Improve conversation quality: better model, enhanced parameters, improved memory context, and natural prompts

app.py

@@ -75,23 +75,30 @@ class MemoryChatApp:
         if any(word in user_input.lower() for word in ["remember", "note", "save"]):
             return user_input
 
-        # Extract personal information
+        # Extract personal information with more detail
         personal_info = []
         if "my name is" in user_input.lower():
-            personal_info.append("User shared their name")
+            # Extract the actual name
+            name_part = user_input.lower().split("my name is")[-1].strip()
+            personal_info.append(f"User's name is {name_part}")
         if "i live in" in user_input.lower():
-            personal_info.append("User shared their location")
+            location_part = user_input.lower().split("i live in")[-1].strip()
+            personal_info.append(f"User lives in {location_part}")
         if "i work at" in user_input.lower():
-            personal_info.append("User shared their workplace")
+            work_part = user_input.lower().split("i work at")[-1].strip()
+            personal_info.append(f"User works at {work_part}")
         if "i study" in user_input.lower():
-            personal_info.append("User shared their studies")
+            study_part = user_input.lower().split("i study")[-1].strip()
+            personal_info.append(f"User studies {study_part}")
         if "my birthday" in user_input.lower():
-            personal_info.append("User shared their birthday")
+            birthday_part = user_input.lower().split("my birthday")[-1].strip()
+            personal_info.append(f"User's birthday is {birthday_part}")
         if "my favorite" in user_input.lower():
-            personal_info.append("User shared a favorite thing")
+            favorite_part = user_input.lower().split("my favorite")[-1].strip()
+            personal_info.append(f"User's favorite {favorite_part}")
 
         if personal_info:
-            return f"User mentioned: {', '.join(personal_info)}. Details: {user_input}"
+            return f"Personal info: {', '.join(personal_info)}"
 
         # Default to user input if no specific patterns found
         return user_input
@@ -114,17 +121,17 @@ class MemoryChatApp:
         self.conversation_history.append({"role": "user", "content": user_input})
 
         # Retrieve relevant memories to provide context
-        relevant_memories = self.memory_manager.retrieve_memories(user_input, k=3)
+        relevant_memories = self.memory_manager.retrieve_memories(user_input, k=5)  # Get more memories
 
-        # Build context from memories
+        # Build context from memories with better formatting
         context = ""
         if relevant_memories:
-            context = "Relevant memories:\n"
-            for memory in relevant_memories[:2]:  # Limit to 2 most relevant
-                context += f"- {memory['content']}\n"
+            context = "Here's what I remember about you:\n"
+            for i, memory in enumerate(relevant_memories[:3], 1):  # Show top 3 memories
+                context += f"{i}. {memory['content']}\n"
             context += "\n"
 
-        # Build the prompt with context and conversation history
+        # Build the prompt with enhanced context and conversation history
         prompt = self.build_prompt(user_input, context)
 
         # Generate AI response
@@ -157,7 +164,12 @@ class MemoryChatApp:
         Returns:
             The prompt to send to the AI model
         """
-        prompt = f"{context}Human: {user_input}\nAI: "
+        # Build a more natural conversation prompt
+        prompt = f"""{context}The user says: "{user_input}"
+
+As an AI assistant, respond naturally and helpfully. Consider any relevant memories above when crafting your response. Be conversational, engaging, and provide helpful information.
+
+Your response: """
         return prompt
 
     def get_current_time(self) -> str:

chat_interface.py

@@ -9,7 +9,7 @@ console = Console()
 class HuggingFaceChat:
     """Interface for chatting with Hugging Face models."""
 
-    def __init__(self, model_name: str = "microsoft/DialoGPT-small"):
+    def __init__(self, model_name: str = "microsoft/DialoGPT-medium"):
         """
         Initialize the chat interface.
 
@@ -66,15 +66,18 @@ class HuggingFaceChat:
             return "I'm sorry, but I couldn't load the model. Please check your internet connection and model availability."
 
         try:
-            # Generate response
+            # Generate response with improved parameters for better quality
             response = self.chatbot(
                 prompt,
                 max_length=max_length,
                 do_sample=True,
-                temperature=0.7,
-                top_p=0.9,
-                repetition_penalty=1.2,
-                pad_token_id=self.tokenizer.eos_token_id
+                temperature=0.8,  # Higher for more creativity
+                top_p=0.95,       # Higher for more diverse responses
+                top_k=50,         # Limit to top 50 tokens
+                repetition_penalty=1.1,  # Lower penalty for more natural flow
+                no_repeat_ngram_size=3,  # Avoid repeating 3-word phrases
+                pad_token_id=self.tokenizer.eos_token_id,
+                truncation=True   # Enable truncation to prevent errors
             )
 
             # Extract the generated text
@@ -84,12 +87,31 @@ class HuggingFaceChat:
             if generated_text.startswith(prompt):
                 generated_text = generated_text[len(prompt):].strip()
 
+            # Clean up the response
+            # Remove any incomplete sentences or hanging punctuation
+            generated_text = self._clean_response(generated_text)
+
             return generated_text
 
         except Exception as e:
             console.print(f"[red]Error generating response: {e}[/red]")
             return "I'm sorry, but I encountered an error while generating a response."
 
+    def _clean_response(self, text: str) -> str:
+        """Clean up the generated response."""
+        # Remove any trailing incomplete sentences
+        if text.endswith(('.', '!', '?')):
+            return text
+
+        # Find the last complete sentence
+        import re
+        sentences = re.split(r'(?<=[.!?])\s+', text)
+        if len(sentences) > 1:
+            # Remove the last incomplete sentence
+            text = ' '.join(sentences[:-1])
+
+        return text.strip()
+
     def check_model_availability(self) -> bool:
         """Check if the model is available."""
         return self.chatbot is not None