Category: provider AliCloud Milvus (Serverless) via PyMilvus This skill uses standard PyMilvus APIs to connect to AliCloud Milvus and run vector search. Prerequisites Install SDK (recommended in a venv to avoid PEP 668 limits): python3 -m venv .venv . .venv/bin/activate python -m pip install --upgrade pymilvus Provide connection via environment variables: MILVUS_URI (e.g. http://:19530 ) MILVUS_TOKEN ( : ) MILVUS_DB (default: default ) Quickstart (Python) import os from pymilvus import MilvusClient client = MilvusClient ( uri = os . getenv ( "MILVUS_URI" ) , token = os . getenv ( "MILVUS_TOKEN" ) , db_name = os . getenv ( "MILVUS_DB" , "default" ) , )

1) Create a collection

client . create_collection ( collection_name = "docs" , dimension = 768 , )

2) Insert data

items

[ { "id" : 1 , "vector" : [ 0.01 ] * 768 , "source" : "kb" , "chunk" : 0 } , { "id" : 2 , "vector" : [ 0.02 ] * 768 , "source" : "kb" , "chunk" : 1 } , ] client . insert ( collection_name = "docs" , data = items )

3) Search

query_vectors

[ [ 0.01 ] * 768 ] res = client . search ( collection_name = "docs" , data = query_vectors , limit = 5 , filter = 'source == "kb" and chunk >= 0' , output_fields = [ "source" , "chunk" ] , ) print ( res ) Script quickstart python skills/ai/search/alicloud-ai-search-milvus/scripts/quickstart.py Environment variables: MILVUS_URI MILVUS_TOKEN MILVUS_DB (optional) MILVUS_COLLECTION (optional) MILVUS_DIMENSION (optional) Optional args: --collection , --dimension , --limit , --filter . Notes for Claude Code/Codex Insert is async; wait a few seconds before searching newly inserted data. Keep vector dimension aligned with your embedding model. Use filters to enforce tenant scoping or dataset partitions. Error handling Auth errors: check MILVUS_TOKEN and instance permissions. Dimension mismatch: ensure all vectors match collection dimension. Network errors: verify VPC/public access settings on the instance. Validation mkdir -p output/alicloud-ai-search-milvus for f in skills/ai/search/alicloud-ai-search-milvus/scripts/*.py ; do python3 -m py_compile " $f " done echo "py_compile_ok"

output/alicloud-ai-search-milvus/validate.txt Pass criteria: command exits 0 and output/alicloud-ai-search-milvus/validate.txt is generated. Output And Evidence Save artifacts, command outputs, and API response summaries under output/alicloud-ai-search-milvus/ . 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 PyMilvus MilvusClient examples for AliCloud Milvus Source list: references/sources.md