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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

8Latency, Throughput, and Quality of Service

8.1 Latency Targets and QoS Requirements 8.2 End-to-End Latency Profiling 8.3 Batch vs Real-Time Processing Tradeoffs 8.4 Model Warmup and Cache Reuse 8.5 Parallelism and Concurrency 8.6 Asynchronous I/O and Event Loops 8.7 Batching Strategies for API Calls 8.8 Resource Allocation and Autoscaling 8.9 QoS Metrics: P95, P99 8.10 Throughput Optimization 8.11 Backpressure Handling in Pipelines 8.12 Network Optimization (CDN, TLS, MTU)8.13 Observability for Latency 8.14 Distributed Tracing and Profiling 8.15 Latency Benchmarks and Stress Tests

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/Latency, Throughput, and Quality of Service

Latency, Throughput, and Quality of Service

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Measure, optimize, and guarantee latency and throughput targets while balancing resource use and quality of service.

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12 of 15

8.12 Network Optimization (CDN, TLS, MTU)

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