Mortgage advisor chatbot

Role: You are a friendly and professional Mortgage Assistant. Your goal
is to respond to greetings or casual conversation in a warm, trustworthy
manner, and subtly steer the conversation back toward the user's
mortgage goals.

Response Guidelines:
- Tone: Warm, professional, and pragmatic.
- Acknowledge & Validate: Respond directly to what the user said.
- Subtle Pivot: End your response with a gentle nudge toward the
  mortgage process, without being pushy.
- Constraint: Keep the response under 3 sentences.

User message: ${userMessage}

Role: You are a mortgage assistant. The user sent a message that did not
match any known intent (greetings, product offer, knowledge base
question, or data input).

Respond with a short, friendly message that acknowledges you did not
understand and offers three concrete options the user can try: asking a
mortgage question, sharing their financial details, or requesting a
product recommendation. Keep it under 3 sentences.

User message: ${userMessage}

message = ""
if "userMessage" in input:
    message = input["userMessage"]
clientProfile = {}
if "clientProfile" in input:
    clientProfile = input["clientProfile"]
words = message.lower().split()
cleanWords = []
for w in words:
    cleaned = ""
    for ch in w:
        if ch.isalnum():
            cleaned = cleaned + ch
    cleanWords.append(cleaned)
words = cleanWords
numWords = len(words)
i = 0
while i < numWords:
    word = words[i]
    if word in ["years", "year"] and i > 0:
        prev = words[i - 1]
        if prev.isdigit():
            val = int(prev)
            if i < numWords - 1 and words[i + 1] == "old":
                if val > 15 and val < 120:
                    clientProfile["age"] = val
            else:
                if val >= 1 and val <= 50:
                    clientProfile["loan_duration"] = val
    if word == "age" and i < numWords - 1:
        nxt = words[i + 1]
        if nxt.isdigit() and int(nxt) > 15 and int(nxt) < 120:
            clientProfile["age"] = int(nxt)
    i = i + 1
i = 0
while i < numWords:
    word = words[i]
    if word in ["income", "salary", "earn", "earning", "make"]:
        j = i + 1
        while j < numWords and j < i + 4:
            val = words[j]
            if val.isdigit() and int(val) > 100:
                clientProfile["income"] = int(val)
            j = j + 1
    i = i + 1
i = 0
while i < numWords:
    word = words[i]
    if word in ["loan", "borrow", "mortgage"]:
        j = i + 1
        while j < numWords and j < i + 5:
            val = words[j]
            if val.isdigit() and int(val) > 1000:
                clientProfile["loan_amount"] = int(val)
            j = j + 1
    i = i + 1
collected = []
keys = list(clientProfile.keys())
k = 0
while k < len(keys):
    key = keys[k]
    collected.append(str(key) + ": " + str(clientProfile[key]))
    k = k + 1
confirmationText = ""
if len(collected) > 0:
    confirmationText = "Got it! I have recorded: " + ", ".join(collected) + ". What else can I help you with?"
else:
    confirmationText = "I could not extract data. Please tell me your age, income, loan amount, or loan duration."
output["confirmationText"] = confirmationText
output["clientProfile"] = clientProfile

Role: You are a mortgage assistant confirming data you captured from the
user.

Return the system-prepared confirmation verbatim, or rephrase it in a
friendly tone if helpful. Do not invent any new numbers — only use what
is in the confirmation.

User message: ${userMessage}
System-prepared confirmation: ${confirmationText}

Role: You are a Mortgage Knowledge Base Expert who uses an integrated
Knowledge Base to provide accurate, data-grounded answers.

Operating Procedure:
1. Query Analysis: Identify key terms from the user's question.
2. Knowledge Base Search: Find the most relevant document fragments.
3. Response Synthesis: Formulate a clear answer using ONLY the
   information found.

Strict Rules:
- If the information is not in the Knowledge Base, respond:
  "Our current knowledge base does not contain specific information
  about this topic."
- Reference the source document when possible.
- Never fabricate rates, terms, or requirements.
- Keep responses concise (under 200 words).

User question: ${userMessage}

answerPersonalisedOffer (Output Focused):
  Start
    → Text Understanding (extract client data + filter products)
      → Script (financial calculations: PMT, DTI, max loan)
        → Script (score, rank, and format report)
          → End Flow (outputs reportText)

mainChat (Chat Driven), Offer branch:
  Intent Classification Agent → Offer output
    → Subworkflow (calls answerPersonalisedOffer, Response Key: offerOutput)
      → Custom Agent (Send as Chat Reply ON, reads ${offerOutput.reportText})
        → End Flow

You are a mortgage product filter. Extract the client's financial
details from their message (provided in the context) and evaluate
available products.

Available products:
1. fixed30 (Alpha Bank): 4.5% fixed, 30 years, min income 3000 EUR,
   max LTV 80%, min age 21, max age at maturity 70, prepayment after
   5 years, 20% down payment
2. variable20 (Beta Bank): 3.5% variable (EURIBOR 6M + 1.8%), 20 years,
   min income 2500 EUR, max LTV 85%, min age 23, max age at maturity 65,
   no prepayment first 3 years, 15% down payment
3. fixed15 (Gamma Bank): 4.0% fixed, 15 years, min income 4000 EUR,
   max LTV 75%, min age 25, max age at maturity 60, prepayment anytime,
   25% down payment

First extract from the message:
- age (number)
- monthly_income (number)
- loan_amount (number)
- loan_duration (number, in years)

Then for each product, evaluate eligibility and return JSON only:
{
  "client": {"age": 0, "monthly_income": 0, "loan_amount": 0,
             "loan_duration": 0},
  "filtered_products": [
    {"product_id": "fixed30", "qualitative_match_score": 0.8,
     "match_reasons": "brief explanation", "disqualified": false}
  ]
}

Only include products where disqualified is false. Return ONLY valid
JSON, no other text.

Client message: ${userMessage}

{
  "type": "object",
  "properties": {
    "client": {
      "type": "object",
      "properties": {
        "age": { "type": "number" },
        "monthly_income": { "type": "number" },
        "loan_amount": { "type": "number" },
        "loan_duration": { "type": "number" }
      }
    },
    "filtered_products": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "product_id": { "type": "string" },
          "qualitative_match_score": { "type": "number" },
          "match_reasons": { "type": "string" },
          "disqualified": { "type": "boolean" }
        }
      }
    }
  }
}

var fp = input.filteredProducts;
var client = fp.get("client");

var income = parseFloat(client.get("monthly_income")) || 0;
var loanAmount = parseFloat(client.get("loan_amount")) || 0;
var loanDuration = parseInt(client.get("loan_duration")) || 20;
var termMonths = loanDuration * 12;

function computePMT(principal, annualRate, months) {
  var r = annualRate / 12;
  if (r === 0) return principal / months;
  return principal * (r * Math.pow(1 + r, months)) /
          (Math.pow(1 + r, months) - 1);
}

var estimatedRate = 0.045;
var monthlyPayment = income > 0
  ? computePMT(loanAmount, estimatedRate, termMonths) : 0;
var dti = income > 0 ? monthlyPayment / income : 1;

var maxDtiShare = 0.43;
var allowablePayment = maxDtiShare * income;
var maxLoan = allowablePayment > 0
  ? allowablePayment * ((Math.pow(1 + estimatedRate / 12, termMonths) - 1) /
    (estimatedRate / 12 * Math.pow(1 + estimatedRate / 12, termMonths)))
  : 0;

output.calculationResults = {
  monthlyPayment: Math.round(monthlyPayment * 100) / 100,
  dti: Math.round(dti * 10000) / 10000,
  maxLoanAmount: Math.round(maxLoan),
  meetsEligibility: dti <= maxDtiShare,
  requestedVsMax: loanAmount <= maxLoan ? "within_limits" : "exceeds_limits"
};

var calcResults = input.calculationResults || {};

var catalog = [
  {id: "fixed30", bankName: "Alpha Bank", rate: 0.045,
   allowsPrepayment: true, flexibleTerm: false, term: 30},
  {id: "variable20", bankName: "Beta Bank", rate: 0.035,
   allowsPrepayment: false, flexibleTerm: true, term: 20},
  {id: "fixed15", bankName: "Gamma Bank", rate: 0.04,
   allowsPrepayment: true, flexibleTerm: true, term: 15}
];

var fp = input.filteredProducts;
var aiProducts = fp.get("filtered_products");
var aiFiltered = [];
for (var i = 0; i < aiProducts.size(); i++) {
  var p = aiProducts.get(i);
  aiFiltered.push({
    product_id: "" + p.get("product_id"),
    qualitative_match_score:
      parseFloat(p.get("qualitative_match_score")) || 0.7,
    match_reasons: "" + p.get("match_reasons")
  });
}

var WEIGHT_RATE = 0.35;
var WEIGHT_QUALITATIVE = 0.25;
var WEIGHT_LTV = 0.20;
var WEIGHT_FLEXIBILITY = 0.20;

var scored = [];
for (var i = 0; i < aiFiltered.length; i++) {
  var product = aiFiltered[i];
  var details = null;
  for (var j = 0; j < catalog.length; j++) {
    if (catalog[j].id === product.product_id) {
      details = catalog[j];
      break;
    }
  }
  if (!details) continue;
  var rateScore = 1 - (details.rate / 0.10);
  var flexScore = (details.allowsPrepayment ? 0.5 : 0) +
                  (details.flexibleTerm ? 0.5 : 0);
  var totalScore = (rateScore * WEIGHT_RATE) +
    (product.qualitative_match_score * WEIGHT_QUALITATIVE) +
    (0.8 * WEIGHT_LTV) +
    (flexScore * WEIGHT_FLEXIBILITY);
  scored.push({
    productId: product.product_id,
    bankName: details.bankName,
    rate: details.rate,
    term: details.term,
    totalScore: Math.round(totalScore * 1000) / 1000,
    matchReasons: product.match_reasons
  });
}

scored.sort(function(a, b) { return b.totalScore - a.totalScore; });
var top = scored.slice(0, 3);

var mp = calcResults.monthlyPayment || 0;
var dti = calcResults.dti || 0;
var maxLoan = calcResults.maxLoanAmount || 0;
var eligible = calcResults.meetsEligibility !== false;

var report = "Mortgage Recommendation Report. ";
report += "Eligibility Assessment: ";
report += "Estimated monthly payment: " + mp + " EUR. ";
report += "Debt-to-income ratio: " + (dti * 100).toFixed(1) + "%. ";
report += "Maximum eligible loan: " + maxLoan + " EUR. ";
report += "Status: " + (eligible ? "Eligible" : "May need adjustment")
  + ". ";
report += "Top Recommendations: ";

for (var k = 0; k < top.length; k++) {
  var p = top[k];
  report += (k + 1) + ". " + p.bankName + " (" + p.productId + ") - "
    + (p.rate * 100) + "% for " + p.term + " years. ";
  report += "Score: " + p.totalScore + ". " + p.matchReasons + ". ";
}

report += "Next Steps: Compare the options above, gather required "
  + "documents, and schedule a consultation with your preferred bank.";

output.reportText = report;
output.rankedProducts = top;

{
  "reportText": "${reportText}"
}

Role: You are a mortgage advisor presenting a personalized
recommendation to the client.

Present the system-prepared report to the client in a warm,
professional tone. Preserve every number and product name exactly —
do not invent rates, terms, or eligibility details. Close with an
invitation to ask follow-up questions.

User message: ${userMessage}
System-prepared report: ${offerOutput.reportText}