liangdabiao / Multimodal-RAG

What is Multimodal-RAG?

This Python-based multimodal RAG system lets you upload PDFs and query them in natural language, pulling relevant pages as images for AI answers via Zilliz vector search and vision LLMs like Qwen. Unlike text-only RAG, it skips OCR and extraction, embedding entire pages directly to keep tables, charts, handwriting, and layouts intact. Run a web app for uploads, indexing, and chat, or hit the API for queries returning answers plus scored page refs.

Why is it gaining traction?

It stands out in the multimodal rag github space by switching seamlessly between Cohere/DashScope for image embedding github and DashScope/OpenRouter for generation, without custom LangChain plumbing. Devs dig the zero-OCR approach for azure multimodal rag github workflows, preserving visual fidelity that text RAG mangles. Quick setup via env vars yields a working multimodal rag chatbot github in minutes, ideal for docking multimodal rag experiments.

Who should use this?

ML engineers prototyping multimodal rag langchain github alternatives for document QA with visuals. Researchers running multimodal rag evaluation or building multimodal rag dataset tools on PDFs heavy with diagrams. Backend devs needing a simple multimodal rag implementation github for internal knowledge bases.

Verdict

Grab it for proofs-of-concept in image embedding github or multimodal agentic rag github—solid user flow despite 15 stars and 1.0% credibility score. Too green for prod (light docs, no tests), but forkable for custom embedding models like jina embedding github tweaks.

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