fix: update type to silence warning and memory chatbot json (#3265)
* fix: update value type in DefaultPromptField from str to Any for increased flexibility in input handling * fix: replace _instantiate_input with instantiate_input for consistency in input instantiation across the codebase * chore: update test durations * update memory chatbot
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
parent
0eb694f1d7
commit
9da2c34551
5 changed files with 369 additions and 361 deletions
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@ -1039,7 +1039,7 @@
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import get_messages, LCBuiltinChatMemory\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.field_typing import BaseChatMemory\nfrom langchain.memory import ConversationBufferMemory\n\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n icon = \"message-square-more\"\n name = \"Memory\"\n\n inputs = [\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"BaseChatMessageHistory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Messages (Data)\", name=\"messages\", method=\"retrieve_messages\"),\n Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n Output(display_name=\"Memory\", name=\"lc_memory\", method=\"build_lc_memory\"),\n ]\n\n def retrieve_messages(self) -> Data:\n sender = self.sender\n sender_name = self.sender_name\n session_id = self.session_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender == \"Machine and User\":\n sender = None\n\n if self.memory:\n # override session_id\n self.memory.session_id = session_id\n\n stored = self.memory.messages\n # langchain memories are supposed to return messages in ascending order\n if order == \"DESC\":\n stored = stored[::-1]\n if n_messages:\n stored = stored[:n_messages]\n stored = [Message.from_lc_message(m) for m in stored]\n if sender:\n expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n stored = get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n self.status = stored\n return stored\n\n def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, self.retrieve_messages())\n self.status = stored_text\n return Message(text=stored_text)\n\n def build_lc_memory(self) -> BaseChatMemory:\n if self.memory:\n chat_memory = self.memory\n else:\n chat_memory = LCBuiltinChatMemory(flow_id=self.graph.flow_id, session_id=self.session_id)\n return ConversationBufferMemory(chat_memory=chat_memory)\n"
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"value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n icon = \"message-square-more\"\n name = \"Memory\"\n\n inputs = [\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"BaseChatMessageHistory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Messages (Data)\", name=\"messages\", method=\"retrieve_messages\"),\n Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n Output(display_name=\"Memory\", name=\"lc_memory\", method=\"build_lc_memory\"),\n ]\n\n def retrieve_messages(self) -> Data:\n sender = self.sender\n sender_name = self.sender_name\n session_id = self.session_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender == \"Machine and User\":\n sender = None\n\n if self.memory:\n # override session_id\n self.memory.session_id = session_id\n\n stored = self.memory.messages\n # langchain memories are supposed to return messages in ascending order\n if order == \"DESC\":\n stored = stored[::-1]\n if n_messages:\n stored = stored[:n_messages]\n stored = [Message.from_lc_message(m) for m in stored]\n if sender:\n expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n stored = get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n self.status = stored\n return stored\n\n def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, self.retrieve_messages())\n self.status = stored_text\n return Message(text=stored_text)\n\n def build_lc_memory(self) -> BaseChatMemory:\n if self.memory:\n chat_memory = self.memory\n else:\n chat_memory = LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n return ConversationBufferMemory(chat_memory=chat_memory)\n"
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},
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"memory": {
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"advanced": false,
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@ -471,7 +471,7 @@ class DefaultPromptField(Input):
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advanced: bool = False
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multiline: bool = True
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input_types: list[str] = DEFAULT_PROMPT_INTUT_TYPES
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value: str = "" # Set the value to empty string
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value: Any = "" # Set the value to empty string
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InputTypes = Union[
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@ -500,7 +500,7 @@ InputTypes = Union[
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InputTypesMap: dict[str, type[InputTypes]] = {t.__name__: t for t in get_args(InputTypes)}
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def _instantiate_input(input_type: str, data: dict) -> InputTypes:
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def instantiate_input(input_type: str, data: dict) -> InputTypes:
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input_type_class = InputTypesMap.get(input_type)
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if "type" in data:
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# Replate with field_type
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@ -2,7 +2,7 @@ from typing import Callable, Union, cast
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from pydantic import BaseModel, Field, model_serializer
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from langflow.inputs.inputs import InputTypes, _instantiate_input
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from langflow.inputs.inputs import InputTypes, instantiate_input
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from langflow.template.field.base import Input
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from langflow.utils.constants import DIRECT_TYPES
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@ -48,7 +48,7 @@ class Template(BaseModel):
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input_type = value.pop("_input_type", None)
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if input_type:
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try:
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_input = _instantiate_input(input_type, value)
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_input = instantiate_input(input_type, value)
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except Exception as e:
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raise ValueError(f"Error instantiating input {input_type}: {e}")
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else:
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@ -1,354 +1,362 @@
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{
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||||
"src/backend/tests/unit/test_initial_setup.py::test_load_starter_projects": 1.2834318330001224,
|
||||
"src/backend/tests/unit/test_initial_setup.py::test_refresh_starter_projects": 2.5242092499997852,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_create_secret": 1.277097417000732,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_delete_secret": 1.2887339569997494,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_email_address": 1.3158049170001505,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_encode_string": 1.2695311679999577,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_encode_uuid": 1.259859792000043,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_ends_with_non_alphanumeric": 1.302864874999159,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_get_secret": 1.2660873319996426,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_long_string": 1.2887962920003702,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_starts_with_non_alphanumeric": 1.3552359580003213,
|
||||
"src/backend/tests/unit/test_kubernetes_secrets.py::test_uuid_case_insensitivity": 1.299499248000302,
|
||||
"src/backend/tests/unit/test_loading.py::test_load_flow_from_json": 0.0960882499998661,
|
||||
"src/backend/tests/unit/test_loading.py::test_load_flow_from_json_object": 0.018880542000260903,
|
||||
"src/backend/tests/unit/test_loading.py::test_load_flow_from_json_with_tweaks": 0.016509083999608265,
|
||||
"src/backend/tests/unit/test_logger.py::test_enabled": 1.0503544179996425,
|
||||
"src/backend/tests/unit/test_logger.py::test_get_after_timestamp": 1.057395667000037,
|
||||
"src/backend/tests/unit/test_logger.py::test_get_before_timestamp": 1.069635374000427,
|
||||
"src/backend/tests/unit/test_logger.py::test_get_last_n": 1.0638129170001775,
|
||||
"src/backend/tests/unit/test_logger.py::test_init_default": 3.221781582999938,
|
||||
"src/backend/tests/unit/test_logger.py::test_init_with_env_variable": 1.1272078760002842,
|
||||
"src/backend/tests/unit/test_logger.py::test_len": 1.0672918339996613,
|
||||
"src/backend/tests/unit/test_logger.py::test_max_size": 1.0532803749997584,
|
||||
"src/backend/tests/unit/test_logger.py::test_write": 1.083741292999548,
|
||||
"src/backend/tests/unit/test_logger.py::test_write_overflow": 1.0727743750007903,
|
||||
"src/backend/tests/unit/test_login.py::test_login_successful": 1.495486374999473,
|
||||
"src/backend/tests/unit/test_login.py::test_login_unsuccessful_wrong_password": 1.2617856669999128,
|
||||
"src/backend/tests/unit/test_login.py::test_login_unsuccessful_wrong_username": 1.0718942079993212,
|
||||
"src/backend/tests/unit/test_messages.py::test_add_messages": 1.0813413340001716,
|
||||
"src/backend/tests/unit/test_messages.py::test_add_messagetables": 1.064473082999939,
|
||||
"src/backend/tests/unit/test_messages.py::test_convert_to_langchain[convert_to_langchain_type]": 1.0604117919997407,
|
||||
"src/backend/tests/unit/test_messages.py::test_convert_to_langchain[message]": 1.0689540419998593,
|
||||
"src/backend/tests/unit/test_messages.py::test_delete_messages": 1.1744598340001176,
|
||||
"src/backend/tests/unit/test_messages.py::test_get_messages": 1.0707767070002774,
|
||||
"src/backend/tests/unit/test_messages.py::test_store_message": 1.0799672490002195,
|
||||
"src/backend/tests/unit/test_process.py::test_load_langchain_object_with_cached_session": 1.0705989160001081,
|
||||
"src/backend/tests/unit/test_process.py::test_load_langchain_object_with_no_cached_session": 1.0660591649993876,
|
||||
"src/backend/tests/unit/test_process.py::test_load_langchain_object_without_session_id": 1.0685117090001768,
|
||||
"src/backend/tests/unit/test_process.py::test_multiple_tweaks": 3.4656270829996174,
|
||||
"src/backend/tests/unit/test_process.py::test_no_tweaks": 1.0528393340005096,
|
||||
"src/backend/tests/unit/test_process.py::test_single_tweak": 1.0588130830001319,
|
||||
"src/backend/tests/unit/test_process.py::test_tweak_no_node_id": 1.0907960420004201,
|
||||
"src/backend/tests/unit/test_process.py::test_tweak_not_in_template": 1.065006457999516,
|
||||
"src/backend/tests/unit/test_setup_superuser.py::test_teardown_superuser_default_superuser": 1.0545740010002191,
|
||||
"src/backend/tests/unit/test_setup_superuser.py::test_teardown_superuser_no_default_superuser": 1.054325458999756,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_gauge": 1.081740833999902,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_gauge_with_counter_method": 1.056230042000152,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_gauge_with_historgram_method": 1.110880250000264,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_gauge_with_up_down_counter_method": 1.1089114589999554,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_increment_counter": 1.091222834000746,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_increment_counter_empty_label": 1.0598395000001801,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_increment_counter_missing_mandatory_label": 1.0523453739997422,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_increment_counter_unregisted_metric": 1.060642084000392,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_init": 1.0586442910002916,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_missing_labels": 1.0542013740000584,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_multithreaded_singleton": 1.0596915410001202,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_multithreaded_singleton_race_condition": 1.078360167000028,
|
||||
"src/backend/tests/unit/test_telemetry.py::test_opentelementry_singleton": 1.0586542500004725,
|
||||
"src/backend/tests/unit/test_template.py::test_build_template_from_function": 1.0667507910002314,
|
||||
"src/backend/tests/unit/test_template.py::test_get_base_classes": 1.0488440839994837,
|
||||
"src/backend/tests/unit/test_template.py::test_get_default_factory": 1.061691208999946,
|
||||
"src/backend/tests/unit/test_validate_code.py::test_create_function": 3.7202681249996203,
|
||||
"src/backend/tests/unit/test_validate_code.py::test_execute_function_missing_function": 1.0697480410003664,
|
||||
"src/backend/tests/unit/test_validate_code.py::test_execute_function_missing_module": 1.0638055839999652,
|
||||
"src/backend/tests/unit/test_validate_code.py::test_execute_function_missing_schema": 1.0542077080003764,
|
||||
"src/backend/tests/unit/test_validate_code.py::test_execute_function_success": 1.0714820839998538,
|
||||
"src/backend/tests/unit/test_validate_code.py::test_validate_code": 1.0665104990002874,
|
||||
"src/backend/tests/unit/test_version.py::test_compute_main": 1.0618379169995933,
|
||||
"src/backend/tests/unit/test_version.py::test_version": 1.0611935000001722,
|
||||
"src/backend/tests/unit/utils/test_connection_string_parser.py::test_transform_connection_string[protocol::password@host-protocol::password@host]": 0.0002089159997922252,
|
||||
"src/backend/tests/unit/utils/test_connection_string_parser.py::test_transform_connection_string[protocol:user:pa:ss:word@host-protocol:user:pa:ss:word@host]": 0.0002251249998153071,
|
||||
"src/backend/tests/unit/utils/test_connection_string_parser.py::test_transform_connection_string[protocol:user:pa@ss@word@host-protocol:user:pa%40ss%40word@host]": 0.0002600429997983156,
|
||||
"src/backend/tests/unit/utils/test_connection_string_parser.py::test_transform_connection_string[protocol:user:pass@word@host-protocol:user:pass%40word@host]": 0.0002567910000834672,
|
||||
"src/backend/tests/unit/utils/test_connection_string_parser.py::test_transform_connection_string[protocol:user:password@-protocol:user:password@]": 0.0002196240002376726,
|
||||
"src/backend/tests/unit/utils/test_connection_string_parser.py::test_transform_connection_string[protocol:user:password@host-protocol:user:password@host]": 0.0002557500001785229,
|
||||
"src/backend/tests/unit/utils/test_connection_string_parser.py::test_transform_connection_string[protocol:user@host-protocol:user@host]": 0.00021862499943381408,
|
||||
"src/backend/tests/unit/utils/test_connection_string_parser.py::test_transform_connection_string[user:password@host-user:password@host]": 0.0002157910003006691
|
||||
}
|
||||
|
|
@ -20,7 +20,7 @@ from langflow.inputs.inputs import (
|
|||
SecretStrInput,
|
||||
StrInput,
|
||||
TableInput,
|
||||
_instantiate_input,
|
||||
instantiate_input,
|
||||
)
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
|
@ -69,14 +69,14 @@ def test_message_text_input_invalid():
|
|||
|
||||
def test_instantiate_input_valid():
|
||||
data = {"name": "valid_input", "value": "This is a string"}
|
||||
input_instance = _instantiate_input("StrInput", data)
|
||||
input_instance = instantiate_input("StrInput", data)
|
||||
assert isinstance(input_instance, StrInput)
|
||||
assert input_instance.value == "This is a string"
|
||||
|
||||
|
||||
def test_instantiate_input_invalid():
|
||||
with pytest.raises(ValueError):
|
||||
_instantiate_input("InvalidInput", {"name": "invalid_input", "value": "This is a string"})
|
||||
instantiate_input("InvalidInput", {"name": "invalid_input", "value": "This is a string"})
|
||||
|
||||
|
||||
def test_handle_input_valid():
|
||||
|
|
@ -218,8 +218,8 @@ def test_instantiate_input_comprehensive():
|
|||
}
|
||||
|
||||
for input_type, data in valid_data.items():
|
||||
input_instance = _instantiate_input(input_type, data)
|
||||
input_instance = instantiate_input(input_type, data)
|
||||
assert isinstance(input_instance, InputTypesMap[input_type])
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
_instantiate_input("InvalidInput", {"name": "invalid_input", "value": "Invalid"})
|
||||
instantiate_input("InvalidInput", {"name": "invalid_input", "value": "Invalid"})
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue