Add MiniMax LLM provider configuration

.env.example

@@ -31,6 +31,20 @@ VIDU_API_KEY=your_vidu_api_key_here
 # 默认使用 DashScope (通过 OpenAI 兼容接口调用，只需上方 DASHSCOPE_API_KEY 即可)
 # LLM_PROVIDER=dashscope
 #
+# MiniMax OpenAI-compatible configuration:
+# LLM_PROVIDER=minimax
+# MINIMAX_API_KEY=your_minimax_api_key_here
+# MINIMAX_MODEL=MiniMax-M3
+# Supported models: MiniMax-M3, MiniMax-M2.7
+# Global endpoint:
+# MINIMAX_BASE_URL=https://api.minimax.io/v1
+# China endpoint:
+# MINIMAX_BASE_URL=https://api.minimaxi.com/v1
+#
+# MiniMax Anthropic-compatible clients can use the matching regional endpoint:
+# Global: ANTHROPIC_BASE_URL=https://api.minimax.io/anthropic
+# China: ANTHROPIC_BASE_URL=https://api.minimaxi.com/anthropic
+#
 # 如需切换到第三方 OpenAI 兼容 API (OpenAI/DeepSeek/Ollama 等):
 # LLM_PROVIDER=openai
 # OPENAI_API_KEY=your_openai_api_key_here
@@ -59,5 +73,6 @@ API_PORT=8000
 # ===============================
 # DASHSCOPE_BASE_URL=https://dashscope-intl.aliyuncs.com
 # KLING_BASE_URL=https://api.klingai.com/v1
+# MINIMAX_BASE_URL=https://api.minimax.io/v1
 # VIDU_BASE_URL=https://api.vidu.cn/ent/v2
 # MULEROUTER_BASE_URL=https://api.mulerouter.ai

src/apps/comic_gen/llm_adapter.py

@@ -1,28 +1,34 @@
 """
-LLM Adapter - Unified interface for DashScope and OpenAI-compatible APIs.
+LLM Adapter - Unified interface for provider-specific, OpenAI-compatible APIs.
 
-Supports two providers:
+Supported providers:
   - dashscope (default): Alibaba Cloud DashScope via OpenAI-compatible endpoint
   - openai: Any OpenAI-compatible API (OpenAI, DeepSeek, Ollama, etc.)
+  - minimax: MiniMax via its OpenAI-compatible endpoint
 
 Configuration via environment variables:
   LLM_PROVIDER=dashscope|openai
   DASHSCOPE_API_KEY=...
   OPENAI_API_KEY=...
   OPENAI_BASE_URL=https://api.openai.com/v1
   OPENAI_MODEL=gpt-4o
+  LLM_PROVIDER=minimax
+  MINIMAX_API_KEY=...
+  MINIMAX_BASE_URL=https://api.minimax.io/v1
+  MINIMAX_MODEL=MiniMax-M3
 """
-import os
+
 import logging
-from typing import Dict, List, Optional, Any
+import os
+from typing import Any, Dict, List, Optional
 
 from ...utils.endpoints import get_provider_base_url
 
 logger = logging.getLogger(__name__)
 
 
 class LLMAdapter:
-    """Unified LLM call interface supporting DashScope and OpenAI-compatible APIs."""
+    """Unified LLM call interface with provider-specific configuration."""
 
     def __init__(self):
         self.provider = os.getenv("LLM_PROVIDER", "dashscope").lower()
@@ -31,6 +37,8 @@ def __init__(self):
 
     @property
     def is_configured(self) -> bool:
+        if self.provider == "minimax":
+            return bool(os.getenv("MINIMAX_API_KEY"))
         if self.provider == "openai":
             return bool(os.getenv("OPENAI_API_KEY"))
         return bool(os.getenv("DASHSCOPE_API_KEY"))
@@ -41,9 +49,14 @@ def _get_client(self):
             try:
                 from openai import OpenAI
             except ImportError:
-                raise RuntimeError(
-                    "openai package not installed. Run: pip install openai>=1.0.0"
+                raise RuntimeError("openai package not installed. Run: pip install openai>=1.0.0")
+
+            if self.provider == "minimax":
+                self._client = OpenAI(
+                    api_key=os.getenv("MINIMAX_API_KEY"),
+                    base_url=get_provider_base_url("MINIMAX"),
                 )
+                return self._client
 
             if self.provider == "openai":
                 self._client = OpenAI(
@@ -64,6 +77,8 @@ def _get_client(self):
     _DASHSCOPE_MODEL_FALLBACK_CHAIN = ["qwen3.7-plus", "qwen3.6-plus", "qwen-plus"]
 
     def _get_default_model(self) -> str:
+        if self.provider == "minimax":
+            return os.getenv("MINIMAX_MODEL", "MiniMax-M3")
         if self.provider == "openai":
             return os.getenv("OPENAI_MODEL", "gpt-4o")
         return self._DASHSCOPE_MODEL_FALLBACK_CHAIN[0]
@@ -94,6 +109,9 @@ def chat(
         if model:
             return self._chat_once(client, model, messages, response_format)
 
+        if self.provider == "minimax":
+            return self._chat_once(client, self._get_default_model(), messages, response_format)
+
         # Provider 默认路径：DashScope 走 fallback chain，OpenAI 单次尝试。
         if self.provider == "openai":
             return self._chat_once(client, self._get_default_model(), messages, response_format)
@@ -106,16 +124,26 @@ def chat(
                 # 仅在 "模型不存在 / 不可用" 类错误时回退；其他错误（鉴权、限流、网络）
                 # 直接抛，不浪费第二次重试。判定关键字宽松匹配 DashScope 文案。
                 msg = str(e).lower()
-                is_model_unavailable = any(k in msg for k in (
-                    "model not found", "invalidmodel", "model_not_found",
-                    "no such model", "not supported", "modelnotfound", "404",
-                ))
+                is_model_unavailable = any(
+                    k in msg
+                    for k in (
+                        "model not found",
+                        "invalidmodel",
+                        "model_not_found",
+                        "no such model",
+                        "not supported",
+                        "modelnotfound",
+                        "404",
+                    )
+                )
                 last_err = e
                 if is_model_unavailable and idx < len(self._DASHSCOPE_MODEL_FALLBACK_CHAIN) - 1:
                     next_candidate = self._DASHSCOPE_MODEL_FALLBACK_CHAIN[idx + 1]
                     logger.warning(
                         "DashScope model %s unavailable (%s); falling back to %s",
-                        candidate, e, next_candidate,
+                        candidate,
+                        e,
+                        next_candidate,
                     )
                     continue
                 raise
@@ -140,5 +168,7 @@ def _chat_once(
             response = client.chat.completions.create(**kwargs)
             return response.choices[0].message.content
         except Exception as e:
+            if self.provider == "minimax":
+                raise RuntimeError(f"MiniMax API error: {e}") from e
             provider_label = "DashScope" if self.provider != "openai" else "OpenAI"
             raise RuntimeError(f"{provider_label} API error: {e}") from e

src/utils/endpoints.py

@@ -4,6 +4,7 @@
 PROVIDER_DEFAULTS = {
     "DASHSCOPE": "https://dashscope.aliyuncs.com",
     "KLING": "https://api-beijing.klingai.com/v1",
+    "MINIMAX": "https://api.minimax.io/v1",
     "VIDU": "https://api.vidu.cn/ent/v2",
     "MULEROUTER": "https://api.mulerouter.ai",
 }

tests/test_llm_adapter.py

@@ -0,0 +1,87 @@
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pytest
+
+from src.apps.comic_gen.llm_adapter import LLMAdapter
+
+
+@pytest.fixture
+def minimax_client(monkeypatch):
+    captured = {}
+    create = Mock(
+        return_value=SimpleNamespace(
+            choices=[SimpleNamespace(message=SimpleNamespace(content="MiniMax response"))]
+        )
+    )
+    client = SimpleNamespace(chat=SimpleNamespace(completions=SimpleNamespace(create=create)))
+
+    def build_client(**kwargs):
+        captured["kwargs"] = kwargs
+        captured["client"] = client
+        return client
+
+    monkeypatch.setitem(sys.modules, "openai", SimpleNamespace(OpenAI=build_client))
+    return captured
+
+
+def test_minimax_configuration_requires_its_api_key(monkeypatch):
+    monkeypatch.setenv("LLM_PROVIDER", "minimax")
+    monkeypatch.delenv("MINIMAX_API_KEY", raising=False)
+
+    adapter = LLMAdapter()
+
+    assert adapter.is_configured is False
+
+    monkeypatch.setenv("MINIMAX_API_KEY", "test-key")
+    assert adapter.is_configured is True
+
+
+def test_minimax_uses_global_defaults(monkeypatch, minimax_client):
+    monkeypatch.setenv("LLM_PROVIDER", "minimax")
+    monkeypatch.setenv("MINIMAX_API_KEY", "test-key")
+    monkeypatch.delenv("MINIMAX_BASE_URL", raising=False)
+    monkeypatch.delenv("MINIMAX_MODEL", raising=False)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    result = LLMAdapter().chat(messages)
+
+    assert result == "MiniMax response"
+    assert minimax_client["kwargs"] == {
+        "api_key": "test-key",
+        "base_url": "https://api.minimax.io/v1",
+    }
+    minimax_client["client"].chat.completions.create.assert_called_once_with(
+        model="MiniMax-M3",
+        messages=messages,
+    )
+
+
+def test_minimax_supports_china_endpoint_and_secondary_model(monkeypatch, minimax_client):
+    monkeypatch.setenv("LLM_PROVIDER", "minimax")
+    monkeypatch.setenv("MINIMAX_API_KEY", "test-key")
+    monkeypatch.setenv("MINIMAX_BASE_URL", "https://api.minimaxi.com/v1")
+    monkeypatch.setenv("MINIMAX_MODEL", "MiniMax-M2.7")
+    messages = [{"role": "user", "content": "Hello"}]
+
+    LLMAdapter().chat(messages, response_format={"type": "json_object"})
+
+    assert minimax_client["kwargs"]["base_url"] == "https://api.minimaxi.com/v1"
+    minimax_client["client"].chat.completions.create.assert_called_once_with(
+        model="MiniMax-M2.7",
+        messages=messages,
+        response_format={"type": "json_object"},
+    )
+
+
+def test_minimax_documents_regional_protocol_endpoints():
+    env_example = (Path(__file__).parents[1] / ".env.example").read_text()
+
+    assert "https://api.minimax.io/v1" in env_example
+    assert "https://api.minimaxi.com/v1" in env_example
+    assert "https://api.minimax.io/anthropic" in env_example
+    assert "https://api.minimaxi.com/anthropic" in env_example
+    assert "MiniMax-M3" in env_example
+    assert "MiniMax-M2.7" in env_example