Welcome

Welcome to the ColiVara documentation! Here you'll get an overview of all the features ColiVara offers to help you build a state of the art retrieval system.

Colivara is a suite of services that allows you to store, search, and retrieve documents based on their visual embeddings.

Why visual embeddings?

Documents are visually rich structures that convey information through text, as well as tables, figures, page layouts, and charts. While legacy document retrieval systems exhibit good performance on query-to-text matching, they struggle to pass visual cues efficiently to large language models, hindering their performance on practical document retrieval applications such as Retrieval Augmented Generation.

It is a web-first implementation of the ColPali paper using ColQwen2 as the LLM model. It works exactly like RAG from the end-user standpoint - but using vision models instead of chunking and text-processing for documents.

Performance

Vision based retrieval achieves state of the art performance on both latency and accuracy. Beating text extraction with BM25 search and LLM image captioning.

Jump right in

Getting Started

Create your first RAG pipeline with 2 lines of code

Guides

Learn all what ColiVara have to offer

API Reference

Try the API live