'use strict';

var chunkDIG2K5CV_cjs = require('./chunk-DIG2K5CV.cjs');
var v4 = require('zod/v4');

var jsonSchemaObject = v4.z.lazy(() => v4.z.record(v4.z.string(), v4.z.unknown()));
var jsonSchemaField = v4.z.union([jsonSchemaObject, v4.z.null()]).optional();
var expectedStepBase = {
  name: v4.z.string().describe("Step name to match"),
  durationMs: v4.z.number().optional(),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  children: v4.z.any().optional().describe("Nested trajectory expectation (untyped at this depth)")
};
var expectedToolCallStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("tool_call"),
  toolArgs: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  toolResult: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  success: v4.z.boolean().optional()
});
var expectedMcpToolCallStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("mcp_tool_call"),
  toolArgs: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  toolResult: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  mcpServer: v4.z.string().optional(),
  success: v4.z.boolean().optional()
});
var expectedModelGenerationStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("model_generation"),
  modelId: v4.z.string().optional(),
  promptTokens: v4.z.number().optional(),
  completionTokens: v4.z.number().optional(),
  finishReason: v4.z.string().optional()
});
var expectedAgentRunStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("agent_run"),
  agentId: v4.z.string().optional()
});
var expectedWorkflowStepStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("workflow_step"),
  stepId: v4.z.string().optional(),
  status: v4.z.string().optional(),
  output: v4.z.record(v4.z.string(), v4.z.unknown()).optional()
});
var expectedWorkflowRunStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("workflow_run"),
  workflowId: v4.z.string().optional(),
  status: v4.z.string().optional()
});
var expectedWorkflowConditionalStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("workflow_conditional"),
  conditionCount: v4.z.number().optional(),
  selectedSteps: v4.z.array(v4.z.string()).optional()
});
var expectedWorkflowParallelStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("workflow_parallel"),
  branchCount: v4.z.number().optional(),
  parallelSteps: v4.z.array(v4.z.string()).optional()
});
var expectedWorkflowLoopStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("workflow_loop"),
  loopType: v4.z.string().optional(),
  totalIterations: v4.z.number().optional()
});
var expectedWorkflowSleepStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("workflow_sleep"),
  sleepDurationMs: v4.z.number().optional(),
  sleepType: v4.z.string().optional()
});
var expectedWorkflowWaitEventStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("workflow_wait_event"),
  eventName: v4.z.string().optional(),
  eventReceived: v4.z.boolean().optional()
});
var expectedProcessorRunStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.literal("processor_run"),
  processorId: v4.z.string().optional()
});
var expectedGenericStepSchema = v4.z.object({
  ...expectedStepBase,
  stepType: v4.z.undefined().optional()
});
var expectedStepSchema = v4.z.union([
  v4.z.discriminatedUnion("stepType", [
    expectedToolCallStepSchema,
    expectedMcpToolCallStepSchema,
    expectedModelGenerationStepSchema,
    expectedAgentRunStepSchema,
    expectedWorkflowStepStepSchema,
    expectedWorkflowRunStepSchema,
    expectedWorkflowConditionalStepSchema,
    expectedWorkflowParallelStepSchema,
    expectedWorkflowLoopStepSchema,
    expectedWorkflowSleepStepSchema,
    expectedWorkflowWaitEventStepSchema,
    expectedProcessorRunStepSchema
  ]),
  expectedGenericStepSchema
]);
var trajectoryExpectationSchema = v4.z.object({
  // Accuracy
  steps: v4.z.array(expectedStepSchema).optional().describe("Expected steps for accuracy checking"),
  ordering: v4.z.enum(["strict", "relaxed", "unordered"]).optional().describe("How to compare step ordering (default: relaxed)"),
  allowRepeatedSteps: v4.z.boolean().optional().describe("Whether to allow repeated steps (default: true)"),
  // Efficiency
  maxSteps: v4.z.number().int().optional().describe("Maximum number of steps allowed"),
  maxTotalTokens: v4.z.number().int().optional().describe("Maximum total tokens across all model_generation steps"),
  maxTotalDurationMs: v4.z.number().optional().describe("Maximum total duration in milliseconds"),
  noRedundantCalls: v4.z.boolean().optional().describe("Whether to penalize redundant calls (same tool + same args consecutively, default: true)"),
  // Blacklist
  blacklistedTools: v4.z.array(v4.z.string()).optional().describe("Tool names that should never appear"),
  blacklistedSequences: v4.z.array(v4.z.array(v4.z.string())).optional().describe("Tool name sequences that should never appear"),
  // Tool failure tolerance
  maxRetriesPerTool: v4.z.number().int().optional().describe("Maximum retries per tool before penalizing (default: 2)")
}).optional().nullable().describe("Expected trajectory configuration for trajectory scoring");
var datasetItemSourceSchema = v4.z.object({
  type: v4.z.enum(["csv", "json", "trace", "llm", "experiment-result"]).describe("How this item was created"),
  referenceId: v4.z.string().optional().describe("Reference identifier (e.g., trace id, csv filename)")
}).optional().describe("Source/provenance of this dataset item");
var datasetIdPathParams = v4.z.object({
  datasetId: v4.z.string().describe("Unique identifier for the dataset")
});
var experimentIdPathParams = v4.z.object({
  experimentId: v4.z.string().describe("Unique identifier for the experiment")
});
var itemIdPathParams = v4.z.object({
  itemId: v4.z.string().describe("Unique identifier for the dataset item")
});
var datasetAndExperimentIdPathParams = v4.z.object({
  datasetId: v4.z.string().describe("Unique identifier for the dataset"),
  experimentId: v4.z.string().describe("Unique identifier for the experiment")
});
var experimentResultIdPathParams = v4.z.object({
  datasetId: v4.z.string().describe("Unique identifier for the dataset"),
  experimentId: v4.z.string().describe("Unique identifier for the experiment"),
  resultId: v4.z.string().describe("Unique identifier for the experiment result")
});
var datasetAndItemIdPathParams = v4.z.object({
  datasetId: v4.z.string().describe("Unique identifier for the dataset"),
  itemId: v4.z.string().describe("Unique identifier for the dataset item")
});
var paginationQuerySchema = v4.z.object({
  page: v4.z.coerce.number().optional().default(0),
  perPage: v4.z.coerce.number().optional().default(10)
});
var listItemsQuerySchema = v4.z.object({
  page: v4.z.coerce.number().optional().default(0),
  perPage: v4.z.coerce.number().optional().default(10),
  version: v4.z.coerce.number().int().optional(),
  // Optional version filter for snapshot semantics
  search: v4.z.string().optional()
});
var createDatasetBodySchema = v4.z.object({
  name: v4.z.string().describe("Name of the dataset"),
  description: v4.z.string().optional().describe("Description of the dataset"),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional().describe("Additional metadata"),
  inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
  groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
  requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
  targetType: v4.z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
  targetIds: v4.z.array(v4.z.string()).optional().describe("IDs of target entities this dataset is attached to"),
  scorerIds: v4.z.array(v4.z.string()).optional().describe("IDs of scorers attached to this dataset")
});
var updateDatasetBodySchema = v4.z.object({
  name: v4.z.string().optional().describe("Name of the dataset"),
  description: v4.z.string().optional().describe("Description of the dataset"),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional().describe("Additional metadata"),
  inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
  groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
  requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
  tags: v4.z.array(v4.z.string()).optional().describe("Tag definitions for categorizing experiment results"),
  targetType: v4.z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
  targetIds: v4.z.array(v4.z.string()).optional().describe("IDs of target entities this dataset is attached to"),
  scorerIds: v4.z.array(v4.z.string()).optional().nullable().describe("IDs of scorers attached to this dataset")
});
var addItemBodySchema = v4.z.object({
  input: v4.z.unknown().describe("Input data for the dataset item"),
  groundTruth: v4.z.unknown().optional().describe("Expected output for comparison"),
  expectedTrajectory: trajectoryExpectationSchema,
  requestContext: v4.z.record(v4.z.string(), v4.z.unknown()).optional().describe("Request context preset for this item"),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional().describe("Additional metadata"),
  source: datasetItemSourceSchema
});
var updateItemBodySchema = v4.z.object({
  input: v4.z.unknown().optional().describe("Input data for the dataset item"),
  groundTruth: v4.z.unknown().optional().describe("Expected output for comparison"),
  expectedTrajectory: trajectoryExpectationSchema,
  requestContext: v4.z.record(v4.z.string(), v4.z.unknown()).optional().describe("Request context preset for this item"),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional().describe("Additional metadata"),
  source: datasetItemSourceSchema
});
var triggerExperimentBodySchema = v4.z.object({
  targetType: v4.z.enum(["agent", "workflow", "scorer"]).describe("Type of target to run against"),
  targetId: v4.z.string().describe("ID of the target"),
  scorerIds: v4.z.array(v4.z.string()).optional().describe("IDs of scorers to apply"),
  version: v4.z.coerce.number().int().optional().describe("Pin to specific dataset version"),
  agentVersion: v4.z.string().optional().describe("Agent version ID to use for experiment"),
  maxConcurrency: v4.z.number().optional().describe("Maximum concurrent executions"),
  requestContext: v4.z.record(v4.z.string(), v4.z.unknown()).optional().describe("Global request context passed to the target"),
  versions: v4.z.object({
    agents: v4.z.record(
      v4.z.string(),
      v4.z.union([v4.z.object({ versionId: v4.z.string() }), v4.z.object({ status: v4.z.enum(["draft", "published"]) })])
    ).optional(),
    defaultStatus: v4.z.enum(["draft", "published"]).optional()
  }).optional().describe("Version overrides for sub-agent delegation during experiment execution")
});
var compareExperimentsBodySchema = v4.z.object({
  experimentIdA: v4.z.string().describe("ID of baseline experiment"),
  experimentIdB: v4.z.string().describe("ID of candidate experiment")
});
var datasetResponseSchema = v4.z.object({
  id: v4.z.string(),
  name: v4.z.string(),
  description: v4.z.string().optional().nullable(),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional().nullable(),
  inputSchema: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  groundTruthSchema: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  requestContextSchema: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  tags: v4.z.array(v4.z.string()).optional().nullable(),
  targetType: v4.z.string().optional().nullable(),
  targetIds: v4.z.array(v4.z.string()).optional().nullable(),
  scorerIds: v4.z.array(v4.z.string()).optional().nullable(),
  version: v4.z.number().int(),
  createdAt: v4.z.coerce.date(),
  updatedAt: v4.z.coerce.date()
});
var datasetItemResponseSchema = v4.z.object({
  id: v4.z.string(),
  datasetId: v4.z.string(),
  datasetVersion: v4.z.number().int(),
  input: v4.z.unknown(),
  groundTruth: v4.z.unknown().optional(),
  expectedTrajectory: v4.z.unknown().optional(),
  requestContext: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  source: datasetItemSourceSchema,
  createdAt: v4.z.coerce.date(),
  updatedAt: v4.z.coerce.date()
});
var experimentResponseSchema = v4.z.object({
  id: v4.z.string(),
  datasetId: v4.z.string().nullable(),
  datasetVersion: v4.z.number().int().nullable(),
  agentVersion: v4.z.string().nullable().optional(),
  targetType: v4.z.enum(["agent", "workflow", "scorer", "processor"]),
  targetId: v4.z.string(),
  name: v4.z.string().optional(),
  description: v4.z.string().optional(),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  status: v4.z.enum(["pending", "running", "completed", "failed"]),
  totalItems: v4.z.number(),
  succeededCount: v4.z.number(),
  failedCount: v4.z.number(),
  skippedCount: v4.z.number(),
  startedAt: v4.z.coerce.date().nullable(),
  completedAt: v4.z.coerce.date().nullable(),
  createdAt: v4.z.coerce.date(),
  updatedAt: v4.z.coerce.date()
});
var scorerResultSchema = v4.z.object({
  scorerId: v4.z.string(),
  scorerName: v4.z.string(),
  score: v4.z.number().nullable(),
  reason: v4.z.string().nullable(),
  error: v4.z.string().nullable()
});
var experimentResultResponseSchema = v4.z.object({
  id: v4.z.string(),
  experimentId: v4.z.string(),
  itemId: v4.z.string(),
  itemDatasetVersion: v4.z.number().int().nullable(),
  input: v4.z.unknown(),
  output: v4.z.unknown().nullable(),
  groundTruth: v4.z.unknown().nullable(),
  expectedTrajectory: v4.z.unknown().optional(),
  error: v4.z.object({
    message: v4.z.string(),
    stack: v4.z.string().optional(),
    code: v4.z.string().optional()
  }).nullable(),
  startedAt: v4.z.coerce.date(),
  completedAt: v4.z.coerce.date(),
  retryCount: v4.z.number(),
  traceId: v4.z.string().nullable(),
  status: v4.z.enum(["needs-review", "reviewed", "complete"]).nullable().optional(),
  tags: v4.z.array(v4.z.string()).nullable().optional(),
  createdAt: v4.z.coerce.date()
});
var updateExperimentResultBodySchema = v4.z.object({
  status: v4.z.enum(["needs-review", "reviewed", "complete"]).nullable().optional(),
  tags: v4.z.array(v4.z.string()).optional()
});
var comparisonItemSchema = v4.z.object({
  itemId: v4.z.string(),
  input: v4.z.unknown().nullable(),
  groundTruth: v4.z.unknown().nullable(),
  results: v4.z.record(
    v4.z.string(),
    v4.z.object({
      output: v4.z.unknown().nullable(),
      scores: v4.z.record(v4.z.string(), v4.z.number().nullable())
    }).nullable()
  )
});
var comparisonResponseSchema = v4.z.object({
  baselineId: v4.z.string(),
  items: v4.z.array(comparisonItemSchema)
});
var experimentSummaryResponseSchema = v4.z.object({
  experimentId: v4.z.string(),
  status: v4.z.enum(["pending", "running", "completed", "failed"]),
  totalItems: v4.z.number(),
  succeededCount: v4.z.number(),
  failedCount: v4.z.number(),
  startedAt: v4.z.coerce.date(),
  completedAt: v4.z.coerce.date().nullable(),
  results: v4.z.array(
    v4.z.object({
      itemId: v4.z.string(),
      itemDatasetVersion: v4.z.number().int().nullable(),
      input: v4.z.unknown(),
      output: v4.z.unknown().nullable(),
      groundTruth: v4.z.unknown().nullable(),
      error: v4.z.string().nullable(),
      startedAt: v4.z.coerce.date(),
      completedAt: v4.z.coerce.date(),
      retryCount: v4.z.number(),
      scores: v4.z.array(
        v4.z.object({
          scorerId: v4.z.string(),
          scorerName: v4.z.string(),
          score: v4.z.number().nullable(),
          reason: v4.z.string().nullable(),
          error: v4.z.string().nullable()
        })
      )
    })
  )
});
var listDatasetsResponseSchema = v4.z.object({
  datasets: v4.z.array(datasetResponseSchema),
  pagination: chunkDIG2K5CV_cjs.paginationInfoSchema
});
var listItemsResponseSchema = v4.z.object({
  items: v4.z.array(datasetItemResponseSchema),
  pagination: chunkDIG2K5CV_cjs.paginationInfoSchema
});
var listExperimentsResponseSchema = v4.z.object({
  experiments: v4.z.array(experimentResponseSchema),
  pagination: chunkDIG2K5CV_cjs.paginationInfoSchema
});
var listExperimentResultsResponseSchema = v4.z.object({
  results: v4.z.array(experimentResultResponseSchema),
  pagination: chunkDIG2K5CV_cjs.paginationInfoSchema
});
var experimentReviewCountsSchema = v4.z.object({
  experimentId: v4.z.string(),
  total: v4.z.number().int(),
  needsReview: v4.z.number().int(),
  reviewed: v4.z.number().int(),
  complete: v4.z.number().int()
});
var reviewSummaryResponseSchema = v4.z.object({
  counts: v4.z.array(experimentReviewCountsSchema)
});
var datasetItemVersionPathParams = v4.z.object({
  datasetId: v4.z.string().describe("Unique identifier for the dataset"),
  itemId: v4.z.string().describe("Unique identifier for the dataset item"),
  datasetVersion: v4.z.coerce.number().int().describe("Dataset version number")
});
var itemVersionResponseSchema = v4.z.object({
  id: v4.z.string(),
  datasetId: v4.z.string(),
  datasetVersion: v4.z.number().int(),
  input: v4.z.unknown(),
  groundTruth: v4.z.unknown().optional(),
  expectedTrajectory: v4.z.unknown().optional(),
  metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
  validTo: v4.z.number().int().nullable(),
  isDeleted: v4.z.boolean(),
  createdAt: v4.z.coerce.date(),
  updatedAt: v4.z.coerce.date()
});
var listItemVersionsResponseSchema = v4.z.object({
  history: v4.z.array(itemVersionResponseSchema)
});
var datasetVersionResponseSchema = v4.z.object({
  id: v4.z.string(),
  datasetId: v4.z.string(),
  version: v4.z.number().int(),
  createdAt: v4.z.coerce.date()
});
var listDatasetVersionsResponseSchema = v4.z.object({
  versions: v4.z.array(datasetVersionResponseSchema),
  pagination: chunkDIG2K5CV_cjs.paginationInfoSchema
});
var batchInsertItemsBodySchema = v4.z.object({
  items: v4.z.array(
    v4.z.object({
      input: v4.z.unknown(),
      groundTruth: v4.z.unknown().optional(),
      expectedTrajectory: trajectoryExpectationSchema,
      requestContext: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
      metadata: v4.z.record(v4.z.string(), v4.z.unknown()).optional(),
      source: datasetItemSourceSchema
    })
  )
});
var batchInsertItemsResponseSchema = v4.z.object({
  items: v4.z.array(datasetItemResponseSchema),
  count: v4.z.number()
});
var batchDeleteItemsBodySchema = v4.z.object({
  itemIds: v4.z.array(v4.z.string())
});
var batchDeleteItemsResponseSchema = v4.z.object({
  success: v4.z.boolean(),
  deletedCount: v4.z.number()
});
var generateItemsBodySchema = v4.z.object({
  modelId: v4.z.string().describe('Model identifier in "provider/model" format (e.g., "openai/gpt-4o")'),
  prompt: v4.z.string().describe("Description of the kind of test data to generate"),
  count: v4.z.number().int().min(1).max(50).default(5).describe("Number of items to generate"),
  agentContext: v4.z.object({
    description: v4.z.string().optional(),
    instructions: v4.z.string().optional(),
    tools: v4.z.array(v4.z.string()).optional()
  }).optional().describe("Context about the agent to generate relevant test data")
});
var generatedItemSchema = v4.z.object({
  input: v4.z.unknown(),
  groundTruth: v4.z.unknown().optional()
});
var generateItemsResponseSchema = v4.z.object({
  items: v4.z.array(generatedItemSchema)
});
var clusterFailuresBodySchema = v4.z.object({
  modelId: v4.z.string().describe('Model identifier in "provider/model" format (e.g., "openai/gpt-4o")'),
  items: v4.z.array(
    v4.z.object({
      id: v4.z.string(),
      input: v4.z.unknown(),
      output: v4.z.unknown().optional(),
      error: v4.z.string().optional(),
      scores: v4.z.record(v4.z.string(), v4.z.number()).optional(),
      existingTags: v4.z.array(v4.z.string()).optional().describe("Tags already applied to this item")
    })
  ).min(1).max(200).describe("Failure items to cluster"),
  availableTags: v4.z.array(v4.z.string()).optional().describe("Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable."),
  prompt: v4.z.string().optional().describe('Optional user instructions to guide the analysis (e.g., "focus on tool usage failures")')
});
var failureClusterSchema = v4.z.object({
  id: v4.z.string().describe("A unique cluster identifier"),
  label: v4.z.string().describe("Short label for this failure pattern"),
  description: v4.z.string().describe("Description of the common pattern"),
  itemIds: v4.z.array(v4.z.string()).describe("IDs of items belonging to this cluster")
});
var clusterFailuresResponseSchema = v4.z.object({
  clusters: v4.z.array(failureClusterSchema),
  /** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */
  proposedTags: v4.z.array(
    v4.z.object({
      itemId: v4.z.string(),
      tags: v4.z.array(v4.z.string()),
      reason: v4.z.string().describe("Brief explanation of why these tags were assigned to this item")
    })
  ).optional()
});

exports.addItemBodySchema = addItemBodySchema;
exports.batchDeleteItemsBodySchema = batchDeleteItemsBodySchema;
exports.batchDeleteItemsResponseSchema = batchDeleteItemsResponseSchema;
exports.batchInsertItemsBodySchema = batchInsertItemsBodySchema;
exports.batchInsertItemsResponseSchema = batchInsertItemsResponseSchema;
exports.clusterFailuresBodySchema = clusterFailuresBodySchema;
exports.clusterFailuresResponseSchema = clusterFailuresResponseSchema;
exports.compareExperimentsBodySchema = compareExperimentsBodySchema;
exports.comparisonResponseSchema = comparisonResponseSchema;
exports.createDatasetBodySchema = createDatasetBodySchema;
exports.datasetAndExperimentIdPathParams = datasetAndExperimentIdPathParams;
exports.datasetAndItemIdPathParams = datasetAndItemIdPathParams;
exports.datasetIdPathParams = datasetIdPathParams;
exports.datasetItemResponseSchema = datasetItemResponseSchema;
exports.datasetItemVersionPathParams = datasetItemVersionPathParams;
exports.datasetResponseSchema = datasetResponseSchema;
exports.datasetVersionResponseSchema = datasetVersionResponseSchema;
exports.experimentIdPathParams = experimentIdPathParams;
exports.experimentResponseSchema = experimentResponseSchema;
exports.experimentResultIdPathParams = experimentResultIdPathParams;
exports.experimentResultResponseSchema = experimentResultResponseSchema;
exports.experimentReviewCountsSchema = experimentReviewCountsSchema;
exports.experimentSummaryResponseSchema = experimentSummaryResponseSchema;
exports.generateItemsBodySchema = generateItemsBodySchema;
exports.generateItemsResponseSchema = generateItemsResponseSchema;
exports.itemIdPathParams = itemIdPathParams;
exports.itemVersionResponseSchema = itemVersionResponseSchema;
exports.listDatasetVersionsResponseSchema = listDatasetVersionsResponseSchema;
exports.listDatasetsResponseSchema = listDatasetsResponseSchema;
exports.listExperimentResultsResponseSchema = listExperimentResultsResponseSchema;
exports.listExperimentsResponseSchema = listExperimentsResponseSchema;
exports.listItemVersionsResponseSchema = listItemVersionsResponseSchema;
exports.listItemsQuerySchema = listItemsQuerySchema;
exports.listItemsResponseSchema = listItemsResponseSchema;
exports.paginationQuerySchema = paginationQuerySchema;
exports.reviewSummaryResponseSchema = reviewSummaryResponseSchema;
exports.scorerResultSchema = scorerResultSchema;
exports.triggerExperimentBodySchema = triggerExperimentBodySchema;
exports.updateDatasetBodySchema = updateDatasetBodySchema;
exports.updateExperimentResultBodySchema = updateExperimentResultBodySchema;
exports.updateItemBodySchema = updateItemBodySchema;
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