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Introduction

RAGatouille is a Python library designed to simplify the integration and training of state-of-the-art late-interaction retrieval methods, particularly ColBERT, within Retrieval-Augmented Generation (RAG) pipelines. It provides a modular and user-friendly interface, enabling developers to enhance their generative AI models with efficient document retrieval and indexing. This guide will explore its features, usage, and practical applications in document retrieval.

Key Features

1. Training and Fine-Tuning ColBERT Models

RAGatouille provides tools to train and fine-tune ColBERT models, allowing for customized retrieval tailored to specific datasets.

2. Embedding and Indexing Documents

Supports embedding and indexing of documents, enabling efficient retrieval operations for large text datasets.

3. Seamless Document Retrieval

Enables retrieval of relevant documents based on queries, integrating smoothly with generative models to improve the relevance of responses.

Setup and Installation

Install RAGatouille using pip:

!pip install ragatouille

Load a Pretrained Model

from ragatouille import RAGPretrainedModel

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

Retrieving Wikipedia Page Content

Before indexing, let’s retrieve text from Wikipedia using an API request.

import requests

def get_wikipedia_page(title: str):
    URL = "https://en.wikipedia.org/w/api.php"
    params = {"action": "query", "format": "json", "titles": title, "prop": "extracts", "explaintext": True}
    headers = {"User-Agent": "RAGatouille_tutorial/0.0.1 (ben@clavie.eu)"}
    response = requests.get(URL, params=params, headers=headers)
    data = response.json()
    page = next(iter(data['query']['pages'].values()))
    return page['extract'] if 'extract' in page else None

Example: Retrieve Content Length of a Wikipedia Page

full_document = get_wikipedia_page("Hayao_Miyazaki")
len(full_document)

Expected Output:

68505

Indexing Wikipedia Content with RAG

RAG.index(
    collection=[full_document],
    document_ids=['miyazaki'],
    document_metadatas=[{"entity": "person", "source": "wikipedia"}],
    index_name="Miyazaki",
    max_document_length=180,
    split_documents=True
)

Retrieving Relevant Information

Let’s query the index for relevant information:

k = 3
results = RAG.search(query="What animation studio did Miyazaki found?", k=k)
results

Expected Output:

[{'content': 'Miyazaki and Takahata founded the animation production company Studio Ghibli on June 15, 1985.',
  'score': 25.71875, 'rank': 1}]

Measuring Search Performance

You can measure the retrieval speed:

%%timeit
RAG.search(query="What animation studio did Miyazaki found?")

Expected Output:

20.7 ms ± 2.57 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Batch Search in RAG

Query multiple questions at once:

all_results = RAG.search(query=["What animation studio did Miyazaki found?", "Miyazaki son name"], k=k)
all_results

Expected Output:

[[{'content': 'Miyazaki and Takahata founded Studio Ghibli on June 15, 1985.', 'rank': 1}],
 [{'content': 'Miyazaki has two sons: Goro, born in January 1967, and Keisuke, born in April 1969.', 'rank': 1}]]

Loading Pretrained RAG Index

If you have a saved index, you can load it directly:

path_to_index = ".ragatouille/colbert/indexes/Miyazaki/"
RAG = RAGPretrainedModel.from_index(path_to_index)

Adding New Documents to RAG Index

new_documents = get_wikipedia_page("Studio_Ghibli")
RAG.add_to_index([new_documents])

Reranking with a Custom Retrieval Pipeline

For more refined search results, integrate Sentence Transformers with Voyager Index:

from sentence_transformers import SentenceTransformer
from voyager import Index, Space

class MyExistingRetrievalPipeline:
    index: Index
    embedder: SentenceTransformer

    def __init__(self, embedder_name: str = "BAAI/bge-small-en-v1.5"):
        self.embedder = SentenceTransformer(embedder_name)
        self.collection_map = {}
        self.index = Index(Space.Cosine, num_dimensions=self.embedder.get_sentence_embedding_dimension())

    def index_documents(self, documents: list[str]) -> None:
        for document in documents:
            self.collection_map[self.index.add_item(self.embedder.encode(document['content']))] = document['content']

    def query(self, query: str, k: int = 10) -> list[str]:
        query_embedding = self.embedder.encode(query)
        return [self.collection_map[idx] for idx in self.index.query(query_embedding, k=k)[0]]

Initialize the Pipeline

existing_pipeline = MyExistingRetrievalPipeline()

Processing Wikipedia Corpus

from ragatouille.utils import get_wikipedia_page
from ragatouille.data import CorpusProcessor

corpus_processor = CorpusProcessor()
documents = [get_wikipedia_page("Hayao Miyazaki"), get_wikipedia_page("Studio Ghibli")]
documents = corpus_processor.process_corpus(documents, chunk_size=200)

Indexing Documents in Custom Pipeline

existing_pipeline.index_documents(documents)

Querying the Custom Pipeline

query = "What's Ghibli's famous policy?"
raw_results = existing_pipeline.query(query, k=10)
raw_results

Conclusion

RAGatouille provides a powerful retrieval system that enhances RAG-based pipelines, making AI-driven search and generation more relevant and accurate. Whether you're indexing Wikipedia pages or creating a domain-specific search engine, RAGatouille streamlines the process with ColBERT-powered retrieval.

References

1. ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
2. Wikipedia API Documentation
3. PyTorch Official Documentation
4. Sentence Transformers (SBERT) for Reranking
5. RAGatouile Build Fast with AI Notebook

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