Top Tweets for #ColPali
✅ Integrating Vision-Language Models into Agentic RAG Systems with ColPali
#AIPipeline #VisionLanguageModel #ColPali #MultimodalAI #CrewAI
https://t.co/ODsPDPScsh
🔍 Agentic Multimodal #RAG with #ColPali, #AmazonNova, #AmazonBedrock & #CrewAI! 📚
@_sumand demo shows a practical approach to building a #multimodal knowledge base by:
✅ Setting up a multimodal knowledge base using ColPali (Vision LLM)
✅ Storing embeddings in a @qdrant_engine vector store
✅ Integrating Amazon Nova Pro as the LLM with CrewAI Agents
A solid solution for extracting insights from complex documents.
FYI, Suman will be presenting this demo at @aiDotEngineer Summit in NYC this week.
[Repo link in next thread]
![crewAIInc's tweet photo. 🔍 Agentic Multimodal #RAG with #ColPali, #AmazonNova, #AmazonBedrock & #CrewAI! 📚
@_sumand demo shows a practical approach to building a #multimodal knowledge base by:
✅ Setting up a multimodal knowledge base using ColPali (Vision LLM)
✅ Storing embeddings in a @qdrant_engine vector store
✅ Integrating Amazon Nova Pro as the LLM with CrewAI Agents
A solid solution for extracting insights from complex documents.
FYI, Suman will be presenting this demo at @aiDotEngineer Summit in NYC this week.
[Repo link in next thread]](https://pbs.twimg.com/media/GkUf8bzXUAAxQbA.jpg)
Real-World Manual Layout
Look at these symbols, warnings, diagrams! Standard OCR-based approaches easily break or skip important call-outs.
ColPali processes the page as an image, preserving vital relationships—no separate text-chunk step required.
#colpali #visionmodel
📢 Join our Webinar TOMORROW: ColPali + Qdrant in Action!
Struggling to retrieve complex, visually rich PDFs with OCR? Tried #ColPali or #ColQwen, but once the dataset hits 10,000+ pages, resources go brrr, and retrieval speed makes you sad?
❗️We'll show you how to optimize ColPali retrieval speed by 13x—using a technique no one's tested before!
👇What's on the agenda:
🔥 New #BentoML example: Serving #ColPali for next-gen document retrieval! https://t.co/6UZij4R82R
Need to search through technical docs, research papers, or complex documents? It understands both text AND visuals - charts, diagrams, everything!
Key features of this example project:
📄 No more complex #OCR pipelines
🪄 Efficient multi-vector embeddings
⚡️ Adaptive batching APIs
🚀 Easy deployment with BentoML
Try it now: https://t.co/6UZij4R82R
Special thanks to @tonywu_71 from the ColPali team for this fantastic contribution to the BentoML community!
#RAG #OpenSource #AI #MachineLearning
When I said we at #starlight are obsessed with optimization, we weren't joking.
Introducing ColPali-Onnx model from starlight-search and how it can be used with our star product #embedanything #colpali
https://t.co/X37GeUxRcd
Curious to learn more? Our latest blog post explains how #ColPali works form a high level perspective. Check it out here 👉 https://t.co/4pIuEq3IPB by @stephenbtl
#DocumentRetrieval #VLMs #RAG #ColBERT #LLMs
Embed-Anything will soon support #ColPali and Splade models 🚀
Late Interaction embeddings are all over the place,
It's much better to use late-interaction embeddings with #embedanything because of features like vector streaming, which allows memory-efficient indexing.
Packed room listening to @jobergum tell us how search over PDFs may be...solved? #ColPali #haystackconf
Directly indexing documents as visual artifacts to support multimodal search with vision language models #VLM, especially #ColPali in a #BiEncoder multivector late interaction architecture. Naturally repped as a Vespa tensor. @@jobergum’s motivating masterclass at #haystackconf
First up after lunch is @jobergum from @vespaengine on a new and exciting way to search PDFs with #ColPali at #haystackconf EU 24
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