NEOPOLIS AKADEMY
End-to-End RAG with Weaviate
Build complete RAG workflows with Weaviate: vector ingestion and indexing, retrieval query formulation, fusion with generation and orchestration to create robust RAG pipelines.
View this course on Akademy ↗Enrolment and practical details are available on Neopolis Akademy.
What you will explore
The course details document ingestion, vector index creation and update options in Weaviate.
It explains retrieval strategies (k-NN, hybrid filters), building contextual prompts and combining retrieved items with a generative model.
Particular attention is given to answer consistency, source control and orchestration of pipeline steps.
STEP BY STEP
Course programme
01End-to-End RAG with Weaviate
Overview of Retrieval‑Augmented Generation (RAG) implemented with Weaviate: fundamentals, building end‑to‑end RAG workflows and extending RAG to multimodal use cases combining text and images.
Introduces core RAG concepts in the Weaviate ecosystem and explains how retrieval and generation are combined to produce context-aware answers.
Describes end‑to‑end RAG workflows with Weaviate, detailing steps to manage vector search and fuse retrieved content into the generator.
Covers multimodal RAG: mixing text and image sources in retrieval pipelines to broaden context and improve answer relevance.
Explore this module on Akademy ↗Programme source: Neopolis Akademy. Original course page