IDDAE - Intent-Driven Document Analysis Engine

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_152":153,"_154":155},"routes/_app.x.$handle.$name",{"_94":93,"_32":45,"_102":84,"_27":103,"_13":14,"_104":-5,"_11":105,"_106":107,"_108":109,"_110":111,"_112":113,"_20":-5,"_114":-5,"_115":-5,"_116":25,"_117":118,"_119":57,"_120":-5,"_121":-5,"_122":123},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",6,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","fe67cdb6-4335-11f0-942b-76b3cceeab13","name","fileType","http","content","import { fetch } from \"https://esm.town/v/std/fetch\";\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { PDFExtract, PDFExtractOptions } from \"npm:pdf.js-extract\";\n\nconst legalTaskSuggestionSystemPromptTemplate = `\nAI legal document review assistant. Your 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, providing in-depth, actionable, and extensive suggestions.\nReturn your suggestions STRICTLY as a JSON object ONLY.\nThe JSON object's keys are category names (e.g., \"Information Extraction\", \"Risk Assessment\").\nValues are arrays of objects, each with \"task\" (string describing the specific task), \"is_high_priority\" (boolean), and \"relevance_score\" (float, 0.0 to 1.0, estimating task relevance to the document).\nMark foundational or highly relevant tasks as \"is_high_priority\": true (aim for at least one per category if appropriate for the document content).\nAim for 3-5 categories, each with 3-7 relevant task suggestions, depending on document richness.\nProvide diverse suggestions covering different angles (e.g., obligations, risks, definitions, timelines).\nExample JSON Output (structure is critical):\n{\n \"Information Extraction\": [\n {\"task\": \"Identify all parties mentioned including their roles and full legal names\", \"is_high_priority\": true, \"relevance_score\": 0.95},\n {\"task\": \"Extract all defined terms and their precise definitions from the glossary or inline\", \"is_high_priority\": false, \"relevance_score\": 0.8},\n {\"task\": \"List all effective dates, expiration dates, and notice periods with their conditions\", \"is_high_priority\": false, \"relevance_score\": 0.85}\n ],\n \"Risk Assessment\": [\n {\"task\": \"Assess potential risks related to data privacy clauses, including GDPR or CCPA compliance\", \"is_high_priority\": true, \"relevance_score\": 0.9},\n {\"task\": \"Identify any ambiguous, conflicting, or potentially unenforceable clauses\", \"is_high_priority\": false, \"relevance_score\": 0.75}\n ]\n}\n\nDOCUMENT TEXT:\n%%DOCUMENT_TEXT%%\n`;\n\nconst legalAIAnalysisSystemPromptTemplate = `\n\nAI System for Precise Legal Information Extraction.\nGoal: Perform user-specified legal task(s) on the provided legal text.\nOutput: MUST be a single JSON object (no markdown formatting) strictly following the schema.\nLEGAL TASK(S) / QUERY: %%LEGAL_TASK_QUERY%% (Address comprehensively. This query might be a synthesis of multiple user-selected/refined items or a complex question. If helpful for accuracy, internally break down the query into sub-steps. Ensure all aspects of the query are addressed in your findings.)\nDOCUMENT SOURCE: %%INPUT_SOURCE_DESCRIPTION%%\nAnalyze document text (provided in user message) using expertise in legal terminology, document structures, and nuanced phrasing. Meticulously map ALL findings to the JSON schema.\nMandatory Steps:\n\n1. Understand Query: Fully grasp all aspects of the LEGAL TASK(S) / QUERY.\n2. Process Input: Carefully review the entire document text. Identify all relevant sections, clauses, and data points pertinent to the query.\n3. Analyze: Execute extraction/analytical tasks. This includes identifying specific information, interpreting legal clauses, synthesizing information if required, and answering questions based only on the provided document text.\n4. Structure Output: Map your analysis and findings precisely into the JSON output format. Ensure every field in the schema is considered.\n5. Neutrality & Accuracy: Present findings objectively. If text is insufficient or ambiguous for parts of the query, note this in the 'limitations_or_warnings' field.\nOutput JSON Schema (Return ALL findings; do NOT trim lists):\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(JSON Schema Field Explanations, Examples, and Notes are as previously defined externally.)\n`;\n\ninterface LogEntry {\n agent: string;\n message: string | object;\n type: \"step\" | \"result\" | \"error\" | \"input\" | \"final\" | \"info\";\n}\n\ninterface LegalAIResponse {\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;\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\ninterface GroupedSuggestions {\n [category: string]: {\n task: string;\n is_high_priority: boolean;\n relevance_score?: number;\n }[];\n}\n\ninterface IDDAEDocument {\n id: string;\n name: string;\n text: string;\n sourceType: \"file\";\n sourceValue: string;\n dateAdded: string;\n type: \"document\";\n}\n\ninterface IDDAEAnalysis {\n id: string;\n docId: string;\n docName?: string;\n query: string;\n result: LegalAIResponse;\n datePerformed: string;\n type: \"analysis\";\n}\n\nconst MAX_TEXT_LENGTH_SUGGESTION = 20000;\nconst MAX_TEXT_LENGTH_ANALYSIS = 30000;\nconst CORS_HEADERS = {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"POST, GET, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, Accept\",\n};\n\nasync function extractPdfTextNative(\n data: ArrayBuffer,\n fileName: string,\n log: LogEntry[],\n): Promise {\n const agent = \"PDF Extraction (Val Server)\";\n log.push({ agent, type: \"step\", message: `Starting PDF text extraction for: ${fileName}` });\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\n if (!text || text.trim().length === 0) {\n log.push({ agent, type: \"error\", message: \"PDF extraction yielded empty text.\" });\n return null;\n }\n log.push({ agent, type: \"info\", message: `Successfully extracted text (approx. ${text.length} chars).` });\n return text;\n } catch (error) {\n const msg = `Error during PDF extraction: ${error.message}`;\n log.push({ agent, type: \"error\", message: msg });\n console.error(agent, msg, error);\n return null;\n }\n}\n\nasync function callOpenAI(\n openaiInstance: OpenAI,\n systemPrompt: string,\n userMessage: string,\n model = \"gpt-4o\",\n expectJson = true,\n log: LogEntry[],\n agentName: string,\n): Promise

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