Inspect embeddings, run similarity search, and tune your index without hand-writing distance SQL every time. No other Mac Postgres client does this.
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Right-click any row with a vector column and TuskDB finds its nearest neighbors, ranked by distance. No query to write, no operator to remember.
vector, halfvec, or sparse columnPoint TuskDB at your own embedding endpoint: a local Ollama model or any OpenAI-compatible API. Type a phrase, TuskDB embeds it against your endpoint and searches your table. Your text goes to the endpoint you chose and nowhere else.
Every vector query in the editor gets a badge showing whether it hit the HNSW or IVFFlat index, or fell back to a scan.
Chart recall against latency across ef_search values, so you tune to a number instead of a hunch.
TuskDB reads the column's declared dimensions and flags a mismatch before it becomes a runtime error.
Every feature, 14 days, no account.