Deploying a Multimodal RAG System Using vLLM and Milvus
Blog post from Zilliz
This blog post guides users through creating a Multimodal Retrieval Augmented Generation (RAG) system using open-source solutions Milvus and vLLM. The tutorial demonstrates how to self-host an AI application, providing full control over the technology while enhancing its capabilities. By leveraging the power of an open-source vector database combined with open-source LLM inference, users can design a system capable of processing and understanding multiple types of data - text, images, audio, and even videos. The resulting multimodal RAG system is flexible, scalable, and under complete user control, mitigating risks associated with relying solely on cloud API providers.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| RAG | 18 | 1,943 | 207 | 76 | -13% |
| Vector Search | 13 | 2,767 | 278 | 102 | -41% |
| LLM | 9 | 3,362 | 423 | 155 | -16% |
| AI Model Fine-tuning | 1 | 570 | 142 | 71 | -38% |
| Serverless | 1 | 518 | 133 | 68 | -46% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.