OpenClaw

alicloud-ai-search-milvus

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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查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install cinience/alicloud-ai-search-milvus

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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 err...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/cinience/alicloud-ai-search-milvus
Version: —
Commit: 976c61fe998b6bf29deaa253920f3089ed141b11
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

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 err...

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