Category: provider DashVector Vector Search Use DashVector to manage collections and perform vector similarity search with optional filters and sparse vectors. Prerequisites Install SDK (recommended in a venv to avoid PEP 668 limits): python3 -m venv .venv . .venv/bin/activate python -m pip install dashvector Provide credentials and endpoint via environment variables: DASHVECTOR_API_KEY DASHVECTOR_ENDPOINT (cluster endpoint) Normalized operations Create collection name (str) dimension (int) metric (str: cosine | dotproduct | euclidean ) fields_schema (optional dict of field types) Upsert docs docs list of {id, vector, fields} or tuples Supports sparse_vector and multi-vector collections Query docs 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) Quickstart (Python SDK) import os import dashvector from dashvector import Doc client = dashvector . Client ( api_key = os . getenv ( "DASHVECTOR_API_KEY" ) , endpoint = os . getenv ( "DASHVECTOR_ENDPOINT" ) , )

1) Create a collection

ret

client . create ( name = "docs" , dimension = 768 , metric = "cosine" , fields_schema = { "title" : str , "source" : str , "chunk" : int } , ) assert ret

2) Upsert docs

collection

client . get ( name = "docs" ) ret = collection . upsert ( [ Doc ( id = "1" , vector = [ 0.01 ] * 768 , fields = { "title" : "Intro" , "source" : "kb" , "chunk" : 0 } ) , Doc ( id = "2" , vector = [ 0.02 ] * 768 , fields = { "title" : "FAQ" , "source" : "kb" , "chunk" : 1 } ) , ] ) assert ret

3) Query

ret

collection . query ( vector = [ 0.01 ] * 768 , topk = 5 , filter = "source = 'kb' AND chunk >= 0" , output_fields = [ "title" , "source" , "chunk" ] , include_vector = False , ) for doc in ret : print ( doc . id , doc . fields ) Script quickstart python skills/ai/search/alicloud-ai-search-dashvector/scripts/quickstart.py Environment variables: DASHVECTOR_API_KEY DASHVECTOR_ENDPOINT DASHVECTOR_COLLECTION (optional) DASHVECTOR_DIMENSION (optional) Optional args: --collection , --dimension , --topk , --filter . Notes for Claude Code/Codex 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. Error handling 401/403: invalid DASHVECTOR_API_KEY 400: invalid collection schema or dimension mismatch 429/5xx: retry with exponential backoff Validation mkdir -p output/alicloud-ai-search-dashvector for f in skills/ai/search/alicloud-ai-search-dashvector/scripts/*.py ; do python3 -m py_compile " $f " done echo "py_compile_ok"

output/alicloud-ai-search-dashvector/validate.txt Pass criteria: command exits 0 and output/alicloud-ai-search-dashvector/validate.txt is generated. Output And Evidence Save artifacts, command outputs, and API response summaries under output/alicloud-ai-search-dashvector/ . Include key parameters (region/resource id/time range) in evidence files for reproducibility. Workflow Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. Run one minimal read-only query first to verify connectivity and permissions. Execute the target operation with explicit parameters and bounded scope. Verify results and save output/evidence files. References DashVector Python SDK: Client.create , Collection.upsert , Collection.query Source list: references/sources.md