Artificial Intelligence

Multimodal RAG with Gemini (GCP)

Standard RAG only reads text. Modern businesses need to query charts, tables, and videos. Build a pipeline that uses Gemini 1.5 Pro to extract and index insights from a library of financial PDF reports containing complex graphs.

Level: AdvancedArtificial Intelligence

What You'll Learn

Limitations of text-only RAG systems
Multimodal document parsing with Gemini 1.5 Pro
Extracting insights from PDFs with charts & tables
Building a multimodal RAG pipeline on GCP
Vertex AI embeddings & Vector Search
Cloud Storage for document management
Querying across text, tables, and images
Production-ready pipeline deployment on Cloud Run

What's Included

🧭AI Tutor🔒

Interactive learning path with AI-generated lessons read aloud by your personal tutor

🧪Practice Lab🔒

Step-by-step lab instructions with AI guidance — run in your own IDE or sandbox.

🎙️Mock Interviews🔒

AI interviewer asks tailored questions, gives real-time feedback and a score report

📝Evaluation🔒

MCQ questions (theory, practical, code) to test your knowledge and earn a badge

⚔️Challenge AI🔒

Compete head-to-head against AI on real production problems — judged on correctness, performance, scalability, code quality, and edge cases

Course Syllabus

Module 1: Multimodal RAG Foundations

  • •Why text-only RAG fails on rich documents
  • •How Gemini 1.5 Pro processes multimodal inputs
  • •Document types: PDFs, charts, tables, images
  • •GCP setup: Vertex AI, Cloud Storage, Vector Search
  • •End-to-end pipeline architecture overview

Module 2: Building the Ingestion Pipeline

  • •Loading financial PDFs from Cloud Storage
  • •Extracting text and visual elements with Gemini
  • •Chunking strategies for multimodal content
  • •Generating multimodal embeddings via Vertex AI
  • •Indexing into Vertex AI Vector Search

Module 3: Query & Retrieval

  • •Natural language queries over financial documents
  • •Retrieving relevant chart and table sections
  • •Re-ranking multimodal results
  • •Generating answers with source citations
  • •Evaluating pipeline accuracy and recall

Module 4: Production Deployment

  • •Containerising the pipeline with Cloud Run
  • •Automating ingestion with Cloud Functions
  • •Monitoring with Cloud Logging and Metrics
  • •Cost optimisation on GCP
  • •Capstone: full financial report Q&A system

You're all set

Everything in this course is unlocked.

Continue Learning →
✓AI Tutor
✓Practice Lab
✓Mock Interviews
✓Evaluation
✓Challenge AI