Hybrid RAG Architectures Integrating Local Vector Stores with Distributed Edge Intelligence Multi‑Agent Systems
Table of Contents Introduction Fundamental Building Blocks 2.1. Retrieval‑Augmented Generation (RAG) 2.2. Local Vector Stores 2.3. Edge Intelligence & Multi‑Agent Systems Why Hybrid RAG? Architectural Blueprint 4.1. Layered View 4.2. Data Flow Diagram Designing the Local Vector Store 5.1. Choosing the Indexing Library 5.2. Schema & Metadata Strategies 5.3. Persistency & Sync Mechanisms Distributed Edge Agents 6.1. Agent Roles & Responsibilities 6.2. Communication Protocols 6.3. Local Inference Engines Integration Patterns 7.1. Query Routing & Load Balancing 7.2. Cache‑Aside Retrieval 7.3. Federated Retrieval Across Edge Nodes Practical End‑to‑End Example 8.1. Scenario Overview 8.2. Code Walk‑through Challenges, Pitfalls, and Best Practices Future Directions & Emerging Trends Conclusion Resources Introduction Retrieval‑Augmented Generation (RAG) has reshaped how large language models (LLMs) interact with external knowledge. By coupling a generative model with a retrieval component, RAG enables grounded, up‑to‑date, and domain‑specific responses without the need to fine‑tune the entire model. ...