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):

CODEBLOCK0

• - Provide connection via environment variables:

Quickstart (Python)

CODEBLOCK1

Script quickstart

CODEBLOCK2

Environment variables:

• - INLINECODE6
• INLINECODE7
• INLINECODE8 (optional)
• INLINECODE9 (optional)
• INLINECODE10 (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

CODEBLOCK3

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

1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.
2) Run one minimal read-only query first to verify connectivity and permissions.
3) Execute the target operation with explicit parameters and bounded scope.
4) Verify results and save output/evidence files.

References

• - PyMilvus MilvusClient examples for AliCloud Milvus

• - Source list: INLINECODE20