feat(dataset api): (1.4/n) fix resolver signature mismatch (#1658)

# What does this PR do?
- fix datasets api signature mis-match so that llama stack run can start

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
```
llama stack run
```
<img width="626" alt="image"
src="https://github.com/user-attachments/assets/59072d1a-ccb6-453a-80e8-d87419896c41"
/>


[//]: # (## Documentation)
This commit is contained in:
Xi Yan 2025-03-15 14:56:11 -07:00 committed by GitHub
parent 72ccdc19a8
commit 2c9d624910
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10 changed files with 105 additions and 80 deletions

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@ -12,7 +12,8 @@ from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.datasetio import DatasetIO, PaginatedRowsResult
from llama_stack.apis.datasetio import DatasetIO, IterrowsResponse
from llama_stack.apis.datasets import DatasetPurpose, DataSource
from llama_stack.apis.eval import (
BenchmarkConfig,
Eval,
@ -160,7 +161,11 @@ class InferenceRouter(Inference):
await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type)
def _construct_metrics(
self, prompt_tokens: int, completion_tokens: int, total_tokens: int, model: Model
self,
prompt_tokens: int,
completion_tokens: int,
total_tokens: int,
model: Model,
) -> List[MetricEvent]:
"""Constructs a list of MetricEvent objects containing token usage metrics.
@ -298,7 +303,12 @@ class InferenceRouter(Inference):
completion_text += chunk.event.delta.text
if chunk.event.event_type == ChatCompletionResponseEventType.complete:
completion_tokens = await self._count_tokens(
[CompletionMessage(content=completion_text, stop_reason=StopReason.end_of_turn)],
[
CompletionMessage(
content=completion_text,
stop_reason=StopReason.end_of_turn,
)
],
tool_config.tool_prompt_format,
)
total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
@ -471,21 +481,36 @@ class DatasetIORouter(DatasetIO):
logger.debug("DatasetIORouter.shutdown")
pass
async def get_rows_paginated(
async def register_dataset(
self,
purpose: DatasetPurpose,
source: DataSource,
metadata: Optional[Dict[str, Any]] = None,
dataset_id: Optional[str] = None,
) -> None:
logger.debug(
f"DatasetIORouter.register_dataset: {purpose=} {source=} {metadata=} {dataset_id=}",
)
await self.routing_table.register_dataset(
purpose=purpose,
source=source,
metadata=metadata,
dataset_id=dataset_id,
)
async def iterrows(
self,
dataset_id: str,
rows_in_page: int,
page_token: Optional[str] = None,
filter_condition: Optional[str] = None,
) -> PaginatedRowsResult:
start_index: Optional[int] = None,
limit: Optional[int] = None,
) -> IterrowsResponse:
logger.debug(
f"DatasetIORouter.get_rows_paginated: {dataset_id}, rows_in_page={rows_in_page}",
f"DatasetIORouter.iterrows: {dataset_id}, {start_index=} {limit=}",
)
return await self.routing_table.get_provider_impl(dataset_id).get_rows_paginated(
return await self.routing_table.get_provider_impl(dataset_id).iterrows(
dataset_id=dataset_id,
rows_in_page=rows_in_page,
page_token=page_token,
filter_condition=filter_condition,
start_index=start_index,
limit=limit,
)
async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: