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{ z } from 'zod/v4';\n\nimport { paginationInfoSchema, createPagePaginationSchema, statusQuerySchema } from './common';\nimport { modelConfigSchema } from './stored-agents';\n\n// ============================================================================\n// Path Parameter Schemas\n// ============================================================================\n\nexport const storedScorerIdPathParams = z.object({\n  storedScorerId: z.string().describe('Unique identifier for the stored scorer definition'),\n});\n\n// ============================================================================\n// Query Parameter Schemas\n// ============================================================================\n\nconst storageOrderBySchema = z.object({\n  field: z.enum(['createdAt', 'updatedAt']).optional(),\n  direction: z.enum(['ASC', 'DESC']).optional(),\n});\n\nexport { statusQuerySchema };\n\nexport const listStoredScorersQuerySchema = createPagePaginationSchema(100).extend({\n  orderBy: storageOrderBySchema.optional(),\n  status: z\n    .enum(['draft', 'published', 'archived'])\n    .optional()\n    .default('published')\n    .describe('Filter scorers by status (defaults to published)'),\n  authorId: z.string().optional().describe('Filter scorers by author identifier'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Filter scorers by metadata key-value pairs'),\n});\n\n// ============================================================================\n// Body Parameter Schemas\n// ============================================================================\n\nconst samplingConfigSchema = z.union([\n  z.object({ type: z.literal('none') }),\n  z.object({ type: z.literal('ratio'), rate: z.number().min(0).max(1) }),\n]);\n\nconst scorerTypeEnum = z\n  .enum([\n    'llm-judge',\n    'answer-relevancy',\n    'answer-similarity',\n    'bias',\n    'context-precision',\n    'context-relevance',\n    'faithfulness',\n    'hallucination',\n    'noise-sensitivity',\n    'prompt-alignment',\n    'tool-call-accuracy',\n    'toxicity',\n  ])\n  .describe('Scorer type: llm-judge for custom, or a preset type name');\n\nconst snapshotConfigSchema = z.object({\n  name: z.string().describe('Name of the scorer'),\n  description: z.string().optional().describe('Description of the scorer'),\n  type: scorerTypeEnum,\n  model: modelConfigSchema.optional().describe('Model configuration for LLM judge'),\n  instructions: z.string().optional().describe('System instructions for the judge LLM (used when type is llm-judge)'),\n  scoreRange: z\n    .object({\n      min: z.number().optional().describe('Minimum score value (default: 0)'),\n      max: z.number().optional().describe('Maximum score value (default: 1)'),\n    })\n    .optional()\n    .describe('Score range configuration (used when type is llm-judge)'),\n  presetConfig: z.record(z.string(), z.unknown()).optional().describe('Serializable config options for preset scorers'),\n  defaultSampling: samplingConfigSchema.optional().describe('Default sampling configuration'),\n});\n\nexport const createStoredScorerBodySchema = z\n  .object({\n    id: z.string().optional().describe('Unique identifier. If not provided, derived from name.'),\n    authorId: z.string().optional().describe('Author identifier for multi-tenant filtering'),\n    metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata for the scorer'),\n  })\n  .merge(snapshotConfigSchema);\n\nexport const updateStoredScorerBodySchema = z\n  .object({\n    authorId: z.string().optional(),\n    metadata: z.record(z.string(), z.unknown()).optional(),\n  })\n  .partial()\n  .merge(snapshotConfigSchema.partial());\n\n// ============================================================================\n// Response Schemas\n// ============================================================================\n\nexport const storedScorerSchema = z.object({\n  id: z.string(),\n  status: z.string().describe('Scorer status: draft, published, or archived'),\n  activeVersionId: z.string().optional(),\n  authorId: z.string().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n  name: z.string().describe('Name of the scorer'),\n  description: z.string().optional().describe('Description of the scorer'),\n  type: scorerTypeEnum,\n  model: modelConfigSchema.optional(),\n  instructions: z.string().optional().describe('System instructions for the judge LLM'),\n  scoreRange: z\n    .object({\n      min: z.number().optional(),\n      max: z.number().optional(),\n    })\n    .optional(),\n  presetConfig: z.record(z.string(), z.unknown()).optional(),\n  defaultSampling: samplingConfigSchema.optional(),\n});\n\nexport const listStoredScorersResponseSchema = paginationInfoSchema.extend({\n  scorerDefinitions: z.array(storedScorerSchema),\n});\n\nexport const getStoredScorerResponseSchema = storedScorerSchema;\nexport const createStoredScorerResponseSchema = storedScorerSchema;\n\nexport const updateStoredScorerResponseSchema = z.union([\n  z.object({\n    id: z.string(),\n    status: z.string(),\n    activeVersionId: z.string().optional(),\n    authorId: z.string().optional(),\n    metadata: z.record(z.string(), z.unknown()).optional(),\n    createdAt: z.coerce.date(),\n    updatedAt: z.coerce.date(),\n  }),\n  storedScorerSchema,\n]);\n\nexport const deleteStoredScorerResponseSchema = z.object({\n  success: z.boolean(),\n  message: z.string(),\n});\n"]}