Indexing PDFs with VLMs and Multi-Vector Search

Problem: Traditional OCR and embedding methods are insufficient for PDFs containing complex layouts, tables, and visual elements, leading to ineffective indexing and poor search retrieval. The article addresses the challenge of indexing diverse PDF structures effectively.

Methodology: A data indexing pipeline is presented that uses a PDF Embedding Decider to dynamically select between traditional text embeddings (after OCR) and multimodal vision embeddings (using VLMs like ColPali) based on document structure analysis (layout, text density, visual elements, tables).

ColPali: This Vision Language Model (VLM) indexes and retrieves information from documents based on visual features, potentially eliminating complex OCR pipelines. It considers both textual and visual content.

PDF Embedding Detector Components:

LayoutAnalyzer:* Quantifies layout complexity via block counting and alignment detection.

TextDensityAnalyzer:* Calculates the ratio of text area to total page area.

VisualElementAnalyzer:* Identifies the presence and distribution of visual elements.

TableDetector:* Identifies and analyzes tables based on text block alignment and spacing.

Multi-Vector Indexing with Vespa: Employs a search engine and vector database to represent documents with multiple vectors (textual and visual embeddings), improving semantic search and retrieval accuracy for multimodal queries. Vespa supports ANN, lexical search, and structured data search in the same query.

Feed Preparation for Vespa: Transforms extracted content into a structured dataset, creating page-level documents with text, Base64-encoded images, and patch-based embeddings for fine-grained multi-vector retrieval.