Efficient Document Retrieval with ColPali and BLIP-2 for AI Queries

Why Use ColPali for Document Extraction Before GPT-4 or BLIP-2?

Using large models like GPT-4 for every document query can be costly and slow. By integrating ColPali—a document relevance extraction model—we can significantly streamline this process. ColPali first analyzes the entire document set and selects the most relevant pages. These pages are then processed by more resource-intensive models like GPT-4 or BLIP-2 for detailed answers, reducing overhead. For those who are eager to dive in, here’s the link to the repository, and please give it a star if you like it.

Document Retrieval Process

Introduction to ColPali

ColPali is designed for document extraction. It processes a large batch of documents and selects those that are most relevant to a given query. The model is fast and efficient, working well with various document formats like images and PDFs.

Key Features:

Efficiently scans documents
Supports batch processing
Lightweight in comparison to large models

By running ColPali first, we save on costs and time.

Introduction to BLIP-2

BLIP-2 excels in visual and textual information. It can analyze images (e.g., scanned pages) and text together. BLIP-2 is cheaper to run than GPT-4, and in many cases, it provides answers that are accurate enough for most tasks.

Why ColPali + BLIP-2 is a Great Combination

The pairing of ColPali and BLIP-2 allows you to process relevant documents and query them effectively without overloading your system with expensive computations. ColPali narrows down the document set, and BLIP-2 extracts meaningful insights.

Understanding ColPali

Our repository includes a demo, test-colpali.py, which shows ColPali’s effectiveness at selecting the most relevant images or documents from a set. Here’s an example from the script:

Assuming a dataset of following images:

I am asking following queries and monitoring the scores:

Is this a car?
Can you see a stop sign?
Can you see a car?
Can you see a Lion?

And the example result is for example:

Query: “Can you see a car?”

Best Match: Image 0 (car.jpg), Score: 13.5
Result: Accurate identification and extraction of the most relevant document before further processing.

Combining ColPali and BLIP-2 for Document Querying: Example Walkthrough

In the examples directory of our repository, we use DDOG Investor Presentation as a test case to demonstrate how ColPali and BLIP-2 work together.

I am using just 4 pages, but if you can use NVIDIA GPU you maybe are able to process bigger documents with ease.

Running the command:

poetry run python main.py query examples/DDOG_Investor_Presentation_Aug-24-extracted_4pages.pdf "Explain the chart trends in the document"

After downloading the models, the application processes the PDF (converted to images) and outputs:

INFO:colpali_service:Processing images and queries in batches.
INFO:colpali_service:Processing batch 1 of 2
INFO:colpali_service:Closest document found at index 1 with score: 10.0
INFO:colpali_service:Generating response using BLIP-2.
INFO:__main__:Generated response: cloud migration market

In this case, ColPali identifies the most relevant page (index 1) with the highest score. BLIP-2 then generates a precise response to the query—"cloud migration market"—giving you insight into the document without processing the entire file.

Benefits of the Combination

This approach highlights the power of first narrowing down the relevant documents using ColPali and then using BLIP-2 to query these documents. It significantly reduces resource consumption, especially when working with larger document sets, and can often provide all the answers you need without invoking more expensive models like GPT-4.

By combining the speed and accuracy of ColPali for retrieval with the interpretive capabilities of BLIP-2, you can build a highly efficient AI-driven document processing system.

For a detailed walkthrough of the repository and code, visit our GitHub.