unsloth/studio/backend/core/data_recipe/service.py
Shine1i f84454ecb8 feat: refactor and extend recipe execution logic
- Extracted shared execution utilities into `execution-helpers.ts` for reusability across features.
- Replaced deprecated `/preview` endpoint and its logic with unified job execution handling.
- Consolidated job execution flows ("Preview" and "Full Run") into shared `runJobExecution` logic.
- Enhanced execution progress tracking with support for column-level progress reporting.
- Added support for handling execution job events and improved error reporting from the backend.
- Updated backend to better manage dataset access errors and provide more informative error messages.
- Cleaned up redundant code in `use-recipe-studio-actions` and streamlined execution APIs.
2026-02-20 12:47:51 +01:00

157 lines
5 KiB
Python

from __future__ import annotations
import os
from typing import Any
def _to_jsonable(value: Any) -> Any:
# pydantic/fastapi can't serialize numpy arrays/scalars.
try:
import numpy as np # type: ignore
except Exception: # pragma: no cover
np = None # type: ignore
if np is not None:
if isinstance(value, np.ndarray):
return value.tolist()
if isinstance(value, np.generic):
return value.item()
if isinstance(value, dict):
return {str(k): _to_jsonable(v) for k, v in value.items()}
if isinstance(value, (list, tuple, set)):
return [_to_jsonable(v) for v in value]
# pandas Timestamp/date-like
if hasattr(value, "isoformat") and callable(value.isoformat):
try:
return value.isoformat()
except Exception:
pass
return value
def build_model_providers(recipe: dict[str, Any]):
from data_designer.config.default_model_settings import get_default_providers
from data_designer.config.models import ModelProvider
providers: list[ModelProvider] = []
for provider in recipe.get("model_providers", []):
api_key = provider.get("api_key")
api_key_env = provider.get("api_key_env")
if not api_key and api_key_env:
api_key = os.getenv(api_key_env)
providers.append(
ModelProvider(
name=provider["name"],
endpoint=provider["endpoint"],
provider_type=provider.get("provider_type", "openai"),
api_key=api_key,
extra_headers=provider.get("extra_headers"),
extra_body=provider.get("extra_body"),
)
)
# DataDesigner currently expects at least one provider even if they only use static samplers,
# but it's fine it gives a warning only.
return providers or get_default_providers()
def build_mcp_providers(
recipe: dict[str, Any],
) -> list:
from data_designer.config.mcp import LocalStdioMCPProvider, MCPProvider
providers: list[MCPProvider | LocalStdioMCPProvider] = []
for provider in recipe.get("mcp_providers", []):
if not isinstance(provider, dict):
continue
provider_type = provider.get("provider_type")
if provider_type == "stdio":
env = provider.get("env")
if not isinstance(env, dict):
env = {}
args = provider.get("args")
if not isinstance(args, list):
args = []
providers.append(
LocalStdioMCPProvider(
name=str(provider.get("name", "")),
command=str(provider.get("command", "")),
args=[str(value) for value in args],
env={str(key): str(value) for key, value in env.items()},
)
)
continue
if provider_type in {"sse", "streamable_http"}:
api_key = provider.get("api_key")
api_key_env = provider.get("api_key_env")
if not api_key and api_key_env:
api_key = os.getenv(str(api_key_env))
providers.append(
MCPProvider(
name=str(provider.get("name", "")),
endpoint=str(provider.get("endpoint", "")),
api_key=str(api_key) if api_key else None,
)
)
return providers
def build_config_builder(recipe: dict[str, Any]):
from data_designer.config import DataDesignerConfigBuilder
recipe_core = {
key: value
for key, value in recipe.items()
if key not in {"model_providers", "mcp_providers"}
}
return DataDesignerConfigBuilder.from_config({"data_designer": recipe_core})
def create_data_designer(
recipe: dict[str, Any],
*,
artifact_path: str | None = None,
):
from data_designer.interface.data_designer import DataDesigner
return DataDesigner(
artifact_path=artifact_path,
model_providers=build_model_providers(recipe),
mcp_providers=build_mcp_providers(recipe),
)
def validate_recipe(recipe: dict[str, Any]) -> None:
builder = build_config_builder(recipe)
designer = create_data_designer(recipe)
designer.validate(builder)
def preview_recipe(
recipe: dict[str, Any],
num_records: int,
) -> tuple[list[dict[str, Any]], dict[str, Any] | None, dict[str, Any] | None]:
builder = build_config_builder(recipe)
designer = create_data_designer(recipe)
results = designer.preview(builder, num_records=num_records)
dataset: list[dict[str, Any]] = []
if results.dataset is not None:
raw_rows = results.dataset.to_dict(orient="records")
dataset = [_to_jsonable(row) for row in raw_rows]
artifacts = (
None
if results.processor_artifacts is None
else _to_jsonable(results.processor_artifacts)
)
analysis = (
None
if results.analysis is None
else _to_jsonable(results.analysis.model_dump(mode="json"))
)
return dataset, artifacts, analysis