# Send Alicloud Ai Search Dashvector to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "alicloud-ai-search-dashvector", "name": "Alicloud Ai Search Dashvector", "source": "tencent", "type": "skill", "category": "效率提升", "sourceUrl": "https://clawhub.ai/cinience/alicloud-ai-search-dashvector", "canonicalUrl": "https://clawhub.ai/cinience/alicloud-ai-search-dashvector", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/alicloud-ai-search-dashvector", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=alicloud-ai-search-dashvector", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "agents/openai.yaml", "references/sources.md", "scripts/quickstart.py" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/alicloud-ai-search-dashvector" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/alicloud-ai-search-dashvector", "downloadUrl": "https://openagent3.xyz/downloads/alicloud-ai-search-dashvector", "agentUrl": "https://openagent3.xyz/skills/alicloud-ai-search-dashvector/agent", "manifestUrl": "https://openagent3.xyz/skills/alicloud-ai-search-dashvector/agent.json", "briefUrl": "https://openagent3.xyz/skills/alicloud-ai-search-dashvector/agent.md" } } ``` ## 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 ## Trust - Source: tencent - Verification: Indexed source record - Publisher: cinience - Version: 1.0.3 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-23T16:43:11.935Z - Expires at: 2026-04-30T16:43:11.935Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/alicloud-ai-search-dashvector) - [Send to Agent page](https://openagent3.xyz/skills/alicloud-ai-search-dashvector/agent) - [JSON manifest](https://openagent3.xyz/skills/alicloud-ai-search-dashvector/agent.json) - [Markdown brief](https://openagent3.xyz/skills/alicloud-ai-search-dashvector/agent.md) - [Download page](https://openagent3.xyz/downloads/alicloud-ai-search-dashvector)