How to Build Multimodal Retrieval with ColQwen and Vespa?

This document explores ColQwen, a cutting-edge multimodal retrieval model, and its integration with Vespa, a powerful vector database, for efficient document retrieval. Unlike traditional methods that rely on text extraction, ColQwen directly embeds entire document pages as images, preserving crucial visual context. This approach is particularly beneficial for complex documents rich in tables, charts, and other visual elements.

Key Learning Objectives:

1. Grasp the functionalities of ColQwen, multi-vector embeddings, and Vespa.
2. Prepare financial PDFs for retrieval by converting pages into images.
3. Generate multi-vector embeddings using ColQwen's Vision Language Model.
4. Optimize Vespa's schema and ranking profile for efficient searching.
5. Implement a two-phase retrieval pipeline using Vespa's Hamming distance and MaxSim calculations.
6. Visualize retrieved pages and utilize ColQwen's explainability features.

Table of Contents:

• Key Learning Objectives
• Introducing ColQwen
• ColQwen's Distinctive Approach
• Understanding Multi-vector Embeddings
• ColPali vs. ColQwen2: Key Improvements
• Vespa: The Vector Database
• Practical Implementation: A Step-by-Step Guide
  • Step 1: Software Installation
  • Step 2: Configuring ColQwen for Image Embedding
  • Step 3: PDF Preparation
  • Step 4: Processing PDFs into Images
  • Step 5: Generating Embeddings
  • Step 6: Base64 Encoding and Data Structuring for Vespa
  • Step 7: Creating the Vespa Schema
  • Step 8: Defining Query Tensors
  • Step 9: Implementing a Multi-Phase Ranking Profile
  • The Rationale Behind Two-Phase Ranking
  • Step 10: Deploying the Vespa Application
  • Step 11: Indexing Data in Vespa
  • Step 12: Querying Vespa and Displaying Results
  • Step 13: Interpretability: Visualizing Relevant Patches
• Frequently Asked Questions

Introducing ColQwen:

ColQwen leverages a Vision Language Model (VLM) to process entire document pages as images, generating rich, multi-vector embeddings that capture both textual and visual context. This significantly improves document retrieval, particularly for visually dense documents.

ColQwen's Distinctive Approach:

Traditional systems often rely on OCR, layout detection, and text embedding, losing valuable visual context. ColQwen's direct image embedding preserves this crucial information, enhancing retrieval accuracy.

Understanding Multi-vector Embeddings:

Unlike single-vector embeddings, multi-vector embeddings create multiple focused embeddings, one for each query token. This allows for more precise matching of query terms to relevant document sections. ColQwen adapts this technique for images, dividing pages into patches, each with its own embedding.

ColPali vs. ColQwen2: Key Improvements:

ColQwen2 improves upon ColPali by processing images at their native resolutions, preserving aspect ratios and offering adjustable resolution for optimized performance and storage.

Vespa: The Vector Database:

Vespa is an open-source vector database that supports multi-vector representations, enabling efficient search and custom ranking strategies. It serves as the query engine in this system.

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Frequently Asked Questions:

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