Multi-modal Retrieval Augmented Generation with LlamaIndex

In this deep dive we'll show you how to build production RAG applications using LlamaIndex's multi-modal capabilities, including
* How RAG works
* What LlamaIndex, LlamaHub and create-llama are
* How to do basic image querying, multi-modal retrieval, multi-modal querying, image-to-image retrieval and image-to-text querying

Linked notebooks:

[cken]

Amazing explanation in 10 mins. I like that every slide is very concise and therefore is easy to follow

[s.moneebahnoman]

Amazing! This was such an organized and easy to understand video. Loved it!

[jorgerios]

One of the best videos with such clear explanations I've seen. Is there a way to use open source LLMs for these multimodal tasks?

[twoplustwo]

🎯 Key Takeaways for quick navigation:

00:02 📚 *Introduction to Retrieval Augmented Generation (RAG) and Llama Index*
- Introduction to the speaker and the topic of Retrieval Augmented Generation (RAG).
- Explanation of how RAG works, including the concept of vector embeddings.
- Introduction to Llama Index and its features.
02:21 🧩 *Stages of a RAG Application*
- Explanation of the six stages of a RAG application.
- Introduction to multimodal RAG applications and how they differ from regular RAG applications.
- Overview of Llama Index's role in managing these stages.
04:53 🛠️ *Building a Multimodal RAG Application with Llama Index*
- Walkthrough of building a multimodal RAG application using Llama Index.
- Explanation of how to load and index text and images.
- Demonstration of querying the multimodal index.
08:02 🖼️ *Image-to-Image Retrieval and Querying*
- Introduction to image-to-image retrieval and querying.
- Walkthrough of setting up a Wikipedia client to download images and text.
- Demonstration of image-to-image retrieval and querying using a Van Gogh painting.
10:41 📝 *Conclusion and Further Learning*
- Recap of the topics covered in the video.
- Encouragement for further learning and exploration of Llama Index's documentation.

Made with HARPA AI

[omegapy]

Great Video!
If possible, can you share the slides?
Very educative.
I am a computer science student at CSU Global.
I will love to have them to follow a long with the code.

[AlejandroErickson]

Very interesting demonstration! Towards the end of the video you talk about image to text querying where you give it starry night and a text query and it returns something post-impressionism. Where is the text context for that response coming from?

[JanghyunBaek]

I hope Azura OpenAI support muli-model soon. I can't find the muli-model Azura OpenAI in Azura OpenAI model list.

[ragsAI]

Hi! Is Langchain integratable/compatible with redshift/databricks? (especially the text-to-sql framework)? Thank you.

[tnnandi]

Do you support integration with open-source multimodal models?

[rutu]

can we do image retrieval using gemini?

[osuoxqg]

Simple directory reader is not reader image files