Enhance README with detailed project description, features, tools used, and supported regulatory sources. Update RegRadarAgent to include memory context in report generation and modify UIHandler to display top past queries. This improves user experience by providing relevant historical insights during regulatory report generation.

README.md

@@ -11,4 +11,30 @@ license: apache-2.0
 short_description: RegRadar watches the worlds regulators so you dont have to.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# RegRadar
+
+RegRadar is an AI-powered regulatory compliance assistant that monitors global regulations so you don't have to. It helps compliance professionals, legal teams, and businesses stay up-to-date with the latest regulatory changes across industries and regions.
+
+## 🚀 Features
+- **Automatic Query Type Detection**: Understands if your message is a regulatory compliance query or a general question, and selects the right tools.
+- **Information Extraction**: Extracts key details (industry, region, keywords) from your queries for precise analysis.
+- **Regulatory Web Crawler**: Crawls official regulatory websites (e.g., SEC, FDA, FTC, ESMA, BIS) for recent updates and compliance changes (last 30 days).
+- **Regulatory Search Engine**: Searches across multiple sources for industry-specific compliance information and aggregates results.
+- **Memory System**: Remembers past queries and responses, personalizing results for each session/user.
+- **AI Analysis Engine**: Summarizes findings and generates actionable compliance recommendations and executive summaries.
+
+## 🛠️ Tools Used
+- **Gradio**: For the interactive web UI.
+- **OpenAI/KeywordsAI LLM**: For natural language understanding, information extraction, summarization, and LLM tracking.
+- **Tavily**: For regulatory web crawling and search.
+- **Mem0**: For session-based memory and personalization.
+
+## 🌍 Supported Regulatory Sources
+- US: SEC, FDA, FTC, Federal Register, CFTC, FDIC, FINRA, Federal Reserve Board
+- EU: ESMA, EBA, EIOPA, European Parliament, ECB
+- Asia: Japan FSA, Reserve Bank of India
+- Global: BIS, IMF, World Bank, OECD
+
+## 📺 Video Demo
+[//]: # (TODO: Embed or link to the video demo here)
+

agents/reg_radar.py

@@ -86,8 +86,17 @@ class RegRadarAgent:
             "memory_results": memory_results,
         }
 
-    def generate_report(self, params, crawl_results):
-        """Generate a comprehensive regulatory report"""
+    def generate_report(self, params, crawl_results, memory_results=None):
+        """Generate a comprehensive regulatory report, including memory context if available"""
+        memory_context = ""
+        if memory_results:
+            # Format memory results for inclusion in the prompt (limit to 3 for brevity)
+            memory_context = "\n\n# 💾 Related Past Queries and Insights\n"
+            for i, mem in enumerate(memory_results[:3], 1):
+                memory_text = mem.get("memory", "N/A")
+                memory_context += f"\n**{i}. Memory:** {memory_text[:300]}...\n"
+            memory_context += "\nIncorporate any relevant insights from these past queries into your analysis.\n"
+
         if not crawl_results["results"]:
             summary_prompt = (
                 f"No regulatory updates found for {params['industry']} in {params['region']} "
@@ -104,7 +113,7 @@ class RegRadarAgent:
 
             summary_prompt = f"""
             Create a comprehensive regulatory compliance report for {params["industry"]} industry in {params["region"]} region.
-            
+            {memory_context}
             Analyze these regulatory updates:
             {json.dumps(by_source, indent=2)}
             

agents/ui_handler.py

@@ -134,12 +134,17 @@ class UIHandler:
 
         # Display memory results if available
         if memory_results:
+            top_memories = memory_results[:3]
+            memory_details = ""
+            for i, mem in enumerate(top_memories, 1):
+                memory_text = mem.get("memory", "N/A")
+                memory_details += f"\n**{i}. Memory:** {memory_text[:300]}...\n"
             memory_msg = f"""
 <details>
 <summary><strong>💾 Related Past Queries</strong> - Click to expand</summary>
 
-Found {len(memory_results)} similar past queries in memory.
-
+Found {len(memory_results)} similar past queries in memory. Top 3 shown below:
+{memory_details}
 </details>
 """
             history.append(ChatMessage(role="assistant", content=memory_msg))
@@ -159,7 +164,7 @@ Found {len(memory_results)} similar past queries in memory.
         streaming_content = ""
         history.append(ChatMessage(role="assistant", content=""))
 
-        for chunk in self.agent.generate_report(params, crawl_results):
+        for chunk in self.agent.generate_report(params, crawl_results, memory_results):
             streaming_content += chunk
             history[-1] = ChatMessage(role="assistant", content=streaming_content)
             yield history, "", gr.update(interactive=False)