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

Validation

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

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

Output And Evidence

• Save artifacts, command outputs, and API response summaries under output/alicloud-ai-search-dashvector/ .

• Include key parameters (region/resource id/time range) in evidence files for reproducibility.

Workflow

• Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.

• Run one minimal read-only query first to verify connectivity and permissions.

• Execute the target operation with explicit parameters and bounded scope.

• Verify results and save output/evidence files.

References

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

Source list: references/sources.md