alicloud-ai-search-dashvector

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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.

3installs

Sourcecinience/alicloud-skills

Added on2026-02-27

NPX Install

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

SKILL.md Content

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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-dashvector/scripts/quickstart.py

Environment variables:

```
DASHVECTOR_API_KEY
```
```
DASHVECTOR_ENDPOINT
```
```
DASHVECTOR_COLLECTION
```
(optional)
```
DASHVECTOR_DIMENSION
```
(optional)

Optional args:

--collection

--dimension

--topk

--filter

Notes for Claude Code/Codex

Prefer
```
upsert
```
for idempotent ingestion.
Keep
```
dimension
```
aligned to your embedding model output size.
Use filters to enforce tenant or dataset scoping.
If using sparse vectors, pass
```
sparse_vector={token_id: weight, ...}
```
when upserting/querying.

Error handling

401/403: invalid
```
DASHVECTOR_API_KEY
```
400: invalid collection schema or dimension mismatch
429/5xx: retry with exponential backoff

References

DashVector Python SDK:

Client.create

Collection.upsert

Collection.query

Source list:
```
references/sources.md
```