Fix: Remove workflow/nodes from pyright exclusion (#26461)

Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
This commit is contained in:
Asuka Minato 2025-09-30 16:39:04 +09:00 committed by GitHub
parent 7242a67f84
commit decf0f3da0
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9 changed files with 15 additions and 12 deletions

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@ -87,7 +87,7 @@ class Executor:
node_data.authorization.config.api_key
).text
self.url: str = node_data.url
self.url = node_data.url
self.method = node_data.method
self.auth = node_data.authorization
self.timeout = timeout
@ -349,11 +349,10 @@ class Executor:
"timeout": (self.timeout.connect, self.timeout.read, self.timeout.write),
"ssl_verify": self.ssl_verify,
"follow_redirects": True,
"max_retries": self.max_retries,
}
# request_args = {k: v for k, v in request_args.items() if v is not None}
try:
response: httpx.Response = _METHOD_MAP[method_lc](**request_args)
response: httpx.Response = _METHOD_MAP[method_lc](**request_args, max_retries=self.max_retries)
except (ssrf_proxy.MaxRetriesExceededError, httpx.RequestError) as e:
raise HttpRequestNodeError(str(e)) from e
# FIXME: fix type ignore, this maybe httpx type issue

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@ -165,6 +165,8 @@ class HttpRequestNode(Node):
body_type = typed_node_data.body.type
data = typed_node_data.body.data
match body_type:
case "none":
pass
case "binary":
if len(data) != 1:
raise RequestBodyError("invalid body data, should have only one item")

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@ -83,7 +83,7 @@ class IfElseNode(Node):
else:
# TODO: Update database then remove this
# Fallback to old structure if cases are not defined
input_conditions, group_result, final_result = _should_not_use_old_function( # ty: ignore [deprecated]
input_conditions, group_result, final_result = _should_not_use_old_function( # pyright: ignore [reportDeprecated]
condition_processor=condition_processor,
variable_pool=self.graph_runtime_state.variable_pool,
conditions=self._node_data.conditions or [],

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@ -107,7 +107,7 @@ class KnowledgeRetrievalNode(Node):
graph_runtime_state=graph_runtime_state,
)
# LLM file outputs, used for MultiModal outputs.
self._file_outputs: list[File] = []
self._file_outputs = []
if llm_file_saver is None:
llm_file_saver = FileSaverImpl(

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@ -161,6 +161,8 @@ class ListOperatorNode(Node):
elif isinstance(variable, ArrayFileSegment):
if isinstance(condition.value, str):
value = self.graph_runtime_state.variable_pool.convert_template(condition.value).text
elif isinstance(condition.value, bool):
raise ValueError(f"File filter expects a string value, got {type(condition.value)}")
else:
value = condition.value
filter_func = _get_file_filter_func(

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@ -46,7 +46,7 @@ class LLMFileSaver(tp.Protocol):
dot (`.`). For example, `.py` and `.tar.gz` are both valid values, while `py`
and `tar.gz` are not.
"""
pass
raise NotImplementedError()
def save_remote_url(self, url: str, file_type: FileType) -> File:
"""save_remote_url saves the file from a remote url returned by LLM.
@ -56,7 +56,7 @@ class LLMFileSaver(tp.Protocol):
:param url: the url of the file.
:param file_type: the file type of the file, check `FileType` enum for reference.
"""
pass
raise NotImplementedError()
EngineFactory: tp.TypeAlias = tp.Callable[[], Engine]

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@ -128,7 +128,7 @@ class LLMNode(Node):
graph_runtime_state=graph_runtime_state,
)
# LLM file outputs, used for MultiModal outputs.
self._file_outputs: list[File] = []
self._file_outputs = []
if llm_file_saver is None:
llm_file_saver = FileSaverImpl(
@ -166,6 +166,7 @@ class LLMNode(Node):
node_inputs: dict[str, Any] = {}
process_data: dict[str, Any] = {}
result_text = ""
clean_text = ""
usage = LLMUsage.empty_usage()
finish_reason = None
reasoning_content = None

View File

@ -68,7 +68,7 @@ class QuestionClassifierNode(Node):
graph_runtime_state=graph_runtime_state,
)
# LLM file outputs, used for MultiModal outputs.
self._file_outputs: list[File] = []
self._file_outputs = []
if llm_file_saver is None:
llm_file_saver = FileSaverImpl(
@ -111,9 +111,9 @@ class QuestionClassifierNode(Node):
query = variable.value if variable else None
variables = {"query": query}
# fetch model config
model_instance, model_config = LLMNode._fetch_model_config(
node_data_model=node_data.model,
model_instance, model_config = llm_utils.fetch_model_config(
tenant_id=self.tenant_id,
node_data_model=node_data.model,
)
# fetch memory
memory = llm_utils.fetch_memory(

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@ -6,7 +6,6 @@
"migrations/",
"core/rag",
"extensions",
"core/workflow/nodes",
"core/app/app_config/easy_ui_based_app/dataset"
],
"typeCheckingMode": "strict",