Update app.py

app.py

@@ -51,19 +51,38 @@ class LegionMariaAssistant:
         
 Available data sections: {available_sections}
 
-Each section contains:
-- about: mission, vision, core values, organizational information
-- office: projects, community outreach, operational details
-- leadership: organizational structure, leadership team, roles
+DETAILED SECTION CONTENTS:
 
-User query: "{message}"
+ABOUT section contains:
+- organization, headquarters_location, head_office, contact info (phone, email, websites)
+- mission, vision, core_values (faithfulness, inclusivity, service, formation, community, integrity)
+- church_beliefs (core_belief, reach, membership, dress_code, sacred_practice)
+- worship (practices, prohibited, prayer_schedule)
+
+OFFICE section contains:
+- departments (30+ departments with officers and roles like administration, treasury, education, procurement, events, medical, auditing, communications, projects, ICT, diaspora, facilities, music, logistics, grants, data engineering, coordination, security, etc.)
+- completed_projects, pending_projects (washrooms, radio/TV station, hospital, mausoleum, refurbishments)
+- current_activities (festivals, sports, workshops, conventions, camps)
+- support_donations (mpesa, bank_account details)
 
-Respond with ONLY the most relevant section name from the available sections. If the query spans multiple sections or is general, respond with "general".
+LEADERSHIP section contains:
+- supreme_leadership (patron_pope, matron_mother_superior, dean_of_cardinals)
+- youth_affairs_director (title, contact)
+- organizational_structure
+
+User query: "{message}"
 
-Examples:
-- "Who is the director?" -> leadership
-- "What is your mission?" -> about
-- "Tell me about your projects" -> office
+Respond with ONLY the most relevant section name. If the query spans multiple sections or is general, respond with "general".
+
+ROUTING EXAMPLES:
+- "Who is the director/pope/patron?" -> leadership
+- "What is your mission/vision/values?" -> about
+- "Where is headquarters/location/office?" -> about
+- "Contact info/phone/email/website?" -> about
+- "Tell me about projects/departments?" -> office
+- "How to donate/support/contribute?" -> office
+- "What activities do you do?" -> office
+- "Church beliefs/worship/practices?" -> about
 - "What do you do?" -> general
 
 Section:"""
@@ -114,22 +133,23 @@ Section:"""
                 conversation_context += "Current conversation:\n"
             
             # Step 3: Response LLM generates answer using only relevant data
-            response_prompt = f"""You are Santa Legion from the Legion Maria Directorate of Youth Affairs. Speak in first person as a member of the organization.
+            response_prompt = f"""You are Santa Legion from the Legion Maria Directorate of Youth Affairs. IMPORTANT: You must ONLY use the information provided below. Do not use any external knowledge or make assumptions.
 
-Your Knowledge:
+ONLY USE THIS INFORMATION:
 {json.dumps(relevant_data, indent=2)}
 
 {conversation_context}User: {message}
 
-Guidelines:
-- You are Santa Legion, speak as "I" and "we" (the organization)
+STRICT RULES:
+- You are Santa Legion, speak as "I" and "we" (the organization)  
+- ONLY answer using the information provided above
+- If headquarters is mentioned in the data, use EXACTLY what is stated
 - Keep responses SHORT (1-3 sentences maximum)
-- Be direct and personal
+- Do not invent or assume any information not in the provided data
 - Never mention being provided documents or data
-- Speak as if this is your natural knowledge
-- Use "our mission", "we believe", "I can help you with"
+- If asked about something not in your data, say "I don't have that information"
 
-Answer:"""
+Answer based ONLY on the provided information:"""
 
             # Get response from specialist LLM
             response = self.response_llm.invoke([{"role": "user", "content": response_prompt}])