refactor: enhance LLM fallback handling and streamline method execution

application/agents/base.py

@@ -256,12 +256,21 @@ class BaseAgent(ABC):
         return retrieved_data
 
     def _llm_gen(self, messages: List[Dict], log_context: Optional[LogContext] = None):
-        resp = self.llm.gen_stream(
-            model=self.gpt_model, messages=messages, tools=self.tools
-        )
+        gen_kwargs = {"model": self.gpt_model, "messages": messages}
+
+        if (
+            hasattr(self.llm, "_supports_tools")
+            and self.llm._supports_tools
+            and self.tools
+        ):
+            gen_kwargs["tools"] = self.tools
+
+        resp = self.llm.gen_stream(**gen_kwargs)
+
         if log_context:
             data = build_stack_data(self.llm, exclude_attributes=["client"])
             log_context.stacks.append({"component": "llm", "data": data})
+
         return resp
 
     def _llm_handler(

application/llm/base.py

@@ -1,53 +1,118 @@
+import logging
 from abc import ABC, abstractmethod
 
 from application.cache import gen_cache, stream_cache
+
+from application.core.settings import settings
 from application.usage import gen_token_usage, stream_token_usage
 
+logger = logging.getLogger(__name__)
+
 
 class BaseLLM(ABC):
-    def __init__(self, decoded_token=None):
+    def __init__(
+        self,
+        decoded_token=None,
+    ):
         self.decoded_token = decoded_token
         self.token_usage = {"prompt_tokens": 0, "generated_tokens": 0}
+        self.fallback_provider = settings.FALLBACK_LLM_PROVIDER
+        self.fallback_model_name = settings.FALLBACK_LLM_NAME
+        self.fallback_llm_api_key = settings.FALLBACK_LLM_API_KEY
+        self._fallback_llm = None
 
-    def _apply_decorator(self, method, decorators, *args, **kwargs):
-        for decorator in decorators:
-            method = decorator(method)
-        return method(self, *args, **kwargs)
+    @property
+    def fallback_llm(self):
+        """Lazy-loaded fallback LLM instance."""
+        if (
+            self._fallback_llm is None
+            and self.fallback_provider
+            and self.fallback_model_name
+        ):
+            try:
+                from llm.llm_creator import LLMCreator
+
+                self._fallback_llm = LLMCreator(
+                    self.fallback_provider,
+                    self.fallback_llm_api_key,
+                    None,
+                    self.decoded_token,
+                )
+            except Exception as e:
+                logger.error(
+                    f"Failed to initialize fallback LLM: {str(e)}", exc_info=True
+                )
+        return self._fallback_llm
+
+    def _execute_with_fallback(
+        self, method_name: str, decorators: list, *args, **kwargs
+    ):
+        """
+        Unified method execution with fallback support.
+
+        Args:
+            method_name: Name of the raw method ('_raw_gen' or '_raw_gen_stream')
+            decorators: List of decorators to apply
+            *args: Positional arguments
+            **kwargs: Keyword arguments
+        """
+
+        def decorated_method():
+            method = getattr(self, method_name)
+            for decorator in decorators:
+                method = decorator(method)
+            return method(self, *args, **kwargs)
+
+        try:
+            return decorated_method()
+        except Exception as e:
+            if not self.fallback_llm:
+                logger.error(f"Primary LLM failed and no fallback available: {str(e)}")
+                raise
+            logger.warning(
+                f"Falling back to {self.fallback_provider}/{self.fallback_model_name}. Error: {str(e)}"
+            )
+            # Retry with fallback (without decorators for accurate token tracking)
+
+            fallback_method = getattr(
+                self.fallback_llm, method_name.replace("_raw_", "")
+            )
+            return fallback_method(*args, **kwargs)
+
+    def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
+        decorators = [gen_token_usage, gen_cache]
+        return self._execute_with_fallback(
+            "_raw_gen",
+            decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs,
+        )
+
+    def gen_stream(self, model, messages, stream=True, tools=None, *args, **kwargs):
+        decorators = [stream_cache, stream_token_usage]
+        return self._execute_with_fallback(
+            "_raw_gen_stream",
+            decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs,
+        )
 
     @abstractmethod
     def _raw_gen(self, model, messages, stream, tools, *args, **kwargs):
         pass
 
-    def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
-        decorators = [gen_token_usage, gen_cache]
-        return self._apply_decorator(
-            self._raw_gen,
-            decorators=decorators,
-            model=model,
-            messages=messages,
-            stream=stream,
-            tools=tools,
-            *args,
-            **kwargs
-        )
-
     @abstractmethod
     def _raw_gen_stream(self, model, messages, stream, *args, **kwargs):
         pass
 
-    def gen_stream(self, model, messages, stream=True, tools=None, *args, **kwargs):
-        decorators = [stream_cache, stream_token_usage]
-        return self._apply_decorator(
-            self._raw_gen_stream,
-            decorators=decorators,
-            model=model,
-            messages=messages,
-            stream=stream,
-            tools=tools,
-            *args,
-            **kwargs
-        )
-
     def supports_tools(self):
         return hasattr(self, "_supports_tools") and callable(
             getattr(self, "_supports_tools")