Multimodal RAG with Gemini: full course guide
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.
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What is inside this guide
01Multimodal 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 Stack: Vertex AI, Cloud Storage & Vector Search
- End-to-End Pipeline Architecture Overview
02Building the Ingestion Pipeline
- Loading Financial PDFs from Cloud Storage
- Extracting Text & Visual Elements with Gemini
- Chunking Strategies for Multimodal Content
- Generating Multimodal Embeddings via Vertex AI
- Indexing into Vertex AI Vector Search
03Query & Retrieval
- Natural Language Queries over Financial Documents
- Retrieving Relevant Chart & Table Sections
- Re-ranking Multimodal Results
- Generating Answers with Source Citations
- Evaluating Pipeline Accuracy & Recall
04Production Deployment on GCP
- Containerising the Pipeline with Cloud Run
- Automating Ingestion with Cloud Functions
- Monitoring with Cloud Logging & Metrics
- Cost Optimisation on GCP
- Capstone: Full Financial Report Q&A System
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