alicloud-ai-search-dashvector

Category: provider Use DashVector to manage collections and perform vector similarity search with optional filters and sparse vectors. Environment variables: Optional args: --collection , --dimension , --topk , --filter . Pass criteria: command exits 0 and output/alicloud-ai-search-dashvector/validate.txt is generated. DashVector Python SDK: Client.create , Collection.upsert , Collection.query Source list: references/sources.md - Install SDK (recommended in a venv to avoid PEP 668 limits): - Provide credentials and endpoint via environment variables: - DASHVECTOR_API_KEY - DASHVECTOR_ENDPOINT (cluster endpoint) - name (str) - dimension (int) - metric (str: cosine | dotproduct | euclidean ) - fields_schema (optional dict of field types) - docs list of {id, vector, fields} or tuples - Supports sparse_vector and multi-vector collections - vector or id (one required; if both empty, only filter is applied) - topk (int) - filter (SQL-like where clause) - output_fields (list of field names) - include_vector (bool) - DASHVECTOR_API_KEY - DASHVECTOR_ENDPOINT - DASHVECTOR_COLLECTION (optional) - DASHVECTOR_DIMENSION (optional) - Prefer upsert for idempotent ingestion. - Keep dimension aligned to your embedding model output size. - Use filters to enforce tenant or dataset scoping. - If using sparse vectors, pass sparse_vector={token_id: weight, ...} when upserting/querying. - 401/403:...