RAG評価の概要

LangChain による検索拡張生成（RAG）

Meri Nova

Machine Learning Engineer

RAG評価の種類

RAGのワークフロー。評価可能な工程（検索、LLMの幻覚、質問への関連性、参照回答との比較）を強調表示。

¹ 画像提供: LangSmith

出力の正確性：文字列評価

query = "What are the main components of RAG architecture?"
predicted_answer = "Training and encoding"
ref_answer = "Retrieval and Generation"

出力の正確性：文字列評価

prompt_template = """You are an expert professor specialized in grading students' answers to questions.
You are grading the following question:{query}
Here is the real answer:{answer}
You are grading the following predicted answer:{result}
Respond with CORRECT or INCORRECT:
Grade:"""

prompt = PromptTemplate(
    input_variables=["query", "answer", "result"],
    template=prompt_template
)

eval_llm = ChatOpenAI(temperature=0, model="gpt-4o-mini", openai_api_key='...')

出力の正確性：文字列評価

from langsmith.evaluation import LangChainStringEvaluator

qa_evaluator = LangChainStringEvaluator(
    "qa",
    config={
        "llm": eval_llm,
        "prompt": PROMPT
    }
)


score = qa_evaluator.evaluator.evaluate_strings(
    prediction=predicted_answer,
    reference=ref_answer,
    input=query
)

出力の正確性：文字列評価

print(f"Score: {score}")

Score: {'reasoning': 'INCORRECT', 'value': 'INCORRECT', 'score': 0}

query = "What are the main components of RAG architecture?"
predicted_answer = "Training and encoding"
ref_answer = "Retrieval and Generation"

Ragasフレームワーク

生成の指標と検索の指標を比較する表。

¹ 画像提供: Ragas

忠実性 (Faithfulness)

生成結果は文脈に忠実か？

$$ \text{Faithfulness} = \frac{\text{文脈から推論可能な主張数}}{\text{主張の総数}} $$

(0, 1)に正規化

忠実性の評価

from langchain_openai import ChatOpenAI, OpenAIEmbeddings

from ragas.integrations.langchain import EvaluatorChain
from ragas.metrics import faithfulness


llm = ChatOpenAI(model="gpt-4o-mini", api_key="...")
embeddings = OpenAIEmbeddings(model="text-embedding-3-small", api_key="...")


faithfulness_chain = EvaluatorChain(
    metric=faithfulness,
    llm=llm,
    embeddings=embeddings
)

忠実性の評価

eval_result = faithfulness_chain({

  "question": "How does the RAG model improve question answering with LLMs?",

  "answer": "The RAG model improves question answering by combining the retrieval of documents...",

  "contexts": [
    "The RAG model integrates document retrieval with LLMs by first retrieving relevant passages...",
    "By incorporating retrieval mechanisms, RAG leverages external knowledge sources, allowing the...",
  ]

})


print(eval_result)

'faithfulness': 1.0

コンテキスト適合率 (Context Precision)

取得文書はクエリにどれだけ関連するか？
(0, 1)に正規化 → 1 = 高関連

from ragas.metrics import context_precision

llm = ChatOpenAI(model="gpt-4o-mini", api_key="...")
embeddings = OpenAIEmbeddings(model="text-embedding-3-small", api_key="...")

context_precision_chain = EvaluatorChain(
    metric=context_precision,
    llm=llm,
    embeddings=embeddings
)

コンテキスト適合率の評価

eval_result = context_precision_chain({
  "question": "How does the RAG model improve question answering with large language models?",
  "ground_truth": "The RAG model improves question answering by combining the retrieval of...",
  "contexts": [
    "The RAG model integrates document retrieval with LLMs by first retrieving...",
    "By incorporating retrieval mechanisms, RAG leverages external knowledge sources...",
  ]
})


print(f"Context Precision: {eval_result['context_precision']}")

Context Precision: 0.99999999995

Ayo berlatih!

LangChain による検索拡張生成（RAG）