Quickstart
Pre-built binaries and a container image are on the roadmap; today you build from source. The whole loop — clone, build, run, first query — takes a few minutes.
Prerequisites
rustupwith the stable toolchainjust(cargo install just)uv(for the demo seed script and the Python SDK)
Clone and run the demo
git clone https://github.com/achref-soua/quiver
cd quiver
just demo # build, start an encrypted server, seed a demo collection
just demo brings up a server with encryption-at-rest on, seeds a small
collection through the Python SDK, and prints how to open the cockpit. Then, in
another terminal:
quiver tui --api-key quiver-demo-key # the retro cockpit
In the cockpit, press v (or enter) on a collection to open the constellation
view — a 2-D random-projection scatter of its vector space with the query’s
nearest neighbour highlighted; move the cursor and press enter to re-query around
any point.
Install the CLI
# from crates.io (the `quiver` binary, published as quiverdb-cli):
cargo install quiverdb-cli
# …or from a cloned repo:
cargo install --path crates/quiver-cli
quiver serve # gRPC + REST, encrypted by default
quiver tui # the cockpit
quiver mcp # MCP server (stdio) for AI agents
Heads-up: Quiver’s CLI publishes as
quiverdb-cli— thequiver-cliname on crates.io is an unrelated third-party project, which is why thequiverdb-*namespace is used (ADR-0056).
Your first query (Python)
from quiver import Client, Point
with Client("http://127.0.0.1:6333", api_key="…") as q:
q.create_collection("items", dim=3, metric="cosine")
q.upsert("items", [Point("a", [0.1, 0.2, 0.3], {"tag": "x"})])
hits = q.search("items", [0.1, 0.2, 0.3], k=5)
print(hits)
The same flow is available over REST & gRPC, the MCP server, and the TypeScript SDK.
Build, test, and the gate
just build # compile the workspace
just verify # the full local quality gate (lint · test · doc · deny · audit)
cargo run -p quiverdb-cli -- --help
just verify is the authoritative gate (the CI workflows are manual-only by
design). Next: Self-hosting & configuration.