output(CitedAnswer)
    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()

result = graph.invoke({"question": "How fast are cheetahs?"})

result["answer"]

CitedAnswer(answer='Cheetahs are capable of running at speeds between 93 to 104 km/h (58 to 65 mph).', citations=[0, 3])

我们可以检查模型引用的索引为 0 的文档：

print(result["context"][0])

page_content='The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in). Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.
The cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran. It lives in a variety of habitats such as savannahs in the Serengeti, arid mountain ranges in the Sahara, and hilly desert terrain.
The cheetah lives in three main social groups: females and their cubs, male "coalitions", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk. It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson's gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year. After a gestation of nearly three months, females give birth to a litter of three or four cubs. Cheetah cubs are highly vulnerable to predation by other large carnivores. They are weaned a' metadata={'title': 'Cheetah', 'summary': 'The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in). Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.\nThe cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran. It lives in a variety of habitats such as savannahs in the Serengeti, arid mountain ranges in the Sahara, and hilly desert terrain.\nThe cheetah lives in three main social groups: females and their cubs, male "coalitions", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk. It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson\'s gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year. After a gestation of nearly three months, females give birth to a litter of three or four cubs. Cheetah cubs are highly vulnerable to predation by other large carnivores. They are weaned at around four months and are independent by around 20 months of age.\nThe cheetah is threatened by habitat loss, conflict with humans, poaching and high susceptibility to diseases. The global cheetah population was estimated in 2021 at 6,517; it is listed as Vulnerable on the IUCN Red List. It has been widely depicted in art, literature, advertising, and animation. It was tamed in ancient Egypt and trained for hunting ungulates in the Arabian Peninsula and India. It has been kept in zoos since the early 19th century.', 'source': 'https://en.wikipedia.org/wiki/Cheetah'}

LangSmith trace: https://smith.langchain.com/public/6f34d136-451d-4625-90c8-2d8decebc21a/r

引用片段

为了返回文本跨度（可能还会返回源标识符），我们可以使用相同的方法。唯一的变化是构建一个更复杂的输出模式，这里使用 Pydantic，它除了源标识符外，还包含一个“引用”。

题外话：请注意，如果我们分解文档，使得文档数量增多但每篇只包含一两句话，而不是几篇长文档，那么引用文档就大致等同于引用片段，并且对模型来说可能更容易，因为模型只需要为每个片段返回一个标识符，而不是实际文本。或许值得尝试这两种方法并进行评估。

class Citation(BaseModel):
    source_id: int = Field(
        ...,
        description="The integer ID of a SPECIFIC source which justifies the answer.",
    )
    quote: str = Field(
        ...,
        description="The VERBATIM quote from the specified source that justifies the answer.",
    )


class QuotedAnswer(BaseModel):
    """Answer the user question based only on the given sources, and cite the sources used."""

    answer: str = Field(
        ...,
        description="The answer to the user question, which is based only on the given sources.",
    )
    citations: List[Citation] = Field(
        ..., description="Citations from the given sources that justify the answer."
    )

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


def generate(state: State):
    formatted_docs = format_docs_with_id(state["context"])
    messages = prompt.invoke({"question": state["question"], "context": formatted_docs})
    structured_llm = llm.with_structured_output(QuotedAnswer)
    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()

在这里我们看到模型从源 0 中提取了一段相关的文本：

result = graph.invoke({"question": "How fast are cheetahs?"})

result["answer"]

QuotedAnswer(answer='Cheetahs are capable of running at speeds of 93 to 104 km/h (58 to 65 mph).', citations=[Citation(source_id=0, quote='The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed.')])

LangSmith 跟踪：https://smith.langchain.com/public/e16dc72f-4261-4f25-a9a7-906238737283/

直接提示

有些模型不支持函数调用。我们可以通过直接提示来实现类似的结果。我们来试着指示模型为其输出生成结构化的 XML：

xml_system = """You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, \
answer the user question and provide citations. If none of the articles answer the question, just say you don't know.

Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that \
justifies the answer and the ID of the quote article. Return a citation for every quote across all articles \
that justify the answer. Use the following format for your final output:

<cited_answer>
    <answer></answer>
    <citations>
        <citation><source_id></source_id><quote></quote></citation>
        <citation><source_id></source_id><quote></quote></citation>
        ...
    </citations>
</cited_answer>

Here are the Wikipedia articles:{context}"""
xml_prompt = ChatPromptTemplate.from_messages(
    [("system", xml_system), ("human", "{question}")]
)

我们现在对链进行类似的微小更新：

更新格式化函数，将检索到的上下文包装在 XML 标签中；
不使用 .with_structured_output（例如，因为模型不存在此功能）；
使用 XMLOutputParser 将答案解析为字典。

from langchain_core.output_parsers import XMLOutputParser


def format_docs_xml(docs: List[Document]) -> str:
    formatted = []
    for i, doc in enumerate(docs):
        doc_str = f"""\
    <source id=\"{i}\">
        <title>{doc.metadata['title']}</title>
        <article_snippet>{doc.page_content}</article_snippet>
    </source>"""
        formatted.append(doc_str)
    return "\n\n<sources>" + "\n".join(formatted) + "</sources>"


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


def generate(state: State):
    formatted_docs = format_docs_xml(state["context"])
    messages = xml_prompt.invoke(
        {"question": state["question"], "context": formatted_docs}
    )
    response = llm.invoke(messages)
    parsed_response = XMLOutputParser().invoke(response)
    return {"answer": parsed_response}


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

API Reference:XMLOutputParser

请注意，引文再次被整合到答案中：

result = graph.invoke({"question": "How fast are cheetahs?"})

result["answer"]

{'cited_answer': [{'answer': 'Cheetahs can run at speeds of 93 to 104 km/h (58 to 65 mph).'},
  {'citations': [{'citation': [{'source_id': '0'},
      {'quote': 'The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph);'}]},
    {'citation': [{'source_id': '3'},
      {'quote': 'The fastest land animal is the cheetah.'}]}]}]}

LangSmith trace：https://smith.langchain.com/public/0c45f847-c640-4b9a-a5fa-63559e413527/r

检索后处理

另一种方法是对检索到的文档进行后处理以压缩内容，这样源内容已经足够精简，无需模型引用特定来源或片段。例如，我们可以将每个文档拆分成两句左右，嵌入这些片段，并只保留最相关的片段。LangChain 内置了一些组件来实现这一点。在这里，我们将使用一个 RecursiveCharacterTextSplitter，它通过分割分隔符子字符串来创建指定大小的块，以及一个 EmbeddingsFilter，它只保留具有最相关嵌入的文本。

这种方法有效地更新了我们的 retrieve 步骤来压缩文档。首先，让我们选择一个嵌入模型：

Select embeddings model:

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")

现在我们可以重写 retrieve 步骤：

from langchain.retrievers.document_compressors import EmbeddingsFilter
from langchain_core.runnables import RunnableParallel
from langchain_text_splitters import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=400,
    chunk_overlap=0,
    separators=["\n\n", "\n", ".", " "],
    keep_separator=False,
)
compressor = EmbeddingsFilter(embeddings=embeddings, k=10)


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


def retrieve(state: State):
    retrieved_docs = retriever.invoke(state["question"])
    split_docs = splitter.split_documents(retrieved_docs)
    stateful_docs = compressor.compress_documents(split_docs, state["question"])
    return {"context": stateful_docs}

API Reference:EmbeddingsFilter | RunnableParallel | RecursiveCharacterTextSplitter

让我们来测试一下：

retrieval_result = retrieve({"question": "How fast are cheetahs?"})

for doc in retrieval_result["context"]:
    print(f"{doc.page_content}\n\n")

Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail

The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in)

2 mph), or 171 body lengths per second. The cheetah, the fastest land mammal, scores at only 16 body lengths per second

It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson's gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year

The cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran

The cheetah lives in three main social groups: females and their cubs, male "coalitions", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk

The Southeast African cheetah (Acinonyx jubatus jubatus) is the nominate cheetah subspecies native to East and Southern Africa. The Southern African cheetah lives mainly in the lowland areas and deserts of the Kalahari, the savannahs of Okavango Delta, and the grasslands of the Transvaal region in South Africa. In Namibia, cheetahs are mostly found in farmlands

Subpopulations have been called "South African cheetah" and "Namibian cheetah."

In India, four cheetahs of the subspecies are living in Kuno National Park in Madhya Pradesh after having been introduced there

Acinonyx jubatus velox proposed in 1913 by Edmund Heller on basis of a cheetah that was shot by Kermit Roosevelt in June 1909 in the Kenyan highlands.
Acinonyx rex proposed in 1927 by Reginald Innes Pocock on basis of a specimen from the Umvukwe Range in Rhodesia.

接下来，我们将其像之前一样组装到我们的链中：

# This step is unchanged from our original RAG implementation
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}


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

result = graph.invoke({"question": "How fast are cheetahs?"})

print(result["answer"])

Cheetahs are capable of running at speeds between 93 to 104 km/h (58 to 65 mph). They are known as the fastest land animals.

请注意，文档内容现在已被压缩，尽管文档对象在其元数据中保留了原始内容的“summary”键。这些摘要不会传递给模型；只有经过精简的内容会被传递。

result["context"][0].page_content  # passed to model

'Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail'

result["context"][0].metadata["summary"]  # original document  # original document

'The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in). Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.\nThe cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran. It lives in a variety of habitats such as savannahs in the Serengeti, arid mountain ranges in the Sahara, and hilly desert terrain.\nThe cheetah lives in three main social groups: females and their cubs, male "coalitions", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk. It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson\'s gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year. After a gestation of nearly three months, females give birth to a litter of three or four cubs. Cheetah cubs are highly vulnerable to predation by other large carnivores. They are weaned at around four months and are independent by around 20 months of age.\nThe cheetah is threatened by habitat loss, conflict with humans, poaching and high susceptibility to diseases. The global cheetah population was estimated in 2021 at 6,517; it is listed as Vulnerable on the IUCN Red List. It has been widely depicted in art, literature, advertising, and animation. It was tamed in ancient Egypt and trained for hunting ungulates in the Arabian Peninsula and India. It has been kept in zoos since the early 19th century.'

LangSmith trace：https://smith.langchain.com/public/21b0dc15-d70a-4293-9402-9c70f9178e66/r

生成后处理

另一种方法是对我们的模型生成进行后处理。在此示例中，我们首先仅生成一个答案，然后要求模型为其自身答案添加引用。当然，这种方法的缺点是速度较慢且成本较高，因为需要进行两次模型调用。

让我们将其应用于我们最初的链。如果需要，我们可以在应用程序中通过第三个步骤来实现这一点。

class Citation(BaseModel):
    source_id: int = Field(
        ...,
        description="The integer ID of a SPECIFIC source which justifies the answer.",
    )
    quote: str = Field(
        ...,
        description="The VERBATIM quote from the specified source that justifies the answer.",
    )


class AnnotatedAnswer(BaseModel):
    """Annotate the answer to the user question with quote citations that justify the answer."""

    citations: List[Citation] = Field(
        ..., description="Citations from the given sources that justify the answer."
    )


structured_llm = llm.with_structured_output(AnnotatedAnswer)

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


def retrieve(state: State):
    retrieved_docs = retriever.invoke(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}


def annotate(state: State):
    formatted_docs = format_docs_with_id(state["context"])
    messages = [
        ("system", system_prompt.format(context=formatted_docs)),
        ("human", state["question"]),
        ("ai", state["answer"]),
        ("human", "Annotate your answer with citations."),
    ]
    response = structured_llm.invoke(messages)
    return {"annotations": response}


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

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

result = graph.invoke({"question": "How fast are cheetahs?"})

print(result["answer"])

Cheetahs are capable of running at speeds between 93 to 104 km/h (58 to 65 mph).

result["annotations"]

AnnotatedAnswer(citations=[Citation(source_id=0, quote='The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph)')])

LangSmith trace: https://smith.langchain.com/public/b8257417-573b-47c4-a750-74e542035f19/

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