OpenClaw

alicloud-ai-search-dashvector

Build vector retrieval with DashVector using the Python SDK. Use when creating collections, upserting docs, and running similarity search with filters in Claude Code/Codex.

cinience0 来源安装数☆ 0 仓库 Starsv1.0.0

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-dashvector

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

DashVector Vector Search

Use DashVector to manage collections and perform vector similarity search with optional filters and sparse vectors.

Prerequisites

Install SDK (recommended in a venv to avoid PEP 668 limits):

python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashvector

Provide credentials and endpoint via environment variables:
- DASHVECTOR_API_KEY
- DASHVECTOR_ENDPOINT (cluster endpoint)

Normalized operations

Create collection

name (str)
dimension (int)
metric (str: cosine | dotproduct | euclidean)
fields_schema (optional dict of field types)

Upsert docs

docs list of {id, vector, fields} or tuples
Supports sparse_vector and multi-vector collections

Query docs

vector or id (one required; if both empty, only filter is applied)
topk (int)
filter (SQL-like where clause)
output_fields (list of field names)
include_vector (bool)

Quickstart (Python SDK)

import os
import dashvector
from dashvector import Doc

client = dashvector.Client(
    api_key=os.getenv("DASHVECTOR_API_KEY"),
    endpoint=os.getenv("DASHVECTOR_ENDPOINT"),
)

# 1) Create a collection
ret = client.create(
    name="docs",
    dimension=768,
    metric="cosine",
    fields_schema={"title": str, "source": str, "chunk": int},
)
assert ret

# 2) Upsert docs
collection = client.get(name="docs")
ret = collection.upsert(
    [
        Doc(id="1", vector=[0.01] * 768, fields={"title": "Intro", "source": "kb", "chunk": 0}),
        Doc(id="2", vector=[0.02] * 768, fields={"title": "FAQ", "source": "kb", "chunk": 1}),
    ]
)
assert ret

# 3) Query
ret = collection.query(
    vector=[0.01] * 768,
    topk=5,
    filter="source = 'kb' AND chunk >= 0",
    output_fields=["title", "source", "chunk"],
    include_vector=False,
)
for doc in ret:
    print(doc.id, doc.fields)

Script quickstart

python skills/ai/search/alicloud-ai-search-dashve...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/cinience/alicloud-ai-search-dashvector
Version: 1.0.0
Commit: c9c537a1bc36fe045af86e9782ce13b370905597
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

# DashVector Vector Search

Use DashVector to manage collections and perform vector similarity search with optional filters and sparse vectors.

## Prerequisites

- Install SDK (recommended in a venv to avoid PEP 668 limits):

```bash
python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashvector
```
- Provide credentials and endpoint via environment variables:
  - `DASHVECTOR_API_KEY`
  - `DASHVECTOR_ENDPOINT` (cluster endpoint)

## Normalized operations

### Create collection
- `name` (str)
- `dimension` (int)
- `metric` (str: `cosine` | `dotproduct` | `euclidean`)
- `fields_schema` (optional dict of field types)

### Upsert docs
- `docs` list of `{id, vector, fields}` or tuples
- Supports `sparse_vector` and multi-vector collections

### Query docs
- `vector` or `id` (one required; if both empty, only filter is applied)
- `topk` (int)
- `filter` (SQL-like where clause)
- `output_fields` (list of field names)
- `include_vector` (bool)

## Quickstart (Python SDK)

```python
import os
import dashvector
from dashvector import Doc

client = dashvector.Client(
    api_key=os.getenv("DASHVECTOR_API_KEY"),
    endpoint=os.getenv("DASHVECTOR_ENDPOINT"),
)

# 1) Create a collection
ret = client.create(
    name="docs",
    dimension=768,
    metric="cosine",
    fields_schema={"title": str, "source": str, "chunk": int},
)
assert ret

# 2) Upsert docs
collection = client.get(name="docs")
ret = collection.upsert(
    [
        Doc(id="1", vector=[0.01] * 768, fields={"title": "Intro", "source": "kb", "chunk": 0}),
        Doc(id="2", vector=[0.02] * 768, fields={"title": "FAQ", "source": "kb", "chunk": 1}),
    ]
)
assert ret

# 3) Query
ret = collection.query(
    vector=[0.01] * 768,
    topk=5,
    filter="source = 'kb' AND chunk >= 0",
    output_fields=["title", "source", "chunk"],
    include_vector=False,
)
for doc in ret:
    print(doc.id, doc.fields)
```

## Script quickstart

```bash
python skills/ai/search/alicloud-ai-search-dashve...

搜索