3 RAG architectures for the same use case
El punto de partida Cuando empecé a hacer proyectos de RAG con Amazon Bedrock, pude ver las ventajas de usar una Knowledge Base gestionada, ya que te resuelve casi todo, como la ingesta, el chunking, los embeddings y la base vectorial. Pero para entender bien la arquitectura, quería ver qué pasaba por dentro. Así que en vez de quedarme solo con lo gestionado, armé el mismo proyecto de tres formas…
A developer created a project called AutoSpec, a technical assistant that answers questions about three fictional vehicle models, Alpha, Beta, and Gamma, using their technical specifications as a source. The project was developed in three phases, using Amazon Bedrock's managed services, then building the components manually, and finally using a decoupled framework for multi-cloud portability with LangChain and Google Gemini API.
The developer found that using a managed service provides speed, building manually provides control, and using decoupled components provides portability. The project's code is available on GitHub. The developer tested the Retrieval Augmented Generation (RAG) system, observing issues such as throttling and information mixing, and implemented solutions like guardrails and observability with SQLite.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.
