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/aliyun-dashvector-search/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

Validation#

bash

mkdir -p output/aliyun-dashvector-search
for f in skills/ai/search/aliyun-dashvector-search/scripts/*.py; do
  python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/aliyun-dashvector-search/validate.txt

Pass criteria: command exits 0 and output/aliyun-dashvector-search/validate.txt is generated.

Output And Evidence#

• Save artifacts, command outputs, and API response summaries under output/aliyun-dashvector-search/.
• Include key parameters (region/resource id/time range) in evidence files for reproducibility.

Workflow#

1. Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.
2. Run one minimal read-only query first to verify connectivity and permissions.
3. Execute the target operation with explicit parameters and bounded scope.
4. Verify results and save output/evidence files.

References#

• DashVector Python SDK: Client.create, Collection.upsert, Collection.query

• Source list: references/sources.md