sach91 bootcamp week8 exercise

community-contributions/sach91-bootcamp/week8/agents/base_agent.py

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+"""
+Base Agent class - Foundation for all specialized agents
+"""
+from abc import ABC, abstractmethod
+import logging
+from typing import Optional, Dict, Any
+from utils.ollama_client import OllamaClient
+
+logger = logging.getLogger(__name__)
+
+class BaseAgent(ABC):
+    """Abstract base class for all agents"""
+    
+    def __init__(self, name: str, llm_client: Optional[OllamaClient] = None, 
+                 model: str = "llama3.2"):
+        """
+        Initialize base agent
+        
+        Args:
+            name: Agent name for logging
+            llm_client: Shared Ollama client (creates new one if None)
+            model: Ollama model to use
+        """
+        self.name = name
+        self.model = model
+        
+        # Use shared client or create new one
+        if llm_client is None:
+            self.llm = OllamaClient(model=model)
+            logger.info(f"{self.name} initialized with new LLM client (model: {model})")
+        else:
+            self.llm = llm_client
+            logger.info(f"{self.name} initialized with shared LLM client (model: {model})")
+    
+    def generate(self, prompt: str, system: Optional[str] = None, 
+                 temperature: float = 0.7, max_tokens: int = 2048) -> str:
+        """
+        Generate text using the LLM
+        
+        Args:
+            prompt: User prompt
+            system: System message (optional)
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            
+        Returns:
+            Generated text
+        """
+        logger.info(f"{self.name} generating response")
+        response = self.llm.generate(
+            prompt=prompt,
+            system=system,
+            temperature=temperature,
+            max_tokens=max_tokens
+        )
+        logger.debug(f"{self.name} generated {len(response)} characters")
+        return response
+    
+    def chat(self, messages: list, temperature: float = 0.7, 
+             max_tokens: int = 2048) -> str:
+        """
+        Chat completion with message history
+        
+        Args:
+            messages: List of message dicts with 'role' and 'content'
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            
+        Returns:
+            Generated text
+        """
+        logger.info(f"{self.name} processing chat with {len(messages)} messages")
+        response = self.llm.chat(
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens
+        )
+        logger.debug(f"{self.name} generated {len(response)} characters")
+        return response
+    
+    @abstractmethod
+    def process(self, *args, **kwargs) -> Any:
+        """
+        Main processing method - must be implemented by subclasses
+        
+        Each agent implements its specialized logic here
+        """
+        pass
+    
+    def __str__(self):
+        return f"{self.name} (model: {self.model})"