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    "sections": [
      {
        "title": "DashVector Vector Search",
        "body": "Use DashVector to manage collections and perform vector similarity search with optional filters and sparse vectors."
      },
      {
        "title": "Prerequisites",
        "body": "Install SDK (recommended in a venv to avoid PEP 668 limits):\n\npython3 -m venv .venv\n. .venv/bin/activate\npython -m pip install dashvector\n\nProvide credentials and endpoint via environment variables:\n\nDASHVECTOR_API_KEY\nDASHVECTOR_ENDPOINT (cluster endpoint)"
      },
      {
        "title": "Create collection",
        "body": "name (str)\ndimension (int)\nmetric (str: cosine | dotproduct | euclidean)\nfields_schema (optional dict of field types)"
      },
      {
        "title": "Upsert docs",
        "body": "docs list of {id, vector, fields} or tuples\nSupports sparse_vector and multi-vector collections"
      },
      {
        "title": "Query docs",
        "body": "vector or id (one required; if both empty, only filter is applied)\ntopk (int)\nfilter (SQL-like where clause)\noutput_fields (list of field names)\ninclude_vector (bool)"
      },
      {
        "title": "Quickstart (Python SDK)",
        "body": "import os\nimport dashvector\nfrom dashvector import Doc\n\nclient = dashvector.Client(\n    api_key=os.getenv(\"DASHVECTOR_API_KEY\"),\n    endpoint=os.getenv(\"DASHVECTOR_ENDPOINT\"),\n)\n\n# 1) Create a collection\nret = client.create(\n    name=\"docs\",\n    dimension=768,\n    metric=\"cosine\",\n    fields_schema={\"title\": str, \"source\": str, \"chunk\": int},\n)\nassert ret\n\n# 2) Upsert docs\ncollection = client.get(name=\"docs\")\nret = collection.upsert(\n    [\n        Doc(id=\"1\", vector=[0.01] * 768, fields={\"title\": \"Intro\", \"source\": \"kb\", \"chunk\": 0}),\n        Doc(id=\"2\", vector=[0.02] * 768, fields={\"title\": \"FAQ\", \"source\": \"kb\", \"chunk\": 1}),\n    ]\n)\nassert ret\n\n# 3) Query\nret = collection.query(\n    vector=[0.01] * 768,\n    topk=5,\n    filter=\"source = 'kb' AND chunk >= 0\",\n    output_fields=[\"title\", \"source\", \"chunk\"],\n    include_vector=False,\n)\nfor doc in ret:\n    print(doc.id, doc.fields)"
      },
      {
        "title": "Script quickstart",
        "body": "python skills/ai/search/alicloud-ai-search-dashvector/scripts/quickstart.py\n\nEnvironment variables:\n\nDASHVECTOR_API_KEY\nDASHVECTOR_ENDPOINT\nDASHVECTOR_COLLECTION (optional)\nDASHVECTOR_DIMENSION (optional)\n\nOptional args: --collection, --dimension, --topk, --filter."
      },
      {
        "title": "Notes for Claude Code/Codex",
        "body": "Prefer upsert for idempotent ingestion.\nKeep dimension aligned to your embedding model output size.\nUse filters to enforce tenant or dataset scoping.\nIf using sparse vectors, pass sparse_vector={token_id: weight, ...} when upserting/querying."
      },
      {
        "title": "Error handling",
        "body": "401/403: invalid DASHVECTOR_API_KEY\n400: invalid collection schema or dimension mismatch\n429/5xx: retry with exponential backoff"
      },
      {
        "title": "Validation",
        "body": "mkdir -p output/alicloud-ai-search-dashvector\nfor f in skills/ai/search/alicloud-ai-search-dashvector/scripts/*.py; do\n  python3 -m py_compile \"$f\"\ndone\necho \"py_compile_ok\" > output/alicloud-ai-search-dashvector/validate.txt\n\nPass criteria: command exits 0 and output/alicloud-ai-search-dashvector/validate.txt is generated."
      },
      {
        "title": "Output And Evidence",
        "body": "Save artifacts, command outputs, and API response summaries under output/alicloud-ai-search-dashvector/.\nInclude key parameters (region/resource id/time range) in evidence files for reproducibility."
      },
      {
        "title": "Workflow",
        "body": "Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.\nRun one minimal read-only query first to verify connectivity and permissions.\nExecute the target operation with explicit parameters and bounded scope.\nVerify results and save output/evidence files."
      },
      {
        "title": "References",
        "body": "DashVector Python SDK: Client.create, Collection.upsert, Collection.query\n\n\nSource list: references/sources.md"
      }
    ],
    "body": "Category: provider\n\nDashVector Vector Search\n\nUse DashVector to manage collections and perform vector similarity search with optional filters and sparse vectors.\n\nPrerequisites\nInstall SDK (recommended in a venv to avoid PEP 668 limits):\npython3 -m venv .venv\n. .venv/bin/activate\npython -m pip install dashvector\n\nProvide credentials and endpoint via environment variables:\nDASHVECTOR_API_KEY\nDASHVECTOR_ENDPOINT (cluster endpoint)\nNormalized operations\nCreate collection\nname (str)\ndimension (int)\nmetric (str: cosine | dotproduct | euclidean)\nfields_schema (optional dict of field types)\nUpsert docs\ndocs list of {id, vector, fields} or tuples\nSupports sparse_vector and multi-vector collections\nQuery docs\nvector or id (one required; if both empty, only filter is applied)\ntopk (int)\nfilter (SQL-like where clause)\noutput_fields (list of field names)\ninclude_vector (bool)\nQuickstart (Python SDK)\nimport os\nimport dashvector\nfrom dashvector import Doc\n\nclient = dashvector.Client(\n    api_key=os.getenv(\"DASHVECTOR_API_KEY\"),\n    endpoint=os.getenv(\"DASHVECTOR_ENDPOINT\"),\n)\n\n# 1) Create a collection\nret = client.create(\n    name=\"docs\",\n    dimension=768,\n    metric=\"cosine\",\n    fields_schema={\"title\": str, \"source\": str, \"chunk\": int},\n)\nassert ret\n\n# 2) Upsert docs\ncollection = client.get(name=\"docs\")\nret = collection.upsert(\n    [\n        Doc(id=\"1\", vector=[0.01] * 768, fields={\"title\": \"Intro\", \"source\": \"kb\", \"chunk\": 0}),\n        Doc(id=\"2\", vector=[0.02] * 768, fields={\"title\": \"FAQ\", \"source\": \"kb\", \"chunk\": 1}),\n    ]\n)\nassert ret\n\n# 3) Query\nret = collection.query(\n    vector=[0.01] * 768,\n    topk=5,\n    filter=\"source = 'kb' AND chunk >= 0\",\n    output_fields=[\"title\", \"source\", \"chunk\"],\n    include_vector=False,\n)\nfor doc in ret:\n    print(doc.id, doc.fields)\n\nScript quickstart\npython skills/ai/search/alicloud-ai-search-dashvector/scripts/quickstart.py\n\n\nEnvironment variables:\n\nDASHVECTOR_API_KEY\nDASHVECTOR_ENDPOINT\nDASHVECTOR_COLLECTION (optional)\nDASHVECTOR_DIMENSION (optional)\n\nOptional args: --collection, --dimension, --topk, --filter.\n\nNotes for Claude Code/Codex\nPrefer upsert for idempotent ingestion.\nKeep dimension aligned to your embedding model output size.\nUse filters to enforce tenant or dataset scoping.\nIf using sparse vectors, pass sparse_vector={token_id: weight, ...} when upserting/querying.\nError handling\n401/403: invalid DASHVECTOR_API_KEY\n400: invalid collection schema or dimension mismatch\n429/5xx: retry with exponential backoff\nValidation\nmkdir -p output/alicloud-ai-search-dashvector\nfor f in skills/ai/search/alicloud-ai-search-dashvector/scripts/*.py; do\n  python3 -m py_compile \"$f\"\ndone\necho \"py_compile_ok\" > output/alicloud-ai-search-dashvector/validate.txt\n\n\nPass criteria: command exits 0 and output/alicloud-ai-search-dashvector/validate.txt is generated.\n\nOutput And Evidence\nSave artifacts, command outputs, and API response summaries under output/alicloud-ai-search-dashvector/.\nInclude key parameters (region/resource id/time range) in evidence files for reproducibility.\nWorkflow\nConfirm user intent, region, identifiers, and whether the operation is read-only or mutating.\nRun one minimal read-only query first to verify connectivity and permissions.\nExecute the target operation with explicit parameters and bounded scope.\nVerify results and save output/evidence files.\nReferences\n\nDashVector Python SDK: Client.create, Collection.upsert, Collection.query\n\nSource list: references/sources.md"
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    "owner": "cinience",
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