Convex MCP server

All this does is import a ton of vectors into a table with a vector index. There's some very simply python parsing to extract embeddings specifically from this file: https://drive.google.com/file/d/1qRJWC4kiM9xZ-oTbiqK9ii0vPciNHhkI/view?usp=drive_link. That's a set of embeddings that originated from https://www.kaggle.com/datasets/stephanst/wikipedia-simple-openai-embeddings which was MIT licensed at the time it was downloaded. Download the file from drive or kaggle, extract the archive into a .jsonl file, then run the script with: 1. `just rush update` 2. `npx convex dev --once` 3. `uv run main <path_to_jsonl>` You can adapt the python code to parse other formats if you'd like. The main purpose of this is to test bulk imports, particularly with vectors.