#pgvector

This project builds a document search system with Next.js, using OpenAI for enhanced search results. It integrates with Vercel and Supabase, processing MDX files and storing embeddings with pgvector in a Postgres database to improve query accuracy. The system dynamically injects content into OpenAI prompts for better user interaction. Easily deploy on Vercel by configuring required API keys to explore documents effectively.

Seamlessly integrate vector similarity search into Postgres with pgvector, supporting various vector types and distance metrics. pgvector offers exact and approximate nearest neighbor searches, and capitalizes on Postgres features like ACID compliance and point-in-time recovery. It ensures flexible compatibility across multiple programming languages. Installation is straightforward on Linux, Mac, and Windows, with options like Docker and package managers. Utilize HNSW and IVFFlat index types for effective vector querying and enhance searches with Postgres full-text search capabilities.

The project transforms .mdx files for use in custom OpenAI text completions, integrating Vercel and Supabase for embedding creation and PostgreSQL storage with pgvector. It facilitates runtime vector similarity search, enhancing ChatGPT frameworks. Additional tools include a writing assistant and AI encyclopedia, with straightforward development setup instructions.

The workshop instructs on developing a secure chat interface for document interaction utilizing OpenAI’s GPT models and retrieval augmented generation (RAG). Features include interactive chat, third-party login, secure document management, and a dynamic REST API with row-level security. It includes detailed guidance for setting up a Supabase project, either locally or in the cloud, optimizing the frontend for file uploads, and is complemented by a comprehensive YouTube tutorial.

The project employs serverless technology and AI to compare submitted ideas with YCombinator's past investments. It uses Neon Postgres for database management, pgvector for similarity search, and OpenAI for embedding generation. The interface is built with TailwindCSS and Next.js, and deployed via Vercel. The app helps users explore startup ideas, fostering innovation and strategic growth.