Run ruff

src/pipecat/services/mem0.py

@@ -25,10 +25,10 @@ except ModuleNotFoundError as e:
 
 class Mem0MemoryService(FrameProcessor):
     """A standalone memory service that integrates with Mem0.
-    
+
     This service intercepts message frames in the pipeline, stores them in Mem0,
     and enhances context with relevant memories before passing them downstream.
-    
+
     Args:
         api_key (str): The API key for accessing Mem0's API
         user_id (str): The user ID to associate with memories in Mem0
@@ -39,9 +39,7 @@ class Mem0MemoryService(FrameProcessor):
         search_limit: int = Field(default=10, ge=1)
         search_threshold: float = Field(default=0.1, ge=0.0, le=1.0)
         api_version: str = Field(default="v2")
-        system_prompt: str = Field(
+        system_prompt: str = Field(default="Based on previous conversations, I recall: \n\n")
-            default="Based on previous conversations, I recall: \n\n"
-        )
         add_as_system_message: bool = Field(default=True)
         position: int = Field(default=1)
 
@@ -61,7 +59,7 @@ class Mem0MemoryService(FrameProcessor):
         # At least one of user_id, agent_id, or run_id must be provided
         if not any([user_id, agent_id, run_id]):
             raise ValueError("At least one of user_id, agent_id, or run_id must be provided")
-        
+
         self.user_id = user_id
         self.agent_id = agent_id
         self.run_id = run_id
@@ -82,7 +80,11 @@ class Mem0MemoryService(FrameProcessor):
         """
         try:
             logger.debug(f"Storing {len(messages)} messages in Mem0")
-            params = {"messages": messages, "metadata": {"platform": "pipecat"}, "output_format": "v1.1"}
+            params = {
+                "messages": messages,
+                "metadata": {"platform": "pipecat"},
+                "output_format": "v1.1",
+            }
             for id in ["user_id", "agent_id", "run_id"]:
                 if getattr(self, id):
                     params[id] = getattr(self, id)
@@ -93,16 +95,20 @@ class Mem0MemoryService(FrameProcessor):
 
     def _retrieve_memories(self, query: str) -> List[Dict[str, Any]]:
         """Retrieve relevant memories from Mem0.
-        
+
         Args:
             query: The query to search for relevant memories
-            
+
         Returns:
             List of relevant memory dictionaries
         """
         try:
             logger.debug(f"Retrieving memories for query: {query}")
-            id_pairs = [("user_id", self.user_id), ("agent_id", self.agent_id), ("run_id", self.run_id)]
+            id_pairs = [
+                ("user_id", self.user_id),
+                ("agent_id", self.agent_id),
+                ("run_id", self.run_id),
+            ]
             clauses = [{name: value} for name, value in id_pairs if value is not None]
             filters = {"AND": clauses} if clauses else {}
             results = self.memory_client.search(
@@ -121,7 +127,7 @@ class Mem0MemoryService(FrameProcessor):
 
     def _enhance_context_with_memories(self, context: OpenAILLMContext, query: str):
         """Enhance the LLM context with relevant memories.
-        
+
         Args:
             context: The OpenAILLMContext to enhance
             query: The query to search for relevant memories
@@ -140,7 +146,7 @@ class Mem0MemoryService(FrameProcessor):
         memory_text = self.system_prompt
         for i, memory in enumerate(memories, 1):
             memory_text += f"{i}. {memory.get('memory', '')}\n\n"
-        
+
         # Add memories as a system message or user message based on configuration
         if self.add_as_system_message:
             context.add_message({"role": "system", "content": memory_text})
@@ -151,22 +157,22 @@ class Mem0MemoryService(FrameProcessor):
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         """Process incoming frames, intercept context frames for memory integration.
-        
+
         Args:
             frame: The incoming frame to process
             direction: The direction of frame flow in the pipeline
         """
         await super().process_frame(frame, direction)
-        
+
         context = None
         messages = None
-        
+
         if isinstance(frame, OpenAILLMContextFrame):
             context = frame.context
         elif isinstance(frame, LLMMessagesFrame):
             messages = frame.messages
             context = OpenAILLMContext.from_messages(messages)
-        
+
         if context:
             try:
                 # Get the latest user message to use as a query for memory retrieval
@@ -180,7 +186,7 @@ class Mem0MemoryService(FrameProcessor):
 
                 if latest_user_message:
                     # Enhance context with memories before passing it downstream
-                    self._enhance_context_with_memories(context, latest_user_message)                
+                    self._enhance_context_with_memories(context, latest_user_message)
                     # Store the conversation in Mem0. Only call this when user message is detected
                     self._store_messages(context_messages)