Multi-modal RAG: Chat with Docs containing Images 

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This is the best AI channel out there, PERIOD. Thanks for sharing your knowledge

Keep going with this approach, it is something I have been struggling with.

a nice open source and self hosted version would be great

Thanks, is there a video of the same project, but with langchain instead of llama index?

Such an insightful information, Eagerly waiting for more multimodel approches.

Use case is to extract the relevant text information along with images available in the file using generative ai, When any prompt is given then relevant text information and image should display as response.

We need more videos on this topic

What about make same, but using LLAMA3 or less local LLM?

do you think all of this is now replaced with Gemini ?

Very nice video but if you can do it with open source embedding model it would be very cool. thank you for the video

Thanks your videos are very helpful. I have several Gigs of pdf ebooks that i would like to process with RAG. What do you think what approach would be the best, this or a graphrag. In my case i'm looking only for local models as the costs would be very high. What if to convert all pdf pages into images first and then process them with local model like phi 3 vision and then process it with Graphrag, would it work out?

can you make it using comeplete open source models?

Can you pls dive deeper into why qdrant was used and other vector dbs limitations to store both text and image embeddings, thx

I appreciate your effort. Pl create one to fine tune the model for efficient retrieval if possible, with lang chain.

Out of interest what is the application called that you used to illustrate the flows? (2:53 in the video) thanks.

Hi your videos are very helpful thank you

Need to do it all in open source. No API Keys.

Is it better than GraphRAG? How does the output quality compare to it?

Lots of good info, thanks

Can we do this method using Langchain ?

it's great job! Thanks

wheres the code used?

It is essential to conduct a thorough preprocessing of the documents before entering them into the RAG. This involves extracting the text, tables, and images, and processing the latter through a vision module. Additionally, it is crucial to maintain content coherence by ensuring that references to tables and images are correctly preserved in the text. Only after this processing should the documents be entered into a LLM.

Thanks

This approach is not good enough to add value. The pictures and text needs to be referenced and linked in both vector stores to create better similarities.

I except image generation will be have another kind of breed... image gen based on image understanding based on facts