RAGatouille는 ColBERT를 최대한 간단하게 사용할 수 있도록 해줍니다! ColBERT는 빠르고 정확한 검색 모델로, 수십 밀리초 내에 대규모 텍스트 컬렉션에 대한 확장 가능한 BERT 기반 검색을 가능하게 합니다. ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction 논문을 참조하세요.
RAGatouille을 사용할 수 있는 여러 가지 방법이 있습니다.

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Setup

이 통합은 ragatouille 패키지에 포함되어 있습니다. 
pip install -U ragatouille

from ragatouille import RAGPretrainedModel

RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")

[Jan 10, 10:53:28] Loading segmented_maxsim_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...

/Users/harrisonchase/.pyenv/versions/3.10.1/envs/langchain/lib/python3.10/site-packages/torch/cuda/amp/grad_scaler.py:125: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
  warnings.warn(

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Retriever

RAGatouille을 retriever로 사용할 수 있습니다. 자세한 내용은 RAGatouille Retriever를 참조하세요.

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Document Compressor

RAGatouille을 reranker로 바로 사용할 수도 있습니다. 이를 통해 ColBERT를 사용하여 일반적인 retriever에서 검색된 결과를 재순위화할 수 있습니다. 이 방법의 장점은 기존 인덱스 위에서 작업할 수 있어 새로운 인덱스를 생성할 필요가 없다는 것입니다.

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Setup Vanilla Retriever

먼저, 예제로 기본 retriever를 설정해보겠습니다. 
import requests
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter


def get_wikipedia_page(title: str):
    """
    Retrieve the full text content of a Wikipedia page.

    :param title: str - Title of the Wikipedia page.
    :return: str - Full text content of the page as raw string.
    """
    # Wikipedia API endpoint
        URL = "https://en.wikipedia.org/w/api.php"

    # Parameters for the API request
        params = {
        "action": "query",
        "format": "json",
        "titles": title,
        "prop": "extracts",
        "explaintext": True,
    }

    # Custom User-Agent header to comply with Wikipedia's best practices
        headers = {"User-Agent": "RAGatouille_tutorial/0.0.1 ([email protected])"}

        response = requests.get(URL, params=params, headers=headers)
        data = response.json()

    # Extracting page content
        page = next(iter(data["query"]["pages"].values()))
    return page["extract"] if "extract" in page else None


text = get_wikipedia_page("Hayao_Miyazaki")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
texts = text_splitter.create_documents([text])

retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever(
        search_kwargs={"k": 10}
)

docs = retriever.invoke("What animation studio did Miyazaki found")
docs[0]

Document(page_content='collaborative projects. In April 1984, Miyazaki opened his own office in Suginami Ward, naming it Nibariki.')
결과가 질문과 크게 관련이 없는 것을 볼 수 있습니다.

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ColBERT를 reranker로 사용하기

from langchain.retrievers import ContextualCompressionRetriever

compression_retriever = ContextualCompressionRetriever(
        base_compressor=RAG.as_langchain_document_compressor(), base_retriever=retriever
)

compressed_docs = compression_retriever.invoke(
    "What animation studio did Miyazaki found"
)

/Users/harrisonchase/.pyenv/versions/3.10.1/envs/langchain/lib/python3.10/site-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
  warnings.warn(

compressed_docs[0]

Document(page_content='In June 1985, Miyazaki, Takahata, Tokuma and Suzuki founded the animation production company Studio Ghibli, with funding from Tokuma Shoten. Studio Ghibli\'s first film, Laputa: Castle in the Sky (1986), employed the same production crew of Nausicaä. Miyazaki\'s designs for the film\'s setting were inspired by Greek architecture and "European urbanistic templates". Some of the architecture in the film was also inspired by a Welsh mining town; Miyazaki witnessed the mining strike upon his first', metadata={'relevance_score': 26.5194149017334})
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