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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

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

7.1 Memory Architectures for RAG 7.2 Short-Term vs Long-Term Memory Tradeoffs 7.3 Context Window Management 7.4 Dynamic Context Sizing 7.5 Contextual Caching Strategies 7.6 User-Specific Context and Personalization 7.7 Context Versioning and Rollbacks 7.8 Memory Decay and Relevance Ranking 7.9 Summary Prompts and Digest Generation 7.10 Knowledge Graph Integration 7.11 External Tool State Persistence 7.12 Privacy-Preserving Memory Techniques 7.13 Memory Health Monitoring 7.14 Data Provenance in Memory 7.15 Memory Consistency Across Nodes

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/Memory and Context Management in Real-Time RAG

Memory and Context Management in Real-Time RAG

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Explore memory architectures, context management strategies, and personalization techniques to maintain coherent, up-to-date responses.

Content

14 of 15

7.14 Data Provenance in Memory

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