Refactored

app.py

@@ -145,6 +145,16 @@ class InternationalStudentDataCollector:
             f"5 facts on credit building for {country} students: cards, history, pitfalls."
         )
 
+# =======================================
+# Shared RAG Knowledge Base Instances
+# =======================================
+
+KB_INSTANCES: Dict[str, 'KnowledgeBase'] = {}
+COMMON_COUNTRIES = ["India", "China"]
+DOMAINS = ["banking", "credit", "tax"]
+
+# Placeholder for class KnowledgeBase declaration
+
 # =======================================
 # RAG Knowledge Base
 # =======================================
@@ -207,11 +217,10 @@ class KnowledgeBase:
         docs = retr.get_relevant_documents(query)
         return [d.page_content for d in docs]
 
-# Preload KBs
-COMMON_COUNTRIES = ["India","China"]
-DOMAINS = ["banking","credit","tax"]
+# Preload and register shared KBs
 for dom in DOMAINS:
     kb = KnowledgeBase(dom)
+    KB_INSTANCES[dom] = kb
     for c in COMMON_COUNTRIES:
         threading.Thread(target=kb._init_country, args=(c,), daemon=True).start()
 
@@ -222,7 +231,7 @@ for dom in DOMAINS:
 class SpecialistAgent:
     def __init__(self, name: str, domain: str):
         self.name = name
-        self.kb = KnowledgeBase(domain)
+        self.kb = KB_INSTANCES[domain]      # use shared, preloaded KB
         self.llm = ChatOpenAI(temperature=0.2)
 
     def run(self, query: str, country: str) -> str:
@@ -234,11 +243,10 @@ class SpecialistAgent:
         log_workflow(f"{self.name} done")
         return resp.content
 
-# Instantiate specialists
+# Instantiate specialists using shared KB
 BankingAdvisor = lambda: SpecialistAgent("Banking Advisor","banking")
-CreditBuilder = lambda: SpecialistAgent("Credit Builder","credit")
+CreditBuilder  = lambda: SpecialistAgent("Credit Builder","credit")
 TaxSpecialist = lambda: SpecialistAgent("Tax Specialist","tax")
-# Add more as needed
 
 # =======================================
 # Coordinator Agent
@@ -249,25 +257,34 @@ class CoordinatorAgent:
         self.llm = ChatOpenAI(temperature=0.3)
         self.specialists = {
             "banking": BankingAdvisor(),
-            "credit": CreditBuilder(),
-            "tax": TaxSpecialist()
+            "credit":  CreditBuilder(),
+            "tax":      TaxSpecialist()
         }
 
     def run(self, query: str, profile: Dict[str,Any]) -> str:
         clear_workflow_log()
         country = profile.get("home_country","unknown")
-        # 1. Gather specialist advice
-        advice_map = {d:agent.run(query,country) for d,agent in self.specialists.items()}
-        # 2. Multi-path plans placeholder
+        advice_map: Dict[str,str] = {}
+        q = query.lower()
+        # domain-based filtering
+        if "bank" in q or "account" in q:
+            advice_map["banking"] = self.specialists["banking"].run(query,country)
+        if "credit" in q:
+            advice_map["credit"] = self.specialists["credit"].run(query,country)
+        if "tax" in q or "treaty" in q:
+            advice_map["tax"] = self.specialists["tax"].run(query,country)
+        # fallback: if no specialist matched, run banking by default
+        if not advice_map:
+            advice_map["banking"] = self.specialists["banking"].run(query,country)
+        # synthesize
         plans = {"conservative":"...","balanced":"...","growth":"..."}
-        # 3. Synthesis & formatting
         lines = ["# Your Personalized Financial Advice\n"]
-        for domain, text in advice_map.items():
-            lines.append(f"## {domain.capitalize()}\n")
-            for para in text.strip().split("\n\n"):
+        for d,t in advice_map.items():
+            lines.append(f"## {d.capitalize()}\n")
+            for para in t.strip().split("\n\n"):
                 lines.append("    "+para.replace("\n","\n    "))
             lines.append("")
-        lines.append("## Multi-Path Financial Plans\n```json")
+        lines.append("## Multi-Path Plans\n```json")
         lines.append(json.dumps(plans,indent=2))
         lines.append("```")
         formatted = "\n".join(lines)