use interceptor class

lumen/ai/llm.py

@@ -24,7 +24,7 @@ class Llm(param.Parameterized):
 
     use_logfire = param.Boolean(default=False)
 
-    interceptor_path = param.String(default=None)
+    interceptor = param.ClassSelector(default=None, class_=OpenAIInterceptor)
 
     # Allows defining a dictionary of default models.
     model_kwargs = param.Dict(default={})
@@ -35,7 +35,6 @@ class Llm(param.Parameterized):
 
     def __init__(self, **params):
         super().__init__(**params)
-        self._interceptor = None
 
     def _get_model_kwargs(self, model_key):
         if model_key in self.model_kwargs:
@@ -229,17 +228,15 @@ def get_client(self, model_key: str, response_model: BaseModel | None = None, **
             model_kwargs["organization"] = self.organization
         llm = openai.AsyncOpenAI(**model_kwargs)
 
-        if self.interceptor_path:
-            if self._interceptor is None:
-                self._interceptor = OpenAIInterceptor(db_path=self.interceptor_path)
-            self._interceptor.patch_client(llm, mode="store_inputs")
+        if self.interceptor:
+            self.interceptor.patch_client(llm, mode="store_inputs")
 
         if response_model:
             llm = patch(llm)
 
-        if self.interceptor_path:
+        if self.interceptor:
             # must be called after instructor
-            self._interceptor.patch_client_response(llm)
+            self.interceptor.patch_client_response(llm)
 
         client_callable = partial(llm.chat.completions.create, model=model)
 
@@ -278,17 +275,15 @@ def get_client(self, model_key: str, response_model: BaseModel | None = None, **
             model_kwargs["azure_endpoint"] = self.azure_endpoint
         llm = openai.AsyncAzureOpenAI(**model_kwargs)
 
-        if self.interceptor_path:
-            if self._interceptor is None:
-                self._interceptor = OpenAIInterceptor(db_path=self.interceptor_path)
-            self._interceptor.patch_client(llm, mode="store_inputs")
+        if self.interceptor:
+            self.interceptor.patch_client(llm, mode="store_inputs")
 
         if response_model:
             llm = patch(llm)
 
-        if self.interceptor_path:
+        if self.interceptor:
             # must be called after instructor
-            self._interceptor.patch_client_response(llm)
+            self.interceptor.patch_client_response(llm)
 
         client_callable = partial(llm.chat.completions.create, model=model)
         return client_callable
@@ -337,16 +332,14 @@ def get_client(self, model_key: str, response_model: BaseModel | None = None, **
         llm.chat.completions = SimpleNamespace(create=None)  # make it like OpenAI for simplicity
         llm.chat.completions.create = llm.chat.stream_async if stream else llm.chat.complete_async
 
-        if self.interceptor_path:
-            if self._interceptor is None:
-                self._interceptor = OpenAIInterceptor(db_path=self.interceptor_path)
-            self._interceptor.patch_client(llm, mode="store_inputs")
+        if self.interceptor:
+            self.interceptor.patch_client(llm, mode="store_inputs")
 
         if response_model:
             llm = patch(llm)
 
-        if self.interceptor_path:
-            self._interceptor.patch_client_response(llm)
+        if self.interceptor:
+            self.interceptor.patch_client_response(llm)
 
         client_callable = partial(llm.chat.completions.create, model=model)
         return client_callable
@@ -410,16 +403,14 @@ async def llm_chat_non_stream_async(*args, **kwargs):
         llm.chat.completions = SimpleNamespace(create=None)  # make it like OpenAI for simplicity
         llm.chat.completions.create = llm.chat.stream_async if stream else llm.chat.complete_async
 
-        if self.interceptor_path:
-            if self._interceptor is None:
-                self._interceptor = OpenAIInterceptor(db_path=self.interceptor_path)
-            self._interceptor.patch_client(llm, mode="store_inputs")
+        if self.interceptor:
+            self.interceptor.patch_client(llm, mode="store_inputs")
 
         if response_model:
             llm = patch(llm)
 
-        if self.interceptor_path:
-            self._interceptor.patch_client_response(llm)
+        if self.interceptor:
+            self.interceptor.patch_client_response(llm)
 
         client_callable = partial(llm.chat.completions.create, model=model)
         return client_callable
@@ -445,7 +436,7 @@ def _client_kwargs(self):
         return {"temperature": self.temperature, "max_tokens": 1024}
 
     def get_client(self, model_key: str, response_model: BaseModel | None = None, **kwargs):
-        if self.interceptor_path:
+        if self.interceptor:
             raise NotImplementedError("Interceptors are not supported for AnthropicAI.")
 
         from anthropic import AsyncAnthropic