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-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