[Hands-on] Multimodal RAG using DeepSeek's Janus

TODAY’S DAILY DOSE OF DATA SCIENCE

MultiModal RAG with DeepSeek Janus

Continuing the discussion from GroundX…

After DeepSeek-R1, DeepSeek dropped more open-weight multimodal models—Janus, Janus-Pro, and Janus-Flow.

They can understand images and generate images from text input.

Moreover, they beat OpenAI's DALL-E 3 and Stable Diffusion in GenEval and DPG-Bench benchmarks.

Today, let’s do a hands-on demo of building a multimodal RAG with Janus-Pro on a complex document shown below:

It has several complex diagrams, text within visualizations, and tables—perfect for multimodal RAG.

We’ll use:

• Colpali to understand and embed docs using vision capabilities.
• Qdrant as the vector database.
• DeepSeek’s latest Janus-Pro multimodal LLM to generate a response.

This image shows the final outcome of today's issue (the code is available towards the end):

Let's build it!

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1) Embed data

We extract each document page as an image and embed it using ColPali.

We did a full architectural breakdown of ColPali in ​Part 9 of the RAG crash course​ and also optimized it with binary quantization.

ColPali uses vision capabilities to understand the context. It produces patches for every page, and each patch gets an embedding vector.

This is implemented below:

2) Vector database

Embeddings are ready. Next, we create a Qdrant vector database and store these embeddings in it, as demonstrated below:

3) Download DeepSeek Janus

Next, we set up our DeepSeek's latest Janus-Pro by downloading it from HuggingFace.

4) Query vector database and generate a response

Next, we:

• Query the vector database to get the most relevant pages.
• Pass the pages (as images) along with the query to DeepSeek Janus-Pro to generate the response.

Done!

We have implemented a 100% local Multimodal RAG powered by DeepSeek's latest Janus-Pro.

There’s some streamlit part we have shown here, but after building it, we get this clear and neat interface.

In this example, it produces the right response by retrieving the correct page and understanding a complex visualization👇

Here's one more example with a correct response:

Wasn’t that easy and straightforward?

The code for today's demo is available here: ​Multimodal RAG with DeepSeek​.

👉 Over to you: What other demos would you like to see with DeepSeek?

Thanks for reading!

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