Загрузка видео...

Не удалось загрузить видео

На главную

Vision-based(Colapli) RAG is becoming popular, so we built a platform to compare: - Simple OCR RAG - VisionRAG - Colpali - Hybrid Colpali 🚀 Introducing VARAG – the Vision-First RAG Engine (Vision Augmented Retrieval and Generation).

95,370 просмотров • 1 год назад •via X (Twitter)

Комментарии: 9

Фото профиля Adithya S K
Adithya S K1 год назад

A few days ago, I wanted to figure out the best vision-based retrieval techniques for a product I was working on. Ended up building a library out of it Tech used : - colpali engine - @lancedb which was a joy to use - its easy to use - very flexible and developer friendly

Фото профиля Adithya S K
Adithya S K1 год назад

Some Experiments and Observations I ingested 30 research papers, and here is how long each technique took to process them.

Фото профиля Adithya S K
Adithya S K1 год назад

note that in the colpali implementation the score is being calculated across all the vectors in the database thus the time taken is high Accuracy and other RAG metrics yet to test out Retrival Speed:

Фото профиля rohit
rohit1 год назад

colpali does everything. what did you build on top of it?

Фото профиля Adithya S K
Adithya S K1 год назад

Colpali-engine mainly provides functions to generate embeddings and calculate the score. Varag primarily offers an abstraction layer on top of it and provides a uniform interface for each retrieval technique, making it easier to compare and modify when needed.

Фото профиля satish1v
satish1v1 год назад

@adithya_s_k What is hybrid colpali RAG. Is it extract metadata from images and indexing ?

Фото профиля Adithya S K
Adithya S K1 год назад

Its a combination of plain visionRAG and colpali plain vision RAG uses clip/jina-clip as the embedding model where we retrive the initial top n images then we use colpali to rank within those top n to get top k

Фото профиля Prasenjit Sarkar
Prasenjit Sarkar1 год назад

Hey 👋🏻 there is a collective repo for RAG papers implementation, would you like to join hands?

Фото профиля ankur
ankur1 год назад

This is cool

Похожие видео

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by Zain, experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

124,458 просмотров • 1 год назад