How to Use ColQwen2 for Efficient Document Retrieval

Welcome to the exciting world of document retrieval using ColQwen2, an innovative model that combines the strengths of Vision Language Models (VLMs) with the ColBERT strategy. This article will guide you through the process of setting up and utilizing ColQwen2 effectively, ensuring that you can retrieve information from visual documents with ease.

What is ColQwen2?

ColQwen2 is a state-of-the-art model designed to efficiently index documents based on their visual features. It extends the Qwen2-VL-2B architecture and generates multi-vector representations of text and images, making it a powerful tool for retrieving PDF-type documents.

Setting Up ColQwen2

Follow these steps to install and get started with ColQwen2:

• Ensure colpali-engine is installed from source or at version superior to 0.3.1.
• Install the compatible version of transformers, specifically 4.45.0.

Installation Steps

Start by using the following command to install the required libraries:

pip install git+https://github.com/illuin-tech/colpali

Using ColQwen2: Example Code

The following code demonstrates how to use the ColQwen2 model. Think of using ColQwen2 like preparing a meal: you gather all your ingredients (images and queries), mix them together (processing inputs), and then serve the delicious final output (the retrieved scores).

import torch
from PIL import Image
from colpali_engine.models import ColQwen2, ColQwen2Processor

model = ColQwen2.from_pretrained(
        "vidorecolqwen2-v0.1",
        torch_dtype=torch.bfloat16,
        device_map="cuda:0",  # or mps if on Apple Silicon
    ).eval()

processor = ColQwen2Processor.from_pretrained("vidorecolqwen2-v0.1")

# Your inputs
images = [
    Image.new("RGB", (32, 32), color="white"),
    Image.new("RGB", (16, 16), color="black"),
]

queries = [
    "Is attention really all you need?",
    "What is the amount of bananas farmed in Salvador?",
]

# 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)

Breaking Down the Code Analogy

In this code, we act as chefs preparing a dish:

• Gathering Ingredients: The model and processor are like your kitchen tools, ready for action.
• Preparing the Inputs: Images and queries are your ingredients. You create them fresh and ready for processing.
• Cooking: The model conducts a forward pass, mixing together the ingredients to produce embeddings.
• Serving Feedback: Finally, the scores are the finished dish, determining how well the model performed in indexing the visual features.

Troubleshooting

While using ColQwen2, you may encounter a few challenges. Here are some common issues and solutions:

• Memory Errors: If you run into memory issues, try reducing the number of image patches or using a model with lower memory requirements.
• Library Compatibility: Ensure all libraries are up to date. Check the installed versions of colpali-engine and transformers to make sure they meet the required specifications.
• Unexpected Outputs: If you get strange or low scores, double-check your inputs. Ensure images and queries are correctly formatted and passed through the processor.

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Limitations of ColQwen2

• Focus: The model is primarily optimized for PDF-type documents and high-resource languages, which may limit versatility.
• Support: Adapting the model to other vector retrieval frameworks might involve significant engineering efforts.

Conclusion

ColQwen2 stands at the forefront of document retrieval technology, combining visual features with effective indexing strategies. By following the steps outlined above, you can harness this model to retrieve and manage documents efficiently. 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.

Happy coding and retrieving with ColQwen2!