chore(api): remove batch inference (#3261)
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# What does this PR do?

APIs removed:
 - POST /v1/batch-inference/completion
 - POST /v1/batch-inference/chat-completion
 - POST /v1/inference/batch-completion
 - POST /v1/inference/batch-chat-completion

note -
- batch-completion & batch-chat-completion were only implemented for
inference=inline::meta-reference
 - batch-inference were not implemented
This commit is contained in:
Matthew Farrellee 2025-09-26 17:35:34 -04:00 committed by GitHub
parent b48d5cfed7
commit 60484c5c4e
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
12 changed files with 190 additions and 979 deletions

View file

@ -20,8 +20,6 @@ from llama_stack.apis.common.content_types import (
)
from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError
from llama_stack.apis.inference import (
BatchChatCompletionResponse,
BatchCompletionResponse,
ChatCompletionResponse,
ChatCompletionResponseEventType,
ChatCompletionResponseStreamChunk,
@ -273,30 +271,6 @@ class InferenceRouter(Inference):
)
return response
async def batch_chat_completion(
self,
model_id: str,
messages_batch: list[list[Message]],
tools: list[ToolDefinition] | None = None,
tool_config: ToolConfig | None = None,
sampling_params: SamplingParams | None = None,
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
) -> BatchChatCompletionResponse:
logger.debug(
f"InferenceRouter.batch_chat_completion: {model_id=}, {len(messages_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}",
)
provider = await self.routing_table.get_provider_impl(model_id)
return await provider.batch_chat_completion(
model_id=model_id,
messages_batch=messages_batch,
tools=tools,
tool_config=tool_config,
sampling_params=sampling_params,
response_format=response_format,
logprobs=logprobs,
)
async def completion(
self,
model_id: str,
@ -338,20 +312,6 @@ class InferenceRouter(Inference):
return response
async def batch_completion(
self,
model_id: str,
content_batch: list[InterleavedContent],
sampling_params: SamplingParams | None = None,
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
) -> BatchCompletionResponse:
logger.debug(
f"InferenceRouter.batch_completion: {model_id=}, {len(content_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}",
)
provider = await self.routing_table.get_provider_impl(model_id)
return await provider.batch_completion(model_id, content_batch, sampling_params, response_format, logprobs)
async def embeddings(
self,
model_id: str,