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feat: add auto-generated CI documentation pre-commit hook (#2890)

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Our CI is entirely undocumented, this commit adds a README.md file with
a table of the current CI and what is does

---------

Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
This commit is contained in:
Nathan Weinberg 2025-07-25 11:57:01 -04:00 • committed by Mustafa Elbehery
parent 7f834339ba
commit b381ed6d64
93 changed files with 495 additions and 477 deletions
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28
llama_stack/core/routers/inference.py
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@ -58,7 +58,7 @@ from llama_stack.providers.utils.inference.inference_store import InferenceStore
from llama_stack.providers.utils.inference.stream_utils import stream_and_store_openai_completion
from llama_stack.providers.utils.telemetry.tracing import get_current_span
logger = get_logger(name=__name__, category="core")
log = get_logger(name=__name__, category="core")
class InferenceRouter(Inference):
@ -70,7 +70,7 @@ class InferenceRouter(Inference):
telemetry: Telemetry | None = None,
store: InferenceStore | None = None,
) -> None:
logger.debug("Initializing InferenceRouter")
log.debug("Initializing InferenceRouter")
self.routing_table = routing_table
self.telemetry = telemetry
self.store = store
@ -79,10 +79,10 @@ class InferenceRouter(Inference):
self.formatter = ChatFormat(self.tokenizer)
async def initialize(self) -> None:
logger.debug("InferenceRouter.initialize")
log.debug("InferenceRouter.initialize")
async def shutdown(self) -> None:
logger.debug("InferenceRouter.shutdown")
log.debug("InferenceRouter.shutdown")
async def register_model(
self,
@ -92,7 +92,7 @@ class InferenceRouter(Inference):
metadata: dict[str, Any] | None = None,
model_type: ModelType | None = None,
) -> None:
logger.debug(
log.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)
@ -117,7 +117,7 @@ class InferenceRouter(Inference):
"""
span = get_current_span()
if span is None:
logger.warning("No span found for token usage metrics")
log.warning("No span found for token usage metrics")
return []
metrics = [
("prompt_tokens", prompt_tokens),
@ -182,7 +182,7 @@ class InferenceRouter(Inference):
logprobs: LogProbConfig | None = None,
tool_config: ToolConfig | None = None,
) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
logger.debug(
log.debug(
f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}",
)
if sampling_params is None:
@ -288,7 +288,7 @@ class InferenceRouter(Inference):
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
) -> BatchChatCompletionResponse:
logger.debug(
log.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)
@ -313,7 +313,7 @@ class InferenceRouter(Inference):
) -> AsyncGenerator:
if sampling_params is None:
sampling_params = SamplingParams()
logger.debug(
log.debug(
f"InferenceRouter.completion: {model_id=}, {stream=}, {content=}, {sampling_params=}, {response_format=}",
)
model = await self.routing_table.get_model(model_id)
@ -374,7 +374,7 @@ class InferenceRouter(Inference):
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
) -> BatchCompletionResponse:
logger.debug(
log.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)
@ -388,7 +388,7 @@ class InferenceRouter(Inference):
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
logger.debug(f"InferenceRouter.embeddings: {model_id}")
log.debug(f"InferenceRouter.embeddings: {model_id}")
model = await self.routing_table.get_model(model_id)
if model is None:
raise ModelNotFoundError(model_id)
@ -426,7 +426,7 @@ class InferenceRouter(Inference):
prompt_logprobs: int | None = None,
suffix: str | None = None,
) -> OpenAICompletion:
logger.debug(
log.debug(
f"InferenceRouter.openai_completion: {model=}, {stream=}, {prompt=}",
)
model_obj = await self.routing_table.get_model(model)
@ -487,7 +487,7 @@ class InferenceRouter(Inference):
top_p: float | None = None,
user: str | None = None,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
logger.debug(
log.debug(
f"InferenceRouter.openai_chat_completion: {model=}, {stream=}, {messages=}",
)
model_obj = await self.routing_table.get_model(model)
@ -558,7 +558,7 @@ class InferenceRouter(Inference):
dimensions: int | None = None,
user: str | None = None,
) -> OpenAIEmbeddingsResponse:
logger.debug(
log.debug(
f"InferenceRouter.openai_embeddings: {model=}, input_type={type(input)}, {encoding_format=}, {dimensions=}",
)
model_obj = await self.routing_table.get_model(model)