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Building Real-Time RAG Systems with Gemini & the Multimodal Live API

Chapters

1Foundations of Real-Time Retrieval-Augmented Generation

2Gemini Fundamentals: Architecture and Multimodal Capabilities

3Data Sources and Vector Stores for Real-Time RAG

3.1 Data Source Discovery and Cataloging 3.2 Embeddings: Techniques and Models 3.3 Vector Stores: Types and Tradeoffs 3.4 Real-Time Indexing Strategies 3.5 Document Splitting and Chaining 3.6 Metadata and Context Personalization 3.7 Access Control and Data Sandboxing 3.8 Data Cleaning and Deduplication 3.9 Relevance Tuning and Ranking 3.10 Hybrid Retrieval (Dense + Sparse)3.11 Temporal Decay and Freshness Handling 3.12 SFT/LLM-assisted Validation 3.13 Metadata Schemas and Standards 3.14 Compliance and Data Residency 3.15 Cache-First Retrieval

4Real-Time Ingestion and Streaming Data Pipelines

5The Multimodal Live API: Authentication, Endpoints, and Workflows

6Prompt Engineering for RAG with Gemini

7Memory and Context Management in Real-Time RAG

8Latency, Throughput, and Quality of Service

9Security, Privacy, and Compliance in RAG Systems

10Evaluation, Metrics, and A/B Testing for RAG

11Deployment and Orchestration: Cloud and Edge

12Case Studies: Real-World RAG Scenarios

13Advanced Retrieval Techniques: Hybrid Search and Re-ranking

14Observability: Monitoring, Logging, and Debugging

15Extending Gemini with Custom Tools and Plugins

Courses/Building Real-Time RAG Systems with Gemini & the Multimodal Live API/Data Sources and Vector Stores for Real-Time RAG

Data Sources and Vector Stores for Real-Time RAG

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Learn how to select, prepare, and index data sources, and how to use vector stores to enable fast, relevant retrieval in real time.

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3.1 Data Source Discovery and Cataloging

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