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Microservices

An architectural approach where specific stages of a RAG pipeline—such as embedding generation, vector retrieval, and LLM synthesis—are decoupled into independent, containerized services. This allows for granular scaling of resource-heavy tasks (like GPU-intensive inference) independently from I/O-heavy tasks, though it introduces network latency and orchestration overhead.

Definition

An architectural approach where specific stages of a RAG pipeline—such as embedding generation, vector retrieval, and LLM synthesis—are decoupled into independent, containerized services. This allows for granular scaling of resource-heavy tasks (like GPU-intensive inference) independently from I/O-heavy tasks, though it introduces network latency and orchestration overhead.

Disambiguation

Refers to network-isolated service boundaries for AI tasks, not just modularized Python functions or classes.

Visual Metaphor

"A specialized construction site where the plumbing team, electrical team, and framing team each arrive in their own trucks with their own tools, working independently but communicating via a central site manager."

Key Tools
KubernetesDockerBentoMLRay ServeLangServeFastAPIgRPC
Related Connections

Conceptual Overview

An architectural approach where specific stages of a RAG pipeline—such as embedding generation, vector retrieval, and LLM synthesis—are decoupled into independent, containerized services. This allows for granular scaling of resource-heavy tasks (like GPU-intensive inference) independently from I/O-heavy tasks, though it introduces network latency and orchestration overhead.

Disambiguation

Refers to network-isolated service boundaries for AI tasks, not just modularized Python functions or classes.

Visual Analog

A specialized construction site where the plumbing team, electrical team, and framing team each arrive in their own trucks with their own tools, working independently but communicating via a central site manager.

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