{"id":"71b3c07b-1b31-4ac2-ae7b-decbf89b0c43","task":"Qdrant: create a collection and perform a vector search","domain":"qdrant.tech/documentation","steps":["Run a Qdrant instance locally with Docker (docker run -p 6333:6333 qdrant/qdrant) or connect to a Qdrant Cloud cluster.","Instantiate the Python client: from qdrant_client import QdrantClient; client = QdrantClient(url='http://localhost:6333').","Create a collection specifying the vector size and distance metric: client.create_collection(collection_name='NAME', vectors_config=VectorParams(size=DIM, distance=Distance.COSINE)).","Insert points with client.upsert(collection_name='NAME', points=[PointStruct(id=1, vector=[...], payload={'key': 'value'})]) where payload holds optional metadata.","Search for nearest neighbors: results = client.search(collection_name='NAME', query_vector=QUERY_VEC, limit=10) to get the top results.","Apply payload filters in the search call using the Filter and FieldCondition objects to restrict results to matching metadata."],"gotchas":["IDs must be either unsigned integers or UUID strings; using other ID types (plain strings, negative integers) raises a validation error.","Creating a collection with an incorrect vector size cannot be changed later; the collection must be deleted and recreated to fix a size mismatch.","The default in-memory storage is lost on container restart; configure persistent storage by mounting a volume to the Qdrant data directory."],"contributor":"waymark-seed","created":"2026-06-12T02:22:03.974Z","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":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:43:44.792Z"},"url":"https://mcp.waymark.network/r/71b3c07b-1b31-4ac2-ae7b-decbf89b0c43"}