forked from phoenix-oss/llama-stack-mirror
159 lines
4.3 KiB
Python
159 lines
4.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from enum import Enum
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from typing import Annotated, Any, Dict, List, Literal, Optional, Protocol, Union
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from pydantic import BaseModel, Field
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from llama_stack.apis.resource import Resource, ResourceType
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from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
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class DatasetPurpose(Enum):
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"""
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Purpose of the dataset. Each type has a different column format.
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:cvar tuning/messages: The dataset contains messages used for post-training. Examples:
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{
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"messages": [
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{"role": "user", "content": "Hello, world!"},
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{"role": "assistant", "content": "Hello, world!"},
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]
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}
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"""
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tuning_messages = "tuning/messages"
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# TODO: add more schemas here
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class DatasetType(Enum):
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huggingface = "huggingface"
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uri = "uri"
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rows = "rows"
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@json_schema_type
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class URIDataSource(BaseModel):
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type: Literal["uri"] = "uri"
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uri: str
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@json_schema_type
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class HuggingfaceDataSource(BaseModel):
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type: Literal["huggingface"] = "huggingface"
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dataset_path: str
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params: Dict[str, Any]
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@json_schema_type
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class RowsDataSource(BaseModel):
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type: Literal["rows"] = "rows"
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rows: List[Dict[str, Any]]
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DataSource = register_schema(
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Annotated[
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Union[URIDataSource, HuggingfaceDataSource, RowsDataSource],
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Field(discriminator="type"),
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],
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name="DataSource",
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)
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class CommonDatasetFields(BaseModel):
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schema: Schema
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data_source: DataSource
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metadata: Dict[str, Any] = Field(
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default_factory=dict,
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description="Any additional metadata for this dataset",
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)
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@json_schema_type
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class Dataset(CommonDatasetFields, Resource):
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type: Literal[ResourceType.dataset.value] = ResourceType.dataset.value
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@property
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def dataset_id(self) -> str:
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return self.identifier
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@property
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def provider_dataset_id(self) -> str:
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return self.provider_resource_id
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class DatasetInput(CommonDatasetFields, BaseModel):
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dataset_id: str
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provider_id: Optional[str] = None
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provider_dataset_id: Optional[str] = None
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class ListDatasetsResponse(BaseModel):
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data: List[Dataset]
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class Datasets(Protocol):
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@webmethod(route="/datasets", method="POST")
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async def register_dataset(
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self,
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purpose: DatasetPurpose,
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source: DataSource,
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metadata: Optional[Dict[str, Any]] = None,
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dataset_id: Optional[str] = None,
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) -> Dataset:
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"""
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Register a new dataset.
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:param schema: The schema format of the dataset. One of
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- messages: The dataset contains a messages column with list of messages for post-training.
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:param data_source: The data source of the dataset. Examples:
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- {
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"type": "uri",
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"uri": "https://mywebsite.com/mydata.jsonl"
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}
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- {
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"type": "uri",
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"uri": "lsfs://mydata.jsonl"
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}
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- {
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"type": "huggingface",
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"dataset_path": "tatsu-lab/alpaca",
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"params": {
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"split": "train"
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}
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}
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- {
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"type": "rows",
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"rows": [
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{
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"messages": [
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{"role": "user", "content": "Hello, world!"},
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{"role": "assistant", "content": "Hello, world!"},
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]
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}
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]
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}
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:param metadata: The metadata for the dataset.
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- E.g. {"description": "My dataset"}
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:param dataset_id: The ID of the dataset. If not provided, a random ID will be generated.
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"""
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...
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@webmethod(route="/datasets/{dataset_id:path}", method="GET")
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async def get_dataset(
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self,
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dataset_id: str,
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) -> Optional[Dataset]: ...
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@webmethod(route="/datasets", method="GET")
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async def list_datasets(self) -> ListDatasetsResponse: ...
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@webmethod(route="/datasets/{dataset_id:path}", method="DELETE")
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async def unregister_dataset(
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self,
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dataset_id: str,
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) -> None: ...
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