Add toolchain from agentic system here

toolchain/dataset/api/__init__.py

@@ -0,0 +1,2 @@
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

toolchain/dataset/api/datatypes.py

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+from enum import Enum
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel
+
+from strong_typing.schema import json_schema_type
+from models.llama3.datatypes import URL
+
+
+@json_schema_type
+class TrainEvalDatasetColumnType(Enum):
+    dialog = "dialog"
+    text = "text"
+    media = "media"
+    number = "number"
+    json = "json"
+
+
+@json_schema_type
+class TrainEvalDataset(BaseModel):
+    """Dataset to be used for training or evaluating language models."""
+
+    # TODO(ashwin): figure out if we need to add an enum for a "dataset type"
+
+    columns: Dict[str, TrainEvalDatasetColumnType]
+    content_url: URL
+    metadata: Optional[Dict[str, Any]] = None

toolchain/dataset/api/endpoints.py

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+from typing import Protocol
+
+from pydantic import BaseModel
+
+from pyopenapi import webmethod
+from strong_typing.schema import json_schema_type
+
+from .datatypes import *  # noqa: F403
+
+
+@json_schema_type
+class CreateDatasetRequest(BaseModel):
+    """Request to create a dataset."""
+
+    uuid: str
+    dataset: TrainEvalDataset
+
+
+class Datasets(Protocol):
+    @webmethod(route="/datasets/create")
+    def create_dataset(
+        self,
+        request: CreateDatasetRequest,
+    ) -> None: ...
+
+    @webmethod(route="/datasets/get")
+    def get_dataset(
+        self,
+        dataset_uuid: str,
+    ) -> TrainEvalDataset: ...
+
+    @webmethod(route="/datasets/delete")
+    def delete_dataset(
+        self,
+        dataset_uuid: str,
+    ) -> None: ...