diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..d5a05bc4208216d462b7b674b8b1b7f7e2c63e11
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,4 @@
+*.pdf filter=lfs diff=lfs merge=lfs -text
+*.docx filter=lfs diff=lfs merge=lfs -text
+*.faiss filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..94eb2a817d1fda37ed93dde7acf9bb4428aba812
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,10 @@
+.venv/
+.env
+__pycache__/
+*.pyc
+.streamlit/
+temp_docs/
+temp_faiss_index/
+debug_*.txt
+test_*.txt
+*.log
diff --git a/agents/graph.py b/agents/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d99e314b10ede4cc04123bcd0fe61b2ccafa8d4
--- /dev/null
+++ b/agents/graph.py
@@ -0,0 +1,171 @@
+"""
+LangGraph Workflow for Insurance RAG System.
+Implements deterministic, compliance-focused retrieval with specialized nodes.
+"""
+
+from langgraph.graph import StateGraph, END
+from agents.states import AgentState
+from agents.nodes import nodes
+
+
+def build_rag_workflow() -> StateGraph:
+    """
+    Builds the LangGraph workflow with the following flow:
+    
+    query_rewriter → query_classifier → entity_extractor → retrieval_router
+                              ↓
+                    [conditional routing by intent]
+                              ↓
+        ┌─────────────────────────────────────────────────────┐
+        │ list_plans: listing_agent → guardrail              │
+        │ plan_details: retriever → aggregator → retrieval → guardrail │
+        │ compare_plans: retriever → aggregator → comparison → guardrail │
+        │ recommendation: retriever → aggregator → advisory → guardrail │
+        │ general_query: retriever → aggregator → faq → guardrail │
+        └─────────────────────────────────────────────────────┘
+    """
+    
+    workflow = StateGraph(AgentState)
+    
+    # =========================================================================
+    # Add all nodes
+    # =========================================================================
+    
+    # Pre-processing nodes
+    workflow.add_node("query_rewriter", nodes.query_rewriter_node)
+    workflow.add_node("query_classifier", nodes.query_classifier_node)
+    workflow.add_node("entity_extractor", nodes.entity_extractor_node)
+    workflow.add_node("retrieval_router", nodes.retrieval_router_node)
+    
+    # Retrieval nodes
+    workflow.add_node("retriever", nodes.retriever_node)
+    workflow.add_node("plan_aggregator", nodes.plan_aggregator_node)
+    
+    # Agent nodes
+    workflow.add_node("listing_agent", nodes.listing_agent)
+    workflow.add_node("retrieval_agent", nodes.retrieval_agent)
+    workflow.add_node("comparison_agent", nodes.comparison_agent)
+    workflow.add_node("advisory_agent", nodes.advisory_agent)
+    workflow.add_node("faq_agent", nodes.faq_agent)
+    
+    # Post-processing
+    workflow.add_node("guardrail", nodes.guardrail_node)
+    
+    # =========================================================================
+    # Define edges
+    # =========================================================================
+    
+    # Entry point
+    workflow.set_entry_point("query_rewriter")
+    
+    # Linear pre-processing chain
+    workflow.add_edge("query_rewriter", "query_classifier")
+    workflow.add_edge("query_classifier", "entity_extractor")
+    workflow.add_edge("entity_extractor", "retrieval_router")
+    
+    # Conditional routing based on intent
+    def route_by_intent(state: AgentState) -> str:
+        """Route to appropriate handler based on classified intent."""
+        intent = state.get("intent", "plan_details")
+        
+        if intent == "list_plans":
+            # Listing doesn't need retrieval, goes direct to listing agent
+            return "listing_agent"
+        else:
+            # All other intents go through retrieval first
+            return "retriever"
+    
+    workflow.add_conditional_edges(
+        "retrieval_router",
+        route_by_intent,
+        {
+            "listing_agent": "listing_agent",
+            "retriever": "retriever"
+        }
+    )
+    
+    # Listing agent goes directly to guardrail
+    workflow.add_edge("listing_agent", "guardrail")
+    
+    # Retriever always goes to aggregator
+    workflow.add_edge("retriever", "plan_aggregator")
+    
+    # Aggregator routes to appropriate agent based on intent
+    def route_to_agent(state: AgentState) -> str:
+        """Route from aggregator to the appropriate agent."""
+        intent = state.get("intent", "plan_details")
+        
+        route_map = {
+            "plan_details": "retrieval_agent",
+            "compare_plans": "comparison_agent",
+            "recommendation": "advisory_agent",
+            "general_query": "faq_agent"
+        }
+        
+        return route_map.get(intent, "retrieval_agent")
+    
+    workflow.add_conditional_edges(
+        "plan_aggregator",
+        route_to_agent,
+        {
+            "retrieval_agent": "retrieval_agent",
+            "comparison_agent": "comparison_agent",
+            "advisory_agent": "advisory_agent",
+            "faq_agent": "faq_agent"
+        }
+    )
+    
+    # All agents end at guardrail
+    workflow.add_edge("retrieval_agent", "guardrail")
+    workflow.add_edge("comparison_agent", "guardrail")
+    workflow.add_edge("advisory_agent", "guardrail")
+    workflow.add_edge("faq_agent", "guardrail")
+    
+    # Guardrail ends the workflow
+    workflow.add_edge("guardrail", END)
+    
+    return workflow
+
+
+# Build and compile the workflow
+workflow = build_rag_workflow()
+app = workflow.compile()
+
+
+if __name__ == "__main__":
+    # Test the graph
+    print("Graph compiled successfully!")
+    
+    # Test cases
+    test_queries = [
+        "List all term plans from Tata AIA",
+        "Explain the TATA AIA Smart Value Income plan",
+        "Compare Tata AIA vs Edelweiss term plans",
+        "Suggest a plan for 30-year-old non-smoker with 1Cr cover"
+    ]
+    
+    for query in test_queries:
+        print(f"\n{'='*60}")
+        print(f"Query: {query}")
+        print('='*60)
+        
+        initial_state = {
+            "input": query,
+            "chat_history": [],
+            "intent": "",
+            "extracted_entities": {},
+            "metadata_filters": {},
+            "retrieval_strategy": "",
+            "context": [],
+            "retrieved_chunks": {},
+            "reasoning_output": "",
+            "answer": "",
+            "next_step": ""
+        }
+        
+        try:
+            result = app.invoke(initial_state)
+            print(f"Intent: {result.get('intent')}")
+            print(f"Answer: {result.get('answer', '')[:500]}...")
+        except Exception as e:
+            print(f"Error: {e}")
diff --git a/agents/nodes.py b/agents/nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..93aba79258a0c97db39e88b28319c0b08015c82f
--- /dev/null
+++ b/agents/nodes.py
@@ -0,0 +1,884 @@
+import re
+from typing import Dict, List, Any, Optional
+from collections import defaultdict
+from agents.states import AgentState, ExtractedEntities
+from rag.retriever import RAGRetriever
+from langchain_core.messages import HumanMessage, SystemMessage
+from models.llm import LLMFactory
+
+
+# Compliance disclaimer to append to all answers
+COMPLIANCE_DISCLAIMER = (
+    "\n\n---\n"
+)
+
+# Prompting rules for all agents
+COMPLIANCE_RULES = """
+CRITICAL RULES:
+- ❌ NO invented plan names - only use plans from the provided context
+- ❌ NO assumptions beyond documents - if info is missing, say so explicitly
+- ❌ NO meta-commentary. DO NOT mention "the provided context", "the documents", "the text", or "internal state".
+- ✅ CIS overrides brochure for: exclusions, charges, conditions
+- ✅ Use structured output (markdown tables) for comparisons
+- ✅ Simple, clear language for end users
+- ✅ Provide "OUTPUT ONLY" - start answering the user's question directly.
+"""
+
+
+class AgentNodes:
+    """
+    Enhanced LangGraph nodes implementing the full RAG specification.
+    """
+    
+    def __init__(self):
+        self.retriever = None
+
+    def _get_retriever(self) -> Optional[RAGRetriever]:
+        """Lazy initialization of retriever."""
+        if not self.retriever:
+            try:
+                self.retriever = RAGRetriever()
+            except Exception:
+                return None
+        return self.retriever
+
+    def reload_retriever(self):
+        """Triggers a reload of the retriever's index."""
+        retriever = self._get_retriever()
+        if retriever:
+            retriever.reload()
+
+    # =========================================================================
+    # NODE 1: Query Rewriter
+    # =========================================================================
+    def query_rewriter_node(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Rewrites query to be self-contained based on chat history.
+        Resolves pronouns and references.
+        """
+        llm = LLMFactory.get_llm("small")
+        query = state["input"]
+        history = state.get("chat_history", [])
+        
+        if not history:
+            return {"input": query}
+            
+        system_prompt = (
+            "You are a query rewriter for an insurance RAG system. "
+            "Your task is to rewrite the latest question to be self-contained.\n\n"
+            "RULES:\n"
+            "1. ALWAYS resolve pronouns (it, they, these) or vague terms (the plan, previous one) using the previous context.\n"
+            "2. If the user asks a follow-up about 'it' or 'the plan', replace it with the specific plan name mentioned last.\n"
+            "3. If the user asks 'is it good for me' or similar, rewrite it to '[Plan Name] recommendation for [user details if any]'.\n"
+            "4. If the query is already very specific and names a plan, keep it mostly as-is but ensure insurer names are present.\n"
+            "5. Do NOT cross-pollinate unrelated queries. If the user switches topics completely, ignore the history.\n"
+            "6. NEVER return a conversational response, suggestion, or question. If you cannot resolve a reference, return the original 'Latest' query as is.\n"
+            "7. Return ONLY the rewritten query text."
+        )
+        
+        history_str = "\n".join([f"- {h}" for h in history[-5:]])  # Last 5 turns
+        prompt = f"History:\n{history_str}\n\nLatest: {query}"
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=prompt)])
+        rewritten = getattr(response, 'content', str(response)).strip()
+        
+        return {"input": rewritten}
+
+    # =========================================================================
+    # NODE 2: Query Classifier
+    # =========================================================================
+    def query_classifier_node(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Classifies user intent into:
+        - list_plans: User wants to see available plans
+        - plan_details: User asks about a specific plan
+        - compare_plans: User wants to compare multiple plans
+        - recommendation: User seeks personalized advice
+        - general_query: General insurance questions
+        """
+        llm = LLMFactory.get_llm("small")
+        query = state["input"].lower()
+        
+        # Fast keyword-based classification first
+        if any(kw in query for kw in ["list", "which plans", "what plans", "all plans", "available plans", "show me plans"]):
+            return {"intent": "list_plans"}
+        
+        if any(kw in query for kw in ["compare", "vs", "versus", "difference between", "which is better"]):
+            return {"intent": "compare_plans"}
+        
+        if any(kw in query for kw in ["suggest", "recommend", "best for", "should i", "suitable for"]):
+            return {"intent": "recommendation"}
+        
+        # LLM-based classification for ambiguous cases
+        system_prompt = (
+            "Classify the user's insurance query into ONE of:\n"
+            "- 'plan_details': Asking about features, benefits, eligibility of a SPECIFIC plan\n"
+            "- 'list_plans': Wants to know WHICH plans are available\n"
+            "- 'compare_plans': Wants to COMPARE 2+ plans side-by-side\n"
+            "- 'recommendation': Seeks personalized advice based on their profile\n"
+            "- 'general_query': General insurance terminology or concepts\n\n"
+            "Return ONLY the category name."
+        )
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=query)])
+        intent = getattr(response, 'content', str(response)).lower().strip()
+        
+        valid_intents = ['list_plans', 'plan_details', 'compare_plans', 'recommendation', 'general_query']
+        if intent not in valid_intents:
+            intent = "plan_details"  # Default fallback
+            
+        return {"intent": intent}
+
+    # =========================================================================
+    # NODE 3: Entity Extractor
+    # =========================================================================
+    def entity_extractor_node(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Extracts structured entities from the query:
+        - provider (insurer names)
+        - insurance_type (term, ulip, savings, etc.)
+        - plan_names (specific plan names mentioned)
+        - user_profile (age, income, smoker, dependents, goal)
+        """
+        query = state["input"].lower()
+        
+        # Extract providers
+        provider_map = {
+            "edelweiss": "Edelweiss Life",
+            "tata": "TATA AIA",
+            "tata aia": "TATA AIA",
+            "generali": "Generali Central",
+            "central": "Generali Central",
+            "pramerica": "PRAMERICA"
+        }
+        providers = []
+        for keyword, name in provider_map.items():
+            if keyword in query and name not in providers:
+                providers.append(name)
+        
+        # Extract insurance types
+        type_map = {
+            "term": ["Term Insurance", "Term Plan"],
+            "ulip": ["Unit Linked Insurance Plan", "ULIP Plan"],
+            "wealth": ["Unit Linked Insurance Plan"],
+            "savings": ["Savings Plan", "Guaranteed Return"],
+            "retirement": ["Retirement and Pension"],
+            "pension": ["Retirement and Pension"],
+            "health": ["Health Insurance"],
+            "group": ["Group Plan"]
+        }
+        insurance_types = []
+        for keyword, types in type_map.items():
+            if keyword in query:
+                for t in types:
+                    if t not in insurance_types:
+                        insurance_types.append(t)
+        
+        # Extract specific plan names using LLM
+        plan_names = self._extract_plan_names_from_query(state["input"])
+        
+        # Extract user profile for recommendation intent
+        user_profile = {}
+        if state.get("intent") == "recommendation":
+            user_profile = self._extract_user_profile(state["input"])
+        
+        entities: ExtractedEntities = {
+            "provider": list(set(providers)) if providers else [],
+            "insurance_type": list(set(insurance_types)) if insurance_types else [],
+            "plan_names": list(set(plan_names)) if plan_names else [],
+            "user_profile": user_profile or {}
+        }
+        
+        # Build metadata filters from entities
+        filters = {}
+        if providers:
+            filters["insurer"] = providers
+        if insurance_types:
+            filters["insurance_type"] = insurance_types
+        
+        return {
+            "extracted_entities": entities,
+            "metadata_filters": filters
+        }
+
+    def _extract_plan_names_from_query(self, query: str) -> List[str]:
+        """Use LLM to extract specific plan names mentioned in query."""
+        llm = LLMFactory.get_llm("small")
+        
+        system_prompt = (
+            "Extract EXACT insurance plan names from the query.\n"
+            "If the user is asking to compare, extract BOTH plan names.\n"
+            "RULES:\n"
+            "- Return one plan name per line\n"
+            "- Include insurer prefix if mentioned (e.g., 'TATA AIA Smart Value Income', 'Edelweiss Saral Jeevan Bima')\n"
+            "- Return EMPTY if no specific plan names found\n"
+            "- Do NOT invent plan names"
+        )
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=query)])
+        result = getattr(response, 'content', str(response)).strip()
+        
+        # Validation: If LLM returns a sentence instead of names, skip it
+        if "mentioned in" in result.lower() or "referring to" in result.lower() or len(result) > 200:
+            return []
+        
+        if not result or result.lower() in ['none', 'empty', 'n/a']:
+            return []
+        
+        # Parse response
+        plan_names = []
+        for line in result.split('\n'):
+            line = re.sub(r'^[\d\.\-\*\u2022]\s*', '', line).strip().strip('"\'')
+            if len(line) > 5:
+                plan_names.append(line)
+        
+        return plan_names
+
+    def _extract_user_profile(self, query: str) -> Dict[str, Any]:
+        """Extract user profile information for recommendations."""
+        llm = LLMFactory.get_llm("small")
+        
+        system_prompt = (
+            "Extract user profile from the insurance query.\n"
+            "Return in format:\n"
+            "age: <number or null>\n"
+            "smoker: <yes/no or null>\n"
+            "cover_amount: <amount or null>\n"
+            "goal: <protection/savings/retirement/wealth or null>\n"
+            "dependents: <number or null>"
+        )
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=query)])
+        result = getattr(response, 'content', str(response))
+        
+        profile = {}
+        for line in result.split('\n'):
+            if ':' in line:
+                key, value = line.split(':', 1)
+                key = key.strip().lower()
+                value = value.strip().lower()
+                if value not in ['null', 'none', 'n/a', '']:
+                    if key == 'age':
+                        try:
+                            profile['age'] = int(re.search(r'\d+', value).group())
+                        except:
+                            pass
+                    elif key == 'smoker':
+                        profile['smoker'] = 'yes' in value
+                    elif key == 'cover_amount':
+                        profile['cover_amount'] = value
+                    elif key == 'goal':
+                        profile['goal'] = value
+                    elif key == 'dependents':
+                        try:
+                            profile['dependents'] = int(re.search(r'\d+', value).group())
+                        except:
+                            pass
+        
+        return profile
+
+    # =========================================================================
+    # NODE 4: Retrieval Router
+    # =========================================================================
+    def retrieval_router_node(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Determines retrieval strategy based on intent.
+        """
+        intent = state.get("intent", "plan_details")
+        
+        strategy_map = {
+            "list_plans": "metadata_only",
+            "plan_details": "plan_level",
+            "compare_plans": "cross_plan",
+            "recommendation": "section_specific",
+            "general_query": "plan_level"
+        }
+        
+        return {"retrieval_strategy": strategy_map.get(intent, "plan_level")}
+
+    # =========================================================================
+    # NODE 5: Retriever
+    # =========================================================================
+    def retriever_node(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Retrieves documents with:
+        - Metadata filtering
+        - CIS boosting for exclusions/charges/conditions
+        - Deduplication by similarity
+        """
+        retriever = self._get_retriever()
+        if not retriever:
+            return {"context": [], "retrieved_chunks": {}}
+        
+        query = state["input"]
+        filters = state.get("metadata_filters", {})
+        entities = state.get("extracted_entities", {})
+        strategy = state.get("retrieval_strategy", "plan_level")
+        
+        # If specific plan names were extracted, use them for precise retrieval
+        plan_names = entities.get("plan_names") or []
+        matched_plans = []
+        if plan_names:
+            # Resolve to actual plan names in index
+            all_plans = self._list_plans_from_index()
+            for name in plan_names:
+                match = self._find_closest_plan_name(name, all_plans)
+                if match:
+                    matched_plans.append(match)
+            
+            # Update filters for non-comparison queries (for comparison, _retrieve_for_comparison handles it)
+            if matched_plans and strategy != "cross_plan":
+                filters = filters.copy()
+                filters["product_name"] = matched_plans[0] if len(matched_plans) == 1 else matched_plans
+        
+        boost_cis = any(kw in query.lower() for kw in 
+                       ["exclusion", "excluded", "not covered", "charges", "fee", "condition", "waiting"])
+        
+        # Retrieve documents
+        if strategy == "cross_plan":
+            # For comparisons, retrieve for each plan separately
+            # Pass matched_plans if we have them, otherwise it will try to find them from filters
+            chunks_by_plan = self._retrieve_for_comparison(query, filters, entities, matched_plans=matched_plans)
+        else:
+            docs = retriever.search(query, filters=filters, k=8)
+            chunks_by_plan = self._group_by_plan_id(docs)
+        
+        # Boost CIS documents if needed
+        if boost_cis:
+            chunks_by_plan = self._boost_cis_chunks(chunks_by_plan)
+        
+        # Format context strings
+        limit_per_plan = 5 if strategy == "cross_plan" else 3
+        context = self._format_context(chunks_by_plan, limit=limit_per_plan)
+        
+        return {
+            "context": context,
+            "retrieved_chunks": chunks_by_plan
+        }
+
+    def _format_context(self, chunks_by_plan: Dict[str, List[Dict]], limit: int = 3) -> List[str]:
+        """Helper to format chunks into LLM-readable context strings."""
+        context = []
+        for plan_id, chunks in chunks_by_plan.items():
+            for chunk in chunks[:limit]:
+                content = chunk.get("content", "")
+                plan_name = chunk.get("product_name", "Unknown")
+                doc_type = chunk.get("document_type", "brochure")
+                section = chunk.get("section", "General")
+                context.append(f"[{plan_name} - {doc_type.upper()} - {section}] {content}")
+        return context
+
+    def _retrieve_for_comparison(self, query: str, filters: Dict, entities: Dict, matched_plans: List[str] = None) -> Dict[str, List]:
+        """Retrieve chunks for each plan separately in comparison mode."""
+        retriever = self._get_retriever()
+        if not retriever:
+            return {}
+        
+        if not matched_plans:
+            plan_names = entities.get("plan_names") or []
+            all_index_plans = self._list_plans_from_index()
+            matched_plans = []
+            for name in plan_names:
+                match = self._find_closest_plan_name(name, all_index_plans)
+                if match:
+                    matched_plans.append(match)
+        
+        if not matched_plans:
+            # Plan A: Deterministic "List & Match" Discovery
+            # For each provider, list all their plans and see if any match the query
+            providers = entities.get("provider") or []
+            
+            if not providers:
+                search_providers = [None]
+            else:
+                search_providers = providers
+                
+            discovered_names = []
+            all_plans_in_index = self._list_plans_from_index()
+            
+            for prov in search_providers:
+                prov_filter = {"insurer": prov} if prov else {}
+                prov_plans = self._list_plans_from_index(filters=prov_filter)
+                self._log_debug(f"Provider: {prov}, Plans found: {len(prov_plans)}")
+                
+                # Try to find which plan from this insurer is mentioned in the query
+                match = self._find_closest_plan_name(query, prov_plans)
+                self._log_debug(f"Match for {prov}: {match} (In list: {match in prov_plans})")
+                
+                if match and match in prov_plans and match not in discovered_names:
+                    discovered_names.append(match)
+            
+            matched_plans = discovered_names
+        
+        if not matched_plans:
+            # Plan B: Fall back to broad similarity-based discovery as a last resort
+            discovery_docs = retriever.search(query, k=20)
+            for d in discovery_docs:
+                p_name = d.metadata.get("product_name")
+                if p_name and p_name not in matched_plans:
+                    matched_plans.append(p_name)
+            matched_plans = matched_plans[:3]
+        
+        if not matched_plans:
+            # Plan B: Fall back to listing plans matching filters (metadata-only)
+            matched_plans = self._list_plans_from_index(filters)[:5]
+        
+        chunks_by_plan = defaultdict(list)
+        for matched in matched_plans:
+            
+            # Use a focused query for each plan instead of the broad comparison query
+            # This helps the retriever find relevant feature chunks for the specific plan
+            focused_query = f"features, benefits, eligibility and exclusions of {matched}"
+            
+            # Use a fresh, strictly focused filter for each plan
+            # IMPORTANT: Search by insurer and manually filter by product_name
+            # This is more robust than passing a combined filter to the vector store
+            matched_insurer = None
+            if hasattr(self, "_cached_plans") and self._cached_plans:
+                for p_meta in self._cached_plans:
+                    if p_meta["product_name"] == matched:
+                        matched_insurer = p_meta.get("insurer")
+                        break
+            
+            search_filters = {"insurer": matched_insurer} if matched_insurer else {}
+            
+            # Search only by insurer and then manually filter by product_name
+            # This is more robust than passing a combined filter to the vector store
+            docs = retriever.search(focused_query, filters=search_filters, k=50)
+            
+            plan_chunks = []
+            for doc in docs:
+                doc_product = doc.metadata.get("product_name", "")
+                # Use fuzzy match for manual filter consistency
+                if self._find_closest_plan_name(doc_product, [matched]) == matched:
+                    plan_chunks.append(doc)
+            
+            for doc in plan_chunks[:10]:
+                plan_id = doc.metadata.get("plan_id", matched)
+                chunks_by_plan[plan_id].append({
+                    "content": doc.page_content,
+                    "product_name": doc.metadata.get("product_name"),
+                    "document_type": doc.metadata.get("document_type", "brochure"),
+                    "section": doc.metadata.get("section", "General")
+                })
+        
+        return dict(chunks_by_plan)
+
+    def _group_by_plan_id(self, docs: List) -> Dict[str, List]:
+        """Group retrieved documents by plan_id."""
+        grouped = defaultdict(list)
+        for doc in docs:
+            plan_id = doc.metadata.get("plan_id", doc.metadata.get("product_name", "unknown"))
+            grouped[plan_id].append({
+                "content": doc.page_content,
+                "product_name": doc.metadata.get("product_name"),
+                "document_type": doc.metadata.get("document_type", "brochure"),
+                "section": doc.metadata.get("section", "General")
+            })
+        return dict(grouped)
+
+    def _boost_cis_chunks(self, chunks_by_plan: Dict[str, List]) -> Dict[str, List]:
+        """Boost CIS documents to appear first for each plan."""
+        boosted = {}
+        for plan_id, chunks in chunks_by_plan.items():
+            cis_chunks = [c for c in chunks if c.get("document_type") == "cis"]
+            brochure_chunks = [c for c in chunks if c.get("document_type") != "cis"]
+            boosted[plan_id] = cis_chunks + brochure_chunks
+        return boosted
+
+    # =========================================================================
+    # NODE 6: Plan Aggregator
+    # =========================================================================
+    def plan_aggregator_node(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Aggregates chunks by plan_id, merging brochure and CIS context.
+        CIS overrides brochure for exclusions, charges, conditions.
+        """
+        chunks_by_plan = state.get("retrieved_chunks", {})
+        
+        # Already grouped, just ensure proper ordering
+        aggregated = {}
+        for plan_id, chunks in chunks_by_plan.items():
+            # Separate by document type
+            cis_chunks = [c for c in chunks if c.get("document_type") == "cis"]
+            brochure_chunks = [c for c in chunks if c.get("document_type") != "cis"]
+            
+            # For exclusions/charges sections, prefer CIS
+            override_sections = ["Exclusions", "Charges", "Waiting Period", "Conditions"]
+            
+            final_chunks = []
+            covered_sections = set()
+            
+            # Add CIS chunks first for override sections
+            for chunk in cis_chunks:
+                section = chunk.get("section", "General")
+                if section in override_sections:
+                    final_chunks.append(chunk)
+                    covered_sections.add(section)
+            
+            # Add brochure chunks, skipping overridden sections
+            for chunk in brochure_chunks:
+                section = chunk.get("section", "General")
+                if section not in covered_sections:
+                    final_chunks.append(chunk)
+            
+            # Add remaining CIS chunks
+            for chunk in cis_chunks:
+                if chunk not in final_chunks:
+                    final_chunks.append(chunk)
+            
+            aggregated[plan_id] = final_chunks
+        
+        # Refresh context strings based on aggregated chunks
+        intent = state.get("intent", "plan_details")
+        limit = 5 if intent == "compare_plans" else 3
+        context = self._format_context(aggregated, limit=limit)
+        
+        return {
+            "retrieved_chunks": aggregated,
+            "context": context
+        }
+
+    # =========================================================================
+    # NODE 7: Listing Agent
+    # =========================================================================
+    def listing_agent(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Lists available plans based on filters.
+        Uses direct index access for accuracy.
+        """
+        llm = LLMFactory.get_llm("small")
+        query = state["input"]
+        filters = state.get("metadata_filters", {})
+        
+        plans = self._list_plans_from_index(filters)
+        plans = sorted(list(set(plans)))
+        
+        if not plans:
+            filter_desc = ", ".join([str(v) for v in filters.values()]) if filters else "your criteria"
+            answer = f"I couldn't find any plans matching {filter_desc}. Please try a different search."
+            return {"context": [], "answer": answer}
+        
+        plans_str = "\n".join([f"- {p}" for p in plans])
+        
+        # Describe the filters
+        filter_parts = []
+        if filters.get("insurer"):
+            insurer_list = filters["insurer"] if isinstance(filters["insurer"], list) else [filters["insurer"]]
+            filter_parts.append(f"from {', '.join(insurer_list)}")
+        if filters.get("insurance_type"):
+            type_list = filters["insurance_type"] if isinstance(filters["insurance_type"], list) else [filters["insurance_type"]]
+            filter_parts.append(f"in {', '.join(type_list)} category")
+        
+        filter_desc = " ".join(filter_parts) if filter_parts else ""
+        
+        system_prompt = (
+            "Present the following insurance plans in a clear, friendly manner.\n"
+            "RULES:\n"
+            "- ONLY include plans from the list below\n"
+            "- Group by insurer if multiple insurers present\n"
+            "- Use bullet points for clarity\n"
+            "- Do NOT mention technical details about data retrieval"
+        )
+        
+        prompt = f"User asked: {query}\n\nAvailable plans {filter_desc}:\n{plans_str}"
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=prompt)])
+        answer = getattr(response, 'content', str(response))
+        
+        return {"context": [f"Plans: {plans}"], "answer": answer}
+
+    # =========================================================================
+    # NODE 8: Plan Details Agent (Retrieval Agent)
+    # =========================================================================
+    def retrieval_agent(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Provides detailed information about a specific plan.
+        Grounds all responses in retrieved documents.
+        """
+        llm = LLMFactory.get_llm("medium")
+        query = state["input"]
+        context = state.get("context", [])
+        
+        if not context:
+            # Fallback retrieval
+            retriever = self._get_retriever()
+            if retriever:
+                docs = retriever.search(query, k=5)
+                context = [f"[{d.metadata.get('product_name')}] {d.page_content}" for d in docs]
+        
+        context_str = "\n\n".join(context)
+        
+        system_prompt = f"""You are an Insurance Policy Specialist providing accurate information.
+
+{COMPLIANCE_RULES}
+
+Answer the user's question using ONLY the Policy Context provided to you.
+If information is not in the context, say "I don't have that specific information in our documents."
+DO NOT mention that you are looking at documents or context. Just provide the answer.
+Be warm and helpful while maintaining accuracy."""
+        
+        prompt = f"Policy Context:\n{context_str}\n\nUser Question: {query}"
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=prompt)])
+        answer = getattr(response, 'content', str(response))
+        
+        return {"answer": answer}
+
+    # =========================================================================
+    # NODE 9: Comparison Agent
+    # =========================================================================
+    def comparison_agent(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Generates structured side-by-side comparisons.
+        Normalizes attributes across plans.
+        """
+        llm = LLMFactory.get_llm("medium")
+        query = state["input"]
+        context = state.get("context", [])
+        chunks_by_plan = state.get("retrieved_chunks", {})
+        
+        # Get plan names being compared
+        plan_names = list(chunks_by_plan.keys()) if chunks_by_plan else []
+        
+        if not context and not plan_names:
+            return {"answer": "I couldn't find the plans you want to compare. Please specify the plan names."}
+        
+        context_str = "\n\n".join(context)
+        plans_info = f"\n\nPlans to compare: {', '.join(plan_names)}" if plan_names else ""
+        
+        system_prompt = f"""You are an Insurance Comparison Expert.
+
+{COMPLIANCE_RULES}
+
+COMPARISON FORMAT:
+- Return comparison as a Markdown TABLE
+- Columns: Features | Plan 1 | Plan 2 | ...
+- Rows: Plan Type, Eligibility, Sum Assured, Premium Terms, Key Benefits, Exclusions
+- If a detail is missing, put "Not specified"
+- Include ALL plans mentioned in the context
+- Be objective and factual"""
+        
+        prompt = f"Policy Context:\n{context_str}{plans_info}\n\nUser Question: {query}"
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=prompt)])
+        answer = getattr(response, 'content', str(response))
+        
+        return {"answer": answer, "reasoning_output": f"Compared {len(plan_names)} plans"}
+
+    # =========================================================================
+    # NODE 10: Recommendation Agent (Advisory)
+    # =========================================================================
+    def advisory_agent(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Provides personalized recommendations based on user profile.
+        Grounds all advice in retrieved documents.
+        """
+        llm = LLMFactory.get_llm("large")
+        query = state["input"]
+        context = state.get("context", [])
+        entities = state.get("extracted_entities", {})
+        user_profile = entities.get("user_profile", {})
+        
+        context_str = "\n\n".join(context) if context else "No specific plans found matching your criteria."
+        
+        profile_info = ""
+        if user_profile:
+            profile_parts = []
+            if user_profile.get("age"):
+                profile_parts.append(f"Age: {user_profile['age']}")
+            if user_profile.get("smoker") is not None:
+                profile_parts.append(f"Smoker: {'Yes' if user_profile['smoker'] else 'No'}")
+            if user_profile.get("cover_amount"):
+                profile_parts.append(f"Cover needed: {user_profile['cover_amount']}")
+            if user_profile.get("goal"):
+                profile_parts.append(f"Goal: {user_profile['goal']}")
+            if profile_parts:
+                profile_info = f"\n\nUser Profile: {', '.join(profile_parts)}"
+        
+        system_prompt = f"""You are an Expert Insurance Advisor.
+
+{COMPLIANCE_RULES}
+
+RECOMMENDATION RULES:
+- Base recommendations ONLY on plans in the context
+- Consider user's age, smoking status, cover requirement if provided
+- Explain WHY a plan suits them based on document features
+- List 2-3 suitable options if available
+- Be clear about eligibility criteria
+- DO NOT reference the "context" or "documents" in your answer. Provide the advice directly."""
+        
+        prompt = f"Policy Context:\n{context_str}{profile_info}\n\nUser Question: {query}"
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=prompt)])
+        answer = getattr(response, 'content', str(response))
+        
+        return {"answer": answer}
+
+    # =========================================================================
+    # NODE 11: General Query Agent (FAQ)
+    # =========================================================================
+    def faq_agent(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Handles general insurance questions.
+        Still attempts to ground in documents when possible.
+        """
+        llm = LLMFactory.get_llm("small")
+        query = state["input"]
+        context = state.get("context", [])
+        
+        context_str = "\n\n".join(context) if context else ""
+        
+        system_prompt = f"""You are an Insurance Helpdesk Assistant.
+
+{COMPLIANCE_RULES}
+
+For general insurance terminology questions:
+- Provide accurate, helpful explanations
+- If context is available, use it to give specific examples
+- Keep explanations simple and jargon-free"""
+        
+        prompt = f"Context (if relevant):\n{context_str}\n\nUser Question: {query}" if context_str else f"User Question: {query}"
+        
+        response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=prompt)])
+        answer = getattr(response, 'content', str(response))
+        
+        return {"answer": answer}
+
+    # =========================================================================
+    # NODE 12: Guardrail
+    # =========================================================================
+    def guardrail_node(self, state: AgentState) -> Dict[str, Any]:
+        """
+        Final validation and compliance disclaimer.
+        - Validates answer is grounded
+        - Adds compliance disclaimer
+        - Blocks hallucinated content
+        """
+        answer = state.get("answer", "")
+        
+        if not answer:
+            answer = "I apologize, but I couldn't generate a response. Please try rephrasing your question."
+        
+        # Add compliance disclaimer
+        if COMPLIANCE_DISCLAIMER not in answer:
+            answer = answer + COMPLIANCE_DISCLAIMER
+        
+        return {"answer": answer}
+
+    # =========================================================================
+    # HELPER METHODS
+    # =========================================================================
+    def _list_plans_from_index(self, filters: Dict = None) -> List[str]:
+        """Returns unique product names matching filters. Optimized with caching."""
+        retriever = self._get_retriever()
+        if not retriever:
+            return []
+        
+        try:
+            # Use a simple cache attribute on the instance if it doesn't exist
+            if not hasattr(self, "_cached_plans") or self._cached_plans is None:
+                store = retriever.vector_store
+                plans_metadata = []
+                for doc in store.docstore._dict.values():
+                    p_name = doc.metadata.get('product_name')
+                    insurer = doc.metadata.get('insurer')
+                    i_type = doc.metadata.get('insurance_type')
+                    if p_name:
+                        plans_metadata.append({
+                            "product_name": p_name,
+                            "insurer": insurer,
+                            "insurance_type": i_type
+                        })
+                self._cached_plans = plans_metadata
+
+            # Filter from cache
+            plans = set()
+            for meta in self._cached_plans:
+                if filters:
+                    match = True
+                    for k, v in filters.items():
+                        doc_val = str(meta.get(k, "")).lower().strip()
+                        if not doc_val:
+                            match = False
+                            break
+                            
+                        # Standardize filter values to list of lowercase strings
+                        filter_values = v if isinstance(v, list) else [v]
+                        filter_values = [str(fv).lower().strip() for fv in filter_values]
+                        
+                        # Robust match: any filter item matches or is matched by doc_val
+                        val_match = False
+                        for fv in filter_values:
+                            if k == "product_name":
+                                if fv in doc_val or doc_val in fv:
+                                    val_match = True
+                                    break
+                            elif k == "insurer": # Strictly match insurer names
+                                if fv == doc_val:
+                                    val_match = True
+                                    break
+                            else: # For other keys like insurance_type, allow exact match
+                                if fv == doc_val:
+                                    val_match = True
+                                    break
+                        
+                        if not val_match:
+                            match = False
+                            break
+                            
+                    if not match:
+                        continue
+                plans.add(meta["product_name"])
+            
+            return sorted(list(plans))
+        except Exception:
+            return []
+
+    def _find_closest_plan_name(self, query_plan: str, all_plans: List[str]) -> Optional[str]:
+        """Finds closest matching plan name using fuzzy matching."""
+        if not all_plans:
+            return query_plan
+            
+        def normalize(s):
+            return s.lower().replace(" ", "").replace("-", "").replace("_", "").replace("edelweisslife", "edelweiss")
+            
+        query_norm = normalize(query_plan)
+        
+        # 1. Exact match (case insensitive)
+        for plan in all_plans:
+            if plan.lower() == query_plan.lower():
+                return plan
+                
+        # 2. Normalized containment match (High Confidence)
+        # Check if the plan name is mentioned in the query
+        for plan in all_plans:
+            plan_norm = normalize(plan)
+            if plan_norm in query_norm or query_norm in plan_norm:
+                return plan
+        
+        # 3. Word overlap (Lower Confidence fallback)
+        query_words = set(query_plan.lower().split())
+        stop_words = {"tata", "aia", "edelweiss", "life", "generali", "central", "plan", "insurance", "the", "a", "of", "with", "compare"}
+        query_significant = query_words - stop_words
+        
+        best_match = None
+        max_overlap = 0
+        
+        for plan in all_plans:
+            plan_words = set(plan.lower().split())
+            plan_significant = plan_words - stop_words
+            
+            # Count significant word overlap
+            overlap = len(query_significant.intersection(plan_significant))
+            
+            if overlap > max_overlap:
+                max_overlap = overlap
+                best_match = plan
+                
+        # Return best match if we found significant overlap (at least 2 words)
+        return best_match if max_overlap >= 2 else query_plan
+
+
+# Singleton instance
+nodes = AgentNodes()
diff --git a/agents/states.py b/agents/states.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3b3641c4c0c87643796cb67e211f00b8ef23eed
--- /dev/null
+++ b/agents/states.py
@@ -0,0 +1,51 @@
+from typing import TypedDict, Annotated, List, Dict, Any, Optional
+import operator
+
+
+class UserProfile(TypedDict, total=False):
+    """User profile for recommendation intent."""
+    age: Optional[int]
+    income: Optional[str]
+    smoker: Optional[bool]
+    dependents: Optional[int]
+    goal: Optional[str]  # "protection", "savings", "retirement", "wealth"
+    cover_amount: Optional[str]  # e.g., "1 Cr", "50 Lakh"
+
+
+class ExtractedEntities(TypedDict, total=False):
+    """Entities extracted from user query."""
+    provider: Optional[List[str]]  # ["TATA AIA", "Edelweiss Life"]
+    insurance_type: Optional[List[str]]  # ["Term Insurance", "ULIP"]
+    plan_names: Optional[List[str]]  # Specific plan names mentioned
+    user_profile: Optional[UserProfile]
+
+
+class AgentState(TypedDict):
+    """
+    Enhanced state for the LangGraph RAG workflow.
+    Supports deterministic, compliance-focused retrieval.
+    """
+    # Input
+    input: str
+    chat_history: List[str]
+    
+    # Query Classification
+    intent: str  # 'list_plans', 'plan_details', 'compare_plans', 'recommendation', 'general_query'
+    
+    # Entity Extraction
+    extracted_entities: ExtractedEntities
+    
+    # Retrieval Configuration
+    metadata_filters: Dict[str, Any]  # Filters for vector store
+    retrieval_strategy: str  # 'metadata_only', 'plan_level', 'section_specific', 'cross_plan'
+    
+    # Retrieved Content
+    context: List[str]  # accumulated context strings
+    retrieved_chunks: Dict[str, List[Dict]]  # Grouped by plan_id: {plan_id: [chunks]}
+    
+    # Reasoning & Output
+    reasoning_output: str  # Structured comparison/recommendation data
+    answer: str  # Final answer to user
+    
+    # Internal Routing
+    next_step: str  # For conditional edges
diff --git a/api/main.py b/api/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..afd3454a99eb9014a0dd32df268ffed023f739cc
--- /dev/null
+++ b/api/main.py
@@ -0,0 +1,52 @@
+from fastapi import FastAPI, HTTPException, Body
+from pydantic import BaseModel
+from typing import List, Dict, Optional, Any
+from agents.graph import app as agent_app
+# from ingestion.pipeline import IngestionPipeline # Optional: Trigger via API
+
+app = FastAPI(title="Insurance Advisory AI Agent", version="1.0.0")
+
+class ChatRequest(BaseModel):
+    message: str
+    chat_history: Optional[List[str]] = []
+
+class ChatResponse(BaseModel):
+    answer: str
+    intent: str
+    context_used: Optional[List[str]] = None
+
+@app.get("/")
+def health_check():
+    return {"status": "active", "system": "Insurance Advisory Agent"}
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat_endpoint(request: ChatRequest):
+    """
+    Main chat endpoint. Routes query through the Multi-Agent Graph.
+    """
+    try:
+        # Initial state
+        initial_state = {
+            "input": request.message,
+            "chat_history": request.chat_history or [],
+            "intent": "",
+            "context": [],
+            "answer": "",
+            "metadata_filters": {}
+        }
+        
+        # Invoke Graph
+        result = agent_app.invoke(initial_state)
+        
+        return ChatResponse(
+            answer=result.get("answer", "No response generated."),
+            intent=result.get("intent", "unknown"),
+            context_used=result.get("context", [])
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f307c7345d12c4cb412dcdc7e41959063818048
--- /dev/null
+++ b/app.py
@@ -0,0 +1,266 @@
+import os
+import json
+import threading
+import speech_recognition as sr
+from flask import Flask, render_template, request, jsonify
+from agents.graph import app as agent_app
+from dotenv import load_dotenv
+from ingestion.pipeline import IngestionPipeline
+from rag.vector_store import VectorStoreManager
+
+load_dotenv()
+
+app = Flask(__name__)
+
+# Global state for ingestion tracking
+ingestion_status = {
+    "status": "Idle",
+    "progress": 0,
+    "last_error": None
+}
+
+def ingest_worker(file_path, delete_source=None):
+    """Worker thread for background ingestion using enhanced pipeline."""
+    global ingestion_status
+    try:
+        ingestion_status["status"] = "Starting..."
+        ingestion_status["progress"] = 0
+        
+        base_docs_dir = "docs"
+        pipeline = IngestionPipeline(base_docs_dir)
+        vector_manager = VectorStoreManager()
+        
+        if delete_source:
+            ingestion_status["status"] = "Removing old version..."
+            vector_manager.delete_documents_by_source(delete_source)
+            ingestion_status["progress"] = 10
+        
+        ingestion_status["status"] = "Processing document..."
+        ingestion_status["progress"] = 30
+        
+        # Use the new unified process_single_file method
+        # Handles metadata extraction, section detection, and proper chunking
+        chunks = pipeline.process_single_file(file_path)
+        
+        if chunks:
+            ingestion_status["status"] = "Updating Vector Store..."
+            ingestion_status["progress"] = 70
+            vector_manager.update_vector_store(chunks)
+            
+            # Reload the retriever in the agent nodes to see new documents
+            from agents.nodes import nodes
+            nodes.reload_retriever()
+            
+            ingestion_status["status"] = "Completed Successfully!"
+            ingestion_status["progress"] = 100
+        else:
+            ingestion_status["status"] = "Failed: No content extracted."
+            ingestion_status["progress"] = 0
+            
+    except Exception as e:
+        ingestion_status["status"] = "Failed"
+        ingestion_status["last_error"] = str(e)
+        ingestion_status["progress"] = 0
+
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+@app.route("/api/chat", methods=["POST"])
+def chat():
+    data = request.json
+    prompt = data.get("prompt")
+    history = data.get("history", [])
+    
+    if not prompt:
+        return jsonify({"error": "Prompt is required"}), 400
+        
+    try:
+        initial_state = {
+            "input": prompt,
+            "chat_history": history,
+            "intent": "",
+            "context": [],
+            "answer": "",
+            "metadata_filters": {}
+        }
+        
+        result = agent_app.invoke(initial_state)
+        return jsonify({
+            "answer": result.get("answer", ""),
+            "context": result.get("context", [])
+        })
+    except Exception as e:
+        return jsonify({"error": str(e), "status": "error"}), 500
+
+@app.route("/api/audio-chat", methods=["POST"])
+def audio_chat():
+    if 'audio' not in request.files:
+        return jsonify({"error": "No audio file part"}), 400
+    
+    file = request.files['audio']
+    history = json.loads(request.form.get("history", "[]"))
+    
+    if file.filename == '':
+        return jsonify({"error": "No selected file"}), 400
+
+    temp_path = "temp_voice_query.wav"
+    file.save(temp_path)
+    
+    r = sr.Recognizer()
+    try:
+        with sr.AudioFile(temp_path) as source:
+            audio_data = r.record(source)
+        
+        raw_text = r.recognize_google(audio_data)
+        
+        # Summarize/Refine the transcribed audio text
+        from models.llm import LLMFactory
+        from langchain_core.messages import SystemMessage, HumanMessage
+        
+        refiner_llm = LLMFactory.get_llm("small")
+        refine_system = (
+            "You are an assistant that cleans up and summarizes noisy speech-to-text transcriptions. "
+            "Your goal is to extract the actual insurance-related question or request from the text.\n\n"
+            "RULES:\n"
+            "1. Remove filler words (um, ah, like, you know).\n"
+            "2. Fix grammatical errors caused by transcription.\n"
+            "3. If multiple things are mentioned, focus on the core request.\n"
+            "4. Return ONLY the cleaned, professional question text."
+        )
+        
+        refine_response = refiner_llm.invoke([
+            SystemMessage(content=refine_system),
+            HumanMessage(content=f"Transcription: {raw_text}")
+        ])
+        summarized_text = getattr(refine_response, 'content', str(refine_response)).strip()
+        
+        # Process with existing Agent using the summarized text
+        initial_state = {
+            "input": summarized_text,
+            "chat_history": history,
+            "intent": "",
+            "context": [],
+            "answer": "",
+            "metadata_filters": {}
+        }
+        
+        result = agent_app.invoke(initial_state)
+        
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+            
+        return jsonify({
+            "transcription": raw_text,
+            "summarized_question": summarized_text,
+            "answer": result.get("answer", ""),
+            "context": result.get("context", [])
+        })
+        
+    except sr.UnknownValueError:
+        if os.path.exists(temp_path): os.remove(temp_path)
+        return jsonify({"error": "Could not understand audio"}), 400
+    except sr.RequestError as e:
+        if os.path.exists(temp_path): os.remove(temp_path)
+        return jsonify({"error": f"Speech service error: {e}"}), 500
+    except Exception as e:
+        if os.path.exists(temp_path): os.remove(temp_path)
+        return jsonify({"error": str(e)}), 500
+
+def update_doc_structure(provider_name, category_name):
+    """Helper to persist new providers/categories to the config file."""
+    try:
+        config_path = os.path.join("configs", "doc_structure.json")
+        if not os.path.exists(config_path):
+            return
+        
+        with open(config_path, "r") as f:
+            config = json.load(f)
+        
+        # Find or create provider
+        provider = next((p for p in config["providers"] if p["name"] == provider_name), None)
+        if not provider:
+            # Insert at the beginning (before 'Other')
+            provider = {"name": provider_name, "categories": []}
+            config["providers"].insert(0, provider)
+        
+        # Add category if new
+        if category_name not in provider["categories"]:
+            provider["categories"].append(category_name)
+            # Sort categories for cleanliness (except if it was General)
+            if len(provider["categories"]) > 1:
+                provider["categories"].sort()
+        
+        with open(config_path, "w") as f:
+            json.dump(config, f, indent=4)
+    except Exception as e:
+        pass
+
+@app.route("/api/upload", methods=["POST"])
+def upload():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file part"}), 400
+    
+    file = request.files['file']
+    provider = request.form.get("provider")
+    category = request.form.get("category")
+    mode = request.form.get("mode", "New Upload") # "New Upload" or "Modify Existing"
+    
+    if file.filename == '' or not provider or not category:
+        return jsonify({"error": "Missing metadata or file"}), 400
+
+    # Persist new structure to JSON
+    update_doc_structure(provider, category)
+
+    base_dir = "docs"
+    target_dir = os.path.join(base_dir, provider, category)
+    os.makedirs(target_dir, exist_ok=True)
+    
+    file_path = os.path.join(target_dir, file.filename)
+    file.save(file_path)
+    
+    delete_source = None
+    if mode == "Modify Existing":
+        file_to_modify = request.form.get("file_to_modify")
+        if file_to_modify:
+            delete_source = os.path.join(base_dir, provider, category, file_to_modify)
+            if os.path.abspath(delete_source) != os.path.abspath(file_path):
+                if os.path.exists(delete_source):
+                    os.remove(delete_source)
+
+    # Start background ingestion
+    thread = threading.Thread(target=ingest_worker, args=(file_path, delete_source))
+    thread.start()
+    
+    return jsonify({"message": "File uploaded, ingestion started.", "path": file_path})
+
+@app.route("/api/status", methods=["GET"])
+def get_status():
+    return jsonify(ingestion_status)
+
+@app.route("/api/config", methods=["GET"])
+def get_config():
+    config_path = os.path.join("configs", "doc_structure.json")
+    if os.path.exists(config_path):
+        with open(config_path, "r") as f:
+            return jsonify(json.load(f))
+    return jsonify({"providers": []})
+
+@app.route("/api/files", methods=["GET"])
+def list_files():
+    provider = request.args.get("provider")
+    category = request.args.get("category")
+    
+    if not provider or not category:
+        return jsonify({"files": []})
+        
+    base_dir = "docs"
+    target_dir = os.path.join(base_dir, provider, category)
+    if os.path.exists(target_dir):
+        files = [f for f in os.listdir(target_dir) if f.lower().endswith(('.pdf', '.docx'))]
+        return jsonify({"files": files})
+    return jsonify({"files": []})
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 8000))
+    app.run(host="0.0.0.0", port=port)
diff --git a/configs/doc_structure.json b/configs/doc_structure.json
new file mode 100644
index 0000000000000000000000000000000000000000..5d524670f9d0a0553f2235921ab19d0607e88631
--- /dev/null
+++ b/configs/doc_structure.json
@@ -0,0 +1,82 @@
+{
+    "providers": [
+        {
+            "name": "Canara HSBC",
+            "categories": [
+                "Term Plan",
+                "Retirement Plan",
+                "ULIP Plan"
+            ]
+        },
+        {
+            "name": "Aviva",
+            "categories": [
+                "Term Plan",
+                "Savings Plan",
+                "ULIP Plan"
+            ]
+        },
+         {
+            "name": "Bharti Axa",
+            "categories": [
+                "Ulip Plan",
+                "Retirement Plan",
+                "Saving Plan"
+            ]
+        },
+        {
+            "name": "PRAMERICA",
+            "categories": [
+                "ULIP Plan",
+                "Health Plan",
+                "Retirement Plan",
+                "Savings Plan",
+                "Term Plan"
+            ]
+        },
+        {
+            "name": "TATA AIA",
+            "categories": [
+                "Combo",
+                "Health Plan",
+                "Retirement and Pension Plan",
+                "Savings Plan",
+                "ULIP Plans",
+                "Term Plans"
+            ]
+        },
+         {
+            "name": "IndusInd Nippon",
+            "categories": [
+                "Combo Plan",
+                "Pension Plan",
+                "Savings Plan",
+                "ULIP Plan",
+                "Term Plan"
+            ]
+        },
+        {
+            "name": "Edelweiss Life",
+            "categories": [
+                "Term Insurance Plans",
+                "Group Solutions",
+                "Guaranteed Income Plans",
+                "Micro Plans",
+                "Wealth Creation Plans"
+            ]
+        },
+        {
+            "name": "Generali Central",
+            "categories": [
+                "Term Plan",
+                "Savings Plan",
+                "ULIP Plan",
+                "Pension Plan"
+            ]
+        },
+        {
+            "name": "Other",
+            "categories": []
+        }
+    ]
+}
\ No newline at end of file
diff --git a/configs/plan_metadata.json b/configs/plan_metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..87e5aa9d6fd7815726e77a96149274935ccb3336
--- /dev/null
+++ b/configs/plan_metadata.json
@@ -0,0 +1,103 @@
+{
+    "EdelweissLife_Wealth_Rise_Plus": {
+        "category": [
+            "Savings Plan",
+            "Unit Linked Insurance Plan"
+        ]
+    },
+    "EdelweissLife_Saral_Jeevan_Bima": {
+        "category": [
+            "Term Insurance"
+        ]
+    },
+    "EdelweissLife_Zindagi_Protect_Plus": {
+        "category": [
+            "Term Insurance"
+        ]
+    },
+    "EdelweissLife_Group_Employee_Benefit_Plus": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Pradhan_Mantri_Jeevan_Jyoti_Bima_Yojana": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Group_Life_Protection": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Group_Total_Secure": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Flexi_Dream_Plan": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Bharat_Savings_STAR": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Flexi_Savings_Plan": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Guaranteed_Flexi_STAR": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Guaranteed_Income_STAR": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Guaranteed_Savings_STAR": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Premier_Guaranteed_STAR": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Premier_Guaranteed_STAR_PRO": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Jan_Suraksha": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Raksha_Kavach_Bima_Yojana": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Wealth_Plus": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Wealth_Premier": {
+        "category": [
+            "Uncategorized"
+        ]
+    },
+    "EdelweissLife_Insurance_Wealth_Ultima": {
+        "category": [
+            "Uncategorized"
+        ]
+    }
+}
\ No newline at end of file
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+size 75176
diff --git a/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Maxima Brochure.pdf b/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Maxima Brochure.pdf
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eacfe405331c52f44ca491be4198017e940a689b218dcff5221d9a22bf88ba48
+size 914962
diff --git a/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Maxima CIS.docx b/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Maxima CIS.docx
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Pro Brochure.pdf b/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Pro Brochure.pdf
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+version https://git-lfs.github.com/spec/v1
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+size 269304
diff --git a/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Pro CIS.docx b/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Pro CIS.docx
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--- /dev/null
+++ b/docs/TATA AIA/ULIP Plans/TATA AIA Wealth Pro CIS.docx	
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+version https://git-lfs.github.com/spec/v1
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diff --git a/docs/TATA AIA/ULIP Plans/TATA AIA iSmart Income Plus Brochure.pdf b/docs/TATA AIA/ULIP Plans/TATA AIA iSmart Income Plus Brochure.pdf
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index 0000000000000000000000000000000000000000..cdef593def1a79f3be4acb9f90f2dfb82c1f0ece
--- /dev/null
+++ b/docs/TATA AIA/ULIP Plans/TATA AIA iSmart Income Plus Brochure.pdf	
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a20253b613d45724af9a84ac7b530ba7a388c54f86f80bdd9815b7b3090cf01
+size 2374298
diff --git a/docs/TATA AIA/ULIP Plans/TATA AIA iSmart Income Plus CIS.docx b/docs/TATA AIA/ULIP Plans/TATA AIA iSmart Income Plus CIS.docx
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--- /dev/null
+++ b/docs/TATA AIA/ULIP Plans/TATA AIA iSmart Income Plus CIS.docx	
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b1677cd5912bdc7893a9eb4285718a7e04069318608f600d32b70010bb29e37
+size 78413
diff --git a/ingest.py b/ingest.py
new file mode 100644
index 0000000000000000000000000000000000000000..86d17320b8d93771640c4c1ff313a2f465fd444c
--- /dev/null
+++ b/ingest.py
@@ -0,0 +1,85 @@
+"""
+Ingestion CLI for the Insurance RAG System.
+Supports incremental and force re-ingestion.
+"""
+
+import os
+import sys
+import argparse
+
+# Ensure project root is in path
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from ingestion.pipeline import IngestionPipeline
+from rag.vector_store import VectorStoreManager
+from ingestion.state import IngestionState
+from tqdm import tqdm
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Insurance Document Ingestion")
+    parser.add_argument("--force", action="store_true", 
+                       help="Force full re-ingestion of all documents")
+    args = parser.parse_args()
+
+    DOCS_DIR = "docs"
+    state = IngestionState()
+    pipeline = IngestionPipeline(DOCS_DIR)
+    vector_manager = VectorStoreManager()
+
+    # 1. Identify files to process
+    all_files = []
+    for root, _, files in os.walk(DOCS_DIR):
+        for file in files:
+            if file.lower().endswith(('.pdf', '.docx')):
+                all_files.append(os.path.join(root, file))
+
+    files_to_process = []
+    if args.force:
+        files_to_process = all_files
+        print("FORCE mode: Re-indexing all files with enhanced metadata.")
+    else:
+        for f in all_files:
+            if state.is_file_changed(f):
+                files_to_process.append(f)
+        
+    if not files_to_process:
+        print("Everything is up to date. No new or modified documents found.")
+        return
+
+    print(f"Found {len(files_to_process)} documents to process.")
+
+    # 2. Process files using enhanced pipeline with progress percentage
+    all_chunks = []
+    total_files = len(files_to_process)
+    for idx, file_path in enumerate(files_to_process, 1):
+        try:
+            # Show percentage progress
+            percent = (idx / total_files) * 100
+            print(f"\r[{percent:5.1f}%] Processing ({idx}/{total_files}): {os.path.basename(file_path)[:40]:<40}", end="", flush=True)
+            
+            chunks = pipeline.process_single_file(file_path)
+            all_chunks.extend(chunks)
+            state.update_file(file_path)
+        except Exception as e:
+            print(f"\nError on {file_path}: {e}")
+
+    print(f"Total chunks created: {len(all_chunks)}")
+
+    # 3. Update Vector Store
+    if all_chunks:
+        if args.force:
+            print("Creating new vector store...")
+            vector_manager.create_vector_store(all_chunks)
+        else:
+            print("Updating existing vector store...")
+            vector_manager.update_vector_store(all_chunks)
+        
+        state.save_state()
+        
+        
+        print("\nIngestion complete!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ingestion/metadata.py b/ingestion/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e70c8945c06f2a346a24826834fd45a1eb1ae78
--- /dev/null
+++ b/ingestion/metadata.py
@@ -0,0 +1,196 @@
+import json
+import re
+from pathlib import Path
+from typing import Dict, List
+
+# Section headers for semantic chunking
+SECTION_HEADERS = [
+    "Overview", "About", "Introduction", "Key Features", "Features",
+    "Eligibility", "Who Can Buy", "Entry Age", "Benefits", "Death Benefit",
+    "Maturity Benefit", "Survival Benefit", "Riders", "Add-on", "Premium",
+    "Charges", "Policy Charges", "Fund Management", "Policy Term", "Term",
+    "Exclusions", "What is Not Covered", "Tax Benefits", "Tax Benefit",
+    "Claim Process", "How to Claim", "Claims", "Disclaimers", "Disclaimer",
+    "Sum Assured", "Cover Amount", "Waiting Period", "Free Look", "Grace Period",
+    "Surrender", "Loan", "Revival", "Settlement", "Nomination"
+]
+
+# Compile regex patterns for section detection
+SECTION_PATTERNS = [re.compile(rf'\b{header}\b', re.IGNORECASE) for header in SECTION_HEADERS]
+
+
+class MetadataExtractor:
+    """
+    Extracts metadata from file paths and enriches them with an external JSON config.
+    docs/Insurer/InsuranceType/Product.pdf + plan_metadata.json
+    """
+    
+    def __init__(self, base_path: str, config_path: str = "configs/plan_metadata.json"):
+        self.base_path = Path(base_path).resolve()
+        self.config_path = Path(config_path)
+        self.external_metadata = self._load_external_metadata()
+
+    def _load_external_metadata(self) -> Dict[str, Dict]:
+        if self.config_path.exists():
+            with open(self.config_path, 'r') as f:
+                return json.load(f)
+        return {}
+
+    def _generate_plan_id(self, provider: str, insurance_type: str, product_name: str) -> str:
+        """
+        Generate deterministic plan_id for linking brochure and CIS.
+        Format: provider_insurancetype_productname (lowercase, underscores)
+        """
+        # Remove doc type suffixes from product name for consistent plan_id
+        clean_product = re.sub(r'[\s_-]?(brochure|cis)$', '', product_name, flags=re.IGNORECASE).strip()
+        normalized = f"{provider}_{insurance_type}_{clean_product}"
+        return re.sub(r'[^a-z0-9]+', '_', normalized.lower()).strip('_')
+
+    def extract_from_path(self, file_path: str) -> Dict[str, str]:
+        """
+        Parses the file path to extract insurer and insurance type (category) from folders.
+        Enhanced with plan_id and document_type for RAG compliance.
+        """
+        path = Path(file_path).resolve()
+        
+        if not str(path).lower().startswith(str(self.base_path).lower()):
+            raise ValueError(f"File {file_path} is not inside base directory {self.base_path}")
+
+        relative_path = path.relative_to(self.base_path)
+        parts = relative_path.parts
+        raw_stem = path.stem
+
+        # Initialize product_name and doc_type
+        product_name = raw_stem
+        doc_type = "brochure"  # Default to brochure if not specified
+
+        # Normalize common typos in product name using regex for safety
+        product_name = re.sub(r'\bEdelwiess\b', 'Edelweiss', product_name, flags=re.IGNORECASE)
+        product_name = re.sub(r'Edelweis(?!s)', 'Edelweiss', product_name, flags=re.IGNORECASE)
+        product_name = re.sub(r'Smapoorna', 'Sampoorna', product_name, flags=re.IGNORECASE)
+        product_name = re.sub(r'Fotune', 'Fortune', product_name, flags=re.IGNORECASE)
+        product_name = re.sub(r'^(Tata\s*AIA|TATA_AIA)\b', 'TATA AIA', product_name, flags=re.IGNORECASE)
+        
+        stem_lower = product_name.lower()
+        
+        # Detect document type (CIS vs Brochure)
+        if "cis" in stem_lower:
+            doc_type = "cis"
+            product_name = re.sub(r'[\s_-]cis\b', '', product_name, flags=re.IGNORECASE).strip()
+        elif "brochure" in stem_lower:
+            doc_type = "brochure"
+            product_name = re.sub(r'[\s_-]brochure\b', '', product_name, flags=re.IGNORECASE).strip()
+
+        # Extract Category (Insurance Type) from Folder Structure
+        # Expected structure: docs/Insurer/Category/Product.pdf
+        category = "General"
+        insurer = "Other"
+        
+        if len(parts) >= 2:
+            insurer = parts[0]
+            if len(parts) >= 3:
+                category = parts[1]
+            else:
+                category = "General"
+
+        # Standardize Categories
+        category_mapping = {
+            "Term Insurance Plans": "Term Insurance",
+            "Term Plans": "Term Insurance",
+            "Term Plan": "Term Insurance",
+            "ULIP Plans": "Unit Linked Insurance Plan",
+            "ULIP Plan": "Unit Linked Insurance Plan",
+            "Wealth Creation Plans": "Unit Linked Insurance Plan",
+            "Savings Plan": "Savings Plan",
+            "Savings Plans": "Savings Plan",
+            "Guaranteed Income Plans": "Savings Plan",
+            "Retirement Plans": "Retirement and Pension",
+            "Retirement and Pension Plan": "Retirement and Pension",
+            "Pension Plan": "Retirement and Pension",
+            "Health Plan": "Health Insurance",
+            "Health Plans": "Health Insurance",
+            "Group Solutions": "Group Plan",
+            "Micro Plans": "Micro Insurance",
+            "Combo": "Combo Plan"
+        }
+        
+        standard_category = category_mapping.get(category, category)
+
+        # Generate plan_id for linking brochure and CIS
+        plan_id = self._generate_plan_id(insurer, standard_category, product_name)
+
+        metadata = {
+            "source": str(file_path),
+            "filename": path.name,
+            "product_name": product_name,
+            "document_type": doc_type,  # "brochure" or "cis"
+            "insurer": insurer,
+            "insurance_type": standard_category,
+            "plan_id": plan_id,  # Links brochure and CIS together
+        }
+
+        # Optional: Merge additional static info if product matches exactly
+        if product_name in self.external_metadata:
+            ext_data = self.external_metadata[product_name]
+            if isinstance(ext_data, dict):
+                for k, v in ext_data.items():
+                    if k == "category":
+                        continue 
+                    if isinstance(v, list):
+                        metadata[k] = ", ".join(v)
+                    else:
+                        metadata[k] = v
+        
+        return metadata
+
+    @staticmethod
+    def detect_section(text: str) -> str:
+        """
+        Detect the most likely section based on content headers.
+        Returns the section name or 'General' if no match.
+        """
+        # Check first 500 chars for section headers
+        sample = text[:500].lower()
+        
+        section_scores = {}
+        for header in SECTION_HEADERS:
+            if header.lower() in sample:
+                section_scores[header] = sample.index(header.lower())
+        
+        if section_scores:
+            # Return the earliest matching section
+            return min(section_scores, key=section_scores.get)
+        
+        # Keyword-based fallback detection
+        keyword_map = {
+            "Eligibility": ["age", "entry age", "minimum age", "maximum age", "who can"],
+            "Benefits": ["death benefit", "maturity benefit", "survival benefit", "sum assured"],
+            "Exclusions": ["not covered", "excluded", "suicide", "war", "pre-existing"],
+            "Charges": ["premium", "fund management", "mortality", "allocation", "admin"],
+            "Tax Benefits": ["80c", "80d", "10(10d)", "income tax", "tax benefit"],
+            "Riders": ["rider", "accidental", "critical illness", "waiver"],
+            "Claim Process": ["claim", "intimation", "documents required", "settlement"]
+        }
+        
+        for section, keywords in keyword_map.items():
+            if any(kw in sample for kw in keywords):
+                return section
+        
+        return "General"
+
+
+# Quick validation block
+if __name__ == "__main__":
+    extractor = MetadataExtractor("docs")
+    
+    # Test brochure
+    sample1 = "docs/TATA AIA/Term Plans/TATA AIA Smart Value Income Plan Brochure.pdf"
+    print("Brochure:", extractor.extract_from_path(sample1))
+    
+    # Test CIS
+    sample2 = "docs/TATA AIA/Term Plans/TATA AIA Maha Raksha Supreme Select CIS.docx"
+    print("CIS:", extractor.extract_from_path(sample2))
+    
+    # Test section detection
+    test_text = "Eligibility Criteria: The minimum entry age is 18 years and maximum is 65 years."
+    print("Section detected:", MetadataExtractor.detect_section(test_text))
diff --git a/ingestion/pipeline.py b/ingestion/pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..318a308a86c615b0f96c50d2db5b5448d717c495
--- /dev/null
+++ b/ingestion/pipeline.py
@@ -0,0 +1,218 @@
+import os
+import re
+import pdfplumber
+import docx
+import warnings
+import logging
+from typing import List, Tuple
+from pathlib import Path
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+from ingestion.metadata import MetadataExtractor
+from tqdm import tqdm
+
+# Suppress PDF extraction warnings that clutter the terminal
+logging.getLogger("pdfminer").setLevel(logging.ERROR)
+warnings.filterwarnings("ignore", message=".*Cannot set gray stroke color.*")
+
+
+class IngestionPipeline:
+    """
+    Enhanced ingestion pipeline with:
+    - Differentiated chunk sizes for brochures vs CIS
+    - Section-aware chunking with semantic header detection
+    - Micro-chunking for tables
+    """
+    
+    def __init__(self, docs_dir: str):
+        self.docs_dir = docs_dir
+        self.metadata_extractor = MetadataExtractor(docs_dir)
+        
+        # Brochure: 500-800 tokens (~650 avg), larger chunks for narrative content
+        self.brochure_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=2600,  # ~650 tokens * 4 chars/token
+            chunk_overlap=400,  # ~100 tokens overlap
+            separators=["\n\n", "\n", ". ", " ", ""]
+        )
+        
+        # CIS: 250-400 tokens (~325 avg), smaller for dense factual content
+        self.cis_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1300,  # ~325 tokens * 4 chars/token
+            chunk_overlap=160,  # ~40 tokens overlap
+            separators=["\n\n", "\n", ". ", " ", ""]
+        )
+        
+        # Table micro-chunker: Very small for precise retrieval
+        self.table_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=800,  # ~200 tokens for tables
+            chunk_overlap=100,
+            separators=["\n", "|", " ", ""]
+        )
+
+    def _extract_with_tables(self, file_path: str) -> List[Tuple[Document, str]]:
+        """
+        Uses pdfplumber to extract text while preserving table layouts.
+        Returns list of (Document, chunk_type) tuples.
+        """
+        documents = []
+        with pdfplumber.open(file_path) as pdf:
+            for i, page in enumerate(pdf.pages):
+                text = page.extract_text() or ""
+                
+                # Add regular text content
+                if text.strip():
+                    documents.append((
+                        Document(page_content=text, metadata={"page": i + 1}),
+                        "text"
+                    ))
+                
+                # Check for tables and add as separate micro-chunks
+                tables = page.extract_tables()
+                if tables:
+                    for table_idx, table in enumerate(tables):
+                        table_str = self._format_table(table)
+                        if table_str.strip():
+                            documents.append((
+                                Document(
+                                    page_content=table_str,
+                                    metadata={"page": i + 1, "table_index": table_idx}
+                                ),
+                                "table"
+                            ))
+        return documents
+
+    def _format_table(self, table: List[List]) -> str:
+        """Format table rows into markdown-style pipe-separated format."""
+        rows = []
+        for row in table:
+            clean_row = [str(cell).replace("\n", " ").strip() if cell else "" for cell in row]
+            rows.append("| " + " | ".join(clean_row) + " |")
+        return "\n".join(rows)
+
+    def _extract_from_docx(self, file_path: str) -> List[Tuple[Document, str]]:
+        """
+        Extracts text from DOCX files with table separation.
+        """
+        doc = docx.Document(file_path)
+        documents = []
+        
+        # Extract paragraphs as text
+        para_text = []
+        for para in doc.paragraphs:
+            if para.text.strip():
+                para_text.append(para.text)
+        
+        if para_text:
+            documents.append((
+                Document(page_content="\n".join(para_text), metadata={"page": 1}),
+                "text"
+            ))
+        
+        # Extract tables separately
+        for table_idx, table in enumerate(doc.tables):
+            rows = []
+            for row in table.rows:
+                cells = [cell.text.replace("\n", " ").strip() for cell in row.cells]
+                rows.append("| " + " | ".join(cells) + " |")
+            
+            table_str = "\n".join(rows)
+            if table_str.strip():
+                documents.append((
+                    Document(
+                        page_content=table_str,
+                        metadata={"page": 1, "table_index": table_idx}
+                    ),
+                    "table"
+                ))
+        
+        return documents
+
+    def _get_splitter_for_doc_type(self, doc_type: str):
+        """Return appropriate splitter based on document type."""
+        if doc_type == "cis":
+            return self.cis_splitter
+        return self.brochure_splitter
+
+    def _chunk_with_metadata(self, raw_docs: List[Tuple[Document, str]], 
+                             file_metadata: dict) -> List[Document]:
+        """
+        Split documents with proper metadata including section and chunk_type.
+        """
+        all_chunks = []
+        doc_type = file_metadata.get("document_type", "brochure")
+        text_splitter = self._get_splitter_for_doc_type(doc_type)
+        
+        for doc, chunk_type in raw_docs:
+            # Choose splitter based on content type
+            if chunk_type == "table":
+                splitter = self.table_splitter
+            else:
+                splitter = text_splitter
+            
+            # Split the document
+            chunks = splitter.split_documents([doc])
+            
+            # Enrich each chunk with metadata
+            for chunk in chunks:
+                # Detect section from content
+                section = MetadataExtractor.detect_section(chunk.page_content)
+                
+                # Merge file-level metadata
+                chunk.metadata.update(file_metadata)
+                chunk.metadata["chunk_type"] = chunk_type
+                chunk.metadata["section"] = section
+                
+                all_chunks.append(chunk)
+        
+        return all_chunks
+
+    def load_and_process_documents(self) -> List[Document]:
+        """Process all documents in the docs directory."""
+        all_chunks = []
+        files_to_process = []
+
+        for root, _, files in os.walk(self.docs_dir):
+            for file in files:
+                if file.lower().endswith(('.pdf', '.docx')):
+                    files_to_process.append(os.path.join(root, file))
+
+        print(f"Found {len(files_to_process)} documents for ingestion.")
+
+        for file_path in tqdm(files_to_process, desc="Processing"):
+            try:
+                file_metadata = self.metadata_extractor.extract_from_path(file_path)
+                
+                # Load based on file extension
+                if file_path.lower().endswith('.pdf'):
+                    raw_docs = self._extract_with_tables(file_path)
+                elif file_path.lower().endswith('.docx'):
+                    raw_docs = self._extract_from_docx(file_path)
+                else:
+                    continue
+                
+                chunks = self._chunk_with_metadata(raw_docs, file_metadata)
+                all_chunks.extend(chunks)
+                
+            except Exception as e:
+                print(f"Error processing {file_path}: {str(e)}")
+
+        print(f"Total chunks created: {len(all_chunks)}")
+        return all_chunks
+
+    def process_single_file(self, file_path: str) -> List[Document]:
+        """
+        Process a single file for incremental ingestion.
+        Used by the Flask upload endpoint.
+        """
+        file_metadata = self.metadata_extractor.extract_from_path(file_path)
+        
+        if file_path.lower().endswith('.pdf'):
+            raw_docs = self._extract_with_tables(file_path)
+        elif file_path.lower().endswith('.docx'):
+            raw_docs = self._extract_from_docx(file_path)
+        else:
+            return []
+        
+        return self._chunk_with_metadata(raw_docs, file_metadata)
+
+
diff --git a/ingestion/state.json b/ingestion/state.json
new file mode 100644
index 0000000000000000000000000000000000000000..db5631581a77a614b5ef0b6fa7c6f8abf5591f4f
--- /dev/null
+++ b/ingestion/state.json
@@ -0,0 +1,175 @@
+{
+    "docs\\Edelweiss Life\\Group Solutions\\EdelweissLife Group Employee Benefit Plus Brochure.pdf": "b74b4ce2d1bc60c89993540e858828b3",
+    "docs\\Edelweiss Life\\Group Solutions\\EdelweissLife Group Life Protection Brochure.pdf": "c3da62cbe3981d80c7eac53e58ea12b6",
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+    "docs\\PRAMERICA\\Retirement Plan\\PRAMERICA LIFE NEXTGEN PENSION PLAN BROCHURE.pdf": "1725fe4d3eb241cee5e7ddc1eb722148",
+    "docs\\PRAMERICA\\Retirement Plan\\PRAMERICA LIFE SARAL PENSION BROCHURE.pdf": "1dbbcda066c8ee1811b6a1f4a75fe9fe",
+    "docs\\PRAMERICA\\Savings Plan\\PRAMERICA LIFE FLEXI INCOME BROCHURE.pdf": "f90fe62c41cf29d43048fa99af7c9f31",
+    "docs\\PRAMERICA\\Savings Plan\\PRAMERICA LIFE GUARANTEED RETURN ON WEALTH BROCHURE.pdf": "851519de5731220b8decc4d17b3c5837",
+    "docs\\PRAMERICA\\Savings Plan\\PRAMERICA LIFE MAGNUM ASSURE BROCHURE.pdf": "d4d7ac8d325709247d7361ddf8a5540f",
+    "docs\\PRAMERICA\\Savings Plan\\PRAMERICA LIFE ROCKSOLID FUTURE BROCHURE.pdf": "157fa4a3d17a62d14c7f093c6976d69c",
+    "docs\\PRAMERICA\\Savings Plan\\PRAMERICA LIFE SIGNATURE WEALTH BROCHURE.pdf": "b7f3418f5a8a91639394211c9c35f41b",
+    "docs\\PRAMERICA\\Savings Plan\\PRAMERICA LIFE SMART ASSURE BROCHURE.pdf": "f0f1dd4d83a6720b4966bfa197152fcb",
+    "docs\\PRAMERICA\\Savings Plan\\PRAMERICA SMART INCOME BROCHURE.pdf": "94e9aff422bc56d4398e930cff316e22",
+    "docs\\PRAMERICA\\Term Plan\\PRAMERICA LIFE SARAL JEEVAN BIMA BROCHURE.pdf": "df6537dba75de5388fac23ada2c803d9",
+    "docs\\PRAMERICA\\Term Plan\\PRAMERICA ROCKSOLID TERM INSURANCE BROCHURE.pdf": "5c7e0e293e1a23969f7933e040c95ef6",
+    "docs\\PRAMERICA\\ULIP Plan\\PRAMERICA LIFE SMART INVEST 1 UP BROCHURE.pdf": "3e8e83e8dd9bcf732b8d803a04a19895",
+    "docs\\PRAMERICA\\ULIP Plan\\PRAMERICA LIFE SMART INVEST BROCHURE.pdf": "096863f1d0acc6bc9956308deadfe462",
+    "docs\\PRAMERICA\\ULIP Plan\\PRAMERICA LIFE SMART WEALTH PLUS BROCHURE.pdf": "14f045a38b68d01b0b79dab8efead39f",
+    "docs\\PRAMERICA\\ULIP Plan\\PRAMERICA LIFE SUPER INVESTMENT PLAN BROCHURE.pdf": "32376fc07d2e2f2e7bae16b39f88ffeb",
+    "docs\\PRAMERICA\\ULIP Plan\\PRAMERICA LIFE WEALTH ENHANCER BROCHURE.pdf": "fa70f660f900368a95b7369f64b0f2b8",
+    "docs\\PRAMERICA\\ULIP Plan\\PRAMERICA LIFE WEALTH PLUS ACE BROCHURE.pdf": "5f8c9e5dd381a2a87e4861e0ba649738",
+    "docs\\PRAMERICA\\ULIP Plan\\PRAMERICA WEALTH MAXIMISER BROCHURE.pdf": "80841a4397a52bef4146d797aedcb384"
+}
\ No newline at end of file
diff --git a/ingestion/state.py b/ingestion/state.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba2e53b471de6c08916104a936263d1697b0e2f3
--- /dev/null
+++ b/ingestion/state.py
@@ -0,0 +1,34 @@
+import json
+import os
+import hashlib
+from typing import Dict
+
+class IngestionState:
+    def __init__(self, state_file: str = "ingestion/state.json"):
+        self.state_file = state_file
+        self.state = self._load_state()
+
+    def _load_state(self) -> Dict[str, str]:
+        if os.path.exists(self.state_file):
+            with open(self.state_file, 'r') as f:
+                return json.load(f)
+        return {}
+
+    def save_state(self):
+        with open(self.state_file, 'w') as f:
+            json.dump(self.state, f, indent=4)
+
+    def get_file_hash(self, file_path: str) -> str:
+        hasher = hashlib.md5()
+        with open(file_path, 'rb') as f:
+            for chunk in iter(lambda: f.read(4096), b""):
+                hasher.update(chunk)
+        return hasher.hexdigest()
+
+    def is_file_changed(self, file_path: str) -> bool:
+        current_hash = self.get_file_hash(file_path)
+        last_hash = self.state.get(file_path)
+        return current_hash != last_hash
+
+    def update_file(self, file_path: str):
+        self.state[file_path] = self.get_file_hash(file_path)
diff --git a/models/llm.py b/models/llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..31da0e091037f0ec0b1179aae606a4726e61826d
--- /dev/null
+++ b/models/llm.py
@@ -0,0 +1,39 @@
+import os
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class LLMFactory:
+
+    @staticmethod
+    def get_llm(model_type="small"):
+        """
+        Returns a Groq LLM instance based on type.
+        """
+        api_key = os.getenv("GROQ_API_KEY")
+        
+        # Groq specific models from environment
+        if model_type == "small":
+            model_name = os.getenv("GROQ_MODEL_SMALL", "llama-3.1-8b-instant")
+        elif model_type == "medium":
+            model_name = os.getenv("GROQ_MODEL_MEDIUM", "llama-3.1-8b-instant")
+        else:
+            model_name = os.getenv("GROQ_MODEL_LARGE", "llama-3.1-8b-instant")
+            
+        if api_key:
+            return ChatGroq(
+                model=model_name,
+                temperature=0,
+                groq_api_key=api_key,
+                max_retries=3,  # Automatically retry on rate limits or transient errors
+                timeout=30      # Prevent hanging on slow responses
+            )
+        
+        # Fallback to mock for testing without key
+        class MockLLM:
+            def invoke(self, msg):
+                return f"[Groq Mock Response for {model_type}]: Model {model_name} processing..."
+        
+        return MockLLM()
+
diff --git a/rag/faiss_index/index.faiss b/rag/faiss_index/index.faiss
new file mode 100644
index 0000000000000000000000000000000000000000..fa3968a7ccb264e7987a76a72aa4acb737a92081
--- /dev/null
+++ b/rag/faiss_index/index.faiss
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cb1fc3e9e704be81c134c7972844e8165eb1dc95b5768cc77915bed54ed0711
+size 29004333
diff --git a/rag/faiss_index/index.pkl b/rag/faiss_index/index.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..44f03633d3afbfe7c576dbd82d49d29b75152180
--- /dev/null
+++ b/rag/faiss_index/index.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:325fc2f0d0a4d093d611e37483211b2daa243faf48d40b024ba0754cc915395a
+size 29007310
diff --git a/rag/retriever.py b/rag/retriever.py
new file mode 100644
index 0000000000000000000000000000000000000000..13b0c391559e7ed195e195db0cfee12bf85bdafb
--- /dev/null
+++ b/rag/retriever.py
@@ -0,0 +1,87 @@
+from typing import List, Dict, Optional
+from langchain_core.documents import Document
+from rag.vector_store import VectorStoreManager
+
+class RAGRetriever:
+    def __init__(self):
+        self.vector_manager = VectorStoreManager()
+        self.vector_store = self.vector_manager.load_vector_store()
+        self.retriever = self.vector_store.as_retriever(
+            search_type="similarity",
+            search_kwargs={"k": 5}
+        )
+
+    def reload(self):
+        """Re-read the vector store from disk."""
+        self.vector_store = self.vector_manager.load_vector_store()
+        self.retriever = self.vector_store.as_retriever(
+            search_type="similarity",
+            search_kwargs={"k": 5}
+        )
+
+    def search(self, query: str, filters: Optional[Dict] = None, k: int = 5) -> List[Document]:
+        """
+        Search for documents with robust metadata filtering.
+        """
+        search_kwargs = {"k": k}
+        
+        filter_fn = None
+        if filters:
+            def filter_fn(metadata):
+                for key, value in filters.items():
+                    met_val = metadata.get(key)
+                    if met_val is None:
+                        return False
+                    
+                    met_val_str = str(met_val).lower().strip()
+                    
+                    if isinstance(value, list):
+                        norm_values = [str(v).lower().strip() for v in value]
+                        match_found = False
+                        for v_item in norm_values:
+                            if key in ["insurer", "insurance_type"]:
+                                # Exact match for categories
+                                if v_item == met_val_str:
+                                    match_found = True
+                                    break
+                            else:
+                                # Fuzzy/substring match for other fields (like product_name)
+                                if v_item in met_val_str or met_val_str in v_item:
+                                    match_found = True
+                                    break
+                        if not match_found:
+                            return False
+                    else:
+                        norm_value = str(value).lower().strip()
+                        if key in ["insurer", "insurance_type"]:
+                            if norm_value != met_val_str:
+                                return False
+                        else:
+                            if norm_value not in met_val_str and met_val_str not in norm_value:
+                                return False
+                return True
+            
+            search_kwargs["filter"] = filter_fn
+            
+        
+        if filter_fn:
+            # Compensate for post-retrieval filtering by increasing search depth
+            # Increased to handle indices with many chunks per plan
+            k_expanded = max(k * 500, 3000)
+            search_kwargs["k"] = k_expanded
+            search_kwargs["fetch_k"] = k_expanded * 2
+            
+        results = self.vector_store.similarity_search(query, **search_kwargs)
+        return results[:k]
+
+    def comparative_search(self, query: str, products: List[str]) -> Dict[str, List[Document]]:
+        """
+        Retrieves documents for the same query across multiple products for comparison.
+        """
+        results = {}
+        for product in products:
+            product_results = self.search(query, filters={"product_name": product}, k=3)
+            results[product] = product_results
+            
+        return results
+
diff --git a/rag/vector_store.py b/rag/vector_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..737d1f438f876ac26655c9bd974114d1e0aff696
--- /dev/null
+++ b/rag/vector_store.py
@@ -0,0 +1,113 @@
+import os
+import threading
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.documents import Document
+from typing import List
+
+class VectorStoreManager:
+    _embeddings = None
+    _lock = threading.Lock()
+
+    def __init__(self, index_path: str = "rag/faiss_index"):
+        self.index_path = index_path
+        if VectorStoreManager._embeddings is None:
+            # Load embeddings model once
+            VectorStoreManager._embeddings = HuggingFaceEmbeddings(
+                model_name="sentence-transformers/all-MiniLM-L6-v2"
+            )
+        self.embeddings = VectorStoreManager._embeddings
+    
+    def create_vector_store(self, documents: List[Document], batch_size: int = 100):
+        """
+        Creates a new FAISS index from the provided documents and saves it locally.
+        Uses batching and tqdm to show progress.
+        """
+        if not documents:
+            print("No documents to index.")
+            return
+
+        from tqdm import tqdm
+        print(f"Creating vector store with {len(documents)} chunks...")
+        
+        # Initialize with first batch
+        first_batch = documents[:batch_size]
+        vector_store = FAISS.from_documents(first_batch, self.embeddings)
+        
+        # Add remaining batches with progress bar
+        if len(documents) > batch_size:
+            for i in tqdm(range(batch_size, len(documents), batch_size), desc="Creating Vectors"):
+                batch = documents[i : i + batch_size]
+                vector_store.add_documents(batch)
+        
+        # Save to disk
+        with VectorStoreManager._lock:
+            vector_store.save_local(self.index_path)
+        print(f"Vector store saved to {self.index_path}")
+        return vector_store
+
+    def load_vector_store(self):
+        """
+        Loads the existing FAISS index from disk.
+        """
+        if not os.path.exists(self.index_path):
+            raise FileNotFoundError(f"Index not found at {self.index_path}. Run ingestion first.")
+        
+        with VectorStoreManager._lock:
+            return FAISS.load_local(
+                self.index_path, 
+                self.embeddings, 
+                allow_dangerous_deserialization=True 
+            )
+
+    def update_vector_store(self, documents: List[Document], batch_size: int = 100):
+        """
+        Loads an existing index, adds new documents, and saves.
+        Uses batching and tqdm to show progress.
+        """
+        if not documents:
+            return
+
+        if not os.path.exists(self.index_path):
+            print("Index doesn't exist. Creating new one...")
+            return self.create_vector_store(documents, batch_size=batch_size)
+
+        from tqdm import tqdm
+        print(f"Updating existing index with {len(documents)} new chunks...")
+        vector_store = self.load_vector_store()
+        
+        for i in tqdm(range(0, len(documents), batch_size), desc="Updating Vectors"):
+            batch = documents[i : i + batch_size]
+            vector_store.add_documents(batch)
+            
+        with VectorStoreManager._lock:
+            vector_store.save_local(self.index_path)
+        print(f"Update complete. Saved to {self.index_path}")
+
+    def delete_documents_by_source(self, source_path: str):
+        """
+        Removes all documents from the index that match the given source path.
+        """
+        if not os.path.exists(self.index_path):
+            return
+
+        vector_store = self.load_vector_store()
+        
+        # Identify IDs to delete
+        ids_to_delete = []
+        # docstore._dict is {id: Document}
+        for doc_id, doc in vector_store.docstore._dict.items():
+            # Check source match (handle both absolute and relative discrepancies if needed)
+            # source_path should be consistent with how it was ingested
+            if doc.metadata.get("source") == source_path:
+                ids_to_delete.append(doc_id)
+        
+        if ids_to_delete:
+            print(f"Deleting {len(ids_to_delete)} chunks for source: {source_path}")
+            with VectorStoreManager._lock:
+                vector_store.delete(ids_to_delete)
+                vector_store.save_local(self.index_path)
+            print("Deletion complete and index saved.")
+        else:
+            print(f"No documents found for source: {source_path}")
+
diff --git a/render.yaml b/render.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..26b9f69b817f3cc18e658727b0c88c031850ef77
--- /dev/null
+++ b/render.yaml
@@ -0,0 +1,11 @@
+services:
+  - type: web
+    name: brochure-rag
+    env: python
+    buildCommand: pip install -r requirements.txt
+    startCommand: gunicorn app:app
+    envVars:
+      - key: PYTHON_VERSION
+        value: 3.10.0
+      - key: PORT
+        value: 10000
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..463543de98fd37ad064c4e8eb8f0f7ec1dc8086a
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,25 @@
+flask
+gunicorn
+flask-cors
+fastapi
+uvicorn
+python-multipart
+langchain
+langchain-community
+langchain-core
+langchain-text-splitters
+langgraph
+pypdf
+faiss-cpu
+sentence-transformers
+pydantic
+python-dotenv
+tiktoken
+tqdm
+langchain-huggingface
+SpeechRecognition
+langchain-groq
+streamlit
+requests
+pdfplumber
+python-docx
\ No newline at end of file
diff --git a/static/css/style.css b/static/css/style.css
new file mode 100644
index 0000000000000000000000000000000000000000..bc0b156fa5ed32fafb0b609d07a4892f24e98356
--- /dev/null
+++ b/static/css/style.css
@@ -0,0 +1,373 @@
+@import url('https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;600;700&display=swap');
+
+:root {
+    --primary: #6366f1;
+    --primary-hover: #4f46e5;
+    --bg-dark: #0f172a;
+    --card-bg: rgba(30, 41, 59, 0.7);
+    --text-main: #f8fafc;
+    --text-muted: #94a3b8;
+    --glass-border: rgba(255, 255, 255, 0.1);
+    --animation-speed: 0.3s;
+}
+
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: 'Outfit', sans-serif;
+    background: radial-gradient(circle at top right, #1e1b4b, #0f172a);
+    color: var(--text-main);
+    height: 100vh;
+    overflow: hidden;
+    display: flex;
+}
+
+/* Sidebar Styling */
+.sidebar {
+    width: 350px;
+    background: rgba(15, 23, 42, 0.8);
+    backdrop-filter: blur(20px);
+    border-right: 1px solid var(--glass-border);
+    padding: 2rem;
+    display: flex;
+    flex-direction: column;
+    z-index: 10;
+}
+
+.logo {
+    display: flex;
+    align-items: center;
+    gap: 12px;
+    margin-bottom: 3rem;
+}
+
+.logo span {
+    font-size: 1.5rem;
+    font-weight: 700;
+    background: linear-gradient(to right, #818cf8, #c084fc);
+    -webkit-background-clip: text;
+    background-clip: text;
+    -webkit-text-fill-color: transparent;
+}
+
+.sidebar-section {
+    margin-bottom: 2rem;
+}
+
+.sidebar-section h3 {
+    font-size: 0.85rem;
+    text-transform: uppercase;
+    letter-spacing: 0.1em;
+    color: var(--text-muted);
+    margin-bottom: 1rem;
+}
+
+/* Main Content Area */
+.main-content {
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+    position: relative;
+    padding: 2rem;
+}
+
+/* Glass Card Component */
+.glass-card {
+    background: var(--card-bg);
+    backdrop-filter: blur(12px);
+    border: 1px solid var(--glass-border);
+    border-radius: 24px;
+    box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.3);
+}
+
+/* Chat Container */
+.chat-container {
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
+    margin-bottom: 1.5rem;
+}
+
+.chat-messages {
+    flex: 1;
+    overflow-y: auto;
+    padding: 1.5rem;
+    display: flex;
+    flex-direction: column;
+    gap: 1.5rem;
+}
+
+/* Scrollbar styling */
+.chat-messages::-webkit-scrollbar {
+    width: 6px;
+}
+
+.chat-messages::-webkit-scrollbar-thumb {
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 10px;
+}
+
+.message {
+    max-width: 80%;
+    padding: 1rem 1.5rem;
+    border-radius: 20px;
+    font-size: 0.95rem;
+    line-height: 1.6;
+    animation: fadeIn 0.4s ease-out forwards;
+}
+
+.message p {
+    margin-bottom: 0.8rem;
+}
+
+.message p:last-child {
+    margin-bottom: 0;
+}
+
+.message ul,
+.message ol {
+    margin-left: 1.5rem;
+    margin-bottom: 1rem;
+}
+
+.message li {
+    margin-bottom: 0.5rem;
+}
+
+/* Table Styling within Messages */
+.message table {
+    width: 100%;
+    border-collapse: collapse;
+    margin: 1rem 0;
+    background: rgba(0, 0, 0, 0.2);
+    border-radius: 8px;
+    overflow: hidden;
+}
+
+.message th,
+.message td {
+    padding: 10px 14px;
+    border: 1px solid var(--glass-border);
+    text-align: left;
+}
+
+.message th {
+    background: rgba(255, 255, 255, 0.1);
+    font-weight: 600;
+}
+
+.message tr:nth-child(even) {
+    background: rgba(255, 255, 255, 0.02);
+}
+
+@keyframes blink {
+    50% {
+        opacity: 0;
+    }
+}
+
+@keyframes fadeIn {
+    from {
+        opacity: 0;
+        transform: translateY(10px);
+    }
+
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.user-message {
+    align-self: flex-end;
+    background: var(--primary);
+    color: white;
+    border-bottom-right-radius: 4px;
+}
+
+.bot-message {
+    align-self: flex-start;
+    background: rgba(255, 255, 255, 0.05);
+    border: 1px solid var(--glass-border);
+    border-top-left-radius: 4px;
+}
+
+/* Audio Player inside message */
+.audio-control {
+    display: flex;
+    align-items: center;
+    gap: 12px;
+}
+
+.play-btn {
+    background: rgba(255, 255, 255, 0.2);
+    border: none;
+    color: white;
+    width: 32px;
+    height: 32px;
+    border-radius: 50%;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: var(--animation-speed);
+}
+
+.play-btn:hover {
+    background: rgba(255, 255, 255, 0.3);
+    transform: scale(1.1);
+}
+
+.play-btn i {
+    pointer-events: none;
+}
+
+.audio-message-bubble {
+    padding-bottom: 0.8rem !important;
+}
+
+/* Input Area */
+.input-area {
+    display: flex;
+    gap: 12px;
+    padding: 1rem;
+    background: rgba(255, 255, 255, 0.03);
+    border-radius: 20px;
+    align-items: center;
+}
+
+.audio-btn {
+    background: rgba(255, 255, 255, 0.05);
+    border: none;
+    color: var(--text-muted);
+    width: 40px;
+    height: 40px;
+    border-radius: 50%;
+    cursor: pointer;
+    transition: var(--animation-speed);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.audio-btn:hover {
+    background: rgba(255, 255, 255, 0.1);
+    color: white;
+    transform: scale(1.1);
+}
+
+#user-input {
+    flex: 1;
+    background: transparent;
+    border: none;
+    color: white;
+    font-family: inherit;
+    font-size: 1rem;
+    padding: 0.5rem;
+}
+
+#user-input:focus {
+    outline: none;
+}
+
+.send-btn {
+    background: var(--primary);
+    border: none;
+    width: 45px;
+    height: 45px;
+    border-radius: 12px;
+    color: white;
+    cursor: pointer;
+    transition: var(--animation-speed);
+}
+
+.send-btn:hover {
+    background: var(--primary-hover);
+    transform: scale(1.05);
+}
+
+/* Form Styling (Sidebar) */
+.form-group {
+    margin-bottom: 1rem;
+}
+
+select,
+input[type="text"] {
+    width: 100%;
+    background: rgba(255, 255, 255, 0.05);
+    border: 1px solid var(--glass-border);
+    padding: 0.8rem 1rem;
+    border-radius: 12px;
+    color: white;
+    font-family: inherit;
+    transition: var(--animation-speed);
+}
+
+select:focus,
+input[type="text"]:focus {
+    outline: none;
+    border-color: var(--primary);
+    background: rgba(255, 255, 255, 0.1);
+}
+
+select option {
+    background-color: #1e293b;
+    color: white;
+}
+
+.primary-btn {
+    width: 100%;
+    background: var(--primary);
+    border: none;
+    padding: 1rem;
+    border-radius: 12px;
+    color: white;
+    font-weight: 600;
+    cursor: pointer;
+    transition: var(--animation-speed);
+    margin-top: 1rem;
+}
+
+.primary-btn:hover {
+    background: var(--primary-hover);
+    box-shadow: 0 0 20px rgba(99, 102, 241, 0.4);
+}
+
+/* Status Bar */
+.status-container {
+    margin-top: auto;
+    padding-top: 1.5rem;
+    border-top: 1px solid var(--glass-border);
+}
+
+.status-row {
+    display: flex;
+    justify-content: space-between;
+    font-size: 0.85rem;
+    margin-bottom: 8px;
+}
+
+.progress-track {
+    width: 100%;
+    height: 6px;
+    background: rgba(255, 255, 255, 0.05);
+    border-radius: 10px;
+    overflow: hidden;
+}
+
+.progress-bar {
+    height: 100%;
+    background: linear-gradient(to right, #6366f1, #c084fc);
+    width: 0%;
+    transition: width 0.5s ease;
+}
+
+/* Animations and polish */
+.hover-glow:hover {
+    box-shadow: 0 0 15px rgba(255, 255, 255, 0.1);
+}
\ No newline at end of file
diff --git a/static/js/app.js b/static/js/app.js
new file mode 100644
index 0000000000000000000000000000000000000000..d13e1b4fdf8cf577c85a8ffe3506c09e2ed726be
--- /dev/null
+++ b/static/js/app.js
@@ -0,0 +1,371 @@
+document.addEventListener('DOMContentLoaded', () => {
+    const chatBox = document.getElementById('chat-box');
+    const userInput = document.getElementById('user-input');
+    const sendTrigger = document.getElementById('send-trigger');
+    const uploadTrigger = document.getElementById('upload-trigger');
+    const docUpload = document.getElementById('doc-upload');
+    const providerSelect = document.getElementById('provider-select');
+    const categorySelect = document.getElementById('category-select');
+    const otherProviderGroup = document.getElementById('other-provider-group');
+    const modifyGroup = document.getElementById('modify-group');
+    const fileToModify = document.getElementById('file-to-modify');
+    const statusBar = document.getElementById('status-bar');
+    const statusText = document.getElementById('status-text');
+    const clearChat = document.getElementById('clear-chat');
+    const audioUpload = document.getElementById('audio-upload');
+    const audioTrigger = document.getElementById('audio-trigger');
+
+    let chatHistory = [];
+    let isProcessing = false;
+    let configData = null;
+
+    // --- Dynamic UI Initialization ---
+    async function loadConfig() {
+        try {
+            const response = await fetch('/api/config');
+            configData = await response.json();
+            populateProviders();
+        } catch (e) { console.error("Could not load config", e); }
+    }
+
+    function populateProviders() {
+        providerSelect.innerHTML = configData.providers.map(p =>
+            `<option value="${p.name}">${p.name}</option>`
+        ).join('');
+        // Trigger initial category load
+        populateCategories();
+    }
+
+    function populateCategories() {
+        const selectedProviderName = providerSelect.value;
+        const provider = configData.providers.find(p => p.name === selectedProviderName);
+        let categories = provider ? [...provider.categories] : [];
+
+        // Ensure "Other..." is available
+        if (!categories.includes("Other...")) {
+            categories.push("Other...");
+        }
+
+        categorySelect.innerHTML = categories.map(c =>
+            `<option value="${c}">${c}</option>`
+        ).join('');
+
+        // Reset category input visibility
+        const otherCategoryGroup = document.getElementById('other-category-group');
+        if (otherCategoryGroup) {
+            otherCategoryGroup.style.display = categorySelect.value === 'Other...' ? 'block' : 'none';
+        }
+
+        updateFileList();
+    }
+
+    loadConfig();
+
+    // --- Chat Logic ---
+    function addMessage(text, role) {
+        if (role === 'bot') {
+            typeMessage(text);
+        } else {
+            const msgDiv = document.createElement('div');
+            msgDiv.className = `message ${role}-message`;
+            msgDiv.textContent = text;
+            chatBox.appendChild(msgDiv);
+            chatBox.scrollTop = chatBox.scrollHeight;
+        }
+    }
+
+    async function typeMessage(fullText) {
+        const msgDiv = document.createElement('div');
+        msgDiv.className = 'message bot-message';
+        chatBox.appendChild(msgDiv);
+        chatBox.scrollTop = chatBox.scrollHeight;
+
+        const words = fullText.split(' ');
+        let currentText = '';
+
+        for (let i = 0; i < words.length; i++) {
+            currentText += words[i] + ' ';
+            msgDiv.innerHTML = marked.parse(currentText + '▌'); // Add cursor effect
+            chatBox.scrollTop = chatBox.scrollHeight;
+            await new Promise(resolve => setTimeout(resolve, 30)); // Snappy speed
+        }
+
+        msgDiv.innerHTML = marked.parse(fullText); // Final render without cursor
+        chatBox.scrollTop = chatBox.scrollHeight;
+    }
+
+    async function handleChat() {
+        const prompt = userInput.value.trim();
+        if (!prompt || isProcessing) return;
+
+        addMessage(prompt, 'user');
+        userInput.value = '';
+        isProcessing = true;
+
+        // Add typing indicator
+        const typingDiv = document.createElement('div');
+        typingDiv.className = 'message bot-message';
+        typingDiv.textContent = 'Typing...';
+        chatBox.appendChild(typingDiv);
+        chatBox.scrollTop = chatBox.scrollHeight;
+
+        try {
+            const response = await fetch('/api/chat', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify({ prompt, history: chatHistory })
+            });
+            const data = await response.json();
+
+            chatBox.removeChild(typingDiv);
+            if (data.answer) {
+                addMessage(data.answer, 'bot');
+                chatHistory.push(prompt);
+            } else if (data.error) {
+                addMessage(`Error: ${data.error}`, 'bot');
+            } else {
+                addMessage("I'm sorry, I encountered an unknown error.", 'bot');
+            }
+        } catch (error) {
+            chatBox.removeChild(typingDiv);
+            addMessage("Connection error. Please try again later.", 'bot');
+        } finally {
+            isProcessing = false;
+        }
+    }
+
+    sendTrigger.addEventListener('click', handleChat);
+    userInput.addEventListener('keypress', (e) => {
+        if (e.key === 'Enter') handleChat();
+    });
+
+    clearChat.addEventListener('click', () => {
+        chatBox.innerHTML = '<div class="message bot-message">Chat history cleared. How can I help?</div>';
+        chatHistory = [];
+    });
+
+    // --- Audio Chat Logic ---
+    audioTrigger.addEventListener('click', () => audioUpload.click());
+
+    audioUpload.addEventListener('change', async () => {
+        if (!audioUpload.files.length || isProcessing) return;
+
+        const file = audioUpload.files[0];
+        const audioUrl = URL.createObjectURL(file);
+        const formData = new FormData();
+        formData.append('audio', file);
+        formData.append('history', JSON.stringify(chatHistory));
+
+        isProcessing = true;
+
+        // Add "Audio Uploaded" user message with Play button
+        const userMsgDiv = document.createElement('div');
+        userMsgDiv.className = 'message user-message audio-message-bubble';
+        userMsgDiv.innerHTML = `
+            <div class="audio-control">
+                <button class="play-btn" id="play-${Date.now()}">
+                    <i class="fas fa-play"></i>
+                </button>
+                <span>Audio: ${file.name}</span>
+            </div>
+        `;
+        chatBox.appendChild(userMsgDiv);
+        chatBox.scrollTop = chatBox.scrollHeight;
+
+        // Play functionality
+        const playBtn = userMsgDiv.querySelector('.play-btn');
+        const audio = new Audio(audioUrl);
+
+        playBtn.addEventListener('click', () => {
+            console.log("Play button clicked for:", file.name);
+            if (audio.paused) {
+                audio.play().then(() => {
+                    console.log("Playback started");
+                    playBtn.innerHTML = '<i class="fas fa-pause"></i>';
+                }).catch(err => {
+                    console.error("Playback failed:", err);
+                    alert("Playback failed: " + err.message);
+                });
+            } else {
+                audio.pause();
+                playBtn.innerHTML = '<i class="fas fa-play"></i>';
+            }
+        });
+
+        audio.onended = () => {
+            playBtn.innerHTML = '<i class="fas fa-play"></i>';
+        };
+
+        audio.onerror = (e) => {
+            console.error("Audio error event:", e);
+            alert("Error loading the audio file for playback.");
+        };
+
+        // Add typing indicator
+        const typingDiv = document.createElement('div');
+        typingDiv.className = 'message bot-message';
+        typingDiv.textContent = 'Transcribing audio...';
+        chatBox.appendChild(typingDiv);
+        chatBox.scrollTop = chatBox.scrollHeight;
+
+        try {
+            const response = await fetch('/api/audio-chat', {
+                method: 'POST',
+                body: formData
+            });
+            const data = await response.json();
+
+            chatBox.removeChild(typingDiv);
+            if (data.transcription) {
+                const transDiv = document.createElement('div');
+                transDiv.innerHTML = `
+                    <div style="font-size: 0.75rem; color: var(--text-muted); margin-bottom: 4px;">Raw Transcript:</div>
+                    <div style="font-size: 0.85rem; font-style: italic; opacity: 0.7; margin-bottom: 12px;">"${data.transcription}"</div>
+                    <div style="font-size: 0.75rem; color: var(--text-muted); margin-bottom: 4px;">Refined Question:</div>
+                    <div style="font-size: 1rem; font-weight: 600; color: #818cf8;">"${data.summarized_question || data.transcription}"</div>
+                `;
+                transDiv.style.marginTop = '12px';
+                transDiv.style.borderTop = '1px solid rgba(255,255,255,0.1)';
+                transDiv.style.paddingTop = '12px';
+                userMsgDiv.appendChild(transDiv);
+
+                if (data.answer) {
+                    addMessage(data.answer, 'bot');
+                    chatHistory.push(data.summarized_question || data.transcription);
+                }
+            } else if (data.error) {
+                addMessage(`Error: ${data.error}`, 'bot');
+            }
+        } catch (error) {
+            if (typingDiv.parentNode) chatBox.removeChild(typingDiv);
+            addMessage("Audio processing error. Please try again.", 'bot');
+        } finally {
+            isProcessing = false;
+            audioUpload.value = '';
+        }
+    });
+
+    // --- Upload and Ingestion Logic ---
+    providerSelect.addEventListener('change', () => {
+        otherProviderGroup.style.display = providerSelect.value === 'Other' ? 'block' : 'none';
+        populateCategories();
+    });
+
+    categorySelect.addEventListener('change', () => {
+        const otherCategoryGroup = document.getElementById('other-category-group');
+        otherCategoryGroup.style.display = categorySelect.value === 'Other...' ? 'block' : 'none';
+        updateFileList();
+    });
+
+    const otherCategoryInput = document.getElementById('other-category');
+    if (otherCategoryInput) {
+        otherCategoryInput.addEventListener('input', updateFileList);
+    }
+
+    const otherProviderInput = document.getElementById('other-provider');
+    if (otherProviderInput) {
+        otherProviderInput.addEventListener('input', updateFileList);
+    }
+
+    categorySelect.addEventListener('change', updateFileList);
+
+    document.querySelectorAll('input[name="upload-mode"]').forEach(radio => {
+        radio.addEventListener('change', (e) => {
+            modifyGroup.style.display = e.target.value === 'Modify Existing' ? 'block' : 'none';
+            if (e.target.value === 'Modify Existing') updateFileList();
+        });
+    });
+
+    async function updateFileList() {
+        const provider = providerSelect.value === 'Other' ? document.getElementById('other-provider').value : providerSelect.value;
+        const category = categorySelect.value === 'Other...' ? document.getElementById('other-category').value : categorySelect.value;
+
+        if (!provider || !category || category === '') {
+            fileToModify.innerHTML = '<option value="">(Waiting for selection...)</option>';
+            return;
+        }
+
+        try {
+            const response = await fetch(`/api/files?provider=${encodeURIComponent(provider)}&category=${encodeURIComponent(category)}`);
+            const data = await response.json();
+            fileToModify.innerHTML = '<option value="">Select file to replace...</option>';
+            data.files.forEach(f => {
+                const opt = document.createElement('option');
+                opt.value = f;
+                opt.textContent = f;
+                fileToModify.appendChild(opt);
+            });
+        } catch (e) { console.error("Could not fetch files", e); }
+    }
+
+    uploadTrigger.addEventListener('click', () => docUpload.click());
+
+    docUpload.addEventListener('change', async () => {
+        if (!docUpload.files.length) return;
+
+        const formData = new FormData();
+        const provider = providerSelect.value === 'Other' ? document.getElementById('other-provider').value : providerSelect.value;
+        const category = categorySelect.value === 'Other...' ? document.getElementById('other-category').value : categorySelect.value;
+        const mode = document.querySelector('input[name="upload-mode"]:checked').value;
+
+        if (!provider || !category) {
+            alert("Please specify both Provider and Category.");
+            return;
+        }
+
+        formData.append('file', docUpload.files[0]);
+        formData.append('provider', provider);
+        formData.append('category', category);
+        formData.append('mode', mode);
+        if (mode === 'Modify Existing') {
+            formData.append('file_to_modify', fileToModify.value);
+        }
+
+        uploadTrigger.disabled = true;
+        uploadTrigger.textContent = 'Uploading...';
+
+        try {
+            const response = await fetch('/api/upload', {
+                method: 'POST',
+                body: formData
+            });
+            const data = await response.json();
+            if (data.error) alert(data.error);
+            else {
+                // Refresh config to include new provider/category if added
+                await loadConfig();
+                // Start polling status
+                pollStatus();
+            }
+        } catch (e) {
+            alert("Upload failed.");
+        } finally {
+            uploadTrigger.disabled = false;
+            uploadTrigger.textContent = 'Choose & Process';
+            docUpload.value = '';
+        }
+    });
+
+    function pollStatus() {
+        const interval = setInterval(async () => {
+            try {
+                const res = await fetch('/api/status');
+                const data = await res.json();
+
+                statusText.textContent = data.status;
+                statusBar.style.width = data.progress + '%';
+
+                if (data.status === 'Completed Successfully!' || data.status.startsWith('Failed')) {
+                    clearInterval(interval);
+                    if (data.status === 'Completed Successfully!') {
+                        statusText.style.color = '#10b981'; // Green
+                    } else {
+                        statusText.style.color = '#ef4444'; // Red
+                    }
+                }
+            } catch (e) {
+                clearInterval(interval);
+            }
+        }, 1000);
+    }
+});
diff --git a/templates/index.html b/templates/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..fce837a0209bd6816b7eae0769fc6c943a8a364a
--- /dev/null
+++ b/templates/index.html
@@ -0,0 +1,112 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Insurance Advisory AI | Premium Advisor</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+</head>
+
+<body>
+    <aside class="sidebar">
+        <div class="logo">
+            <i class="fas fa-shield-halved fa-2x" style="color: #818cf8;"></i>
+            <span>AI ADVISOR</span>
+        </div>
+
+        <div class="sidebar-section">
+            <h3>Document Manager</h3>
+            <div class="form-group">
+                <select id="provider-select">
+                    <!-- Loaded dynamically -->
+                </select>
+            </div>
+            <div class="form-group" id="other-provider-group" style="display: none;">
+                <input type="text" id="other-provider" placeholder="Provider Name">
+            </div>
+            <div class="form-group">
+                <select id="category-select">
+                    <!-- Loaded dynamically -->
+                </select>
+            </div>
+            <div class="form-group" id="other-category-group" style="display: none;">
+                <input type="text" id="other-category" placeholder="Category Name">
+            </div>
+
+            <div class="form-group">
+                <label
+                    style="font-size: 0.8rem; color: var(--text-muted); display: block; margin-bottom: 0.5rem;">Mode</label>
+                <div style="display: flex; gap: 10px;">
+                    <label style="font-size: 0.85rem;"><input type="radio" name="upload-mode" value="New Upload"
+                            checked> New</label>
+                    <label style="font-size: 0.85rem;"><input type="radio" name="upload-mode" value="Modify Existing">
+                        Modify</label>
+                </div>
+            </div>
+
+            <div class="form-group" id="modify-group" style="display: none;">
+                <select id="file-to-modify">
+                    <option value="">Select file...</option>
+                </select>
+            </div>
+
+            <div class="form-group">
+                <input type="file" id="doc-upload" hidden accept=".pdf,.docx">
+                <button class="primary-btn" id="upload-trigger">
+                    <i class="fas fa-file-upload"></i> &nbsp; Choose & Process
+                </button>
+            </div>
+        </div>
+
+        <div class="status-container">
+            <div class="status-row">
+                <span>Ingestion Status</span>
+                <span id="status-text" style="color: #fbbf24;">Idle</span>
+            </div>
+            <div class="progress-track">
+                <div class="progress-bar" id="status-bar"></div>
+            </div>
+        </div>
+
+        <button class="primary-btn" style="background: rgba(255,255,255,0.05); margin-top: 1rem;" id="clear-chat">
+            <i class="fas fa-trash-alt"></i> &nbsp; Clear History
+        </button>
+    </aside>
+
+    <main class="main-content">
+        <header style="margin-bottom: 2rem;">
+            <h1 style="font-size: 1.8rem;">Direct-to-Agent Policy Advisory</h1>
+            <p style="color: var(--text-muted);">Get grounded answers from your insurance documents.</p>
+            <p style="color: var(--text-muted);">Please Note :- This response is based solely on insurer-provided
+                documents and is not financial advice.</p>
+        </header>
+
+        <div class="chat-container glass-card">
+            <div class="chat-messages" id="chat-box">
+                <!-- Welcome Message -->
+                <div class="message bot-message">
+                    Hello! I'm your Insurance AI Advisor. How can I help you today?
+                </div>
+            </div>
+
+            <div class="input-area">
+                <input type="file" id="audio-upload" hidden accept=".wav">
+                <button class="audio-btn" id="audio-trigger" title="Upload Audio (.wav)">
+                    <i class="fas fa-upload"></i>
+                </button>
+                <input type="text" id="user-input"
+                    placeholder="Ask about waiting periods, eligibility, or compare plans..." autocomplete="off">
+                <button class="send-btn" id="send-trigger">
+                    <i class="fas fa-paper-plane"></i>
+                </button>
+            </div>
+        </div>
+    </main>
+
+    <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
+    <script src="{{ url_for('static', filename='js/app.js') }}"></script>
+</body>
+
+</html>
\ No newline at end of file