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 us

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2024-11-16 19:00:03

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.

If you prefer Swagger, you can try our endpoints at ColiVara API Swagger. You can also import an openAPI spec (for example for Postman) from the swagger documentation endpoint at v1/docs/openapi.json

RAG (Retrieval Augmented Generation) is a powerful technique that allows us to enhance LLMs (Language Models) output with private documents and proprietary knowledge that is not available elsewhere. (For example, a company's internal documents or a researcher's notes).

However, it is limited by the quality of the text extraction pipeline. With limited ability to extract visual cues and other non-textual information, RAG can be suboptimal for documents that are visually rich.

ColiVara uses vision models to generate embeddings for documents, allowing you to retrieve documents based on their visual content.

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