Paix2-router

.env.example

@@ -40,3 +40,12 @@ XAI_API_KEY = "xai-<YOUR_KEY>"
 
 # ZHIPU_API_KEY
 ZHIPU_API_KEY = "<YOUR_KEY>"
+
+# MINIMAX_API_KEY
+MINIMAX_API_KEY = "<YOUR_KEY>"
+
+# AGNES_API_KEY
+AGNES_API_KEY = "<YOUR_KEY>"
+
+# SILICONFLOW_API_KEY
+SILICONFLOW_API_KEY = "<YOUR_KEY>"

llm_inference/model_inference.py

@@ -40,6 +40,11 @@ def __init__(self):
 
         self.gpt2_enc = tiktoken.get_encoding("gpt2")
 
+        # add by Paix2-router
+        self.minimax_api_key = os.getenv("MINIMAX_API_KEY")
+        self.agnes_api_key = os.getenv("AGNES_API_KEY")
+        self.siliconflow_api_key = os.getenv("SILICONFLOW_API_KEY")
+
     def infer(
         self, model_name: str, prompt: str, max_retries: int = 3
     ) -> Dict[str, Any]:
@@ -86,6 +91,13 @@ def infer(
                     return self._call_xai(model_name, prompt)
                 elif provider == "zhipu":
                     return self._call_zhipu(model_name, prompt)
+                elif provider == "minimax":
+                    return self._call_minimax(model_name, prompt)
+                elif provider == "agnes":
+                    return self._call_agnes(model_name, prompt)
+                elif provider == "siliconflow":
+                    return self._call_siliconflow(model_name, prompt)
+
                 else:
                     # Default to Together API for most open-source models
                     return self._call_together(model_name, prompt)
@@ -197,6 +209,13 @@ def _get_provider(self, model_name: str) -> str:
             "glm-4-air": "zhipu",
             "glm-4-flash": "zhipu",
             "glm-4-plus": "zhipu",
+            # MiniMax
+            "MiniMax-M3": "minimax",
+            # Agens
+            "agnes-2.0-flash": "agnes",
+            # Siliconflow
+            "THUDM/GLM-4-9B-0414": "siliconflow",
+            "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B": "siliconflow",
         }
 
         # Check if exact model name is in mapping
@@ -634,6 +653,116 @@ def _call_perplexity(self, model_name: str, prompt: str) -> Dict[str, Any]:
             "provider": "perplexity",
         }
 
+    def _call_agnes(self, model_name: str, prompt: str) -> Dict[str, Any]:
+        """Call agnes API."""
+        agnes_api_key = os.getenv("AGNES_API_KEY")
+        client = OpenAI(
+            base_url="https://apihub.agnes-ai.com/v1",
+            api_key=agnes_api_key,
+        )
+
+        response = client.chat.completions.create(
+            model=model_name, messages=[{"role": "user", "content": prompt}]
+        )
+
+        usage = getattr(response, "usage", None)
+        input_tokens = getattr(usage, "prompt_tokens", 0) if usage is not None else 0
+        completion_tokens = (
+            getattr(usage, "completion_tokens", 0) if usage is not None else 0
+        )
+        total_tokens = (
+            getattr(usage, "total_tokens", 0)
+            if usage is not None
+            else input_tokens + completion_tokens
+        )
+
+        return {
+            "response": response.choices[0].message.content,
+            "success": True,
+            "token_usage": {
+                "input_tokens": input_tokens,
+                "output_tokens": completion_tokens,
+                "total_tokens": total_tokens,
+            },
+            "model_used": model_name,
+            "provider": "agnes",
+        }
+
+    def _call_minimax(self, model_name: str, prompt: str) -> Dict[str, Any]:
+        """Call minimax API."""
+        import anthropic
+
+        client = anthropic.Anthropic(
+            base_url="https://api.minimaxi.com/anthropic", api_key=self.minimax_api_key
+        )
+
+        clean_model_name = model_name.replace("anthropic/", "")
+
+        response = client.messages.create(
+            model=clean_model_name,
+            max_tokens=2048,
+            messages=[{"role": "user", "content": prompt}],
+        )
+
+        usage = getattr(response, "usage", None)
+        input_tokens = getattr(usage, "input_tokens", 0) if usage is not None else 0
+        output_tokens = getattr(usage, "output_tokens", 0) if usage is not None else 0
+        total_tokens = input_tokens + output_tokens
+
+        content0 = response.content[0]
+        text = getattr(content0, "text", str(content0))
+        return {
+            "response": text,
+            "success": True,
+            "token_usage": {
+                "input_tokens": input_tokens,
+                "output_tokens": output_tokens,
+                "total_tokens": total_tokens,
+            },
+            "model_used": model_name,
+            "provider": "minimax",
+        }
+
+    def _call_siliconflow(self, model_name: str, prompt: str) -> Dict[str, Any]:
+        """Call siliconflow API."""
+        import openai
+
+        client = openai.OpenAI(
+            api_key=self.siliconflow_api_key, base_url="https://api.siliconflow.cn/v1"
+        )
+
+        clean_model_name = model_name.replace("siliconflow/", "")
+
+        response = client.chat.completions.create(
+            model=clean_model_name,
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=1024,
+            temperature=0.7,
+        )
+
+        usage = getattr(response, "usage", None)
+        input_tokens = getattr(usage, "prompt_tokens", 0) if usage is not None else 0
+        completion_tokens = (
+            getattr(usage, "completion_tokens", 0) if usage is not None else 0
+        )
+        total_tokens = (
+            getattr(usage, "total_tokens", 0)
+            if usage is not None
+            else input_tokens + completion_tokens
+        )
+
+        return {
+            "response": response.choices[0].message.content,
+            "success": True,
+            "token_usage": {
+                "input_tokens": input_tokens,
+                "output_tokens": completion_tokens,
+                "total_tokens": total_tokens,
+            },
+            "model_used": model_name,
+            "provider": "siliconflow",
+        }
+
     def _call_aws(self, model_name: str, prompt: str) -> Dict[str, Any]:
         """Call AWS Bedrock API."""
         import boto3

model_cost/model_cost.json

@@ -331,6 +331,22 @@
     "input_token_price_per_million": 0.4,
     "output_token_price_per_million": 1.5
   },
+  "MiniMax-M3": {
+    "input_token_price_per_million": 0.60,
+    "output_token_price_per_million": 2.40
+  },
+  "agnes-2.0-flash": {
+    "input_token_price_per_million": 0.03,
+    "output_token_price_per_million": 0.15
+  },
+  "THUDM/GLM-4-9B-0414": {
+    "input_token_price_per_million": 0.05,
+    "output_token_price_per_million": 0.61
+  },
+  "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B": {
+    "input_token_price_per_million": 0.18,
+    "output_token_price_per_million": 0.85
+  },
   "grok-4.3": {
     "input_token_price_per_million": 1.25,
     "output_token_price_per_million": 2.5

router_inference/config/Paix2-router.json

@@ -0,0 +1,13 @@
+{
+  "pipeline_params": {
+    "router_name": "Paix2-router",
+    "router_cls_name": "MyPaix2Router",
+    "models": [
+	  "MiniMax-M3",
+      "agnes-2.0-flash",
+      "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
+	  "THUDM/GLM-4-9B-0414"
+
+    ]
+  }
+}