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TypeScript semantic layer for ClickHouse datasets, metrics, multi-tenancy, and AI agents","maintainers":[{"name":"lureilly2","email":"lureilly1@gmail.com"}],"readme":"# @hypequery/datasets\n\nThe code-first TypeScript semantic layer for ClickHouse.\n\n`@hypequery/datasets` gives product teams one trusted definition for dimensions, measures, metrics, relationships, time grains, and multi-tenant isolation. Keep analytics meaning in the same repo as your application, then reuse it in backend jobs, HTTP APIs, React dashboards, and MCP tools for AI agents.\n\nIt builds on the `@hypequery/clickhouse` type-safe query builder, so semantic models and direct ClickHouse queries share generated schema types.\n\nNo YAML project. No separate semantic-layer server. Just typed, testable product analytics in code.\n\n## Install\n\n```bash\nnpm install @hypequery/datasets @hypequery/clickhouse\n```\n\n## Define analytics once\n\n```ts\nimport { dataset, dimension, measure } from '@hypequery/datasets';\n\nexport const Orders = dataset('orders', {\n  source: 'orders',\n  tenantKey: 'tenant_id',\n  timeKey: 'created_at',\n  dimensions: {\n    region: dimension.string(),\n    status: dimension.string(),\n    createdAt: dimension.timestamp({ column: 'created_at' }),\n  },\n  measures: {\n    revenue: measure.sum('amount'),\n    orderCount: measure.count('id'),\n    p95OrderValue: measure.percentile('amount', 0.95),\n    latestStatus: measure.argMax('status', 'createdAt'),\n  },\n});\n\nexport const revenue = Orders.metric('revenue', {\n  measure: 'revenue',\n  label: 'Revenue',\n});\n```\n\nThe model is now the contract. Callers can only use the dimensions, measures, filters, and relationships you publish. A declared `tenantKey` requires trusted runtime scope and rejects unscoped execution.\n\n## Query it anywhere\n\n```ts\nconst result = await analytics.execute(\n  revenue,\n  {\n    dimensions: ['region'],\n    orderBy: [{ field: 'revenue', direction: 'desc' }],\n    limit: 10,\n  },\n  {\n    runtime: { tenant: session.accountId },\n    abortSignal: request.signal,\n  },\n);\n```\n\nPassing an `AbortSignal` cancels the semantic query and forwards cancellation to\nthe backing query builder/database request.\n\nThe same definition can become:\n\n- a named KPI or flexible dataset query;\n- a validated `@hypequery/serve` endpoint;\n- a typed `@hypequery/react` hook;\n- an OpenAI, AI SDK, or MCP tool schema;\n- a stable semantic contract for CI and deployment.\n\n`buildCanonicalSemanticQuerySchemas()` compiles the same catalog into strict\ndataset and metric Zod validators, JSON Schemas, and a deterministic manifest\nhash. It accepts either live local Dataset definitions or an already-normalized\ncatalog from a hosted activation, so Serve and MCP do not maintain separate\nfield or operator lists.\n\n## Analytics teams actually need\n\n- sums, counts, distinct counts, averages, min, and max;\n- percentiles, median, `argMax`, `argMin`, standard deviation, and variance;\n- filtered measures and derived metric formulas;\n- daily through yearly time grains;\n- fail-closed multi-tenant analytics;\n- one-hop typed `belongsTo` and `hasOne` dimensions;\n- validated filtering, sorting, and pagination.\n\nSee the [current capability matrix](https://hypequery.com/docs/capabilities) for exact syntax and the complete shipped surface.\n\n## Why code-first\n\nMetric changes travel through the workflow your team already trusts: TypeScript, code review, tests, CI, and version control. Backend, frontend, and agent consumers stop maintaining their own definition of “revenue.”\n\n## Learn more\n\n- [Datasets overview](https://hypequery.com/docs/datasets/overview)\n- [Measures and metrics](https://hypequery.com/docs/datasets/measures)\n- [Multi-tenancy](https://hypequery.com/docs/datasets/multi-tenancy)\n- [Relationships](https://hypequery.com/docs/datasets/relationships)\n- [MCP tool generation](https://hypequery.com/docs/datasets/tool-generation)\n\n## License\n\nApache-2.0.\n","readmeFilename":"README.md"}