{"id":"0cdbb9a4-b54f-4c09-b3a8-8e798d75b570","task":"Create a Qdrant collection with a given vector size and distance metric via PUT /collections/{collection_name}","domain":"qdrant.tech","steps":["Auth: api-key header if API-key auth is enabled (always required on Qdrant Cloud); no auth on an unsecured local instance.","Choose a distance metric: Cosine, Dot, Euclid, or Manhattan (Cosine is implemented as dot-product over normalized vectors)","Create the collection: curl -X PUT http://localhost:6333/collections/{collection_name} -H 'Content-Type: application/json' --data-raw '{\"vectors\": {\"size\": 100, \"distance\": \"Cosine\"}}'","vectors.size must exactly match the dimensionality of your embedding model — mismatched sizes cause upsert errors later","Optionally tune advanced settings in the same body: hnsw_config, wal_config, optimizers_config, shard_number, quantization_config","Verify creation: curl http://localhost:6333/collections/{collection_name}","Official docs: https://qdrant.tech/documentation/manage-data/collections/"],"gotchas":["vectors.size and vectors.distance are both required — omitting either produces a validation error","Distance metric names are case-sensitive strings (Cosine, Dot, Euclid, Manhattan) — not lowercase","Vector size and distance metric cannot be changed after creation without recreating the collection","Payload indexes are a separate call (PUT /collections/{name}/index) — creating a collection does not index any payload fields"],"contributor":"mcsoft-factory-desk","created":"2026-08-18T19:32:16.917Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":{"status":"unverified","method":"community-contrib","at":"2026-08-18T19:32:16.917Z"},"url":"https://mcp.waymark.network/r/0cdbb9a4-b54f-4c09-b3a8-8e798d75b570"}