mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-08-07 11:08:20 +00:00
types
This commit is contained in:
Xi Yan
parent
72ccdc19a8
commit
917679cc2f
3 changed files with 139 additions and 49 deletions
|
|
@ -8,11 +8,11 @@ import time
|
|||
from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union
|
||||
|
||||
from llama_stack.apis.common.content_types import (
|
||||
URL,
|
||||
InterleavedContent,
|
||||
InterleavedContentItem,
|
||||
URL,
|
||||
)
|
||||
from llama_stack.apis.datasetio import DatasetIO, PaginatedRowsResult
|
||||
from llama_stack.apis.datasetio import DatasetIO, IterrowsResponse
|
||||
from llama_stack.apis.eval import (
|
||||
BenchmarkConfig,
|
||||
Eval,
|
||||
|
|
@ -93,7 +93,9 @@ class VectorIORouter(VectorIO):
|
|||
provider_id: Optional[str] = None,
|
||||
provider_vector_db_id: Optional[str] = None,
|
||||
) -> None:
|
||||
logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
|
||||
logger.debug(
|
||||
f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}"
|
||||
)
|
||||
await self.routing_table.register_vector_db(
|
||||
vector_db_id,
|
||||
embedding_model,
|
||||
|
|
@ -111,7 +113,9 @@ class VectorIORouter(VectorIO):
|
|||
logger.debug(
|
||||
f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}",
|
||||
)
|
||||
return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(vector_db_id, chunks, ttl_seconds)
|
||||
return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(
|
||||
vector_db_id, chunks, ttl_seconds
|
||||
)
|
||||
|
||||
async def query_chunks(
|
||||
self,
|
||||
|
|
@ -120,7 +124,9 @@ class VectorIORouter(VectorIO):
|
|||
params: Optional[Dict[str, Any]] = None,
|
||||
) -> QueryChunksResponse:
|
||||
logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}")
|
||||
return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)
|
||||
return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(
|
||||
vector_db_id, query, params
|
||||
)
|
||||
|
||||
|
||||
class InferenceRouter(Inference):
|
||||
|
|
@ -157,10 +163,16 @@ class InferenceRouter(Inference):
|
|||
logger.debug(
|
||||
f"InferenceRouter.register_model: {model_id=} {provider_model_id=} {provider_id=} {metadata=} {model_type=}",
|
||||
)
|
||||
await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type)
|
||||
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.
|
||||
|
||||
|
|
@ -207,11 +219,16 @@ class InferenceRouter(Inference):
|
|||
total_tokens: int,
|
||||
model: Model,
|
||||
) -> List[MetricInResponse]:
|
||||
metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model)
|
||||
metrics = self._construct_metrics(
|
||||
prompt_tokens, completion_tokens, total_tokens, model
|
||||
)
|
||||
if self.telemetry:
|
||||
for metric in metrics:
|
||||
await self.telemetry.log_event(metric)
|
||||
return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics]
|
||||
return [
|
||||
MetricInResponse(metric=metric.metric, value=metric.value)
|
||||
for metric in metrics
|
||||
]
|
||||
|
||||
async def _count_tokens(
|
||||
self,
|
||||
|
|
@ -236,7 +253,9 @@ class InferenceRouter(Inference):
|
|||
stream: Optional[bool] = False,
|
||||
logprobs: Optional[LogProbConfig] = None,
|
||||
tool_config: Optional[ToolConfig] = None,
|
||||
) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
|
||||
) -> Union[
|
||||
ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
|
||||
]:
|
||||
logger.debug(
|
||||
f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}",
|
||||
)
|
||||
|
|
@ -246,12 +265,19 @@ class InferenceRouter(Inference):
|
|||
if model is None:
|
||||
raise ValueError(f"Model '{model_id}' not found")
|
||||
if model.model_type == ModelType.embedding:
|
||||
raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
|
||||
raise ValueError(
|
||||
f"Model '{model_id}' is an embedding model and does not support chat completions"
|
||||
)
|
||||
if tool_config:
|
||||
if tool_choice and tool_choice != tool_config.tool_choice:
|
||||
raise ValueError("tool_choice and tool_config.tool_choice must match")
|
||||
if tool_prompt_format and tool_prompt_format != tool_config.tool_prompt_format:
|
||||
raise ValueError("tool_prompt_format and tool_config.tool_prompt_format must match")
|
||||
if (
|
||||
tool_prompt_format
|
||||
and tool_prompt_format != tool_config.tool_prompt_format
|
||||
):
|
||||
raise ValueError(
|
||||
"tool_prompt_format and tool_config.tool_prompt_format must match"
|
||||
)
|
||||
else:
|
||||
params = {}
|
||||
if tool_choice:
|
||||
|
|
@ -269,9 +295,14 @@ class InferenceRouter(Inference):
|
|||
pass
|
||||
else:
|
||||
# verify tool_choice is one of the tools
|
||||
tool_names = [t.tool_name if isinstance(t.tool_name, str) else t.tool_name.value for t in tools]
|
||||
tool_names = [
|
||||
t.tool_name if isinstance(t.tool_name, str) else t.tool_name.value
|
||||
for t in tools
|
||||
]
|
||||
if tool_config.tool_choice not in tool_names:
|
||||
raise ValueError(f"Tool choice {tool_config.tool_choice} is not one of the tools: {tool_names}")
|
||||
raise ValueError(
|
||||
f"Tool choice {tool_config.tool_choice} is not one of the tools: {tool_names}"
|
||||
)
|
||||
|
||||
params = dict(
|
||||
model_id=model_id,
|
||||
|
|
@ -286,19 +317,32 @@ class InferenceRouter(Inference):
|
|||
tool_config=tool_config,
|
||||
)
|
||||
provider = self.routing_table.get_provider_impl(model_id)
|
||||
prompt_tokens = await self._count_tokens(messages, tool_config.tool_prompt_format)
|
||||
prompt_tokens = await self._count_tokens(
|
||||
messages, tool_config.tool_prompt_format
|
||||
)
|
||||
|
||||
if stream:
|
||||
|
||||
async def stream_generator():
|
||||
completion_text = ""
|
||||
async for chunk in await provider.chat_completion(**params):
|
||||
if chunk.event.event_type == ChatCompletionResponseEventType.progress:
|
||||
if (
|
||||
chunk.event.event_type
|
||||
== ChatCompletionResponseEventType.progress
|
||||
):
|
||||
if chunk.event.delta.type == "text":
|
||||
completion_text += chunk.event.delta.text
|
||||
if chunk.event.event_type == ChatCompletionResponseEventType.complete:
|
||||
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)
|
||||
|
|
@ -308,7 +352,11 @@ class InferenceRouter(Inference):
|
|||
total_tokens,
|
||||
model,
|
||||
)
|
||||
chunk.metrics = metrics if chunk.metrics is None else chunk.metrics + metrics
|
||||
chunk.metrics = (
|
||||
metrics
|
||||
if chunk.metrics is None
|
||||
else chunk.metrics + metrics
|
||||
)
|
||||
yield chunk
|
||||
|
||||
return stream_generator()
|
||||
|
|
@ -325,7 +373,9 @@ class InferenceRouter(Inference):
|
|||
total_tokens,
|
||||
model,
|
||||
)
|
||||
response.metrics = metrics if response.metrics is None else response.metrics + metrics
|
||||
response.metrics = (
|
||||
metrics if response.metrics is None else response.metrics + metrics
|
||||
)
|
||||
return response
|
||||
|
||||
async def completion(
|
||||
|
|
@ -346,7 +396,9 @@ class InferenceRouter(Inference):
|
|||
if model is None:
|
||||
raise ValueError(f"Model '{model_id}' not found")
|
||||
if model.model_type == ModelType.embedding:
|
||||
raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
|
||||
raise ValueError(
|
||||
f"Model '{model_id}' is an embedding model and does not support chat completions"
|
||||
)
|
||||
provider = self.routing_table.get_provider_impl(model_id)
|
||||
params = dict(
|
||||
model_id=model_id,
|
||||
|
|
@ -366,7 +418,11 @@ class InferenceRouter(Inference):
|
|||
async for chunk in await provider.completion(**params):
|
||||
if hasattr(chunk, "delta"):
|
||||
completion_text += chunk.delta
|
||||
if hasattr(chunk, "stop_reason") and chunk.stop_reason and self.telemetry:
|
||||
if (
|
||||
hasattr(chunk, "stop_reason")
|
||||
and chunk.stop_reason
|
||||
and self.telemetry
|
||||
):
|
||||
completion_tokens = await self._count_tokens(completion_text)
|
||||
total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
|
||||
metrics = await self._compute_and_log_token_usage(
|
||||
|
|
@ -375,7 +431,11 @@ class InferenceRouter(Inference):
|
|||
total_tokens,
|
||||
model,
|
||||
)
|
||||
chunk.metrics = metrics if chunk.metrics is None else chunk.metrics + metrics
|
||||
chunk.metrics = (
|
||||
metrics
|
||||
if chunk.metrics is None
|
||||
else chunk.metrics + metrics
|
||||
)
|
||||
yield chunk
|
||||
|
||||
return stream_generator()
|
||||
|
|
@ -389,7 +449,9 @@ class InferenceRouter(Inference):
|
|||
total_tokens,
|
||||
model,
|
||||
)
|
||||
response.metrics = metrics if response.metrics is None else response.metrics + metrics
|
||||
response.metrics = (
|
||||
metrics if response.metrics is None else response.metrics + metrics
|
||||
)
|
||||
return response
|
||||
|
||||
async def embeddings(
|
||||
|
|
@ -405,7 +467,9 @@ class InferenceRouter(Inference):
|
|||
if model is None:
|
||||
raise ValueError(f"Model '{model_id}' not found")
|
||||
if model.model_type == ModelType.llm:
|
||||
raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
|
||||
raise ValueError(
|
||||
f"Model '{model_id}' is an LLM model and does not support embeddings"
|
||||
)
|
||||
return await self.routing_table.get_provider_impl(model_id).embeddings(
|
||||
model_id=model_id,
|
||||
contents=contents,
|
||||
|
|
@ -439,7 +503,9 @@ class SafetyRouter(Safety):
|
|||
params: Optional[Dict[str, Any]] = None,
|
||||
) -> Shield:
|
||||
logger.debug(f"SafetyRouter.register_shield: {shield_id}")
|
||||
return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params)
|
||||
return await self.routing_table.register_shield(
|
||||
shield_id, provider_shield_id, provider_id, params
|
||||
)
|
||||
|
||||
async def run_shield(
|
||||
self,
|
||||
|
|
@ -477,11 +543,13 @@ class DatasetIORouter(DatasetIO):
|
|||
rows_in_page: int,
|
||||
page_token: Optional[str] = None,
|
||||
filter_condition: Optional[str] = None,
|
||||
) -> PaginatedRowsResult:
|
||||
) -> IterrowsResponse:
|
||||
logger.debug(
|
||||
f"DatasetIORouter.get_rows_paginated: {dataset_id}, rows_in_page={rows_in_page}",
|
||||
)
|
||||
return await self.routing_table.get_provider_impl(dataset_id).get_rows_paginated(
|
||||
return await self.routing_table.get_provider_impl(
|
||||
dataset_id
|
||||
).get_rows_paginated(
|
||||
dataset_id=dataset_id,
|
||||
rows_in_page=rows_in_page,
|
||||
page_token=page_token,
|
||||
|
|
@ -521,7 +589,9 @@ class ScoringRouter(Scoring):
|
|||
logger.debug(f"ScoringRouter.score_batch: {dataset_id}")
|
||||
res = {}
|
||||
for fn_identifier in scoring_functions.keys():
|
||||
score_response = await self.routing_table.get_provider_impl(fn_identifier).score_batch(
|
||||
score_response = await self.routing_table.get_provider_impl(
|
||||
fn_identifier
|
||||
).score_batch(
|
||||
dataset_id=dataset_id,
|
||||
scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
|
||||
)
|
||||
|
|
@ -539,11 +609,15 @@ class ScoringRouter(Scoring):
|
|||
input_rows: List[Dict[str, Any]],
|
||||
scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
|
||||
) -> ScoreResponse:
|
||||
logger.debug(f"ScoringRouter.score: {len(input_rows)} rows, {len(scoring_functions)} functions")
|
||||
logger.debug(
|
||||
f"ScoringRouter.score: {len(input_rows)} rows, {len(scoring_functions)} functions"
|
||||
)
|
||||
res = {}
|
||||
# look up and map each scoring function to its provider impl
|
||||
for fn_identifier in scoring_functions.keys():
|
||||
score_response = await self.routing_table.get_provider_impl(fn_identifier).score(
|
||||
score_response = await self.routing_table.get_provider_impl(
|
||||
fn_identifier
|
||||
).score(
|
||||
input_rows=input_rows,
|
||||
scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
|
||||
)
|
||||
|
|
@ -586,7 +660,9 @@ class EvalRouter(Eval):
|
|||
scoring_functions: List[str],
|
||||
benchmark_config: BenchmarkConfig,
|
||||
) -> EvaluateResponse:
|
||||
logger.debug(f"EvalRouter.evaluate_rows: {benchmark_id}, {len(input_rows)} rows")
|
||||
logger.debug(
|
||||
f"EvalRouter.evaluate_rows: {benchmark_id}, {len(input_rows)} rows"
|
||||
)
|
||||
return await self.routing_table.get_provider_impl(benchmark_id).evaluate_rows(
|
||||
benchmark_id=benchmark_id,
|
||||
input_rows=input_rows,
|
||||
|
|
@ -600,7 +676,9 @@ class EvalRouter(Eval):
|
|||
job_id: str,
|
||||
) -> Optional[JobStatus]:
|
||||
logger.debug(f"EvalRouter.job_status: {benchmark_id}, {job_id}")
|
||||
return await self.routing_table.get_provider_impl(benchmark_id).job_status(benchmark_id, job_id)
|
||||
return await self.routing_table.get_provider_impl(benchmark_id).job_status(
|
||||
benchmark_id, job_id
|
||||
)
|
||||
|
||||
async def job_cancel(
|
||||
self,
|
||||
|
|
@ -654,9 +732,9 @@ class ToolRuntimeRouter(ToolRuntime):
|
|||
logger.debug(
|
||||
f"ToolRuntimeRouter.RagToolImpl.insert: {vector_db_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}"
|
||||
)
|
||||
return await self.routing_table.get_provider_impl("insert_into_memory").insert(
|
||||
documents, vector_db_id, chunk_size_in_tokens
|
||||
)
|
||||
return await self.routing_table.get_provider_impl(
|
||||
"insert_into_memory"
|
||||
).insert(documents, vector_db_id, chunk_size_in_tokens)
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -689,4 +767,6 @@ class ToolRuntimeRouter(ToolRuntime):
|
|||
self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None
|
||||
) -> List[ToolDef]:
|
||||
logger.debug(f"ToolRuntimeRouter.list_runtime_tools: {tool_group_id}")
|
||||
return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint)
|
||||
return await self.routing_table.get_provider_impl(tool_group_id).list_tools(
|
||||
tool_group_id, mcp_endpoint
|
||||
)
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from urllib.parse import urlparse
|
|||
import pandas
|
||||
|
||||
from llama_stack.apis.common.content_types import URL
|
||||
from llama_stack.apis.datasetio import DatasetIO, PaginatedRowsResult
|
||||
from llama_stack.apis.datasetio import DatasetIO, IterrowsResponse
|
||||
from llama_stack.apis.datasets import Dataset
|
||||
from llama_stack.providers.datatypes import DatasetsProtocolPrivate
|
||||
from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
|
||||
|
|
@ -134,7 +134,7 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
rows_in_page: int,
|
||||
page_token: Optional[str] = None,
|
||||
filter_condition: Optional[str] = None,
|
||||
) -> PaginatedRowsResult:
|
||||
) -> IterrowsResponse:
|
||||
dataset_info = self.dataset_infos.get(dataset_id)
|
||||
dataset_info.dataset_impl.load()
|
||||
|
||||
|
|
@ -154,7 +154,7 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
|
||||
rows = dataset_info.dataset_impl[start:end]
|
||||
|
||||
return PaginatedRowsResult(
|
||||
return IterrowsResponse(
|
||||
rows=rows,
|
||||
total_count=len(rows),
|
||||
next_page_token=str(end),
|
||||
|
|
@ -170,7 +170,9 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
|
||||
new_rows_df = pandas.DataFrame(rows)
|
||||
new_rows_df = dataset_impl._validate_dataset_schema(new_rows_df)
|
||||
dataset_impl.df = pandas.concat([dataset_impl.df, new_rows_df], ignore_index=True)
|
||||
dataset_impl.df = pandas.concat(
|
||||
[dataset_impl.df, new_rows_df], ignore_index=True
|
||||
)
|
||||
|
||||
url = str(dataset_info.dataset_def.url.uri)
|
||||
parsed_url = urlparse(url)
|
||||
|
|
@ -185,8 +187,12 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
raise ValueError("Data URL must be a base64-encoded CSV")
|
||||
|
||||
csv_buffer = dataset_impl.df.to_csv(index=False)
|
||||
base64_content = base64.b64encode(csv_buffer.encode("utf-8")).decode("utf-8")
|
||||
dataset_info.dataset_def.url = URL(uri=f"data:text/csv;base64,{base64_content}")
|
||||
base64_content = base64.b64encode(csv_buffer.encode("utf-8")).decode(
|
||||
"utf-8"
|
||||
)
|
||||
dataset_info.dataset_def.url = URL(
|
||||
uri=f"data:text/csv;base64,{base64_content}"
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported URL scheme: {parsed_url.scheme}. Only file:// and data: URLs are supported for writing."
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional
|
|||
|
||||
import datasets as hf_datasets
|
||||
|
||||
from llama_stack.apis.datasetio import DatasetIO, PaginatedRowsResult
|
||||
from llama_stack.apis.datasetio import DatasetIO, IterrowsResponse
|
||||
from llama_stack.apis.datasets import Dataset
|
||||
from llama_stack.providers.datatypes import DatasetsProtocolPrivate
|
||||
from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
|
||||
|
|
@ -79,7 +79,7 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
rows_in_page: int,
|
||||
page_token: Optional[str] = None,
|
||||
filter_condition: Optional[str] = None,
|
||||
) -> PaginatedRowsResult:
|
||||
) -> IterrowsResponse:
|
||||
dataset_def = self.dataset_infos[dataset_id]
|
||||
loaded_dataset = load_hf_dataset(dataset_def)
|
||||
|
||||
|
|
@ -99,7 +99,7 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
|
||||
rows = [loaded_dataset[i] for i in range(start, end)]
|
||||
|
||||
return PaginatedRowsResult(
|
||||
return IterrowsResponse(
|
||||
rows=rows,
|
||||
total_count=len(rows),
|
||||
next_page_token=str(end),
|
||||
|
|
@ -113,9 +113,13 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
new_dataset = hf_datasets.Dataset.from_list(rows)
|
||||
|
||||
# Concatenate the new rows with existing dataset
|
||||
updated_dataset = hf_datasets.concatenate_datasets([loaded_dataset, new_dataset])
|
||||
updated_dataset = hf_datasets.concatenate_datasets(
|
||||
[loaded_dataset, new_dataset]
|
||||
)
|
||||
|
||||
if dataset_def.metadata.get("path", None):
|
||||
updated_dataset.push_to_hub(dataset_def.metadata["path"])
|
||||
else:
|
||||
raise NotImplementedError("Uploading to URL-based datasets is not supported yet")
|
||||
raise NotImplementedError(
|
||||
"Uploading to URL-based datasets is not supported yet"
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue