Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Productivity
Alicloud Ai Search Dashvector
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.
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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.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, agents/openai.yaml, references/sources.md, scripts/quickstart.py
Validation
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- Review SKILL.md after the package is downloaded.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.3
Provenance
- Publisher
- cinience
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Documentation
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)
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
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Scripts1 Config
- SKILL.md
- references/sources.md
- scripts/quickstart.py
- agents/openai.yaml