feat: add MiniMax LLM provider (#4132)

tests/llms/test_minimax.py

@@ -0,0 +1,169 @@
+import os
+from unittest.mock import Mock, patch
+
+import pytest
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.minimax import MinimaxConfig
+from mem0.llms.minimax import MiniMaxLLM
+from mem0.utils.factory import LlmFactory
+
+
+@pytest.fixture
+def mock_minimax_client():
+    with patch("mem0.llms.minimax.OpenAI") as mock_openai:
+        mock_client = Mock()
+        mock_openai.return_value = mock_client
+        yield mock_client
+
+
+def test_minimax_llm_default_base_url():
+    """Default config uses MiniMax official base URL."""
+    config = BaseLlmConfig(
+        model="MiniMax-M2.1", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key"
+    )
+    llm = MiniMaxLLM(config)
+    # OpenAI client may normalize URL with trailing slash
+    assert str(llm.client.base_url).rstrip("/") == "https://api.minimaxi.io/v1"
+
+
+def test_minimax_llm_env_base_url():
+    """Config uses MINIMAX_API_BASE env variable when set."""
+    provider_base_url = "https://api.provider.com/v1/"
+    os.environ["MINIMAX_API_BASE"] = provider_base_url
+    try:
+        config = MinimaxConfig(
+            model="MiniMax-M2.1",
+            temperature=0.7,
+            max_tokens=100,
+            top_p=1.0,
+            api_key="api_key",
+        )
+        llm = MiniMaxLLM(config)
+        assert str(llm.client.base_url).rstrip("/") == provider_base_url.rstrip("/")
+    finally:
+        os.environ.pop("MINIMAX_API_BASE", None)
+
+
+def test_minimax_llm_config_base_url():
+    """Config uses minimax_base_url when provided."""
+    config_base_url = "https://api.config.com/v1/"
+    config = MinimaxConfig(
+        model="MiniMax-M2.1",
+        temperature=0.7,
+        max_tokens=100,
+        top_p=1.0,
+        api_key="api_key",
+        minimax_base_url=config_base_url,
+    )
+    llm = MiniMaxLLM(config)
+    assert str(llm.client.base_url).rstrip("/") == config_base_url.rstrip("/")
+
+
+def test_minimax_llm_default_model(mock_minimax_client):
+    """Default model is MiniMax-M2.1 when not specified."""
+    config = MinimaxConfig(temperature=0.7, max_tokens=100, api_key="api_key")
+    llm = MiniMaxLLM(config)
+    assert llm.config.model == "MiniMax-M2.1"
+
+
+def test_minimax_llm_env_api_key():
+    """Uses MINIMAX_API_KEY env when api_key not in config."""
+    os.environ["MINIMAX_API_KEY"] = "env-api-key"
+    try:
+        with patch("mem0.llms.minimax.OpenAI") as mock_openai:
+            mock_client = Mock()
+            mock_openai.return_value = mock_client
+            config = MinimaxConfig(model="MiniMax-M2.1", api_key=None)
+            MiniMaxLLM(config)
+            mock_openai.assert_called_once_with(
+                api_key="env-api-key",
+                base_url="https://api.minimaxi.io/v1",
+            )
+    finally:
+        os.environ.pop("MINIMAX_API_KEY", None)
+
+
+def test_generate_response_without_tools(mock_minimax_client):
+    """generate_response returns text when no tools provided."""
+    config = BaseLlmConfig(
+        model="MiniMax-M2.1", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key"
+    )
+    llm = MiniMaxLLM(config)
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello, how are you?"},
+    ]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
+    mock_minimax_client.chat.completions.create.return_value = mock_response
+
+    response = llm.generate_response(messages)
+
+    mock_minimax_client.chat.completions.create.assert_called_once_with(
+        model="MiniMax-M2.1", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0
+    )
+    assert response == "I'm doing well, thank you for asking!"
+
+
+def test_generate_response_with_tools(mock_minimax_client):
+    """generate_response returns tool_calls when tools provided."""
+    config = BaseLlmConfig(
+        model="MiniMax-M2.1", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key"
+    )
+    llm = MiniMaxLLM(config)
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Add a new memory: Today is a sunny day."},
+    ]
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "add_memory",
+                "description": "Add a memory",
+                "parameters": {
+                    "type": "object",
+                    "properties": {"data": {"type": "string", "description": "Data to add to memory"}},
+                    "required": ["data"],
+                },
+            },
+        }
+    ]
+
+    mock_response = Mock()
+    mock_message = Mock()
+    mock_message.content = "I've added the memory for you."
+
+    mock_tool_call = Mock()
+    mock_tool_call.function.name = "add_memory"
+    mock_tool_call.function.arguments = '{"data": "Today is a sunny day."}'
+
+    mock_message.tool_calls = [mock_tool_call]
+    mock_response.choices = [Mock(message=mock_message)]
+    mock_minimax_client.chat.completions.create.return_value = mock_response
+
+    response = llm.generate_response(messages, tools=tools)
+
+    mock_minimax_client.chat.completions.create.assert_called_once_with(
+        model="MiniMax-M2.1",
+        messages=messages,
+        temperature=0.7,
+        max_tokens=100,
+        top_p=1.0,
+        tools=tools,
+        tool_choice="auto",
+    )
+
+    assert response["content"] == "I've added the memory for you."
+    assert len(response["tool_calls"]) == 1
+    assert response["tool_calls"][0]["name"] == "add_memory"
+    assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}
+
+
+def test_factory_creates_minimax_llm(mock_minimax_client):
+    """LlmFactory.create returns MiniMaxLLM for provider 'minimax'."""
+    llm = LlmFactory.create("minimax", {"model": "MiniMax-M2.1", "api_key": "test-key"})
+    assert isinstance(llm, MiniMaxLLM)
+    assert llm.config.model == "MiniMax-M2.1"