Refactor app.py to create a dedicated demo function for UI initialization. Update UIHandler to generate a unique user ID per session and pass it to the regulatory query processing. Enhance memory management by ensuring user-specific data is utilized. Modify LLM streaming function to include a system prompt for improved response context.

agents/reg_radar.py

@@ -60,7 +60,9 @@ class RegRadarAgent:
         intent = call_llm(intent_prompt).strip().lower()
         return not intent.startswith("n")
 
-    def process_regulatory_query(self, message: str, params: dict = None):
+    def process_regulatory_query(
+        self, message: str, params: dict = None, user_id: str = "user"
+    ):
         """Process a regulatory query and return results"""
         # Determine the intended tool
         tool_key, tool_name = self.determine_intended_tool(message)
@@ -75,7 +77,7 @@ class RegRadarAgent:
         )
 
         # Check memory for similar queries
-        memory_results = self.memory_tools.search_memory("user", message)
+        memory_results = self.memory_tools.search_memory(user_id, message)
 
         return {
             "tool_name": tool_name,

agents/ui_handler.py

@@ -1,3 +1,4 @@
+import random
 import threading
 import time
 
@@ -12,6 +13,9 @@ from tools.llm import stream_llm
 class UIHandler:
     def __init__(self):
         self.agent = RegRadarAgent()
+        self.user_id = (
+            f"user-{random.randint(1000, 9999)}"  # User ID per session, small number
+        )
 
     def streaming_chatbot(self, message, history):
         """Process messages with tool visibility and lock input during response generation"""
@@ -82,7 +86,9 @@ class UIHandler:
         yield history, "", gr.update(interactive=False)
 
         # Process the regulatory query
-        results = self.agent.process_regulatory_query(message, params)
+        results = self.agent.process_regulatory_query(
+            message, params, user_id=self.user_id
+        )
         crawl_results = results["crawl_results"]
         memory_results = results["memory_results"]
 
@@ -173,7 +179,7 @@ Found {len(memory_results)} similar past queries in memory.
         # Save to memory in the background
         threading.Thread(
             target=self.agent.memory_tools.save_to_memory,
-            args=("user", message, streaming_content),
+            args=(self.user_id, message, streaming_content),
             daemon=True,
         ).start()
 
@@ -238,6 +244,14 @@ Found {len(memory_results)} similar past queries in memory.
                 gr.Markdown("""
                 ### RegRadar uses these intelligent tools:
                 
+                **🧠 Query Type Detection**
+                - Automatically detects if your message is a regulatory compliance query or a general question
+                - Selects the appropriate tools and response style based on your intent
+                
+                **📩 Information Extraction**
+                - Extracts key details (industry, region, keywords) from your command
+                - Ensures accurate and relevant regulatory analysis
+                
                 **🔍 Regulatory Web Crawler**
                 - Crawls official regulatory websites (SEC, FDA, FTC, etc.)
                 - Searches for recent updates and compliance changes
@@ -252,6 +266,7 @@ Found {len(memory_results)} similar past queries in memory.
                 - Remembers past queries and responses
                 - Learns from your compliance interests
                 - Provides context from previous interactions
+                - Each session creates a new user for personalization
                 
                 **🤖 AI Analysis Engine**
                 - Analyzes and summarizes regulatory findings

app.py

@@ -7,12 +7,17 @@ compliance guidance for various industries and regions.
 
 from agents.ui_handler import UIHandler
 
+
+def create_demo():
+    ui_handler = UIHandler()  # New user for each session
+    return ui_handler.create_ui()
+
+
 def main():
     """Initialize and launch the RegRadar application"""
-    ui_handler = UIHandler()
-    demo = ui_handler.create_ui()
+    demo = create_demo()
     demo.launch()
 
+
 if __name__ == "__main__":
     main()
-

tools/llm.py

@@ -28,12 +28,19 @@ def call_llm(prompt: str, temperature: float = DEFAULT_LLM_TEMPERATURE) -> str:
         return "I apologize, but I encountered an error processing your request."
 
 
-def stream_llm(prompt: str, temperature: float = DEFAULT_LLM_TEMPERATURE):
-    """Stream LLM response"""
+def stream_llm(
+    prompt: str,
+    temperature: float = DEFAULT_LLM_TEMPERATURE,
+    system_prompt: str = "You are an expert AI assistant specializing in regulatory updates. Provide thorough, insightful, and actionable analysis based on the user's request, focusing on compliance, recent changes, and best practices.",
+):
+    """Stream LLM response with an expert regulatory system prompt"""
     try:
         stream = client.chat.completions.create(
             model=DEFAULT_LLM_MODEL,
-            messages=[{"role": "user", "content": prompt}],
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": prompt},
+            ],
             temperature=temperature,
             stream=True,
         )