A vector database that mocks VC culture while actually working

What it does

HyperDB stores documents as vectors and lets you query them by semantic similarity. Feed it JSON, point it at a text field, and ask questions like “Likes to sleep.” It returns ranked results—Snorlax first, obviously. Data persists via gzipped pickle files. The pitch deck is a Rickroll.

The interesting bit

The joke is the packaging, not the payload. Underneath the meme benchmarks and fake fundraising, there’s a C++ vector store using Intel MKL BLAS for hardware-accelerated similarity search. The Python surface is deliberately thin—load, query, save, done—because the author seems to believe most LLM agent use cases don’t need a distributed cluster to find a sleepy Pokémon.

Key highlights

• Single-file persistence: db.save("file.pickle.gz") and db.load()—no server, no config
• C++ backend with MKL BLAS acceleration for the actual vector math
• Optional local embeddings via sentence-transformers; otherwise bring your own vectors
• Indexes documents with IDs and metadata, though the README doesn’t detail query filters on either
• “Not entirely a joke”—the author’s words, not mine

Caveats

• The “benchmark” image is unlabeled and credited to “Benim Kıçım” (Turkish for “my butt”); interpret performance claims accordingly
• No visible docs on metadata filtering, update semantics, or concurrent access
• Gzipped pickle isn’t exactly a durable transaction log

Verdict

Worth a look if you want local vector search without spinning up Docker or signing up for a cloud API. Skip it if you need replication, streaming ingestion, or serious observability—the satire is thick, but the feature set is honestly thin.