Building an AI-powered ChatBot using Vercel, OpenAI, and Postgres

The article presents a comprehensive guide on building an AI-powered chatbot using Vercel, OpenAI, and Postgres, with a focus on the implementation of word embeddings to tailor a ChatGPT-like application. It explains the concept and utility of word embeddings, which represent words as vectors and enhance the efficiency of language models by using low-dimensional feature vectors. The guide illustrates how to store these embeddings in Postgres using the pgvector extension and perform similarity analysis, leading to the creation of a chatbot application, Ask Neon, that can answer questions related to Postgres and Neon. The article details the steps to build the backend of the application using Vercel Edge Functions and the OpenAI API, from generating question embeddings to retrieving context and creating text completions. It emphasizes the potential for fine-tuning models by collecting user feedback to improve response quality and details the process for implementing such improvements. The guide further discusses strategies to enhance performance, reduce costs, and streamline user experience, culminating in a discussion on fine-tuning models based on user feedback using OpenAI's CLI tools.