Upload settings.py

config/settings.py

@@ -42,15 +42,16 @@ class ModelSettings:
     agent2_model: str = field(default_factory=lambda: os.getenv("AGENT2_MODEL", "microsoft/Phi-3.5-mini-instruct"))
     
     # Agent 3 (Advisor): Strong reasoning - MOST IMPORTANT
-    # Default: Meta Llama 70B (excellent reasoning)
-    agent3_model: str = field(default_factory=lambda: os.getenv("AGENT3_MODEL", "meta-llama/Llama-3.1-70B-Instruct"))
+    # Default: Qwen 2.5 72B (freely available on HF serverless, no gated access needed)
+    # Alternative: meta-llama/Llama-3.1-70B-Instruct (requires Meta license acceptance)
+    agent3_model: str = field(default_factory=lambda: os.getenv("AGENT3_MODEL", "Qwen/Qwen2.5-72B-Instruct"))
     
     # Agent 4 (Generator): Code/JSON specialist
     # Default: Mistral Codestral (code specialist)
     agent4_model: str = field(default_factory=lambda: os.getenv("AGENT4_MODEL", "mistralai/Codestral-22B-v0.1"))
     
-    # Fallback
-    fallback_model: str = field(default_factory=lambda: os.getenv("FALLBACK_MODEL", "mistralai/Mistral-7B-Instruct-v0.3"))
+    # Fallback (must be freely available on HF serverless inference)
+    fallback_model: str = field(default_factory=lambda: os.getenv("FALLBACK_MODEL", "Qwen/Qwen2.5-7B-Instruct"))
 
 
 @dataclass
@@ -161,9 +162,17 @@ class Settings:
     def get_model_for_agent(self, agent_name: str) -> str:
         """Get the model ID for a specific agent."""
         model_map = {
+            # Legacy agents
             "normalizer": self.models.agent2_model,
             "advisor": self.models.agent3_model,
             "generator": self.models.agent4_model,
+            
+            # Stage 2 New Architecture agents
+            "brand_identifier": self.models.agent3_model,  # Llama 70B - needs context understanding
+            "benchmark_advisor": self.models.agent3_model,  # Llama 70B - needs reasoning
+            "best_practices_validator": self.models.agent3_model,  # Llama 70B - needs judgment
+            "head_synthesizer": self.models.agent3_model,  # Llama 70B - needs synthesis
+            "benchmark_extractor": self.models.agent2_model,  # Phi-3.5 - structured extraction
         }
         return model_map.get(agent_name, self.models.fallback_model)