refactor: LLM plugin invoke parsing (#31499)

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

api/tests/unit_tests/core/model_runtime/__base/test_increase_tool_call.py

@@ -1,5 +1,7 @@
 from unittest.mock import MagicMock, patch
 
+import pytest
+
 from core.model_runtime.entities.message_entities import AssistantPromptMessage
 from core.model_runtime.model_providers.__base.large_language_model import _increase_tool_call
 
@@ -97,3 +99,14 @@ def test__increase_tool_call():
     mock_id_generator.side_effect = [_exp_case.id for _exp_case in EXPECTED_CASE_4]
     with patch("core.model_runtime.model_providers.__base.large_language_model._gen_tool_call_id", mock_id_generator):
         _run_case(INPUTS_CASE_4, EXPECTED_CASE_4)
+
+
+def test__increase_tool_call__no_id_no_name_first_delta_should_raise():
+    inputs = [
+        ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="", arguments='{"arg1": ')),
+        ToolCall(id="", type="function", function=ToolCall.ToolCallFunction(name="func_foo", arguments='"value"}')),
+    ]
+    actual: list[ToolCall] = []
+    with patch("core.model_runtime.model_providers.__base.large_language_model._gen_tool_call_id", MagicMock()):
+        with pytest.raises(ValueError):
+            _increase_tool_call(inputs, actual)

api/tests/unit_tests/core/model_runtime/__base/test_large_language_model_non_stream_parsing.py

@@ -0,0 +1,103 @@
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.model_providers.__base.large_language_model import _normalize_non_stream_plugin_result
+
+
+def _make_chunk(
+    *,
+    model: str = "test-model",
+    content: str | list[TextPromptMessageContent] | None,
+    tool_calls: list[AssistantPromptMessage.ToolCall] | None = None,
+    usage: LLMUsage | None = None,
+    system_fingerprint: str | None = None,
+) -> LLMResultChunk:
+    message = AssistantPromptMessage(content=content, tool_calls=tool_calls or [])
+    delta = LLMResultChunkDelta(index=0, message=message, usage=usage)
+    return LLMResultChunk(model=model, delta=delta, system_fingerprint=system_fingerprint)
+
+
+def test__normalize_non_stream_plugin_result__from_first_chunk_str_content_and_tool_calls():
+    prompt_messages = [UserPromptMessage(content="hi")]
+
+    tool_calls = [
+        AssistantPromptMessage.ToolCall(
+            id="1",
+            type="function",
+            function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="func_foo", arguments=""),
+        ),
+        AssistantPromptMessage.ToolCall(
+            id="",
+            type="function",
+            function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments='{"arg1": '),
+        ),
+        AssistantPromptMessage.ToolCall(
+            id="",
+            type="function",
+            function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments='"value"}'),
+        ),
+    ]
+
+    usage = LLMUsage.empty_usage().model_copy(update={"prompt_tokens": 1, "total_tokens": 1})
+    chunk = _make_chunk(content="hello", tool_calls=tool_calls, usage=usage, system_fingerprint="fp-1")
+
+    result = _normalize_non_stream_plugin_result(
+        model="test-model", prompt_messages=prompt_messages, result=iter([chunk])
+    )
+
+    assert result.model == "test-model"
+    assert result.prompt_messages == prompt_messages
+    assert result.message.content == "hello"
+    assert result.usage.prompt_tokens == 1
+    assert result.system_fingerprint == "fp-1"
+    assert result.message.tool_calls == [
+        AssistantPromptMessage.ToolCall(
+            id="1",
+            type="function",
+            function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="func_foo", arguments='{"arg1": "value"}'),
+        )
+    ]
+
+
+def test__normalize_non_stream_plugin_result__from_first_chunk_list_content():
+    prompt_messages = [UserPromptMessage(content="hi")]
+
+    content_list = [TextPromptMessageContent(data="a"), TextPromptMessageContent(data="b")]
+    chunk = _make_chunk(content=content_list, usage=LLMUsage.empty_usage())
+
+    result = _normalize_non_stream_plugin_result(
+        model="test-model", prompt_messages=prompt_messages, result=iter([chunk])
+    )
+
+    assert result.message.content == content_list
+
+
+def test__normalize_non_stream_plugin_result__passthrough_llm_result():
+    prompt_messages = [UserPromptMessage(content="hi")]
+    llm_result = LLMResult(
+        model="test-model",
+        prompt_messages=prompt_messages,
+        message=AssistantPromptMessage(content="ok"),
+        usage=LLMUsage.empty_usage(),
+    )
+
+    assert (
+        _normalize_non_stream_plugin_result(model="test-model", prompt_messages=prompt_messages, result=llm_result)
+        == llm_result
+    )
+
+
+def test__normalize_non_stream_plugin_result__empty_iterator_defaults():
+    prompt_messages = [UserPromptMessage(content="hi")]
+
+    result = _normalize_non_stream_plugin_result(model="test-model", prompt_messages=prompt_messages, result=iter([]))
+
+    assert result.model == "test-model"
+    assert result.prompt_messages == prompt_messages
+    assert result.message.content == []
+    assert result.message.tool_calls == []
+    assert result.usage == LLMUsage.empty_usage()
+    assert result.system_fingerprint is None