SpringAI Retrieval Augmented Generation (RAG) With PgVector Part 2

Author(s): Adil

Originally published on Towards AI.

SpringAI Retrieval Augmented Generation (RAG) With PgVector Part 2

Welcome back, folks! 👋 This is the second part of our series on using SpringAI RAG with an embedded Ollama model.

This article builds upon the previous installment by guiding you through the complete process of utilizing SpringAI Retrieval-Augmented Generation (RAG) with a focus on calling the LLM, embedding documents into a pgvector database, and evaluating the model’s responses. The discussion highlights the importance of document injection in enhancing the model’s accuracy and context-awareness, while also addressing the practical implementation of APIs and the overall user experience with SpringAI. Additionally, significant points about the response time of local models versus cloud-based APIs are elaborated, underscoring the real-world limitations and considerations when using local setups.

Read the full blog for free on Medium.

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Published via Towards AI