Create base_agent.py

base_agent.py

@@ -0,0 +1,52 @@
+from typing import Dict, Any, Optional
+import os
+import time
+import json
+import openai
+
+class BaseAgent:
+    """Base class for all agents with common functionality."""
+    
+    def __init__(self, model: str = DEFAULT_LLM_MODEL):
+        """Initialize the base agent with the specified model."""
+        if model in None:
+            model = os.getenv("DEFAULT_LLM_MODEL")
+            
+        openai_key = os.getenv("OPENAI_API_KEY")
+        self.model = model
+        self.client = openai.OpenAI(api_key=openai_key)
+        
+    def __call__(self, question: str, task_id: Optional[str] = None, **kwargs) -> str:
+        """Process the question and return an answer."""
+        raise NotImplementedError("Subclasses must implement this method")
+    
+    def llm_call(self, system_prompt: str, user_prompt: str, 
+                 temperature: float = 0.1, model: Optional[str] = None) -> str:
+        """
+        Make a call to the LLM with the given prompts.
+        
+        Args:
+            system_prompt: The system prompt to guide the LLM behavior
+            user_prompt: The user prompt containing the question or task
+            temperature: Controls randomness (lower is more deterministic)
+            model: Optional override for the model to use
+            
+        Returns:
+            The LLM response content as a string
+        """
+        if model is None:
+            model = self.model
+            
+        try:
+            response = self.client.chat.completions.create(
+                model=model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt}
+                ],
+                temperature=temperature
+            )
+            return response.choices[0].message.content
+        except Exception as e:
+            print(f"Error calling LLM: {str(e)}")
+            return f"Error: {str(e)}"