Update mcp_hub/config.py

Updated the nebius model for code generation. The Qwen coder model did not follow instructions

mcp_hub/config.py

@@ -1,120 +1,120 @@
-"""Configuration management for the MCP Hub project."""
-
-import os
-from dataclasses import dataclass
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-@dataclass
-class APIConfig:
-    """API configuration settings."""
-    # Provider selection
-    llm_provider: str = "nebius"  # Options: "nebius", "openai", "anthropic", "huggingface"
-    
-    # Provider API keys
-    nebius_api_key: str = ""
-    openai_api_key: str = ""
-    anthropic_api_key: str = ""
-    huggingface_api_key: str = ""
-    
-    # Other APIs
-    tavily_api_key: str = ""
-    
-    # Provider URLs
-    nebius_base_url: str = "https://api.studio.nebius.com/v1/"
-    huggingface_base_url: str = "https://api-inference.huggingface.co"
-    
-    # Other settings
-    current_year: str = "2025"
-    
-    def __post_init__(self):
-        """Validate required API keys based on selected provider."""
-        # Always require Tavily for search functionality
-        if not self.tavily_api_key or not self.tavily_api_key.startswith("tvly-"):
-            raise RuntimeError("A valid TAVILY_API_KEY is required in your .env file.")
-        
-        # Validate LLM provider selection
-        valid_providers = ["nebius", "openai", "anthropic", "huggingface"]
-        if self.llm_provider not in valid_providers:
-            raise RuntimeError(f"LLM_PROVIDER must be one of: {', '.join(valid_providers)}")
-        
-        # Validate required API key for selected provider
-        if self.llm_provider == "nebius" and not self.nebius_api_key:
-            raise RuntimeError("NEBIUS_API_KEY is required when using nebius provider.")
-        elif self.llm_provider == "openai" and not self.openai_api_key:
-            raise RuntimeError("OPENAI_API_KEY is required when using openai provider.")
-        elif self.llm_provider == "anthropic" and not self.anthropic_api_key:
-            raise RuntimeError("ANTHROPIC_API_KEY is required when using anthropic provider.")
-        elif self.llm_provider == "huggingface" and not self.huggingface_api_key:
-            raise RuntimeError("HUGGINGFACE_API_KEY is required when using huggingface provider.")
-
-@dataclass
-class ModelConfig:
-    """Model configuration settings."""
-    # Default models (Nebius/HuggingFace compatible)
-    question_enhancer_model: str = "Qwen/Qwen3-4B-fast"
-    llm_processor_model: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"
-    code_generator_model: str = "Qwen/Qwen2.5-Coder-32B-Instruct-fast"
-    orchestrator_model: str = "Qwen/Qwen3-32B-fast"
-    
-    def get_model_for_provider(self, task: str, provider: str) -> str:
-        """Get appropriate model for the given task and provider."""
-        
-        # Model mappings by provider
-        provider_models = {
-            "nebius": {
-                "question_enhancer": self.question_enhancer_model,
-                "llm_processor": self.llm_processor_model,
-                "code_generator": self.code_generator_model,
-                "orchestrator": self.orchestrator_model,
-            },
-            "openai": {
-                "question_enhancer": "gpt-4.1-nano",
-                "llm_processor": "gpt-4.1-nano",
-                "code_generator": "gpt-4.1",
-                "orchestrator": "gpt-4.1",
-            },
-            "anthropic": {
-                "question_enhancer": "claude-3-5-haiku-latest",#
-                "llm_processor": "claude-3-5-sonnet-latest",
-                "code_generator": "claude-sonnet-4-0",
-                "orchestrator": "claude-sonnet-4-0",
-            },
-            "huggingface": {
-                "question_enhancer": "microsoft/phi-4",
-                "llm_processor": "microsoft/phi-4", 
-                "code_generator": "Qwen/Qwen2.5-Coder-32B-Instruct",
-                "orchestrator": "microsoft/phi-4",
-            }
-        }
-        
-        if provider not in provider_models:
-            # Fall back to default models
-            return getattr(self, f"{task}_model", self.llm_processor_model)
-        
-        return provider_models[provider].get(task, provider_models[provider]["llm_processor"])
-
-@dataclass
-class AppConfig:
-    """Application configuration settings."""
-    modal_app_name: str = "my-sandbox-app"
-    max_search_results: int = 2
-    max_code_generation_attempts: int = 3
-    llm_temperature: float = 0.6
-    code_gen_temperature: float = 0.1
-
-# Create global configuration instances
-api_config = APIConfig(
-    llm_provider=os.environ.get("LLM_PROVIDER", "nebius"),
-    nebius_api_key=os.environ.get("NEBIUS_API_KEY", ""),
-    openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
-    anthropic_api_key=os.environ.get("ANTHROPIC_API_KEY", ""),
-    huggingface_api_key=os.environ.get("HUGGINGFACE_API_KEY", ""),
-    tavily_api_key=os.environ.get("TAVILY_API_KEY", ""),
-    current_year=os.environ.get("CURRENT_YEAR", "2025")
-)
-
-model_config = ModelConfig()
-app_config = AppConfig()
+"""Configuration management for the MCP Hub project."""
+
+import os
+from dataclasses import dataclass
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+@dataclass
+class APIConfig:
+    """API configuration settings."""
+    # Provider selection
+    llm_provider: str = "nebius"  # Options: "nebius", "openai", "anthropic", "huggingface"
+    
+    # Provider API keys
+    nebius_api_key: str = ""
+    openai_api_key: str = ""
+    anthropic_api_key: str = ""
+    huggingface_api_key: str = ""
+    
+    # Other APIs
+    tavily_api_key: str = ""
+    
+    # Provider URLs
+    nebius_base_url: str = "https://api.studio.nebius.com/v1/"
+    huggingface_base_url: str = "https://api-inference.huggingface.co"
+    
+    # Other settings
+    current_year: str = "2025"
+    
+    def __post_init__(self):
+        """Validate required API keys based on selected provider."""
+        # Always require Tavily for search functionality
+        if not self.tavily_api_key or not self.tavily_api_key.startswith("tvly-"):
+            raise RuntimeError("A valid TAVILY_API_KEY is required in your .env file.")
+        
+        # Validate LLM provider selection
+        valid_providers = ["nebius", "openai", "anthropic", "huggingface"]
+        if self.llm_provider not in valid_providers:
+            raise RuntimeError(f"LLM_PROVIDER must be one of: {', '.join(valid_providers)}")
+        
+        # Validate required API key for selected provider
+        if self.llm_provider == "nebius" and not self.nebius_api_key:
+            raise RuntimeError("NEBIUS_API_KEY is required when using nebius provider.")
+        elif self.llm_provider == "openai" and not self.openai_api_key:
+            raise RuntimeError("OPENAI_API_KEY is required when using openai provider.")
+        elif self.llm_provider == "anthropic" and not self.anthropic_api_key:
+            raise RuntimeError("ANTHROPIC_API_KEY is required when using anthropic provider.")
+        elif self.llm_provider == "huggingface" and not self.huggingface_api_key:
+            raise RuntimeError("HUGGINGFACE_API_KEY is required when using huggingface provider.")
+
+@dataclass
+class ModelConfig:
+    """Model configuration settings."""
+    # Default models (Nebius/HuggingFace compatible)
+    question_enhancer_model: str = "Qwen/Qwen3-4B-fast"
+    llm_processor_model: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+    code_generator_model: str = "Qwen/Qwen3-32B-fast"
+    orchestrator_model: str = "Qwen/Qwen3-32B-fast"
+    
+    def get_model_for_provider(self, task: str, provider: str) -> str:
+        """Get appropriate model for the given task and provider."""
+        
+        # Model mappings by provider
+        provider_models = {
+            "nebius": {
+                "question_enhancer": self.question_enhancer_model,
+                "llm_processor": self.llm_processor_model,
+                "code_generator": self.code_generator_model,
+                "orchestrator": self.orchestrator_model,
+            },
+            "openai": {
+                "question_enhancer": "gpt-4.1-nano",
+                "llm_processor": "gpt-4.1-nano",
+                "code_generator": "gpt-4.1",
+                "orchestrator": "gpt-4.1",
+            },
+            "anthropic": {
+                "question_enhancer": "claude-3-5-haiku-latest",#
+                "llm_processor": "claude-3-5-sonnet-latest",
+                "code_generator": "claude-sonnet-4-0",
+                "orchestrator": "claude-sonnet-4-0",
+            },
+            "huggingface": {
+                "question_enhancer": "microsoft/phi-4",
+                "llm_processor": "microsoft/phi-4", 
+                "code_generator": "Qwen/Qwen2.5-Coder-32B-Instruct",
+                "orchestrator": "microsoft/phi-4",
+            }
+        }
+        
+        if provider not in provider_models:
+            # Fall back to default models
+            return getattr(self, f"{task}_model", self.llm_processor_model)
+        
+        return provider_models[provider].get(task, provider_models[provider]["llm_processor"])
+
+@dataclass
+class AppConfig:
+    """Application configuration settings."""
+    modal_app_name: str = "my-sandbox-app"
+    max_search_results: int = 2
+    max_code_generation_attempts: int = 3
+    llm_temperature: float = 0.6
+    code_gen_temperature: float = 0.1
+
+# Create global configuration instances
+api_config = APIConfig(
+    llm_provider=os.environ.get("LLM_PROVIDER", "nebius"),
+    nebius_api_key=os.environ.get("NEBIUS_API_KEY", ""),
+    openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
+    anthropic_api_key=os.environ.get("ANTHROPIC_API_KEY", ""),
+    huggingface_api_key=os.environ.get("HUGGINGFACE_API_KEY", ""),
+    tavily_api_key=os.environ.get("TAVILY_API_KEY", ""),
+    current_year=os.environ.get("CURRENT_YEAR", "2025")
+)
+
+model_config = ModelConfig()
+app_config = AppConfig()