How I Built a 768-Dimensional Vector Search Engine with Node.js, TypeScript, and Supabase
How paragraph-aware chunking, embeddings, and PostgreSQL pgvector can make AI retrieval more efficient. When you build an AI application that needs to work with large documents, one problem shows up quickly: how do you give an LLM the information it needs without sending the entire document with every request? Sending more text isn't always the answer. It can increase token usage, introduce…
In this article, the author explains how to create an efficient vector search engine for AI applications using Node.js, TypeScript, and Supabase. The key steps involve breaking down documents into meaningful paragraph-based chunks, generating 768-dimensional embeddings for each chunk, storing them in Supabase along with pgvector support for vector storage and similarity operations, and retrieving relevant chunks by comparing the user's query embedding using cosine similarity.
This architecture is beneficial for various applications like documentation search, internal knowledge assistants, and retrieval-augmented generation pipelines. The author emphasizes that treating retrieval as a separate engineering problem and optimizing chunk size, overlap, and indexing configurations based on specific use cases can lead to sub-200ms semantic search responses while preserving relevance.
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