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评估 RAG 应用

关键概念

RAG 评估 | 评估器 | LLM 作为评委的评估器

检索增强生成 (RAG) 是一种通过为大型语言模型 (LLM) 提供相关的外部知识来增强它们的技术。它已成为构建 LLM 应用程序最广泛使用的方法之一。

本教程将向您展示如何使用 LangSmith 评估您的 RAG 应用程序。您将学习

  1. 如何创建测试数据集
  2. 如何在这些数据集上运行您的 RAG 应用程序
  3. 如何使用不同的评估指标来衡量您的应用程序的性能

概述​

典型的 RAG 评估工作流程包括三个主要步骤

  1. 创建包含问题及其预期答案的数据集
  2. 在这些问题上运行您的 RAG 应用程序
  3. 使用评估器来衡量您的应用程序的性能，查看以下因素：
    • 答案相关性
    • 答案准确性
    • 检索质量

在本教程中，我们将创建并评估一个机器人，该机器人回答有关 Lilian Weng 的一些富有洞察力的博客文章的问题。

设置​

环境​

首先，让我们设置我们的环境变量

import os

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = "YOUR LANGSMITH API KEY"
os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY"

并安装我们将需要的依赖项

pip install -U langsmith langchain[openai] langchain-community

应用程序​

框架灵活性

虽然本教程使用 LangChain，但此处演示的评估技术和 LangSmith 功能适用于任何框架。请随意使用您喜欢的工具和库。

在本节中，我们将构建一个基本的检索增强生成 (RAG) 应用程序。

我们将坚持一个简单的实现，它执行以下操作：

  • 索引：将 Lilian Weng 的一些博客分块并索引到向量存储中
  • 检索：根据用户问题检索这些块
  • 生成：将问题和检索到的文档传递给 LLM。

索引和检索​

首先，让我们加载我们想要为其构建聊天机器人的博客文章，并对其进行索引。

from langchain_community.document_loaders import WebBaseLoader
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

# List of URLs to load documents from
urls = [
    "https://lilianweng.github.io/posts/2023-06-23-agent/",
    "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
    "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/",
]

# Load documents from the URLs
docs = [WebBaseLoader(url).load() for url in urls]
docs_list = [item for sublist in docs for item in sublist]

# Initialize a text splitter with specified chunk size and overlap
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
    chunk_size=250, chunk_overlap=0
)

# Split the documents into chunks
doc_splits = text_splitter.split_documents(docs_list)

# Add the document chunks to the "vector store" using OpenAIEmbeddings
vectorstore = InMemoryVectorStore.from_documents(
    documents=doc_splits,
    embedding=OpenAIEmbeddings(),
)

# With langchain we can easily turn any vector store into a retrieval component:
retriever = vectorstore.as_retriever(k=6)

生成​

我们现在可以定义生成管道。

from langchain_openai import ChatOpenAI
from langsmith import traceable

llm = ChatOpenAI(model="gpt-4o", temperature=1)

# Add decorator so this function is traced in LangSmith
@traceable()
def rag_bot(question: str) -> dict:
    # LangChain retriever will be automatically traced
    docs = retriever.invoke(question)
    docs_string = "

".join(doc.page_content for doc in docs)

    instructions = f"""You are a helpful assistant who is good at analyzing source information and answering questions.       Use the following source documents to answer the user's questions.       If you don't know the answer, just say that you don't know.       Use three sentences maximum and keep the answer concise.

Documents:
{docs_string}"""

    # langchain ChatModel will be automatically traced
    ai_msg = llm.invoke([
            {"role": "system", "content": instructions},
            {"role": "user", "content": question},
        ],
    )

    return {"answer": ai_msg.content, "documents": docs}

数据集​

现在我们已经有了应用程序，让我们构建一个数据集来评估它。在本例中，我们的数据集将非常简单：我们将有示例问题和参考答案。

from langsmith import Client

client = Client()

# Define the examples for the dataset
examples = [
    {
        "inputs": {"question": "How does the ReAct agent use self-reflection? "},
        "outputs": {"answer": "ReAct integrates reasoning and acting, performing actions - such tools like Wikipedia search API - and then observing / reasoning about the tool outputs."},
    },
    {
        "inputs": {"question": "What are the types of biases that can arise with few-shot prompting?"},
        "outputs": {"answer": "The biases that can arise with few-shot prompting include (1) Majority label bias, (2) Recency bias, and (3) Common token bias."},
    },
    {
        "inputs": {"question": "What are five types of adversarial attacks?"},
        "outputs": {"answer": "Five types of adversarial attacks are (1) Token manipulation, (2) Gradient based attack, (3) Jailbreak prompting, (4) Human red-teaming, (5) Model red-teaming."},
    }
]

# Create the dataset and examples in LangSmith
dataset_name = "Lilian Weng Blogs Q&A"
dataset = client.create_dataset(dataset_name=dataset_name)
client.create_examples(
    dataset_id=dataset.id,
    examples=examples
)

评估器​

考虑不同类型的 RAG 评估器的一种方法是将其视为被评估的内容 X 与之进行评估的内容的元组

  1. 正确性：响应 vs 参考答案
  • 目标：衡量“RAG 链的答案相对于真实答案有多相似/正确”
  • 模式：需要通过数据集提供的真实（参考）答案
  • 评估器：使用 LLM 作为评委来评估答案的正确性。
  1. 相关性：响应 vs 输入
  • 目标：衡量“生成的响应在多大程度上解决了用户的初始输入”
  • 模式：不需要参考答案，因为它会将答案与输入问题进行比较
  • 评估器：使用 LLM 作为评委来评估答案的相关性、帮助性等。
  1. 根据性：响应 vs 检索到的文档
  • 目标：衡量“生成的响应在多大程度上与检索到的上下文一致”
  • 模式：不需要参考答案，因为它会将答案与检索到的上下文进行比较
  • 评估器：使用 LLM 作为评委来评估忠实度、幻觉等。
  1. 检索相关性：检索到的文档 vs 输入
  • 目标：衡量“对于此查询，我的检索结果有多相关”
  • 模式：不需要参考答案，因为它会将问题与检索到的上下文进行比较
  • 评估器：使用 LLM 作为评委来评估相关性

正确性：响应 vs 参考答案​

from typing_extensions import Annotated, TypedDict

# Grade output schema
class CorrectnessGrade(TypedDict):
    # Note that the order in the fields are defined is the order in which the model will generate them.
    # It is useful to put explanations before responses because it forces the model to think through
    # its final response before generating it:
    explanation: Annotated[str, ..., "Explain your reasoning for the score"]
    correct: Annotated[bool, ..., "True if the answer is correct, False otherwise."]

# Grade prompt
correctness_instructions = """You are a teacher grading a quiz. 

You will be given a QUESTION, the GROUND TRUTH (correct) ANSWER, and the STUDENT ANSWER. 

Here is the grade criteria to follow:
(1) Grade the student answers based ONLY on their factual accuracy relative to the ground truth answer. 
(2) Ensure that the student answer does not contain any conflicting statements.
(3) It is OK if the student answer contains more information than the ground truth answer, as long as it is factually accurate relative to the  ground truth answer.

Correctness:
A correctness value of True means that the student's answer meets all of the criteria.
A correctness value of False means that the student's answer does not meet all of the criteria.

Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. 

Avoid simply stating the correct answer at the outset."""

# Grader LLM
grader_llm = ChatOpenAI(model="gpt-4o", temperature=0).with_structured_output(CorrectnessGrade, method="json_schema", strict=True)

def correctness(inputs: dict, outputs: dict, reference_outputs: dict) -> bool:
    """An evaluator for RAG answer accuracy"""
    answers = f"""\
QUESTION: {inputs['question']}
GROUND TRUTH ANSWER: {reference_outputs['answer']}
STUDENT ANSWER: {outputs['answer']}"""

    # Run evaluator
    grade = grader_llm.invoke([
        {"role": "system", "content": correctness_instructions}, 
        {"role": "user", "content": answers}
    ])
    return grade["correct"]

相关性：响应 vs 输入​

流程与上述类似，但我们只需查看 inputs 和 outputs，而无需 reference_outputs。在没有参考答案的情况下，我们无法评估准确性，但仍然可以评估相关性——例如，模型是否解决了用户的问题。

# Grade output schema
class RelevanceGrade(TypedDict):
    explanation: Annotated[str, ..., "Explain your reasoning for the score"]
    relevant: Annotated[bool, ..., "Provide the score on whether the answer addresses the question"]

# Grade prompt
relevance_instructions="""You are a teacher grading a quiz. 

You will be given a QUESTION and a STUDENT ANSWER. 

Here is the grade criteria to follow:
(1) Ensure the STUDENT ANSWER is concise and relevant to the QUESTION
(2) Ensure the STUDENT ANSWER helps to answer the QUESTION

Relevance:
A relevance value of True means that the student's answer meets all of the criteria.
A relevance value of False means that the student's answer does not meet all of the criteria.

Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. 

Avoid simply stating the correct answer at the outset."""

# Grader LLM
relevance_llm = ChatOpenAI(model="gpt-4o", temperature=0).with_structured_output(RelevanceGrade, method="json_schema", strict=True)

# Evaluator
def relevance(inputs: dict, outputs: dict) -> bool:
    """A simple evaluator for RAG answer helpfulness."""
    answer = f"QUESTION: {inputs['question']}\nSTUDENT ANSWER: {outputs['answer']}"
    grade = relevance_llm.invoke([
        {"role": "system", "content": relevance_instructions}, 
        {"role": "user", "content": answer}
    ])
    return grade["relevant"]

根据性：响应 vs 检索到的文档​

另一种评估响应的有用方法（无需参考答案）是检查响应是否由检索到的文档证明（或“基于”）。

# Grade output schema
class GroundedGrade(TypedDict):
    explanation: Annotated[str, ..., "Explain your reasoning for the score"]
    grounded: Annotated[bool, ..., "Provide the score on if the answer hallucinates from the documents"]

# Grade prompt
grounded_instructions = """You are a teacher grading a quiz. 

You will be given FACTS and a STUDENT ANSWER. 

Here is the grade criteria to follow:
(1) Ensure the STUDENT ANSWER is grounded in the FACTS. 
(2) Ensure the STUDENT ANSWER does not contain "hallucinated" information outside the scope of the FACTS.

Grounded:
A grounded value of True means that the student's answer meets all of the criteria.
A grounded value of False means that the student's answer does not meet all of the criteria.

Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. 

Avoid simply stating the correct answer at the outset."""

# Grader LLM 
grounded_llm = ChatOpenAI(model="gpt-4o", temperature=0).with_structured_output(GroundedGrade, method="json_schema", strict=True)

# Evaluator
def groundedness(inputs: dict, outputs: dict) -> bool:
    """A simple evaluator for RAG answer groundedness."""
    doc_string = "\n\n".join(doc.page_content for doc in outputs["documents"])
    answer = f"FACTS: {doc_string}\nSTUDENT ANSWER: {outputs['answer']}"
    grade = grounded_llm.invoke([{"role": "system", "content": grounded_instructions}, {"role": "user", "content": answer}])
    return grade["grounded"]

检索相关性：检索到的文档 vs 输入​

# Grade output schema
class RetrievalRelevanceGrade(TypedDict):
    explanation: Annotated[str, ..., "Explain your reasoning for the score"]
    relevant: Annotated[bool, ..., "True if the retrieved documents are relevant to the question, False otherwise"]

# Grade prompt
retrieval_relevance_instructions = """You are a teacher grading a quiz. 

You will be given a QUESTION and a set of FACTS provided by the student. 

Here is the grade criteria to follow:
(1) You goal is to identify FACTS that are completely unrelated to the QUESTION
(2) If the facts contain ANY keywords or semantic meaning related to the question, consider them relevant
(3) It is OK if the facts have SOME information that is unrelated to the question as long as (2) is met

Relevance:
A relevance value of True means that the FACTS contain ANY keywords or semantic meaning related to the QUESTION and are therefore relevant.
A relevance value of False means that the FACTS are completely unrelated to the QUESTION.

Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. 

Avoid simply stating the correct answer at the outset."""

# Grader LLM
retrieval_relevance_llm = ChatOpenAI(model="gpt-4o", temperature=0).with_structured_output(RetrievalRelevanceGrade, method="json_schema", strict=True)

def retrieval_relevance(inputs: dict, outputs: dict) -> bool:
    """An evaluator for document relevance"""
    doc_string = "\n\n".join(doc.page_content for doc in outputs["documents"])
    answer = f"FACTS: {doc_string}\nQUESTION: {inputs['question']}"

    # Run evaluator
    grade = retrieval_relevance_llm.invoke([
        {"role": "system", "content": retrieval_relevance_instructions}, 
        {"role": "user", "content": answer}
    ])
    return grade["relevant"]

运行评估​

我们现在可以使用我们所有不同的评估器启动我们的评估作业。

def target(inputs: dict) -> dict:
    return rag_bot(inputs["question"])

experiment_results = client.evaluate(
    target,
    data=dataset_name,
    evaluators=[correctness, groundedness, relevance, retrieval_relevance],
    experiment_prefix="rag-doc-relevance",
    metadata={"version": "LCEL context, gpt-4-0125-preview"},
)
# Explore results locally as a dataframe if you have pandas installed
# experiment_results.to_pandas()

您可以在此处查看这些结果的示例：LangSmith 链接

参考代码​

这是一个包含上述所有代码的综合脚本

from langchain_community.document_loaders import WebBaseLoader
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langsmith import Client, traceable
from typing_extensions import Annotated, TypedDict

# List of URLs to load documents from
urls = [
    "https://lilianweng.github.io/posts/2023-06-23-agent/",
    "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
    "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/",
]

# Load documents from the URLs
docs = [WebBaseLoader(url).load() for url in urls]
docs_list = [item for sublist in docs for item in sublist]

# Initialize a text splitter with specified chunk size and overlap
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
    chunk_size=250, chunk_overlap=0
)

# Split the documents into chunks
doc_splits = text_splitter.split_documents(docs_list)

# Add the document chunks to the "vector store" using OpenAIEmbeddings
vectorstore = InMemoryVectorStore.from_documents(
    documents=doc_splits,
    embedding=OpenAIEmbeddings(),
)

# With langchain we can easily turn any vector store into a retrieval component:
retriever = vectorstore.as_retriever(k=6)

llm = ChatOpenAI(model="gpt-4o", temperature=1)


# Add decorator so this function is traced in LangSmith
@traceable()
def rag_bot(question: str) -> dict:
    # langchain Retriever will be automatically traced
    docs = retriever.invoke(question)

    docs_string = "

".join(doc.page_content for doc in docs)
    instructions = f"""You are a helpful assistant who is good at analyzing source information and answering questions.       Use the following source documents to answer the user's questions.       If you don't know the answer, just say that you don't know.       Use three sentences maximum and keep the answer concise.

Documents:
{docs_string}"""
    # langchain ChatModel will be automatically traced
    ai_msg = llm.invoke(
        [
            {"role": "system", "content": instructions},
            {"role": "user", "content": question},
        ],
    )

    return {"answer": ai_msg.content, "documents": docs}


client = Client()

# Define the examples for the dataset
examples = [
    {
        "inputs": {"question": "How does the ReAct agent use self-reflection? "},
        "outputs": {"answer": "ReAct integrates reasoning and acting, performing actions - such tools like Wikipedia search API - and then observing / reasoning about the tool outputs."},
    },
    {
        "inputs": {"question": "What are the types of biases that can arise with few-shot prompting?"},
        "outputs": {"answer": "The biases that can arise with few-shot prompting include (1) Majority label bias, (2) Recency bias, and (3) Common token bias."},
    },
    {
        "inputs": {"question": "What are five types of adversarial attacks?"},
        "outputs": {"answer": "Five types of adversarial attacks are (1) Token manipulation, (2) Gradient based attack, (3) Jailbreak prompting, (4) Human red-teaming, (5) Model red-teaming."},
    },
]

# Create the dataset and examples in LangSmith
dataset_name = "Lilian Weng Blogs Q&A"
if not client.has_dataset(dataset_name=dataset_name):
    dataset = client.create_dataset(dataset_name=dataset_name)
    client.create_examples(
        dataset_id=dataset.id,
        examples=examples
    )


# Grade output schema
class CorrectnessGrade(TypedDict):
    # Note that the order in the fields are defined is the order in which the model will generate them.
    # It is useful to put explanations before responses because it forces the model to think through
    # its final response before generating it:
    explanation: Annotated[str, ..., "Explain your reasoning for the score"]
    correct: Annotated[bool, ..., "True if the answer is correct, False otherwise."]


# Grade prompt
correctness_instructions = """You are a teacher grading a quiz. 

You will be given a QUESTION, the GROUND TRUTH (correct) ANSWER, and the STUDENT ANSWER. 

Here is the grade criteria to follow:
(1) Grade the student answers based ONLY on their factual accuracy relative to the ground truth answer. 
(2) Ensure that the student answer does not contain any conflicting statements.
(3) It is OK if the student answer contains more information than the ground truth answer, as long as it is factually accurate relative to the  ground truth answer.

Correctness:
A correctness value of True means that the student's answer meets all of the criteria.
A correctness value of False means that the student's answer does not meet all of the criteria.

Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. 

Avoid simply stating the correct answer at the outset."""

# Grader LLM
grader_llm = ChatOpenAI(model="gpt-4o", temperature=0).with_structured_output(
    CorrectnessGrade, method="json_schema", strict=True
)


def correctness(inputs: dict, outputs: dict, reference_outputs: dict) -> bool:
    """An evaluator for RAG answer accuracy"""
    answers = f"""\
QUESTION: {inputs['question']}
GROUND TRUTH ANSWER: {reference_outputs['answer']}
STUDENT ANSWER: {outputs['answer']}"""

    # Run evaluator
    grade = grader_llm.invoke(
        [
            {"role": "system", "content": correctness_instructions},
            {"role": "user", "content": answers},
        ]
    )
    return grade["correct"]


# Grade output schema
class RelevanceGrade(TypedDict):
    explanation: Annotated[str, ..., "Explain your reasoning for the score"]
    relevant: Annotated[
        bool, ..., "Provide the score on whether the answer addresses the question"
    ]


# Grade prompt
relevance_instructions = """You are a teacher grading a quiz. 

You will be given a QUESTION and a STUDENT ANSWER. 

Here is the grade criteria to follow:
(1) Ensure the STUDENT ANSWER is concise and relevant to the QUESTION
(2) Ensure the STUDENT ANSWER helps to answer the QUESTION

Relevance:
A relevance value of True means that the student's answer meets all of the criteria.
A relevance value of False means that the student's answer does not meet all of the criteria.

Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. 

Avoid simply stating the correct answer at the outset."""

# Grader LLM
relevance_llm = ChatOpenAI(model="gpt-4o", temperature=0).with_structured_output(
    RelevanceGrade, method="json_schema", strict=True
)


# Evaluator
def relevance(inputs: dict, outputs: dict) -> bool:
    """A simple evaluator for RAG answer helpfulness."""
    answer = f"QUESTION: {inputs['question']}\nSTUDENT ANSWER: {outputs['answer']}"
    grade = relevance_llm.invoke(
        [
            {"role": "system", "content": relevance_instructions},
            {"role": "user", "content": answer},
        ]
    )
    return grade["relevant"]


# Grade output schema
class GroundedGrade(TypedDict):
    explanation: Annotated[str, ..., "Explain your reasoning for the score"]
    grounded: Annotated[
        bool, ..., "Provide the score on if the answer hallucinates from the documents"
    ]


# Grade prompt
grounded_instructions = """You are a teacher grading a quiz. 

You will be given FACTS and a STUDENT ANSWER. 

Here is the grade criteria to follow:
(1) Ensure the STUDENT ANSWER is grounded in the FACTS. 
(2) Ensure the STUDENT ANSWER does not contain "hallucinated" information outside the scope of the FACTS.

Grounded:
A grounded value of True means that the student's answer meets all of the criteria.
A grounded value of False means that the student's answer does not meet all of the criteria.

Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. 

Avoid simply stating the correct answer at the outset."""

# Grader LLM
grounded_llm = ChatOpenAI(model="gpt-4o", temperature=0).with_structured_output(
    GroundedGrade, method="json_schema", strict=True
)


# Evaluator
def groundedness(inputs: dict, outputs: dict) -> bool:
    """A simple evaluator for RAG answer groundedness."""
    doc_string = "\n\n".join(doc.page_content for doc in outputs["documents"])
    answer = f"FACTS: {doc_string}\nSTUDENT ANSWER: {outputs['answer']}"
    grade = grounded_llm.invoke(
        [
            {"role": "system", "content": grounded_instructions},
            {"role": "user", "content": answer},
        ]
    )
    return grade["grounded"]


# Grade output schema
class RetrievalRelevanceGrade(TypedDict):
    explanation: Annotated[str, ..., "Explain your reasoning for the score"]
    relevant: Annotated[
        bool,
        ...,
        "True if the retrieved documents are relevant to the question, False otherwise",
    ]


# Grade prompt
retrieval_relevance_instructions = """You are a teacher grading a quiz. 

You will be given a QUESTION and a set of FACTS provided by the student. 

Here is the grade criteria to follow:
(1) You goal is to identify FACTS that are completely unrelated to the QUESTION
(2) If the facts contain ANY keywords or semantic meaning related to the question, consider them relevant
(3) It is OK if the facts have SOME information that is unrelated to the question as long as (2) is met

Relevance:
A relevance value of True means that the FACTS contain ANY keywords or semantic meaning related to the QUESTION and are therefore relevant.
A relevance value of False means that the FACTS are completely unrelated to the QUESTION.

Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. 

Avoid simply stating the correct answer at the outset."""

# Grader LLM
retrieval_relevance_llm = ChatOpenAI(
    model="gpt-4o", temperature=0
).with_structured_output(RetrievalRelevanceGrade, method="json_schema", strict=True)


def retrieval_relevance(inputs: dict, outputs: dict) -> bool:
    """An evaluator for document relevance"""
    doc_string = "\n\n".join(doc.page_content for doc in outputs["documents"])
    answer = f"FACTS: {doc_string}\nQUESTION: {inputs['question']}"

    # Run evaluator
    grade = retrieval_relevance_llm.invoke(
        [
            {"role": "system", "content": retrieval_relevance_instructions},
            {"role": "user", "content": answer},
        ]
    )
    return grade["relevant"]


def target(inputs: dict) -> dict:
    return rag_bot(inputs["question"])


experiment_results = client.evaluate(
    target,
    data=dataset_name,
    evaluators=[correctness, groundedness, relevance, retrieval_relevance],
    experiment_prefix="rag-doc-relevance",
    metadata={"version": "LCEL context, gpt-4-0125-preview"},
)

# Explore results locally as a dataframe if you have pandas installed
# experiment_results.to_pandas()

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