RAG architecture for SaaS – Learnings from building an AI code assistant
The article discusses the development of an AI Code Assistant SaaS tool using GPT-4o-mini, Langchain, Postgres, and pg_vector. It explores RAG architecture, model selection criteria, LangChain usage, and challenges in AI model switching.
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The article discusses the creation of an AI Code Assistant SaaS tool built on technologies like GPT-4o-mini, Langchain, Postgres, and pg_vector. The tool allows users to explore new code bases by interacting with a virtual "senior developer" familiar with the code. The author shares insights into the AI concepts learned during the development process and details the RAG architecture used for the assistant. The RAG architecture involves two phases: ingestion and conversation, utilizing Postgres with pg_vector for data storage. The article delves into the data model, vector sizes, model selection criteria, and the decision-making process for choosing embedding and conversational models. It also touches on the use of LangChain as an AI SDK for handling user queries and responses. The author highlights the importance of model quality, input tokens, dimensions, deployment ease, and cost considerations when selecting models. Additionally, the article discusses the challenges and considerations in switching between different models and vendors for AI applications.
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