improve some statements and promote some code effiency

src/memary/agent/base_agent.py

@@ -185,7 +185,7 @@ def locate(self, query: str) -> str:
 
         reverse_geocode_result = self.gmaps.reverse_geocode((lattitude, longitude))
         formatted_address = reverse_geocode_result[0]["formatted_address"]
-        return "Your address is" + formatted_address
+        return "Your address is " + formatted_address
 
     def vision(self, query: str, img_url: str) -> str:
         """Uses computer vision to process the image specified by the image url and answers the question based on the CV results"""
@@ -285,7 +285,7 @@ def _change_llm_message_chat(self) -> dict:
         llm_message_chat["messages"].append(
             {
                 "role": "user",
-                "content": "Knowledge Entity Store:" + str(top_entities),
+                "content": "Knowledge Entity Store: " + str(top_entities),
             }
         )
         llm_message_chat["messages"].extend(

src/memary/agent/chat_agent.py

@@ -41,7 +41,7 @@ def add_chat(self, role: str, content: str, entities: Optional[List[str]] = None
         Args:
             role (str): 'system' or 'user'
             content (str): content of the chat
-            entities (Optional[List[str]], optional): entities from Memory systems. Defaults to None.
+            entities (Optional[List[str]]): entities from Memory systems. Defaults to None.
         """
         # Add a chat to the agent's memory.
         self._add_contexts_to_llm_message(role, content)

src/memary/agent/data_types.py

@@ -44,12 +44,9 @@ def __init__(self, system_persona_txt, user_persona_txt, past_chat_json, model):
 
     def __str__(self):
         llm_message_str = f"System Persona: {self.system_persona}\nUser Persona: {self.user_persona}\n"
-        for context in self.contexts:
-            llm_message_str += f"{str(context)},"
-        for memory in self.llm_message["memory_stream"]:
-            llm_message_str += f"{str(memory)},"
-        for entity in self.llm_message["knowledge_entity_store"]:
-            llm_message_str += f"{str(entity)},"
+        llm_message_str += ','.join(str(context) for context in self.contexts)
+        llm_message_str += ','.join(str(memory) for memory in self.llm_message["memory_stream"])
+        llm_message_str += ','.join(str(entity) for entity in self.llm_message["knowledge_entity_store"])
         return llm_message_str
 
     def _init_persona_to_messages(self):

src/memary/memory/entity_knowledge_store.py

@@ -46,7 +46,7 @@ def add_memory(self, memory_stream: list[MemoryItem]):
         Args:
             memory_stream (list): list of MemoryItem
         """
-        knowledge_meory = self._convert_memory_to_knowledge_memory(
+        knowledge_memory = self._convert_memory_to_knowledge_memory(
             memory_stream)
         self._update_knowledge_memory(knowledge_meory)
 

streamlit_app/app.py

@@ -222,7 +222,7 @@ def get_models(llm_models, vision_models):
             edges = []  # (node1, node2, [relationships])
             fill_graph(nodes, edges, cypher_query)
 
-            st.subheader("Knoweldge Graph")
+            st.subheader("Knowledge Graph")
             st.code("# Current Cypher Used\n" + cypher_query)
             st.write("")
             st.text("Subgraph:")