How to Build Multimodal RAG with ColQwen2, Milvus, and Qwen3.5

What this post added

This post demonstrates a practical implementation of a multimodal RAG pipeline. It details the use of ColQwen2 for encoding PDF pages as images into multi-vector embeddings, Milvus Lite for storing and searching these embeddings, and Qwen3.5 for generating answers based on retrieved page images. The implementation includes environment setup, PDF page conversion to images, encoding and insertion into Milvus with a specific schema (id, doc_id, patch_idx, vector), and a retrieval strategy using MaxSim aggregation for page ranking. The retrieval process involves encoding the query, searching Milvus for each token vector, and aggregating scores by page to identify the most relevant content for the LLM.