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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).

Adithya S K

5,699 subscribers

95,370 görüntüleme • 1 yıl önce •via X (Twitter)

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Adithya S K profil fotoğrafı

Adithya S K1 yıl önce

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

Adithya S K profil fotoğrafı

Adithya S K1 yıl önce

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

Adithya S K profil fotoğrafı

Adithya S K1 yıl önce

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:

rohit profil fotoğrafı

rohit1 yıl önce

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

Adithya S K profil fotoğrafı

Adithya S K1 yıl önce

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.

satish1v profil fotoğrafı

satish1v1 yıl önce

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

Adithya S K profil fotoğrafı

Adithya S K1 yıl önce

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

Prasenjit Sarkar profil fotoğrafı

Prasenjit Sarkar1 yıl önce

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

ankur profil fotoğrafı

ankur1 yıl önce

This is cool

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Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

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Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

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Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

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