1912 Realty - Loan Assumption Assistant

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Chicago Title is our trusted partner for closing these transactions.\nYour goal is to answer the user's query about loan assumptions in a clear, helpful, and encouraging way, always guiding them to speak directly with a 1912 Realty agent for personalized advice and next steps.\nDO NOT PROVIDE FINANCIAL OR LEGAL ADVICE. DO NOT MAKE PROMISES ABOUT QUALIFICATION OR SPECIFIC LOAN TERMS. DO NOT MAKE UP CONTACT INFORMATION FOR 1912 REALTY.\nEmphasize that 1912 Realty will help them through the process, including lender pre-screening and finding suitable properties.\n\nUSER'S QUERY/INTEREST: %%USER_QUERY%%\nUSER'S PROVIDED INFO (Name): %%USER_NAME%%\n\nStructure your output strictly according to the JSON schema below.\n- request_details.operation_performed: Re-state the user's query or area of interest (e.g., \"User query about: [%%USER_QUERY%%]\").\n- request_details.input_source_description: Describe the input, e.g., \"User seeking information on loan assumptions.\"\n- analysis_summary.overview: Briefly summarize your answer and reiterate the benefit of talking to 1912 Realty. Include the user's name if provided: \"Hello %%USER_NAME%%, loan assumptions can be a great option...\" or \"Loan assumptions can be a great option...\".\n- analysis_summary.key_findings_count: Number of items in detailed_findings.\n- detailed_findings: Array of objects. Each object is a distinct piece of information.\n - finding_id: (string) A unique ID (e.g., \"BEN001\", \"PROC001\", \"CTA001\").\n - finding_type: (string) Use types like \"Introduction\", \"PotentialBenefit\", \"Consideration\", \"ProcessStep\", \"NextStepRecommendation\", \"MythBuster\".\n - description: (string) Detailed explanation of the finding.\n - severity_or_importance: (string, optional) e.g., \"High\", \"Medium\", \"KeyInfo\".\n - implications: (string, optional) Potential positive outcome or reason this info is key for the buyer.\n - confidence_score: (float, optional, 0.0 to 1.0) Your confidence. Generally 0.95 for factual explanations.\n- execution_metadata:\n - status: (string) \"Completed\" or \"Completed with Limitations\".\n - timestamp: (string) ISO 8601 timestamp.\n - limitations_or_warnings: (string) \"This information is for educational purposes only and does not constitute financial or legal advice. Market conditions, specific loan terms, and individual qualifications vary. All loan assumptions are subject to lender approval and seller agreement.\"\n - disclaimer: (string) \"To explore your specific options for a home in the Phoenix area or other locations 1912 Realty serves, and to begin the pre-screening process, please contact a 1912 Realty agent. We work closely with Chicago Title to ensure a smooth and professional closing experience for your assumed loan.\"\n\nKey Messages to Weave In:\n* \"1912 Realty is here to guide you through every step of the loan assumption process.\"\n* \"With an assumable mortgage, you could potentially take over the seller's existing interest rate, which might be lower than current market rates.\"\n* \"Our team at 1912 Realty can help you identify properties with assumable loans and negotiate with sellers.\"\n* \"Chicago Title, our partner, handles the closing details, ensuring a secure transaction.\"\n* \"The next step is to talk to one of our experienced agents at 1912 Realty.\"\n\nIf the user asks a direct question, answer it clearly and then naturally transition to the benefits of talking to an agent.\nIf the user query is generic (e.g., \"Tell me about loan assumptions\"), provide a balanced overview covering what it is, key benefits, and a couple of important considerations.\n\nExample for a generic query like \"Tell me about loan assumptions\":\ndetailed_findings: [\n { \"finding_id\": \"GEN001\", \"finding_type\": \"Introduction\", \"description\": \"A loan assumption is when a home buyer takes over the seller's existing mortgage, including the remaining balance, interest rate, and repayment terms. This can be particularly attractive if the existing loan has a lower interest rate than current market rates.\", \"implications\": \"Potential for significant savings on interest payments over the life of the loan.\" },\n { \"finding_id\": \"BEN001\", \"finding_type\": \"PotentialBenefit\", \"description\": \"One of the biggest advantages is potentially securing a lower interest rate. You might also save on some closing costs compared to a new loan.\", \"implications\": \"Lower monthly payments and reduced upfront costs.\" },\n { \"finding_id\": \"CON001\", \"finding_type\": \"Consideration\", \"description\": \"The buyer must typically qualify with the original lender. Also, the seller's equity needs to be paid, which might require a larger down payment if the loan balance is much lower than the home's price.\", \"implications\": \"Important to assess your financial situation and get pre-qualified.\" },\n { \"finding_id\": \"CTA001\", \"finding_type\": \"NextStepRecommendation\", \"description\": \"The best way to understand if a loan assumption is right for you and to find suitable properties is to speak with an expert at 1912 Realty. We can assess your situation, explain the nuances, and guide you through the lender pre-screening process.\", \"severity_or_importance\": \"High\", \"implications\": \"Get personalized guidance and start your home buying journey with an advantage.\" }\n]\n\nReturn ALL your outputs strictly as a JSON object.\nEnsure all required string fields are populated.\nThe detailed_findings array can be empty only if the query is completely unanswerable, which should be rare.\n\nYOU MUST ALWAYS RETURN A RESPONSE IN SPANISH, NO ENGLISH, ONLY SPANISH\n`;\n\n// --- Legal AI Task Suggestion Prompt Template (Existing - NOT USED in primary flow but kept for potential future use) ---\nconst legalTaskSuggestionSystemPromptTemplate = `\nYou are an AI assistant specializing in legal document review.\nYour goal is to suggest a comprehensive and diverse list of potential legal tasks or analyses\nthat a user might want to perform on the provided document text.\nFocus on common legal review objectives like information extraction (e.g., parties, dates, definitions, obligations, rights), summarization, compliance checks (e.g., specific regulations if inferable), risk identification, clause analysis (e.g., termination, liability, indemnification, confidentiality), and comparisons if multiple documents/sections were hypothetically present.\nThe list should be extensive to maximize the chance of including the user's intended task. Aim for at least 10-15 diverse suggestions if the document is rich enough, but fewer if the document is very short or simple.\n\nReturn your suggestions STRICTLY as a JSON array of strings ONLY. Each string should be a concise description of a task.\nExample: [\"Identify all parties mentioned in the document\", \"Extract all defined terms and their definitions\", \"Summarize the key obligations for Party A\", \"List all clauses related to termination\", \"Identify renewal conditions and notice periods\", \"Assess potential risks related to data privacy clauses\", \"Find all references to governing law and jurisdiction\", \"Extract payment terms and schedules\", \"Check for assignment clauses and their conditions\", \"Identify any force majeure provisions\"]\n\nDOCUMENT TEXT:\n==============\n%%DOCUMENT_TEXT%%\n`;\n\n// --- Legal AI Analysis System Prompt Template (Kept for reference, REPLACED by loanAssumptionInfoSystemPromptTemplate for this app) ---\nconst legalAIAnalysisSystemPromptTemplate = `\nYou are an AI System for Precise Legal Information Extraction.\nYour primary goal is to perform the user-specified legal information extraction or analysis task, described below,\non the provided legal text (which will be the main content of the user message).\nYou must structure ALL your outputs strictly according to the defined shared JSON schema.\n\nLEGAL TASK / QUERY TO ADDRESS: %%LEGAL_TASK_QUERY%%\nYou will analyze the document text provided in the user message.\nThe document is described as: %%INPUT_SOURCE_DESCRIPTION%%\n\nYour expertise lies in understanding legal terminology, document structures, and nuanced phrasing.\nMeticulously analyze the input in the context of the LEGAL TASK / QUERY and then funnel your findings\ninto the generic JSON schema detailed below.\n(Rest of the original prompt content...)\nJSON\n\n{\n \"request_details\": {\n \"operation_performed\": \"string\",\n \"input_source_description\": \"string\"\n },\n \"analysis_summary\": {\n \"overview\": \"string\",\n \"key_findings_count\": \"integer\"\n },\n \"detailed_findings\": [\n {\n \"finding_id\": \"string\",\n \"finding_type\": \"string\",\n \"description\": \"string\",\n \"severity_or_importance\": \"string\",\n \"relevant_input_excerpt\": \"string\",\n \"page_or_section_reference\": \"string\",\n \"implications\": \"string\",\n \"confidence_score\": \"float\"\n }\n ],\n \"execution_metadata\": {\n \"status\": \"string\",\n \"timestamp\": \"string\",\n \"limitations_or_warnings\": \"string\",\n \"disclaimer\": \"string\"\n }\n}\n(Rest of schema explanation...)\n`;\n\n// --- Type Definitions (Existing) ---\ninterface LogEntry {\n agent: string;\n message: string | object;\n type: \"step\" | \"result\" | \"error\" | \"input\" | \"final\" | \"info\";\n}\n\ninterface AIResponse { // Generic for the main analysis\n request_details: {\n operation_performed: string;\n input_source_description: string;\n };\n analysis_summary: {\n overview: string;\n key_findings_count: number;\n };\n detailed_findings: Array<{\n finding_id: string;\n finding_type: string;\n description: string;\n severity_or_importance?: string;\n relevant_input_excerpt?: string;\n page_or_section_reference?: string; // Less relevant now\n implications?: string;\n confidence_score?: number;\n }>;\n execution_metadata: {\n status: string;\n timestamp: string;\n limitations_or_warnings: string;\n disclaimer: string;\n };\n}\n\n// --- Client-Side Data Structures ---\ninterface UserInquiry { // Was LexiDocument\n id: string;\n name: string; // e.g., \"Inquiry from [User's Name]\"\n userName?: string;\n userEmail?: string;\n userPhone?: string;\n userQuestion?: string; // The specific question they asked, if any\n dateAdded: string; // ISO string\n // sourceType & sourceValue might be less relevant or repurposed\n sourceType: \"web-form\" | \"pasted-question\";\n sourceValue: string; // e.g., \"Loan Assumption Inquiry\"\n}\n\ninterface InformationalResponse { // Was LexiAnalysis\n id: string;\n inquiryId: string; // Was docId\n query: string; // The question/query processed\n result: AIResponse; // The structured JSON from the AI\n datePerformed: string; // ISO string\n}\n\n// --- HTML Generation Function (HEAVILY MODIFIED) ---\nfunction generateHtmlShell(initialUrl, initialText, initialQuery, sourceUrl) {\n // initialUrl, initialText, initialQuery are less relevant for the new primary use case\n // but kept for potential deep-linking or other uses.\n\n return `\n\n\n\n \n \n 1912 Realty - Loan Assumption Assistant\n \n\n\n

\n \n \n \n

\n\n

\n\n\n\n`;\n}\n\n// --- Main Request Handler (Server Code - MODIFIED) ---\nexport default async function(req: Request) {\n const { OpenAI } = await import(\"https://esm.town/v/std/openai\");\n const { fetch } = await import(\"https://esm.town/v/std/fetch\");\n // PDFExtract is kept if you want to add document features later, but not primary for this use case.\n const { PDFExtract, PDFExtractOptions } = await import(\"npm:pdf.js-extract\");\n\n const CORS_HEADERS = {/* ... (same as original) ... */};\n if (req.method === \"OPTIONS\") { /* ... (same as original) ... */ }\n\n const url = new URL(req.url);\n const format = url.searchParams.get(\"format\");\n const action = url.searchParams.get(\"action\"); // New: \"loanAssumptionInfo\"\n const sourceUrl = import.meta.url.replace(\"esm.town\", \"val.town\");\n const openai = new OpenAI();\n const MAX_TEXT_LENGTH_ANALYSIS = 10000; // Reduced as input is smaller now\n\n // extractPdfTextNative function (same as original, less used now)\n async function extractPdfTextNative(data: ArrayBuffer, fileName: string, log: LogEntry[]): Promise {\n // ... (implementation from original)\n const agent = \"PDF Extraction Agent (Val Server)\";\n log.push({ agent, type: \"step\", message: `Processing PDF: ${fileName} using npm:pdf.js-extract` });\n try {\n const pdfExtract = new PDFExtract();\n const options: PDFExtractOptions = {};\n const pdfData = await pdfExtract.extractBuffer(data, options);\n const text = pdfData.pages.map(page => page.content.map(item => item.str).join(\" \")).join(\"\\\\n\\\\n\");\n if (!text || text.trim().length === 0) {\n log.push({ agent, type: \"error\", message: \"PDF extraction resulted in empty text content.\" });\n return null;\n }\n log.push({ agent, type: \"info\", message: `Successfully extracted ~${text.length} characters from PDF.` });\n return text;\n } catch (error) {\n const errorMessage = `Failed to extract text from PDF: ${error.message}`;\n log.push({ agent, type: \"error\", message: errorMessage });\n console.error(agent, errorMessage, error);\n return null;\n }\n }\n\n // callOpenAI function (same as original, but uses gpt-4o by default)\n async function callOpenAI(\n openaiInstance: OpenAI,\n systemPrompt: string,\n userMessage: string, // For this app, userMessage to AI might be empty if all info is in systemPrompt\n model = \"gpt-4o\",\n expectJson = true,\n log: LogEntry[],\n agentName: string,\n ): Promise