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The SDK’s public types are Zod schemas, exported from @gaussia/sdk/schemas. Each schema is a single source of truth: it validates values at runtime, and its TypeScript type is inferred from the schema rather than hand-written. Build values through the schemas so they are validated as you create them. 
import { Batch, Dataset } from "@gaussia/sdk/schemas";

const batch = Batch.parse({
  qaId: "q-1",
  query: "Say hello",
  assistant: "Hello!",
  groundTruthAssistant: "Hello!",
});
Batch.parse returns a validated, fully typed value and throws on invalid input.

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Core data model

These describe the evaluation input consumed by the Gaussia base class.
  • Dataset — one evaluation session: sessionId, assistantId, context, a conversation of Batch[], and an optional language.
  • Batch — one query/answer unit: qaId, query, assistant, groundTruthAssistant, and an optional weight.
  • IterationLevel — full_dataset, stream_sessions, or stream_batches; selects how a retriever’s data is consumed.
  • SessionMetadata and StreamedBatch — the streaming shapes used by the streaming iteration levels.
  • Logprobs — token log-probability data passed through evaluation.

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Generator schemas

These describe the structured output of generators.
  • Chunk — a unit of context produced by a loader.
  • GeneratedQuery and GeneratedQueriesOutput — single-turn generation output.
  • ConversationTurn and GeneratedConversationOutput — multi-turn generation output.

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Prompt-optimizer schemas

These describe the result of GEPA optimization.
  • OptimizationResult — the final result: optimized prompt, initial and final scores, and history.
  • IterationResult and CandidateResult — per-iteration detail in the history.
  • FailingExample — an example that scored below threshold and drove candidate generation.

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Why Zod

Deriving types from schemas keeps the static type and the runtime check in lockstep. Structured model output uses the same approach: generateObject on a LanguageModel validates the model’s response against the Zod schema you pass.