How to Use the Crispy Rerank Family with Mixedbread's ColBERT Model

How to Use the Crispy Rerank Family with Mixedbread’s ColBERT Model

July 24, 2024

If you’re diving into the world of semantic search with advanced models, you’ve landed on the right page. This guide will seamlessly walk you through the process of deploying the crispy rerank family from the Mixedbread ecosystem, particularly the ColBERT model, an efficient solution for fine-tuning search results based on semantic understanding.

Quickstart: Installing and Using ColBERT

Before you can start utilizing the ColBERT model, you need to install it and set it up with the RAGatouille framework. Follow these steps:

First, install the RAGatouille package by running:

pip install ragatouille

Next, import the necessary classes from the library:

from ragatouille import RAGPretrainedModel

Create an instance of the RAGPretrainedModel:

RAG = RAGPretrainedModel.from_pretrained("mixedbread-aimxbai-colbert-v1")

Indexing Documents

Now, it’s time to index some documents to prepare for semantic searching.

documents = [
    "To Kill a Mockingbird is a novel by Harper Lee published in 1960.",
    "The novel Moby-Dick was written by Herman Melville and first published in 1851.",
    "Harper Lee, an American novelist widely known for her novel To Kill a Mockingbird, was born in 1926 in Monroeville, Alabama.",
    "Jane Austen was an English novelist known primarily for her six major novels.",
    "The Harry Potter series, written by J.K. Rowling, is among the most popular books of the modern era.",
    "The Great Gatsby, published in 1925 by F. Scott Fitzgerald, is set in the Jazz Age."
]

RAG.index(documents, index_name="mockingbird")

Executing Searches

With your model and documents ready, you can now perform searches to retrieve relevant information.

query = "Who wrote To Kill a Mockingbird?"
results = RAG.search(query)
# Output
print(results)

This snippet should yield results with relevant content, rankings, and scores that highlight how well the model identifies relevant documents.

Understanding the Code: An Analogy

Imagine you’re a librarian faced with a sea of dusty books in a giant library, and a visitor asks you a specific question. The first thing you do is categorize the books based on subjects (this relates to indexing your documents). After organizing the books, you quickly search through your system to find relevant titles and pull out the top candidates for the visitor’s question (which is akin to executing a search with your model).

Just as the librarian uses their knowledge to direct the query and anticipate the visitor’s needs, the ColBERT model processes queries through layers, aiming to provide the most suitable responses based on the indexed data.

Troubleshooting Tips

While using ColBERT, you might run into a few hurdles. Here are some tips on how to troubleshoot effectively:

If you receive unexpected results, ensure your documents are indexed properly and that no critical information is omitted.
Check that you’ve correctly installed the RAGatouille library and the ColBERT model by running the installation commands again.
If the model struggles with performance, consider using the flagship embedding model mixedbread-aimxbai-embed-large-v1 for retrieval tasks.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

This guide should equip you with the knowledge to set up and utilize the crispy rerank family from Mixedbread effectively. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.