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Fix precommit check after moving to ruff (#927)
Lint check in main branch is failing. This fixes the lint check after we moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We need to move to a `ruff.toml` file as well as fixing and ignoring some additional checks. Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
This commit is contained in:
Yuan Tang
GitHub
parent
4773092dd1
commit
34ab7a3b6c
217 changed files with 981 additions and 2681 deletions
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@ -85,9 +85,7 @@ class VectorIORouter(VectorIO):
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chunks: List[Chunk],
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ttl_seconds: Optional[int] = None,
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) -> None:
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return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(
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vector_db_id, chunks, ttl_seconds
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)
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return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(vector_db_id, chunks, ttl_seconds)
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async def query_chunks(
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self,
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@ -95,9 +93,7 @@ class VectorIORouter(VectorIO):
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query: InterleavedContent,
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params: Optional[Dict[str, Any]] = None,
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) -> QueryChunksResponse:
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return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(
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vector_db_id, query, params
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)
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return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)
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class InferenceRouter(Inference):
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@ -123,9 +119,7 @@ class InferenceRouter(Inference):
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metadata: Optional[Dict[str, Any]] = None,
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model_type: Optional[ModelType] = None,
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) -> None:
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await self.routing_table.register_model(
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model_id, provider_model_id, provider_id, metadata, model_type
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)
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await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type)
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async def chat_completion(
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self,
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@ -143,9 +137,7 @@ class InferenceRouter(Inference):
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if model is None:
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raise ValueError(f"Model '{model_id}' not found")
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if model.model_type == ModelType.embedding:
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raise ValueError(
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f"Model '{model_id}' is an embedding model and does not support chat completions"
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)
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raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
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params = dict(
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model_id=model_id,
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messages=messages,
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@ -176,9 +168,7 @@ class InferenceRouter(Inference):
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if model is None:
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raise ValueError(f"Model '{model_id}' not found")
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if model.model_type == ModelType.embedding:
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raise ValueError(
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f"Model '{model_id}' is an embedding model and does not support chat completions"
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)
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raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
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provider = self.routing_table.get_provider_impl(model_id)
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params = dict(
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model_id=model_id,
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@ -202,9 +192,7 @@ class InferenceRouter(Inference):
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if model is None:
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raise ValueError(f"Model '{model_id}' not found")
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if model.model_type == ModelType.llm:
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raise ValueError(
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f"Model '{model_id}' is an LLM model and does not support embeddings"
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)
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raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
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return await self.routing_table.get_provider_impl(model_id).embeddings(
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model_id=model_id,
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contents=contents,
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@ -231,9 +219,7 @@ class SafetyRouter(Safety):
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provider_id: Optional[str] = None,
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params: Optional[Dict[str, Any]] = None,
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) -> Shield:
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return await self.routing_table.register_shield(
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shield_id, provider_shield_id, provider_id, params
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)
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return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params)
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async def run_shield(
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self,
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@ -268,9 +254,7 @@ class DatasetIORouter(DatasetIO):
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page_token: Optional[str] = None,
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filter_condition: Optional[str] = None,
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) -> PaginatedRowsResult:
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return await self.routing_table.get_provider_impl(
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dataset_id
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).get_rows_paginated(
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return await self.routing_table.get_provider_impl(dataset_id).get_rows_paginated(
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dataset_id=dataset_id,
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rows_in_page=rows_in_page,
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page_token=page_token,
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@ -305,9 +289,7 @@ class ScoringRouter(Scoring):
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) -> ScoreBatchResponse:
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res = {}
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for fn_identifier in scoring_functions.keys():
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score_response = await self.routing_table.get_provider_impl(
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fn_identifier
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).score_batch(
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score_response = await self.routing_table.get_provider_impl(fn_identifier).score_batch(
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dataset_id=dataset_id,
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scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
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)
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@ -328,9 +310,7 @@ class ScoringRouter(Scoring):
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res = {}
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# look up and map each scoring function to its provider impl
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for fn_identifier in scoring_functions.keys():
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score_response = await self.routing_table.get_provider_impl(
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fn_identifier
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).score(
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score_response = await self.routing_table.get_provider_impl(fn_identifier).score(
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input_rows=input_rows,
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scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
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)
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@ -381,9 +361,7 @@ class EvalRouter(Eval):
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task_id: str,
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job_id: str,
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) -> Optional[JobStatus]:
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return await self.routing_table.get_provider_impl(task_id).job_status(
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task_id, job_id
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)
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return await self.routing_table.get_provider_impl(task_id).job_status(task_id, job_id)
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async def job_cancel(
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self,
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@ -420,9 +398,9 @@ class ToolRuntimeRouter(ToolRuntime):
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vector_db_ids: List[str],
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query_config: Optional[RAGQueryConfig] = None,
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) -> RAGQueryResult:
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return await self.routing_table.get_provider_impl(
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"query_from_memory"
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).query(content, vector_db_ids, query_config)
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return await self.routing_table.get_provider_impl("query_from_memory").query(
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content, vector_db_ids, query_config
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)
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async def insert(
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self,
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@ -430,9 +408,9 @@ class ToolRuntimeRouter(ToolRuntime):
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vector_db_id: str,
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chunk_size_in_tokens: int = 512,
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) -> None:
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return await self.routing_table.get_provider_impl(
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"insert_into_memory"
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).insert(documents, vector_db_id, chunk_size_in_tokens)
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return await self.routing_table.get_provider_impl("insert_into_memory").insert(
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documents, vector_db_id, chunk_size_in_tokens
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)
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def __init__(
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self,
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@ -460,6 +438,4 @@ class ToolRuntimeRouter(ToolRuntime):
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async def list_runtime_tools(
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self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None
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) -> List[ToolDef]:
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return await self.routing_table.get_provider_impl(tool_group_id).list_tools(
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tool_group_id, mcp_endpoint
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)
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return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint)
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