alicloud-ai-search-milvus

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Use AliCloud Milvus (serverless) with PyMilvus to create collections, insert vectors, and run filtered similarity search. Optimized for Claude Code/Codex vector retrieval flows.

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Sourcecinience/alicloud-skills

Added on2026-02-26

NPX Install

npx skill4agent add cinience/alicloud-skills alicloud-ai-search-milvus

SKILL.md Content

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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):

bash

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)

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

bash

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.

References

PyMilvus
```
MilvusClient
```
examples for AliCloud Milvus
Source list:
```
references/sources.md
```