feat: add MiniMax as first-class LLM provider

README.md

@@ -31,6 +31,23 @@ SQLBot 是一款基于大语言模型和 RAG 的智能问数系统，由 DataEas
 - **易于集成**：支持多种集成方式，提供 Web 嵌入、弹窗嵌入、MCP 调用等能力；能够快速嵌入到 n8n、Dify、MaxKB、DataEase 等应用，让各类应用快速拥有智能问数能力。
 - **越问越准**：支持自定义提示词、术语库配置，可维护 SQL 示例校准逻辑，精准匹配业务场景；高效运营，基于用户交互数据持续迭代优化，问数效果随使用逐步提升，越问越准。
 
+## 支持的大模型服务商
+
+| 服务商 | API 兼容 |
+|--------|----------|
+| 阿里云百炼 | OpenAI 兼容 |
+| 千帆大模型 | OpenAI 兼容 |
+| DeepSeek | OpenAI 兼容 |
+| 腾讯混元 | OpenAI 兼容 |
+| 讯飞星火 | OpenAI 兼容 |
+| Gemini | OpenAI 兼容 |
+| OpenAI | 原生 |
+| Kimi | OpenAI 兼容 |
+| 腾讯云 | OpenAI 兼容 |
+| 火山引擎 | OpenAI 兼容 |
+| MiniMax | OpenAI 兼容 |
+| 通用 OpenAI 兼容 | 自定义 |
+
 ## 快速开始
 
 ### 安装部署

docs/README.en.md

@@ -30,6 +30,23 @@ SQLBot is an intelligent data query system based on large language models and RA
 - **Easy Integration:** Supports multiple integration methods, providing capabilities such as web embedding, pop-up embedding, and MCP invocation. It can be quickly embedded into applications such as n8n, Dify, MaxKB, and DataEase, allowing various applications to quickly acquire intelligent data collection capabilities.
 - **Increasingly Accurate with Use:** Supports customizable prompts and terminology library configurations, maintainable SQL example calibration logic, and accurate matching of business scenarios. Efficient operation, based on continuous iteration and optimization using user interaction data, the data collection effect gradually improves with use, becoming more accurate with each use.
 
+## Supported LLM Providers
+
+| Provider | API Compatibility |
+|----------|-------------------|
+| Alibaba Cloud Bailian | OpenAI Compatible |
+| Qianfan Model | OpenAI Compatible |
+| DeepSeek | OpenAI Compatible |
+| Tencent Hunyuan | OpenAI Compatible |
+| iFlytek Spark | OpenAI Compatible |
+| Gemini | OpenAI Compatible |
+| OpenAI | Native |
+| Kimi | OpenAI Compatible |
+| Tencent Cloud | OpenAI Compatible |
+| Volcano Engine | OpenAI Compatible |
+| MiniMax | OpenAI Compatible |
+| Generic OpenAI Compatible | Custom |
+
 ## Quick Start
 
 ### Installation and Deployment

frontend/src/entity/supplier.ts

@@ -10,6 +10,7 @@ import icon_txhy_colorful from '@/assets/model/icon_txhy_colorful.png'
 import icon_hsyq_colorful from '@/assets/model/icon_hsyq_colorful.png'
 // import icon_vllm_colorful from '@/assets/model/icon_vllm_colorful.png'
 import icon_common_openai from '@/assets/model/icon_common_openai.png'
+import icon_minimax_colorful from '@/assets/model/icon_minimax_colorful.png'
 // import icon_azure_openAI_colorful from '@/assets/model/icon_Azure_OpenAI_colorful.png'
 
 type ModelArg = { key: string; val?: string | number; type: string; range?: string }
@@ -276,6 +277,23 @@ export const supplierList: Array<{
       },
     },
   },
+  {
+    id: 13,
+    name: 'MiniMax',
+    i18nKey: 'supplier.minimax',
+    icon: icon_minimax_colorful,
+    model_config: {
+      0: {
+        api_domain: 'https://api.minimax.io/v1',
+        common_args: [{ key: 'temperature', val: 0.7, type: 'number', range: '[0, 1]' }],
+        model_options: [
+          { name: 'MiniMax-M2.7' },
+          { name: 'MiniMax-M2.5' },
+          { name: 'MiniMax-M2.5-highspeed' },
+        ],
+      },
+    },
+  },
   /* {
     id: 11,
     name: 'vLLM',

frontend/src/i18n/en.json

@@ -915,6 +915,7 @@
     "kimi": "Kimi",
     "tencent_cloud": "Tencent Cloud",
     "volcano_engine": "Volcano Engine",
+    "minimax": "MiniMax",
     "generic_openai": "Generic OpenAI"
   },
   "modelType": {

frontend/src/i18n/ko-KR.json

@@ -915,6 +915,7 @@
     "kimi": "Kimi",
     "tencent_cloud": "텐센트 클라우드",
     "volcano_engine": "볼케이노 엔진",
+    "minimax": "MiniMax",
     "generic_openai": "범용 OpenAI"
   },
   "modelType": {

frontend/src/i18n/zh-CN.json

@@ -915,6 +915,7 @@
     "kimi": "Kimi",
     "tencent_cloud": "腾讯云",
     "volcano_engine": "火山引擎",
+    "minimax": "MiniMax",
     "generic_openai": "通用OpenAI"
   },
   "modelType": {

tests/test_minimax_integration.py

@@ -0,0 +1,91 @@
+"""
+Integration tests for MiniMax LLM provider in SQLBot.
+
+These tests validate that the MiniMax API is reachable and functioning
+correctly via the OpenAI-compatible protocol.
+
+Requires MINIMAX_API_KEY environment variable to be set.
+"""
+
+import json
+import os
+import unittest
+
+import requests
+
+MINIMAX_API_KEY = os.environ.get("MINIMAX_API_KEY", "")
+MINIMAX_BASE_URL = "https://api.minimax.io/v1"
+
+
+def skip_without_api_key(func):
+    """Skip test if MINIMAX_API_KEY is not set."""
+    return unittest.skipUnless(MINIMAX_API_KEY, "MINIMAX_API_KEY not set")(func)
+
+
+class TestMiniMaxAPIConnectivity(unittest.TestCase):
+    """Test MiniMax API endpoint reachability."""
+
+    @skip_without_api_key
+    def test_api_endpoint_reachable(self):
+        """MiniMax API endpoint should be reachable (chat completions)."""
+        resp = requests.post(
+            f"{MINIMAX_BASE_URL}/chat/completions",
+            headers={
+                "Authorization": f"Bearer {MINIMAX_API_KEY}",
+                "Content-Type": "application/json",
+            },
+            json={
+                "model": "MiniMax-M2.5-highspeed",
+                "messages": [{"role": "user", "content": "Hi"}],
+                "max_tokens": 1,
+            },
+            timeout=15,
+        )
+        self.assertEqual(resp.status_code, 200)
+
+    @skip_without_api_key
+    def test_chat_completions_basic(self):
+        """MiniMax chat completions should return a valid response."""
+        resp = requests.post(
+            f"{MINIMAX_BASE_URL}/chat/completions",
+            headers={
+                "Authorization": f"Bearer {MINIMAX_API_KEY}",
+                "Content-Type": "application/json",
+            },
+            json={
+                "model": "MiniMax-M2.5-highspeed",
+                "messages": [{"role": "user", "content": "Say hello in one word."}],
+                "temperature": 0.7,
+                "max_tokens": 10,
+            },
+            timeout=30,
+        )
+        self.assertEqual(resp.status_code, 200)
+        data = resp.json()
+        self.assertIn("choices", data)
+        self.assertGreater(len(data["choices"]), 0)
+        content = data["choices"][0]["message"]["content"]
+        self.assertTrue(len(content) > 0, "Response content should not be empty")
+
+    @skip_without_api_key
+    def test_temperature_zero_accepted(self):
+        """MiniMax API should accept temperature=0."""
+        resp = requests.post(
+            f"{MINIMAX_BASE_URL}/chat/completions",
+            headers={
+                "Authorization": f"Bearer {MINIMAX_API_KEY}",
+                "Content-Type": "application/json",
+            },
+            json={
+                "model": "MiniMax-M2.5-highspeed",
+                "messages": [{"role": "user", "content": "Reply with OK."}],
+                "temperature": 0,
+                "max_tokens": 5,
+            },
+            timeout=30,
+        )
+        self.assertEqual(resp.status_code, 200)
+
+
+if __name__ == "__main__":
+    unittest.main()