changes made

ai_agent_tutorials/ai_customer_support_agent/README.md

@@ -28,7 +28,7 @@ The app expects Qdrant to be running on localhost:6333. Adjust the configuration
 docker pull qdrant/qdrant
 
 docker run -p 6333:6333 -p 6334:6334 \
-    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
+    -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
     qdrant/qdrant
 ```
 

ai_agent_tutorials/ai_customer_support_agent/customer_support_agent.py

@@ -17,32 +17,41 @@ if openai_api_key:
 
     class CustomerSupportAIAgent:
         def __init__(self):
+            # Initialize Mem0 with Qdrant as the vector store
             config = {
                 "vector_store": {
                     "provider": "qdrant",
                     "config": {
-                        "model": "gpt-4o-mini",
                         "host": "localhost",
                         "port": 6333,
                     }
                 },
             }
+            try:
                 self.memory = Memory.from_config(config)
+            except Exception as e:
+                st.error(f"Failed to initialize memory: {e}")
+                st.stop()  # Stop execution if memory initialization fails
+
             self.client = OpenAI()
             self.app_id = "customer-support"
 
         def handle_query(self, query, user_id=None):
+            try:
+                # Search for relevant memories
                 relevant_memories = self.memory.search(query=query, user_id=user_id)
+                
+                # Build context from relevant memories
                 context = "Relevant past information:\n"
                 if relevant_memories and "results" in relevant_memories:
                     for memory in relevant_memories["results"]:
                         if "memory" in memory:
                             context += f"- {memory['memory']}\n"
 
+                # Generate a response using OpenAI
                 full_prompt = f"{context}\nCustomer: {query}\nSupport Agent:"
-
                 response = self.client.chat.completions.create(
-                model="gpt-4o-mini",
+                    model="gpt-4",
                     messages=[
                         {"role": "system", "content": "You are a customer support AI agent for TechGadgets.com, an online electronics store."},
                         {"role": "user", "content": full_prompt}
@@ -50,15 +59,25 @@ if openai_api_key:
                 )
                 answer = response.choices[0].message.content
 
+                # Add the query and answer to memory
                 self.memory.add(query, user_id=user_id, metadata={"app_id": self.app_id, "role": "user"})
                 self.memory.add(answer, user_id=user_id, metadata={"app_id": self.app_id, "role": "assistant"})
 
                 return answer
+            except Exception as e:
+                st.error(f"An error occurred while handling the query: {e}")
+                return "Sorry, I encountered an error. Please try again later."
 
         def get_memories(self, user_id=None):
+            try:
+                # Retrieve all memories for a user
                 return self.memory.get_all(user_id=user_id)
+            except Exception as e:
+                st.error(f"Failed to retrieve memories: {e}")
+                return None
 
-        def generate_synthetic_data(self, user_id):
+        def generate_synthetic_data(self, user_id: str) -> dict | None:
+            try:
                 today = datetime.now()
                 order_date = (today - timedelta(days=10)).strftime("%B %d, %Y")
                 expected_delivery = (today + timedelta(days=2)).strftime("%B %d, %Y")
@@ -75,12 +94,11 @@ if openai_api_key:
                 Format the output as a JSON object."""
 
                 response = self.client.chat.completions.create(
-            model="gpt-4o-mini",
+                    model="gpt-4",
                     messages=[
                         {"role": "system", "content": "You are a data generation AI that creates realistic customer profiles and order histories. Always respond with valid JSON."},
                         {"role": "user", "content": prompt}
-            ],
+                    ]
-            response_format={"type": "json_object"}
                 )
 
                 customer_data = json.loads(response.choices[0].message.content)
@@ -89,11 +107,22 @@ if openai_api_key:
                 for key, value in customer_data.items():
                     if isinstance(value, list):
                         for item in value:
-                        self.memory.add(json.dumps(item), user_id=user_id, metadata={"app_id": self.app_id, "role": "system"})
+                            self.memory.add(
+                                json.dumps(item), 
+                                user_id=user_id, 
+                                metadata={"app_id": self.app_id, "role": "system"}
+                            )
                     else:
-                    self.memory.add(f"{key}: {json.dumps(value)}", user_id=user_id, metadata={"app_id": self.app_id, "role": "system"})
+                        self.memory.add(
+                            f"{key}: {json.dumps(value)}", 
+                            user_id=user_id, 
+                            metadata={"app_id": self.app_id, "role": "system"}
+                        )
 
                 return customer_data
+            except Exception as e:
+                st.error(f"Failed to generate synthetic data: {e}")
+                return None
 
     # Initialize the CustomerSupportAIAgent
     support_agent = CustomerSupportAIAgent()
@@ -113,7 +142,10 @@ if openai_api_key:
         if customer_id:
             with st.spinner("Generating customer data..."):
                 st.session_state.customer_data = support_agent.generate_synthetic_data(customer_id)
+            if st.session_state.customer_data:
                 st.sidebar.success("Synthetic data generated successfully!")
+            else:
+                st.sidebar.error("Failed to generate synthetic data.")
         else:
             st.sidebar.error("Please enter a customer ID first.")
 
@@ -156,6 +188,7 @@ if openai_api_key:
             st.markdown(query)
 
         # Generate and display response
+        with st.spinner("Generating response..."):
             answer = support_agent.handle_query(query, user_id=customer_id)
 
         # Add assistant response to chat history