briqbriq

Qdrant

A vector database, for a knowledge file the agent can search by meaning, not grep.

  • database
  • vector
  • ai

Guide: Connect to a database from outside

Run it

briq_run({
  recipe: "qdrant",
  params: {
    "size": "small",
    "diskGb": 2
  },
  wait: true
})

Parameters

NameTypeOptionsDefault
sizeenumnano, small, medium, large"small"
diskGbnumber—2

Outputs

Returned by briq_run once the stack is healthy. Hosts resolve inside your team network; secrets are generated per run.

QDRANT_URL
http://qdrant.<stack>.internal:6333
QDRANT_GRPC_URL
http://qdrant.<stack>.internal:6334
QDRANT_API_KEY
<QDRANT_API_KEY>

What it runs

BriqImagePortsSizeVolume
qdrantqdrant/qdrant:latest6333, 6334small2 GB at /qdrant/storage

When to use it

  • The agent's context is a growing pile of documents or past runs, and keyword search misses what is phrased differently.
  • You are building or testing RAG and need real upsert, filtering and nearest-neighbour search.
  • The agent should store what it learns and query it back by meaning.

Tips

  • Send the API key on every request: api-key: <QDRANT_API_KEY>.
  • gRPC for bulk upserts (QDRANT_GRPC_URL); REST (QDRANT_URL) for queries.
  • gRPC from outside: briq_expose port 6334 with protocol: "grpc", then send the API key. See Connect to a database from outside.
  • Create collections first: PUT /collections/<name> with the vector size and distance.
  • Dashboard: briq_expose port 6333 with mode: "private" (the briq restarts to publish it), then open /dashboard.
  • Many vectors? The volume starts small: pass diskGb, e.g. params: { diskGb: 20 }. Set it when the briq is created, and it counts toward your plan's disk.

On this page

Free to try

Let your agent try it, on us.

Every new account starts with €10 of credits. No card.

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Message to the briq team
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