vector-embeddings

What problem does it solve?

Vector embeddings enable semantic search, similarity matching, and scalable retrieval by converting text into numerical representations that machines can compare efficiently.

Core Features & Use Cases

• Generate embeddings from text using OpenAI models.
• Store embeddings in Upstash Vector or PostgreSQL pg-vector with optional metadata.
• Search by cosine similarity and filter results by metadata to refine relevance.
• Use cases include knowledge bases, document search, and product content discovery.

Quick Start

Index a sample text and run a vector search using the provided embedding and search utilities.