Unleashing the Power of Multimodal Retrieval-Augmented Generation with Gemini 2.5 Flash and Cohere

Hello, dear readers!

I am excited to share my recent project: a cutting-edge Multimodal Retrieval-Augmented Generation (RAG) system that integrates insights from both text and images found in PDFs. By harnessing the strengths of Gemini 2.5 Flash and Cohere’s multimodal embeddings, this system marks a significant advancement in how we can extract information.

Why This Innovation Matters

Conventional RAG systems often overlook the importance of visual data. Important elements such as pie charts, tables, and infographics—which are essential in domains like finance and research—are typically ignored. This new approach not only fills that gap but also enhances the depth of insights we can gather from diverse document types.

Experience the Demo

If you’re curious about how this works in practice, check out the demo video linked below:

Watch the Demo

How Multimodal RAG Operates:

1. Upload a financial PDF containing relevant data.
2. Embed both text and images using advanced algorithms.
3. Pose your questions—for instance, “What percentage does Apple represent in the S&P 500?”
4. The system will provide responses grounded in the visuals, allowing it to refer directly to charts and other graphical data.

Key Features to Note:

• Utilizes a mixed FAISS index that combines text and image embeddings.
• Applies visual grounding techniques with Gemini 2.5 Flash.
• Capable of addressing inquiries related to tables, charts, and timelines.
• Entirely local setup achieved through Streamlit and FAISS.

Technical Stack Breakdown:

• Cohere embed-v4.0 for mixing text and image embeddings.
• Gemini 2.5 Flash for tailored visual question answering.
• FAISS for efficient retrieval operations.
• Utilizes **