feat: add MiniMax provider support for voice agent LLM

examples/voice_agent/server/server_configs/llm_configs/minimax.yaml

@@ -0,0 +1,21 @@
+# This is an example config for using MiniMax as the LLM backend for a NeMo Voice Agent server.
+# Please refer to https://github.com/NVIDIA-NeMo/NeMo/tree/main/examples/voice_agent/README.md for more details
+# MiniMax API documentation: https://platform.minimax.io/docs/api-reference/text-openai-api
+
+# type: minimax
+# model: "MiniMax-M2.7"  # choices: ["MiniMax-M2.7", "MiniMax-M2.7-highspeed"]
+
+##################################
+######## MiniMax config ##########
+##################################
+# API key for MiniMax. If not set here, MINIMAX_API_KEY environment variable will be used.
+# api_key: null
+base_url: "https://api.minimax.io/v1"
+
+# Inference parameters passed to the MiniMax OpenAI-compatible API
+# Note: MiniMax temperature must be in range (0.0, 1.0], default is 1.0
+minimax_generation_params:
+  temperature: 1.0          # Sampling temperature (0.0 exclusive to 1.0 inclusive)
+  top_p: 0.9                # Top-p (nucleus) sampling (0.0 to 1.0)
+  max_completion_tokens: 256  # Max number of output tokens
+  extra: null               # Additional model-specific params in dict format

examples/voice_agent/tests/test_minimax_llm.py

@@ -0,0 +1,161 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Unit tests for MiniMaxService LLM provider."""
+
+import os
+import sys
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+# Add the local NeMo directory to Python path to use development version
+nemo_root = Path(__file__).resolve().parents[3]
+sys.path.insert(0, str(nemo_root))
+
+# Mock heavy dependencies before importing llm module
+sys.modules["vllm"] = MagicMock()
+sys.modules["vllm.config"] = MagicMock()
+sys.modules["transformers"] = MagicMock()
+sys.modules["psutil"] = MagicMock()
+
+from omegaconf import OmegaConf
+
+from nemo.agents.voice_agent.pipecat.services.nemo.llm import MiniMaxService, get_llm_service_from_config
+
+
+class TestMiniMaxService:
+    """Tests for MiniMaxService."""
+
+    def test_instantiation_with_api_key(self):
+        """MiniMaxService can be created with an explicit api_key."""
+        with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None):
+            svc = MiniMaxService(model="MiniMax-M2.7", api_key="test-key-123")
+        assert svc is not None
+
+    def test_instantiation_uses_env_var(self):
+        """MiniMaxService reads MINIMAX_API_KEY from environment when api_key is not provided."""
+        with patch.dict(os.environ, {"MINIMAX_API_KEY": "env-key-456"}):
+            with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None):
+                svc = MiniMaxService(model="MiniMax-M2.7")
+        assert svc is not None
+
+    def test_missing_api_key_raises(self):
+        """MiniMaxService raises ValueError when no API key is available."""
+        env_without_key = {k: v for k, v in os.environ.items() if k != "MINIMAX_API_KEY"}
+        with patch.dict(os.environ, env_without_key, clear=True):
+            with pytest.raises(ValueError, match="MINIMAX_API_KEY"):
+                MiniMaxService(model="MiniMax-M2.7")
+
+    def test_default_base_url(self):
+        """MiniMaxService uses the MiniMax overseas API base URL by default."""
+        assert MiniMaxService.DEFAULT_BASE_URL == "https://api.minimax.io/v1"
+
+    def test_custom_base_url(self):
+        """MiniMaxService accepts a custom base_url."""
+        custom_url = "https://custom.minimax.io/v1"
+        with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None) as mock_init:
+            MiniMaxService(model="MiniMax-M2.7", api_key="test-key", base_url=custom_url)
+            call_kwargs = mock_init.call_args[1]
+            assert call_kwargs.get("base_url") == custom_url
+
+    def test_supported_models_list(self):
+        """MiniMaxService exposes the two supported models."""
+        assert "MiniMax-M2.7" in MiniMaxService.SUPPORTED_MODELS
+        assert "MiniMax-M2.7-highspeed" in MiniMaxService.SUPPORTED_MODELS
+        assert len(MiniMaxService.SUPPORTED_MODELS) == 2
+
+    def test_api_key_passed_to_super(self):
+        """The resolved API key is forwarded to OpenAILLMService."""
+        with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None) as mock_init:
+            MiniMaxService(model="MiniMax-M2.7", api_key="my-secret-key")
+            call_kwargs = mock_init.call_args[1]
+            assert call_kwargs.get("api_key") == "my-secret-key"
+
+    def test_default_model_is_m27(self):
+        """MiniMaxService defaults to MiniMax-M2.7 model."""
+        with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None) as mock_init:
+            with patch.dict(os.environ, {"MINIMAX_API_KEY": "key"}):
+                MiniMaxService()
+                call_kwargs = mock_init.call_args[1]
+                assert call_kwargs.get("model") == "MiniMax-M2.7"
+
+    def test_highspeed_model(self):
+        """MiniMaxService accepts MiniMax-M2.7-highspeed model."""
+        with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None) as mock_init:
+            MiniMaxService(model="MiniMax-M2.7-highspeed", api_key="key")
+            call_kwargs = mock_init.call_args[1]
+            assert call_kwargs.get("model") == "MiniMax-M2.7-highspeed"
+
+
+class TestGetLLMServiceFromConfigMiniMax:
+    """Tests for get_llm_service_from_config with minimax backend."""
+
+    def test_factory_creates_minimax_service(self):
+        """get_llm_service_from_config returns a MiniMaxService for type=minimax."""
+        cfg = OmegaConf.create(
+            {
+                "type": "minimax",
+                "model": "MiniMax-M2.7",
+                "api_key": "test-factory-key",
+            }
+        )
+        with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None):
+            svc = get_llm_service_from_config(cfg)
+        assert isinstance(svc, MiniMaxService)
+
+    def test_factory_minimax_uses_env_key(self):
+        """Factory resolves MINIMAX_API_KEY when api_key is not in config."""
+        cfg = OmegaConf.create({"type": "minimax", "model": "MiniMax-M2.7"})
+        with patch.dict(os.environ, {"MINIMAX_API_KEY": "env-key"}):
+            with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None):
+                svc = get_llm_service_from_config(cfg)
+        assert isinstance(svc, MiniMaxService)
+
+    def test_factory_invalid_backend_raises(self):
+        """get_llm_service_from_config raises AssertionError for unknown backend."""
+        cfg = OmegaConf.create({"type": "unknown_backend", "model": "some-model"})
+        with pytest.raises(AssertionError):
+            get_llm_service_from_config(cfg)
+
+    def test_factory_minimax_highspeed_model(self):
+        """Factory correctly passes MiniMax-M2.7-highspeed model to MiniMaxService."""
+        cfg = OmegaConf.create(
+            {
+                "type": "minimax",
+                "model": "MiniMax-M2.7-highspeed",
+                "api_key": "key",
+            }
+        )
+        with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None) as mock_init:
+            svc = get_llm_service_from_config(cfg)
+        assert isinstance(svc, MiniMaxService)
+        call_kwargs = mock_init.call_args[1]
+        assert call_kwargs.get("model") == "MiniMax-M2.7-highspeed"
+
+    def test_factory_minimax_custom_base_url(self):
+        """Factory forwards a custom base_url to MiniMaxService."""
+        cfg = OmegaConf.create(
+            {
+                "type": "minimax",
+                "model": "MiniMax-M2.7",
+                "api_key": "key",
+                "base_url": "https://custom.minimax.io/v1",
+            }
+        )
+        with patch("pipecat.services.openai.llm.OpenAILLMService.__init__", return_value=None) as mock_init:
+            svc = get_llm_service_from_config(cfg)
+        call_kwargs = mock_init.call_args[1]
+        assert call_kwargs.get("base_url") == "https://custom.minimax.io/v1"

nemo/agents/voice_agent/pipecat/services/nemo/llm.py

@@ -668,6 +668,46 @@ async def _get_response_from_client(
         return chunks
 
 
+class MiniMaxService(OpenAILLMService):
+    """
+    LLM service that connects to MiniMax API using the OpenAI-compatible interface.
+    Supports MiniMax-M2.7 and MiniMax-M2.7-highspeed models.
+
+    Requires the MINIMAX_API_KEY environment variable or passing api_key explicitly.
+    API documentation: https://platform.minimax.io/docs/api-reference/text-openai-api
+    """
+
+    SUPPORTED_MODELS = [
+        "MiniMax-M2.7",
+        "MiniMax-M2.7-highspeed",
+    ]
+    DEFAULT_BASE_URL = "https://api.minimax.io/v1"
+
+    def __init__(
+        self,
+        *,
+        model: str = "MiniMax-M2.7",
+        api_key: Optional[str] = None,
+        base_url: Optional[str] = None,
+        params: Optional[OpenAILLMService.InputParams] = None,
+        **kwargs,
+    ):
+        resolved_api_key = api_key or os.environ.get("MINIMAX_API_KEY")
+        if not resolved_api_key:
+            raise ValueError(
+                "MiniMax API key is required. Set the MINIMAX_API_KEY environment variable or pass api_key."
+            )
+        resolved_base_url = base_url or self.DEFAULT_BASE_URL
+        super().__init__(
+            model=model,
+            api_key=resolved_api_key,
+            base_url=resolved_base_url,
+            params=params,
+            **kwargs,
+        )
+        logger.info(f"MiniMaxService initialized with model: {model}, base_url: {resolved_base_url}")
+
+
 def get_llm_service_from_config(config: DictConfig) -> OpenAILLMService:
     """Get an LLM service from the configuration."""
     backend = config.type
@@ -696,7 +736,8 @@ def get_llm_service_from_config(config: DictConfig) -> OpenAILLMService:
         "hf",
         "vllm",
         "auto",
-    ], f"Invalid backend: {backend}, only `hf`, `vllm`, and `auto` are supported."
+        "minimax",
+    ], f"Invalid backend: {backend}, only `hf`, `vllm`, `auto`, and `minimax` are supported."
 
     if backend == "hf":
         llm_model = config.model
@@ -756,5 +797,26 @@ def get_llm_service_from_config(config: DictConfig) -> OpenAILLMService:
             start_vllm_on_init=config.get("start_vllm_on_init", False),
             vllm_server_params=vllm_server_params,
         )
+    elif backend == "minimax":
+        llm_model = config.get("model", "MiniMax-M2.7")
+        llm_api_key = config.get("api_key", None)
+        llm_base_url = config.get("base_url", None)
+        llm_params = config.get("minimax_generation_params", None)
+        if llm_params is not None:
+            llm_params = OmegaConf.to_container(llm_params, resolve=True)
+            extra = llm_params.get("extra", None)
+            if extra is None:
+                llm_params["extra"] = {}
+            elif not isinstance(extra, dict):
+                raise ValueError(f"extra must be a dictionary, got {type(extra)}")
+            llm_params = OpenAILLMService.InputParams(**llm_params)
+        else:
+            llm_params = OpenAILLMService.InputParams()
+        return MiniMaxService(
+            model=llm_model,
+            api_key=llm_api_key,
+            base_url=llm_base_url,
+            params=llm_params,
+        )
     else:
         raise ValueError(f"Invalid LLM backend: {backend}")