Vision Language Models

Transform How You Retrieve Visually Rich Documents

ColPali makes retrieval-augmented generation (RAG) faster, simpler, and more accurate—directly from complex document images like PDFs

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Go Beyond Text

Traditional RAG pipelines often rely on text extraction and OCR, missing the full picture—literally. In fields like healthcare and finance, important context is often found in charts, tables, or document layout. These visual elements get lost in text-only systems, reducing accuracy and insight.

ColPali changes that.

Built on PaliGemma vision-language models and designed for late interaction retrieval, ColPali embeds the entire visual structure of a document—not just the text. This allows RAG systems to understand documents as humans do: visually and contextually.

What Makes ColPali Different?

Understands Visual Context

ColPali treats each document page as an image and encodes both text and visual information into a unified vector format. That means charts, diagrams, photos, and layout are all part of what’s searchable

No Preprocessing Required

Skip OCR, layout parsing, and complex metadata extraction. ColPali works directly from rendered page images.

Smarter, More Relevant Retrieval

With late interaction and MaxSim (Maximum Similarity) scoring, ColPali compares query tokens with visual grid embeddings at retrieval time—matching relevance with precision.

Powered by Vespa’s Tensor Engine

ColPali integrates natively with Vespa’s tensor ranking framework. Vespa performs efficient, in-place MaxSim scoring across large-scale datasets—ideal for production-grade search applications.

See It in Action

Explore how Vespa and ColPali power multi-modal search in this demo application. Try searching for “helmet”—even without the word in the text, ColPali finds a relevant result using visual cues: a photo of a boy wearing a bicycle helmet. That’s the power of visual understanding.

Behind the Scenes: How It Works

Vision Embeddings with PaliGemma

Lightweight vision-language models create high-quality embeddings from document pages.

Late Interaction Architecture

Query and document vectors are processed independently until final scoring—reducing compute and increasing scalability.

MaxSim Scoring in Vespa

Vespa evaluates document relevance using MaxSim operations directly on stored tensor data, accelerating performance and reducing memory usage.

Hybrid Ranking Support

Combine visual relevance scores with traditional text signals using Vespa’s ranking functions (e.g., Reciprocal Rank Fusion).

Why It Matters

Improves search over PDFs, scanned forms, financial reports, and medical documents etc
Eliminates manual text extraction and OCR pipelines
Reduces latency and increases RAG relevance
Scales to millions of documents using Vespa

Try It Yourself

Explore ColPali with our interactive notebook and see how visual document retrieval can elevate your RAG pipeline. No text extraction needed—just smarter, faster, multi-modal search.

Vespa Platform Key Capabilities

Vespa provides all the building blocks of an AI application, including vector database, hybrid search, retrieval augmented generation (RAG), natural language processing (NLP), machine learning, and support for large language models (LLM).
Build AI applications that meet your requirements precisely. Seamlessly integrate your operational systems and databases using Vespa’s APIs and SDKs, ensuring efficient integration without redundant data duplication.
Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.
Enhance content analysis with NLP through advanced text retrieval, vector search with embeddings and integration with custom or pre-trained machine learning models. Vespa enables efficient semantic search, allowing users to match queries to documents based on meaning rather than just keywords.
Search and retrieve data using detailed contextual clues that combine images and text. By enhancing the cross-referencing of posts, images, and descriptions, Vespa makes retrieval more intelligent and visually intuitive, transforming search into a seamless, human-like experience.
Ensure seamless user experience and reduce management costs with Vespa Cloud. Applications dynamically adjust to fluctuating loads, optimizing performance and cost to eliminate the need for over-provisioning.
Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa actions data with AI across the enterprise.
Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.
Bring computation to the data distributed across multiple nodes. Vespa reduces network bandwidth costs, minimizes latency from data transfers, and ensures your AI applications comply with existing data residency and security policies. All internal communications between nodes are secured with mutual authentication and encryption, and data is further protected through encryption at rest.
Avoid catastrophic run-time costs with Vespa’s highly efficient and controlled resource consumption architecture. Pricing is transparent and usage-based.

Vespa at Work

By building on Vespa’s platform, Perplexity delivers accurate, near-real-time responses to more than 15 million monthly users and handles more than 100 million queries each week.

“RavenPack has trusted Vespa.ai open source for over five years–no other RAG platform performs at the scale we need to support our users. Following rapid business expansion, we transitioned to Vespa Cloud. This simplifies our infrastructure and gives us access to expert guidance from Vespa engineers on billion-scale vector deployment.”

“We chose Vespa because of its richness of features, the amazing team behind it, and their commitment to staying up to date on every innovation in the search and NLP space. We look forward to the exciting features that the Vespa team is building and are excited to finalize our own migration to Vespa Cloud.” Yuhong Sun, CoFounder/CoCEO Onyx.