How to Use ColPali: Efficient Document Retrieval with Vision Language Models

Welcome to the exciting world of document retrieval! In this article, we will guide you through using ColPali, an innovative model that utilizes Vision Language Models (VLMs) for efficient document indexing based on visual features.

Understanding ColPali

ColPali is like a highly skilled librarian specializing in finding documents not just by words, but also by what they visually represent. Imagine a librarian who can identify books based on pictures or diagrams instead of just titles or summaries – that’s what ColPali delivers! This clever model builds onto PaliGemma-3B, employing the ColBERT strategy to create representations that marry text with images.

Getting Started with ColPali

Step 1: Install the ColPali Engine

Before you can start using ColPali, you’ll need to install the engine. Use the command below:

pip install colpali-engine==0.3.0,0.4.0

Step 2: Load the Model

Next, you’ll need to load the pre-trained model. Below is a sample code to help you:

import torch
from PIL import Image
from colpali_engine.models import ColPali, ColPaliProcessor

model_name = "vidorecolpali-v1.2-merged"
model = ColPali.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="cuda:0"  # or "mps" if on Apple Silicon
).eval()
processor = ColPaliProcessor.from_pretrained(model_name)

Step 3: Prepare Your Inputs

Now it’s time to process your images and queries. You can create sample images and text queries as follows:

images = [
    Image.new("RGB", (32, 32), color="white"),
    Image.new("RGB", (16, 16), color="black"),
]
queries = [
    "Is attention really all you need?",
    "Are Benjamin, Antoine, Merve, and Jo best friends?",
]

Step 4: Process and Retrieve

Finally, you’ll need to process your inputs and obtain results:

# Process the inputs
batch_images = processor.process_images(images).to(model.device)
batch_queries = processor.process_queries(queries).to(model.device)

# Forward pass
with torch.no_grad():
    image_embeddings = model(**batch_images)
    query_embeddings = model(**batch_queries)
    
scores = processor.score_multi_vector(query_embeddings, image_embeddings)

Limitations to Consider

• Focus: ColPali primarily specializes in PDF-type documents and high-resource languages, which could affect its performance with less common formats or languages.
• Support: Adapting the ColBERT late interaction mechanism may require additional engineering effort if you aim to integrate it with other commonly used vector retrieval frameworks.

Troubleshooting Tips

While using ColPali, you may encounter some bumps along the road. Here are a few troubleshooting tips:

• Installation Issues: Ensure you are using the correct version of Python and have all dependencies installed.
• Tensor Size Errors: Double-check that your input images and queries are processed correctly. Ensure that they match in dimensions as expected by the model. Each image and query should be properly formatted.
• Performance Problems: If the model is slow, consider using a system with better GPU support or optimizing your batch sizes.

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Conclusion

ColPali offers an exciting leap in the field of document retrieval by combining both visual and textual elements. With the steps above, you are well on your way to leveraging this powerful tool for your own projects.

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.