ColiVara: Revolutionizing Document Retrieval with State-of-the-Art Technology

ColiVara is a cutting-edge retrieval API that empowers developers to build intelligent applications by harnessing the power of visual embeddings. With its delightful developer experience, ColiVara offers unparalleled performance on both text and visual documents, making it an ideal solution for businesses seeking to enhance their document retrieval capabilities.

Features

• State-of-the-Art Retrieval Performance: ColiVara boasts advanced vision models that understand documents with the same level of human understanding as text-based systems.
• Delectable Developer Experience: The API provides a seamless experience, making it easy for developers to integrate and use its features in their applications.
• Support for Visual Documents: Whether dealing with complex financial reports, technical diagrams, or data-rich tables, ColiVara's vision models see and understand documents just like humans do.
• Pricing Flexibility: The API offers simple, transparent pricing that adapts to the size of your usage, ensuring you get the best value for your needs.

Benefits

• Effortless Integration: With ColiVara, developers can easily integrate its features into their existing applications without requiring extensive knowledge or resources.
• Improved Document Retrieval Capabilities: By utilizing visual embeddings, ColiVarA enhances document retrieval performance, reducing errors and increasing the accuracy of search results.
• Enhanced User Experience: As a result of improved document retrieval capabilities, users can access relevant information more quickly and easily than ever before.

Getting Started

To begin using ColiVara, you can install it via pip: pip install colivara_py. Once installed, import the API with from colivara_py import ColivarA and initialize a client instance. You can then upload documents to the default collection and search for relevant information using the provided methods.

About ColiVara

ColiVara is built on top of the ColPali paper using ColQwen2 as the LLM model, making it a web-first implementation that offers both performance and developer experience similar to RAG but leveraging vision models instead of text processing for documents.