8292e699e4Trim and tighten code comments and docstrings across studio/ Python. Comment-only: every changed file verified code-identical to main via AST/token comparison.
142 lines
4.6 KiB
Python
142 lines
4.6 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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"""Pydantic schemas for Data Recipe (DataDesigner) API."""
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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, model_validator
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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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# Legacy single-file flow (mutually exclusive with file_ids)
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filename: str | None = None
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content_base64: str | None = None
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# Multi-file flow (mutually exclusive with content_base64)
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block_id: str | None = None
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file_ids: list[str] | None = None
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file_names: list[str] | None = None
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# Shared fields
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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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@model_validator(mode = "after")
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def _check_mutual_exclusivity(self) -> "SeedInspectUploadRequest":
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has_legacy = self.content_base64 is not None
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has_multi = self.file_ids is not None
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if has_legacy and has_multi:
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raise ValueError("Provide either content_base64 or file_ids, not both")
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if not has_legacy and not has_multi:
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raise ValueError("Provide either content_base64 or file_ids")
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if has_multi:
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if len(self.file_ids) == 0:
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raise ValueError("file_ids must not be empty")
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if not self.block_id:
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raise ValueError("block_id is required when using file_ids")
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if self.file_names is None or len(self.file_ids) != len(self.file_names):
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raise ValueError("file_names must be provided and same length as file_ids")
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if has_legacy:
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if not self.filename:
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raise ValueError("filename is required when using content_base64")
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return self
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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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resolved_paths: list[str] | None = None
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class UnstructuredFileUploadResponse(BaseModel):
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file_id: str
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filename: str
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size_bytes: int
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status: str # "ok" or "error"
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error: 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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