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fix: invalid new tool call creation logic during response handling in OAI-Compat model (#17781)
This commit is contained in:
Vitor
GitHub
parent
b6b608219a
commit
defd5520ea
@ -1,5 +1,6 @@
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import logging
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import time
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import uuid
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from collections.abc import Generator, Sequence
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from typing import Optional, Union
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@ -24,6 +25,58 @@ from core.plugin.manager.model import PluginModelManager
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logger = logging.getLogger(__name__)
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def _gen_tool_call_id() -> str:
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return f"chatcmpl-tool-{str(uuid.uuid4().hex)}"
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def _increase_tool_call(
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new_tool_calls: list[AssistantPromptMessage.ToolCall], existing_tools_calls: list[AssistantPromptMessage.ToolCall]
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):
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"""
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Merge incremental tool call updates into existing tool calls.
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:param new_tool_calls: List of new tool call deltas to be merged.
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:param existing_tools_calls: List of existing tool calls to be modified IN-PLACE.
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"""
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def get_tool_call(tool_call_id: str):
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"""
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Get or create a tool call by ID
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:param tool_call_id: tool call ID
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:return: existing or new tool call
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"""
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if not tool_call_id:
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return existing_tools_calls[-1]
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_tool_call = next((_tool_call for _tool_call in existing_tools_calls if _tool_call.id == tool_call_id), None)
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if _tool_call is None:
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_tool_call = AssistantPromptMessage.ToolCall(
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id=tool_call_id,
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type="function",
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function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
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)
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existing_tools_calls.append(_tool_call)
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return _tool_call
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for new_tool_call in new_tool_calls:
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# generate ID for tool calls with function name but no ID to track them
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if new_tool_call.function.name and not new_tool_call.id:
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new_tool_call.id = _gen_tool_call_id()
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# get tool call
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tool_call = get_tool_call(new_tool_call.id)
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# update tool call
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if new_tool_call.id:
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tool_call.id = new_tool_call.id
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if new_tool_call.type:
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tool_call.type = new_tool_call.type
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if new_tool_call.function.name:
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tool_call.function.name = new_tool_call.function.name
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if new_tool_call.function.arguments:
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tool_call.function.arguments += new_tool_call.function.arguments
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class LargeLanguageModel(AIModel):
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"""
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Model class for large language model.
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@ -109,44 +162,13 @@ class LargeLanguageModel(AIModel):
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system_fingerprint = None
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tools_calls: list[AssistantPromptMessage.ToolCall] = []
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def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
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def get_tool_call(tool_name: str):
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if not tool_name:
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return tools_calls[-1]
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tool_call = next(
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(tool_call for tool_call in tools_calls if tool_call.function.name == tool_name), None
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)
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if tool_call is None:
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tool_call = AssistantPromptMessage.ToolCall(
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id="",
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type="",
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function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tool_name, arguments=""),
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)
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tools_calls.append(tool_call)
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return tool_call
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for new_tool_call in new_tool_calls:
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# get tool call
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tool_call = get_tool_call(new_tool_call.function.name)
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# update tool call
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if new_tool_call.id:
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tool_call.id = new_tool_call.id
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if new_tool_call.type:
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tool_call.type = new_tool_call.type
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if new_tool_call.function.name:
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tool_call.function.name = new_tool_call.function.name
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if new_tool_call.function.arguments:
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tool_call.function.arguments += new_tool_call.function.arguments
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for chunk in result:
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if isinstance(chunk.delta.message.content, str):
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content += chunk.delta.message.content
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elif isinstance(chunk.delta.message.content, list):
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content_list.extend(chunk.delta.message.content)
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if chunk.delta.message.tool_calls:
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increase_tool_call(chunk.delta.message.tool_calls)
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_increase_tool_call(chunk.delta.message.tool_calls, tools_calls)
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usage = chunk.delta.usage or LLMUsage.empty_usage()
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system_fingerprint = chunk.system_fingerprint
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@ -0,0 +1,99 @@
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from unittest.mock import MagicMock, patch
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from core.model_runtime.entities.message_entities import AssistantPromptMessage
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from core.model_runtime.model_providers.__base.large_language_model import _increase_tool_call
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ToolCall = AssistantPromptMessage.ToolCall
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# CASE 1: Single tool call
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INPUTS_CASE_1 = [
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ToolCall(id="1", type="function", function=ToolCall.ToolCallFunction(name="func_foo", arguments="")),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='{"arg1": ')),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='"value"}')),
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]
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EXPECTED_CASE_1 = [
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ToolCall(
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id="1", type="function", function=ToolCall.ToolCallFunction(name="func_foo", arguments='{"arg1": "value"}')
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),
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]
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# CASE 2: Tool call sequences where IDs are anchored to the first chunk (vLLM/SiliconFlow ...)
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INPUTS_CASE_2 = [
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ToolCall(id="1", type="function", function=ToolCall.ToolCallFunction(name="func_foo", arguments="")),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='{"arg1": ')),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='"value"}')),
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ToolCall(id="2", type="function", function=ToolCall.ToolCallFunction(name="func_bar", arguments="")),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='{"arg2": ')),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='"value"}')),
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]
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EXPECTED_CASE_2 = [
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ToolCall(
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id="1", type="function", function=ToolCall.ToolCallFunction(name="func_foo", arguments='{"arg1": "value"}')
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),
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ToolCall(
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id="2", type="function", function=ToolCall.ToolCallFunction(name="func_bar", arguments='{"arg2": "value"}')
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),
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]
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# CASE 3: Tool call sequences where IDs are anchored to every chunk (SGLang ...)
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INPUTS_CASE_3 = [
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ToolCall(id="1", type="function", function=ToolCall.ToolCallFunction(name="func_foo", arguments="")),
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ToolCall(id="1", type="function", function=ToolCall.ToolCallFunction(name="", arguments='{"arg1": ')),
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ToolCall(id="1", type="function", function=ToolCall.ToolCallFunction(name="", arguments='"value"}')),
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ToolCall(id="2", type="function", function=ToolCall.ToolCallFunction(name="func_bar", arguments="")),
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ToolCall(id="2", type="function", function=ToolCall.ToolCallFunction(name="", arguments='{"arg2": ')),
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ToolCall(id="2", type="function", function=ToolCall.ToolCallFunction(name="", arguments='"value"}')),
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]
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EXPECTED_CASE_3 = [
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ToolCall(
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id="1", type="function", function=ToolCall.ToolCallFunction(name="func_foo", arguments='{"arg1": "value"}')
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),
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ToolCall(
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id="2", type="function", function=ToolCall.ToolCallFunction(name="func_bar", arguments='{"arg2": "value"}')
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),
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]
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# CASE 4: Tool call sequences with no IDs
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INPUTS_CASE_4 = [
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="func_foo", arguments="")),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='{"arg1": ')),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='"value"}')),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="func_bar", arguments="")),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='{"arg2": ')),
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ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='"value"}')),
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]
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EXPECTED_CASE_4 = [
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ToolCall(
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id="RANDOM_ID_1",
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type="function",
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function=ToolCall.ToolCallFunction(name="func_foo", arguments='{"arg1": "value"}'),
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),
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ToolCall(
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id="RANDOM_ID_2",
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type="function",
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function=ToolCall.ToolCallFunction(name="func_bar", arguments='{"arg2": "value"}'),
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),
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]
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def _run_case(inputs: list[ToolCall], expected: list[ToolCall]):
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actual = []
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_increase_tool_call(inputs, actual)
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assert actual == expected
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def test__increase_tool_call():
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# case 1:
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_run_case(INPUTS_CASE_1, EXPECTED_CASE_1)
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# case 2:
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_run_case(INPUTS_CASE_2, EXPECTED_CASE_2)
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# case 3:
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_run_case(INPUTS_CASE_3, EXPECTED_CASE_3)
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# case 4:
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mock_id_generator = MagicMock()
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mock_id_generator.side_effect = [_exp_case.id for _exp_case in EXPECTED_CASE_4]
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with patch("core.model_runtime.model_providers.__base.large_language_model._gen_tool_call_id", mock_id_generator):
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_run_case(INPUTS_CASE_4, EXPECTED_CASE_4)
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