Merge branch 'fixbug/issues/1016' into HEAD

tests/metagpt/roles/di/test_data_interpreter.py

@@ -25,7 +25,6 @@ async def test_interpreter(mocker, auto_run):
 @pytest.mark.asyncio
 async def test_interpreter_react_mode(mocker):
     mocker.patch("metagpt.actions.di.execute_nb_code.ExecuteNbCode.run", return_value=("a successful run", True))
-    mocker.patch("builtins.input", return_value="confirm")
 
     requirement = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy."
 

tests/metagpt/strategy/test_planner.py

@@ -0,0 +1,37 @@
+from metagpt.schema import Plan, Task
+from metagpt.strategy.planner import Planner
+from metagpt.strategy.task_type import TaskType
+
+MOCK_TASK_MAP = {
+    "1": Task(
+        task_id="1",
+        instruction="test instruction for finished task",
+        task_type=TaskType.EDA.type_name,
+        dependent_task_ids=[],
+        code="some finished test code",
+        result="some finished test result",
+        is_finished=True,
+    ),
+    "2": Task(
+        task_id="2",
+        instruction="test instruction for current task",
+        task_type=TaskType.DATA_PREPROCESS.type_name,
+        dependent_task_ids=["1"],
+    ),
+}
+MOCK_PLAN = Plan(
+    goal="test goal",
+    tasks=list(MOCK_TASK_MAP.values()),
+    task_map=MOCK_TASK_MAP,
+    current_task_id="2",
+)
+
+
+def test_planner_get_plan_status():
+    planner = Planner(plan=MOCK_PLAN)
+    status = planner.get_plan_status()
+
+    assert "some finished test code" in status
+    assert "some finished test result" in status
+    assert "test instruction for current task" in status
+    assert TaskType.DATA_PREPROCESS.value.guidance in status  # current task guidance

tests/metagpt/utils/test_text.py

@@ -22,7 +22,7 @@ def _paragraphs(n):
 @pytest.mark.parametrize(
     "msgs, model_name, system_text, reserved, expected",
     [
-        (_msgs(), "gpt-3.5-turbo", "System", 1500, 1),
+        (_msgs(), "gpt-3.5-turbo-0613", "System", 1500, 1),
         (_msgs(), "gpt-3.5-turbo-16k", "System", 3000, 6),
         (_msgs(), "gpt-3.5-turbo-16k", "Hello," * 1000, 3000, 5),
         (_msgs(), "gpt-4", "System", 2000, 3),
@@ -32,22 +32,23 @@ def _paragraphs(n):
     ],
 )
 def test_reduce_message_length(msgs, model_name, system_text, reserved, expected):
-    assert len(reduce_message_length(msgs, model_name, system_text, reserved)) / (len("Hello,")) / 1000 == expected
+    length = len(reduce_message_length(msgs, model_name, system_text, reserved)) / (len("Hello,")) / 1000
+    assert length == expected
 
 
 @pytest.mark.parametrize(
     "text, prompt_template, model_name, system_text, reserved, expected",
     [
-        (" ".join("Hello World." for _ in range(1000)), "Prompt: {}", "gpt-3.5-turbo", "System", 1500, 2),
+        (" ".join("Hello World." for _ in range(1000)), "Prompt: {}", "gpt-3.5-turbo-0613", "System", 1500, 2),
         (" ".join("Hello World." for _ in range(1000)), "Prompt: {}", "gpt-3.5-turbo-16k", "System", 3000, 1),
         (" ".join("Hello World." for _ in range(4000)), "Prompt: {}", "gpt-4", "System", 2000, 2),
         (" ".join("Hello World." for _ in range(8000)), "Prompt: {}", "gpt-4-32k", "System", 4000, 1),
-        (" ".join("Hello World" for _ in range(8000)), "Prompt: {}", "gpt-3.5-turbo", "System", 1000, 8),
+        (" ".join("Hello World" for _ in range(8000)), "Prompt: {}", "gpt-3.5-turbo-0613", "System", 1000, 8),
     ],
 )
 def test_generate_prompt_chunk(text, prompt_template, model_name, system_text, reserved, expected):
-    ret = list(generate_prompt_chunk(text, prompt_template, model_name, system_text, reserved))
-    assert len(ret) == expected
+    chunk = len(list(generate_prompt_chunk(text, prompt_template, model_name, system_text, reserved)))
+    assert chunk == expected
 
 
 @pytest.mark.parametrize(

tests/mock/mock_llm.py

@@ -8,7 +8,6 @@ from metagpt.provider.azure_openai_api import AzureOpenAILLM
 from metagpt.provider.constant import GENERAL_FUNCTION_SCHEMA
 from metagpt.provider.openai_api import OpenAILLM
 from metagpt.schema import Message
-from metagpt.utils.common import process_message
 
 OriginalLLM = OpenAILLM if config.llm.api_type == LLMType.OPENAI else AzureOpenAILLM
 
@@ -105,7 +104,7 @@ class MockLLM(OriginalLLM):
         return rsp
 
     async def aask_code(self, messages: Union[str, Message, list[dict]], **kwargs) -> dict:
-        msg_key = json.dumps(process_message(messages), ensure_ascii=False)
+        msg_key = json.dumps(self.format_msg(messages), ensure_ascii=False)
         rsp = await self._mock_rsp(msg_key, self.original_aask_code, messages, **kwargs)
         return rsp