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BytesAgain β€Ί Skills β€Ί Alicloud Ai Search Dashvector
πŸ¦€ ClawHub

Alicloud Ai Search Dashvector

by @cinience

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

Versionv1.0.3
Downloads1,397
Installs2
#clawhub
TERMINAL
clawhub install alicloud-ai-search-dashvector

πŸ“– About This Skill

name: alicloud-ai-search-dashvector description: 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. version: 1.0.0

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

    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

    • βš™οΈ Configuration

  • 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)

    πŸ”’ Constraints

    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.

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