Upload models_configuration.py

models_configuration.py

@@ -0,0 +1,63 @@
+from typing import Any, List, Dict, Optional
+from langchain_community.chat_models import ChatOllama
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+
+model_configurations: Dict[str, Dict[str, Any]] = {
+    "qwen3:32b": {
+        "type": "Ollama",
+        "model_name": "qwen3:32b",
+        "temperature": 0.3,
+    },
+    "mistral-small:24b": {
+        "type": "Ollama",
+        "model_name": "mistral-small:24b",
+        "temperature": 0.3,
+    },
+    "deepseek-r1:32b": {
+        "type": "Ollama",
+        "model_name": "deepseek-r1:32b",
+        "temperature": 0.3,
+    }
+}
+
+class OllamaWithDebug(ChatOllama):
+    def __init__(self, model, **kwargs):
+        super().__init__(model=model, **kwargs)
+        print(f"--- DEBUG INIT: Model {model} ---")
+        print(f"Injected parameters: {kwargs}")
+        print("Actual instance attributes:")
+        for attr in ['temperature', 'top_p', 'base_url']:
+            print(f"  - {attr}: {getattr(self, attr, 'UNDEFINED')}")
+        print("-" * 40)
+
+    def invoke(self, messages: Any, **kwargs):
+        print(f"\n[DEBUG INVOKE] Sending to {self.model}")
+        temp = getattr(self, 'temperature', 'N/A')
+        print(f"Active config -> Temp: {temp}")
+
+        preview = str(messages)[:150]
+        print(f"Input preview: {preview}...")
+
+        try:
+            response = super().invoke(messages, **kwargs)
+            print(f"[DEBUG RESPONSE] Success! Response length: {len(str(response.content))} chars")
+            return response
+
+        except Exception as e:
+            print(f"!!! DEBUG ERROR !!! Ollama call failed: {e}")
+            raise
+
+def get_model_instance(model_key: str) -> Any:
+    config = model_configurations.get(model_key)
+    if config is None:
+        raise ValueError(f"Model not configured: {model_key}")
+
+    if config.get("type", "").lower() == "ollama":
+        return OllamaWithDebug(
+            model=config["model_name"],
+            base_url="http://localhost:11434",
+            temperature=config.get("temperature", 0.3),
+            top_p=config.get("top_p"),
+        )
+    else:
+        raise ValueError(f"Unsupported or missing model type: {config.get('type')}")