e280b0bebc* miscallenous studio * chore: upload dataset misc * chore: redudancy studio cleanup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: adress the pr comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: adress comments about recipes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Pydantic schemas for Data Recipe (DataDesigner) API.
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"""
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from __future__ import annotations
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from typing import Any
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from pydantic import BaseModel, Field
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class RecipePayload(BaseModel):
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recipe: dict[str, Any] = Field(default_factory = dict)
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run: dict[str, Any] | None = None
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ui: dict[str, Any] | None = None
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class PreviewResponse(BaseModel):
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dataset: list[dict[str, Any]] = Field(default_factory = list)
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processor_artifacts: dict[str, Any] | None = None
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analysis: dict[str, Any] | None = None
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class ValidateError(BaseModel):
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message: str
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path: str | None = None
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code: str | None = None
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class ValidateResponse(BaseModel):
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valid: bool
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errors: list[ValidateError] = Field(default_factory = list)
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raw_detail: str | None = None
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class JobCreateResponse(BaseModel):
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job_id: str
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class PublishDatasetRequest(BaseModel):
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repo_id: str = Field(min_length = 3, description = "Hugging Face dataset repo ID")
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description: str = Field(
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min_length = 1,
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max_length = 4000,
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description = "Short dataset description for the dataset card",
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)
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hf_token: str | None = Field(
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default = None,
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description = "Optional Hugging Face token for private or write-protected repos",
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)
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private: bool = Field(
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default = False,
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description = "Create or update the dataset repo as private",
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)
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artifact_path: str | None = Field(
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default = None,
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description = "Execution artifact path captured by the UI for completed runs",
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)
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class PublishDatasetResponse(BaseModel):
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success: bool = True
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url: str
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message: str
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class SeedInspectRequest(BaseModel):
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dataset_name: str = Field(min_length = 1)
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hf_token: str | None = None
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subset: str | None = None
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split: str | None = "train"
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preview_size: int = Field(default = 10, ge = 1, le = 50)
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class SeedInspectUploadRequest(BaseModel):
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filename: str = Field(min_length = 1)
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content_base64: str = Field(min_length = 1)
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preview_size: int = Field(default = 10, ge = 1, le = 50)
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seed_source_type: str | None = None
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unstructured_chunk_size: int | None = Field(default = None, ge = 1, le = 20000)
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unstructured_chunk_overlap: int | None = Field(default = None, ge = 0, le = 20000)
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class SeedInspectResponse(BaseModel):
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dataset_name: str
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resolved_path: str
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columns: list[str] = Field(default_factory = list)
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preview_rows: list[dict[str, Any]] = Field(default_factory = list)
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split: str | None = None
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subset: str | None = None
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class McpToolsListRequest(BaseModel):
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mcp_providers: list[dict[str, Any]] = Field(default_factory = list)
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timeout_sec: float | None = Field(default = None, gt = 0)
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class McpToolsProviderResult(BaseModel):
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name: str
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tools: list[str] = Field(default_factory = list)
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error: str | None = None
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class McpToolsListResponse(BaseModel):
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providers: list[McpToolsProviderResult] = Field(default_factory = list)
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duplicate_tools: dict[str, list[str]] = Field(default_factory = dict)
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