aliyun-milvus-search

name	aliyun-milvus-search
description	Use when working with AliCloud Milvus (serverless) with PyMilvus to create collections, insert vectors, and run filtered similarity search. Optimized for Claude Code/Codex vector retrieval flows.
version	1.0.0

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/aliyun-milvus-search/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/aliyun-milvus-search
for f in skills/ai/search/aliyun-milvus-search/scripts/*.py; do
  python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/aliyun-milvus-search/validate.txt

Pass criteria: command exits 0 and output/aliyun-milvus-search/validate.txt is generated.

Output And Evidence

• Save artifacts, command outputs, and API response summaries under output/aliyun-milvus-search/.
• 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: references/sources.md