R2R 集成

R2R 是一个一体化的人工智能检索增强生成 (RAG) 解决方案，具备生产就绪的功能，包括多模态内容注入、混合搜索功能、用户/文档管理等多种特性。

概述

在本教程中，我们将

• 利用 R2R 的 /rag 端点，对一个小数据集执行检索增强生成 (RAG)。
• 评估生成的响应。
• 分析评估的追踪记录。

R2R 设置

安装依赖项

首先，安装必要的软件包

%pip install r2r -q

设置本地环境

配置 R2R_API_KEY、OPENAI_API_KEY 和 RAGAS_APP_TOKEN（可选）。

from dotenv import load_dotenv

load_dotenv()

获取数据

dataset = [
    "OpenAI is one of the most recognized names in the large language model space, known for its GPT series of models. These models excel at generating human-like text and performing tasks like creative writing, answering questions, and summarizing content. GPT-4, their latest release, has set benchmarks in understanding context and delivering detailed responses.",
    "Anthropic is well-known for its Claude series of language models, designed with a strong focus on safety and ethical AI behavior. Claude is particularly praised for its ability to follow complex instructions and generate text that aligns closely with user intent.",
    "DeepMind, a division of Google, is recognized for its cutting-edge Gemini models, which are integrated into various Google products like Bard and Workspace tools. These models are renowned for their conversational abilities and their capacity to handle complex, multi-turn dialogues.",
    "Meta AI is best known for its LLaMA (Large Language Model Meta AI) series, which has been made open-source for researchers and developers. LLaMA models are praised for their ability to support innovation and experimentation due to their accessibility and strong performance.",
    "Meta AI with it's LLaMA models aims to democratize AI development by making high-quality models available for free, fostering collaboration across industries. Their open-source approach has been a game-changer for researchers without access to expensive resources.",
    "Microsoft’s Azure AI platform is famous for integrating OpenAI’s GPT models, enabling businesses to use these advanced models in a scalable and secure cloud environment. Azure AI powers applications like Copilot in Office 365, helping users draft emails, generate summaries, and more.",
    "Amazon’s Bedrock platform is recognized for providing access to various language models, including its own models and third-party ones like Anthropic’s Claude and AI21’s Jurassic. Bedrock is especially valued for its flexibility, allowing users to choose models based on their specific needs.",
    "Cohere is well-known for its language models tailored for business use, excelling in tasks like search, summarization, and customer support. Their models are recognized for being efficient, cost-effective, and easy to integrate into workflows.",
    "AI21 Labs is famous for its Jurassic series of language models, which are highly versatile and capable of handling tasks like content creation and code generation. The Jurassic models stand out for their natural language understanding and ability to generate detailed and coherent responses.",
    "In the rapidly advancing field of artificial intelligence, several companies have made significant contributions with their large language models. Notable players include OpenAI, known for its GPT Series (including GPT-4); Anthropic, which offers the Claude Series; Google DeepMind with its Gemini Models; Meta AI, recognized for its LLaMA Series; Microsoft Azure AI, which integrates OpenAI’s GPT Models; Amazon AWS (Bedrock), providing access to various models including Claude (Anthropic) and Jurassic (AI21 Labs); Cohere, which offers its own models tailored for business use; and AI21 Labs, known for its Jurassic Series. These companies are shaping the landscape of AI by providing powerful models with diverse capabilities.",
]

设置 R2R 客户端

from r2r import R2RClient

client = R2RClient()

数据注入

ingest_response = client.documents.create(
    chunks=dataset,
)

使用 /rag 端点

/rag 端点通过将搜索结果与语言模型输出相结合，来促进检索增强生成。生成过程可以使用 rag_generation_config 参数进行自定义，而检索过程可以使用 search_settings 进行配置。

query = "What makes Meta AI’s LLaMA models stand out?"

search_settings = {
        "limit": 2,
        "graph_settings": {"enabled": False, "limit": 2},
    }

response = client.retrieval.rag(
    query=query,
    search_settings=search_settings
)

print(response.results.generated_answer)

输出

Meta AI’s LLaMA models stand out due to their open-source nature, which supports innovation and experimentation by making high-quality models accessible to researchers and developers [1]. This approach democratizes AI development, fostering collaboration across industries and enabling researchers without access to expensive resources to work with advanced AI models [2].

评估

使用 Ragas 评估 R2R 客户端

在 R2R 客户端 就绪后，我们可以使用 Ragas 的 r2r 集成进行评估。该过程涉及以下关键组件：

• 1. R2R 客户端和配置 R2RClient 和指定 RAG 设置的 /rag 配置。

• 2. 评估数据集 您需要一个 Ragas EvaluationDataset，其中包含 Ragas 指标所需的所有必要输入。

• 3. Ragas 指标 Ragas 提供多种评估指标，用于评估 RAG 的不同方面，例如忠实度、答案相关性和上下文召回率。您可以在 Ragas 文档中探索可用的完整指标列表。

构建 Ragas 评估数据集

EvaluationDataset 是 Ragas 中用于表示评估样本的一种数据类型。您可以在核心概念部分找到有关其结构和用法的更多详细信息。

我们将使用 ragas 中的 transform_to_ragas_dataset 函数来为我们的数据获取 EvaluationDataset。

questions = [
    "Who are the major players in the large language model space?",
    "What is Microsoft’s Azure AI platform known for?",
    "What kind of models does Cohere provide?",
]

references = [
    "The major players include OpenAI (GPT Series), Anthropic (Claude Series), Google DeepMind (Gemini Models), Meta AI (LLaMA Series), Microsoft Azure AI (integrating GPT Models), Amazon AWS (Bedrock with Claude and Jurassic), Cohere (business-focused models), and AI21 Labs (Jurassic Series).",
    "Microsoft’s Azure AI platform is known for integrating OpenAI’s GPT models, enabling businesses to use these models in a scalable and secure cloud environment.",
    "Cohere provides language models tailored for business use, excelling in tasks like search, summarization, and customer support.",
]

r2r_responses = []

search_settings = {
    "limit": 2,
    "graph_settings": {"enabled": False, "limit": 2},
}

for que in questions:
    response = client.retrieval.rag(query=que, search_settings=search_settings)
    r2r_responses.append(response)

from ragas.integrations.r2r import transform_to_ragas_dataset

ragas_eval_dataset = transform_to_ragas_dataset(
    user_inputs=questions, r2r_responses=r2r_responses, references=references
)

输出

EvaluationDataset(features=['user_input', 'retrieved_contexts', 'response', 'reference'], len=3)

选择评估指标

为了评估我们的 RAG 端点，我们将使用以下指标：

• 响应相关性 (Response Relevancy)：衡量响应与用户输入（查询）的相关程度。
• 上下文精确率 (Context Precision)：衡量成功检索到的相关文档（或信息片段）的数量。
• 忠实度 (Faithfulness)：衡量响应在事实上与检索到的上下文的一致程度。

from ragas.metrics import AnswerRelevancy, ContextPrecision, Faithfulness
from ragas import evaluate
from langchain_openai import ChatOpenAI
from ragas.llms import LangchainLLMWrapper

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

ragas_metrics = [AnswerRelevancy(llm=evaluator_llm), ContextPrecision(llm=evaluator_llm), Faithfulness(llm=evaluator_llm)]

results = evaluate(dataset=ragas_eval_dataset, metrics=ragas_metrics)

输出

Querying Client: 100%|██████████| 3/3 [00:00<?, ?it/s]

Evaluating: 100%|██████████| 9/9 [00:00<?, ?it/s]

	user_input	retrieved_contexts	response	reference	answer_relevancy	context_precision	faithfulness
0	大型语言领域的主要参与者有哪些...	[在人工智能快速发展的领域中...	大型语言模型领域的主要参与者...	主要参与者包括 OpenAI（GPT系列），...	1.000000	1.0	1.000000
1	微软的 Azure AI 平台以什么闻名？	[微软的 Azure AI 平台以其...	微软的 Azure AI 平台以其集成...	微软的 Azure AI 平台以其集成...	0.948908	1.0	0.833333
2	Cohere 提供什么样的模型？	[Cohere 以其语言模型而闻名...	Cohere 提供专为企业量身定制的语言模型...	Cohere 提供专为企业量身定制的语言模型...	0.903765	1.0	1.000000

追踪评估过程

为了更好地理解评估的分数，我们可以使用以下代码获取评估的追踪记录和结论原因。

results.upload()

编码愉快