Diving Deeper into Advanced Document Retrieval with ColPali - Session 2
Are you struggling with retrieving information from complex documents that contain a mix of text, images, and tables?
Welcome back to our exploration of ColPali, the cutting-edge technique for advanced document retrieval. In this second session, we'll be diving deeper into the indexing process, examining how ColPali handles PDF documents, and exploring the full end-to-end architecture of a retrieval-augmented generation (RAG) system incorporating this powerful technique.
The Indexing Process: From PDF to Searchable Data
The heart of ColPali's power lies in its sophisticated indexing process. Let's break down the key steps:
1. Page Processing
The journey begins with converting each page of a PDF document into an image. This transformation allows ColPali to work with visual information, not just text.
2. Patch Creation
Next, each page image is divided into smaller "patches" - for instance, an input PDF document broken down into 1,030 patches of roughly 32x32 pixels each. This granular approach allows for more precise information retrieval later on.
3. SigLIP Encoding
Each patch is then passed through a vision encoder called SigLIP. This step converts the visual information in each patch into a compact 128-dimensional vector representation in the example in the paper.
4. Projection Layer
The encoded patches are then passed through a projection layer. This crucial step ensures that the data is in an appropriate format for the next stage of processing.
5. Gemma-2B Processing
All the projected patch embeddings are then fed into Gemma-2B, a large language model (LLM). This step allows the model to understand the relationships between patches, adding crucial contextual information.
6. Final Projection and Indexing
After LLM processing, there's a final projection step to ensure each patch is represented by a 128-dimensional vector. These final embeddings are then stored in a vector database for quick retrieval.
Online Querying: How ColPali Handles User Questions
When a user submits a query, ColPali employs a clever "late interaction" approach:
The query is encoded using a language model.
The system calculates similarities between query tokens and document patches.
For each query token, the highest similarity score across all patches is selected.
These scores are used to rank and retrieve the most relevant documents.
This method, inspired by the ColBERT model, allows for more nuanced and explainable search results compared to traditional keyword or dense semantic search.
The Full RAG Architecture with ColPali
Integrating ColPali into a full retrieval-augmented generation system involves several components:
Document Indexing: PDF pages are processed, embedded, and stored as described above.
Image Retrieval: When a query is received, the system retrieves the top-k relevant PDF pages.
Vision-Language Model: Retrieved pages (as images) are passed to a vision-language model.
Response Generation: The model generates a human-readable response based on the visual and textual information.
Curious to learn more?
Join Professor Mehdi and myself for a discussion about this topic below:
What you’ll learn in Session 1🤓:
🔎 The full indexing steps including page processing, patch creation, SigLIP encoding, and projection
🪄 How online querying is handled throughout the architecture
🛠 ColBERT late interaction
🌶️ End-to-end RAG with ColPali
🎃 Caveat of the ColPali approach
👇
🛠️✨ Happy practicing and happy building! 🚀🌟
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