Refactor Anthropic bot for small/large model switching and update developer prompt for autonomy

anthropic_telegram_inference_bot.py

@@ -10,13 +10,19 @@ class AnthropicTelegramInferenceBot(BaseTelegramInferenceBot):
         super().__init__()
         self.anthropic_client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
         
+        # Initialize with the small model by default
+        self.small_model_name = os.environ.get("ANTHROPIC_SMALL_MODEL", "claude-3-haiku-20240307")
+        self.small_model_max_tokens = os.environ.get("ANTHROPIC_SMALL_MODEL_MAX_TOKENS", "2048")
+        self.large_model_name = os.environ.get("ANTHROPIC_LARGE_MODEL", "claude-3-opus-20240229")
+        self.large_model_max_tokens = os.environ.get("ANTHROPIC_LARGE_MODEL_MAX_TOKENS", "4096")
+
         self._configure_model_and_tokens(
-            os.environ.get("ANTHROPIC_MODEL", "claude-3-5-sonnet-20240620"),
+            self.small_model_name,
-            os.environ.get("ANTHROPIC_MAX_TOKENS", "4096")
+            self.small_model_max_tokens
         )
 
-    def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=4096):
+    def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=2048): # Default max_tokens adjusted for typical "small"
-        self.model = model_name if model_name else "claude-3-5-sonnet-20240620"
+        self.model = model_name
         try:
             self.max_tokens = int(max_tokens_str) if max_tokens_str is not None else default_max_tokens
         except ValueError:
@@ -173,26 +179,35 @@ class AnthropicTelegramInferenceBot(BaseTelegramInferenceBot):
             return "No active processing found to abort. Conversation cleared."
 
     async def switch_model(self):
-        primary_model = os.environ.get("ANTHROPIC_MODEL", "claude-3-5-sonnet-20240620")
+        # Ensure ANTHROPIC_SMALL_MODEL and ANTHROPIC_LARGE_MODEL related env vars are loaded in __init__
-        primary_max_tokens = os.environ.get("ANTHROPIC_MAX_TOKENS", "4096")
+        # or ensure they are freshly checked here if they can change during runtime (less common for model names).
-        
+        # For this implementation, we rely on the values stored during __init__.
-        secondary_model_env = os.environ.get("ANTHROPIC_SECONDARY_MODEL")
-        secondary_max_tokens_env = os.environ.get("ANTHROPIC_SECONDARY_MAX_TOKENS")
 
-        if not secondary_model_env: 
+        if not self.small_model_name or not self.large_model_name:
-            logging.warning("ANTHROPIC_SECONDARY_MODEL not defined. Cannot switch model.")
+            logging.warning("Small or Large model names for Anthropic are not defined. Cannot switch model.")
-            return f"Model switching not configured. Currently using {self.model}."
+            return f"Model switching not fully configured. Currently using {self.model}."
 
-        if self.model == primary_model:
+        if self.model == self.small_model_name:
-            target_model = secondary_model_env
+            target_model = self.large_model_name
-            target_max_tokens = secondary_max_tokens_env if secondary_max_tokens_env else "2048" 
+            target_max_tokens = self.large_model_max_tokens
+            # Use default large max_tokens if specific one isn't set or invalid
+            default_max_tokens_for_large = "4096" 
+        elif self.model == self.large_model_name:
+            target_model = self.small_model_name
+            target_max_tokens = self.small_model_max_tokens
+            # Use default small max_tokens if specific one isn't set or invalid
+            default_max_tokens_for_large = "2048"
         else:
-            target_model = primary_model
+            # Current model is neither the designated small nor large, switch to small as a reset
-            target_max_tokens = primary_max_tokens
+            logging.warning(f"Current model {self.model} is neither the configured small nor large model. Switching to small model.")
-        
+            target_model = self.small_model_name
-        self._configure_model_and_tokens(target_model, target_max_tokens)
+            target_max_tokens = self.small_model_max_tokens
+            default_max_tokens_for_large = "2048"
+
+
+        self._configure_model_and_tokens(target_model, target_max_tokens, default_max_tokens=int(default_max_tokens_for_large)) # Pass appropriate default
         logging.info(f"Switched Anthropic model to: {self.model}")
-        return f"Switched to Anthropic model: {self.model}"
+        return f"Switched to Anthropic model: {self.model} (Max Tokens: {self.max_tokens})"#Provide token info
 
 def main():
     if not os.environ.get("ANTHROPIC_API_KEY"):