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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

4.1 Streaming Ingestion Fundamentals 4.2 Choosing Between Kafka, Kinesis, Pub/Sub 4.3 Data Schemas and Schema Evolution 4.4 Backpressure and Flow Control 4.5 Exactly-Once Processing vs At-Least-Once 4.6 Windowing and Micro-batching 4.7 Connectors for Data Sources 4.8 Data Quality Checks in Streaming 4.9 Idempotency and Deduplication 4.10 Real-Time ETL vs ELT 4.11 Stateful vs Stateless Processing 4.12 Event Time vs Processing Time 4.13 Monitoring Streaming Pipelines 4.14 Failure Handling and Retry Policies 4.15 Scaling Streaming Services

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/Real-Time Ingestion and Streaming Data Pipelines

Real-Time Ingestion and Streaming Data Pipelines

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Build robust streaming architectures to ingest, validate, and propagate data in real time for RAG systems.

Content

12 of 15

4.12 Event Time vs Processing Time

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