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如何让RAG应用程序添加引用

本指南回顾了获取模型引用其在生成响应时参考的源文档部分的方法。

我们将介绍五种方法：

  1. 使用工具调用来引用文档ID；
  2. 使用工具调用来引用文档ID并提供文本片段；
  3. 直接提示;
  4. 检索后处理（即压缩检索到的上下文以使其更相关）；
  5. 生成后处理（即发出第二次LLM调用，以引用注释生成答案）。

我们通常建议使用适用于您用例的列表中的第一项。也就是说，如果您的模型支持工具调用，请尝试方法1或方法2；否则，或者如果这些方法失败，请继续向下查看列表。

首先，让我们创建一个简单的RAG链。开始时，我们将使用WikipediaRetriever从维基百科检索。我们将使用与RAG教程中相同的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：

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 ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

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()
================================ System Message ================================

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: {context}

================================ Human Message =================================

{question}

现在我们已经有了一个模型、检索器和提示，让我们将它们全部链接在一起。按照添加引用到RAG应用程序的操作指南，我们将使我们的链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 trace。

工具调用​

如果您选择的LLM实现了工具调用功能，您可以使用它来让模型在生成答案时指定它引用了哪些提供的文档。LangChain工具调用模型实现了.with_structured_output方法，该方法将强制生成符合所需模式的输出（详见此处）。

引用文档​

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

首先我们为输出定义一个模式。.with_structured_output 支持多种格式，包括 JSON 模式和 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])

或者作为一个字典：

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

现在我们将源标识符结构化为提示，以便与我们的链进行复制。我们将进行三个更改：

  1. 更新提示以包含源标识符;
  2. 使用 structured_llm（即 llm.with_structured_output(CitedAnswer)）；
  3. 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 跟踪: https://smith.langchain.com/public/6f34d136-451d-4625-90c8-2d8decebc21a/r

引用代码片段​

为了返回文本片段（可能还包括源标识符），我们可以使用相同的方法。唯一的改变将是构建一个更复杂的输出模式，这里使用Pydantic，其中包括一个“引用”以及一个源标识符。

旁注：请注意，如果我们将文档拆分为许多只有一两句话的文档，而不是几个长文档，引用文档就大致等同于引用片段，这可能对模型来说更容易，因为模型只需要返回每个片段的标识符，而不是实际文本。可能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已经从源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/r

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。让我们尝试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}")]
)

我们现在对我们的链进行类似的小更新：

  1. 我们更新格式化函数，将检索到的上下文包装在XML标签中；
  2. 我们不使用 .with_structured_output（例如，因为某个模型不存在该功能）；
  3. 我们使用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，引用再次被结构化为答案：

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

检索后处理​

另一种方法是对我们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简洁，我们不需要模型引用特定的来源或范围。例如，我们可以将每个文档分解为一两句话，嵌入这些句子并仅保留最相关的部分。LangChain 为此提供了一些内置组件。这里我们将使用一个RecursiveCharacterTextSplitter，它通过在分隔符子字符串上分割来创建指定大小的块，以及一个EmbeddingsFilter，它仅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步骤以压缩文档。首先，我们选择一个嵌入模型：

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")

我们现在可以重写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}

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，我们像之前一样将其组装到我们的链中：

# 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的“summary”键中保留了原始内容。这些摘要不会传递给模型；只有压缩后的内容会被传递。

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 跟踪: 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。在这个例子中，我们首先只生成一个答案，然后要求模型用引用来注释它自己的答案。这种方法的缺点当然是它更慢且更昂贵，因为需要进行两次模型调用。

让我们将其应用到我们的初始链中。如果需要，我们可以通过应用程序中的第三步来实现这一点。

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

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