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://<host>:19530 )

• MILVUS_TOKEN (<username>:<password> )

• 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