Multimodal RAG in Action: A Developer's Guide to Building Reliable AI Agents with Text, Image, and Knowledge Graph Retrieval

By Todd Chandler

Multimodal RAG in Action: A Developer's Guide to Building Reliable AI Agents with Text, Image, and Knowledge Graph Retrieval
What if your AI agent could answer the hardest questions, not just by searching documents, but by reading diagrams, connecting graphs, and piecing together evidence from every corner of your data? Imagine building assistants that don't get stumped by visuals, don't hallucinate facts, and always show their work. That's the new standard. The real challenge: how do you get there as a developer?

Multimodal RAG in Action is the essential playbook for engineers who want more than another text-only chatbot. It shows you how to architect, code, and optimize production-grade Retrieval-Augmented Generation (RAG) systems that search, reason, and explain using text, images, and structured knowledge graphs, the same way experts do.

With this hands-on guide, you'll master the workflows and frameworks that leading AI teams use to build explainable, trustworthy, and adaptable agents. Go far beyond simple document QA and step into the real world of multimodal retrieval, where insights hide in diagrams, charts, and relational data as often as in paragraphs.

Inside, you'll learn how to:

Combine state-of-the-art embedding models, like CLIP, MiniLM, and Node2Vec, for seamless cross-modal search

Assemble fast, scalable pipelines with LangChain, LlamaIndex, Pinecone, Chroma, and FAISS

Engineer hybrid retrievers and fusion logic to surface the right evidence every time

Craft prompts that guarantee every answer is supported, cited, and ready for audit

Troubleshoot, evaluate, and refine your agents with industry benchmarks and best practices

Handle the realities of scaling: latency, cost, versioning, and data privacy

Apply practical patterns for compliance, monitoring, and agentic workflows

Whether you're building enterprise assistants, research copilots, or regulatory tools, this book delivers the battle-tested strategies and sample code you need to succeed. If you're ready to create AI agents that don't just answer, but prove, and win user trust in every domain, Multimodal RAG in Action is your blueprint.

Level up your AI engineering. Build agents that see, retrieve, and reason with everything. Get your copy now and take your projects further.

• Format: Paperback
• Language: English
• ISBN: B0FP17RR26
• ISBN13: 9798262487073
• Release Date: August 2025
• Publisher: Independently Published
• Length: 130 Pages
• Weight: 0.53 lbs.
• Dimensions: 0.3" x 7.0" x 10.0"