DocJoc - AI Document Analysis

[{"_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",8,"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","cba8fae4-4e16-11f0-932a-76b3cceeab13","name","fileType","http","content","const domainIdentificationSystemPromptTemplate = `\nYou are an AI assistant, DocJoc, designed to quickly and accurately classify documents. Your goal is to analyze the provided text and determine its domain and primary purpose. Return your findings STRICTLY as a JSON object.\n\nThe \"domain\" should be a concise category (e.g., \"Academic Paper\", \"Financial Report\", \"Technical Manual\", \"Business Contract\", \"News Article\", \"Creative Writing\").\nThe \"purpose\" should be a brief description of what the document aims to achieve (e.g., \"To present research findings on particle physics\", \"To report quarterly earnings and financial health\", \"To provide instructions for operating a device\").\n\nExample:\n- Input (a scientific abstract): \"We investigate the effects of...\"\n- Output: { \"domain\": \"Academic Paper\", \"purpose\": \"To present research findings and methodology.\" }\n\nDOCUMENT TEXT:\n==============\n%%DOCUMENT_TEXT%%\n`;\n\nconst taskSuggestionSystemPromptTemplate = `\nAI assistant, DocJoc. Your goal is to suggest a comprehensive and diverse list of potential tasks or analyses that a user might want to perform on the provided document.\n\nYou must give a complete rhetorical list of likely tasks or analyses, even the hyperbolic ones you think one might not be interested in. Trust me, we are.. Name that group Misc.\n\nYou have been given the document's likely domain and purpose. Use this context to generate highly relevant and useful suggestions.\n\n- DOCUMENT DOMAIN: %%DOCUMENT_DOMAIN%%\n- DOCUMENT PURPOSE: %%DOCUMENT_PURPOSE%%\n\nReturn your suggestions STRICTLY as a JSON object. The keys are category names (e.g., \"Information Extraction\", \"Summarization\", \"Structural Analysis\"). Values are arrays of objects, each with \"task\" (a string describing the task) and \"is_high_priority\" (boolean). Foundational tasks like summarization and key point identification should be marked as \"is_high_priority: true\".\n\nExample for a 'Financial Report':\n- \"Information Extraction\": \"Extract all key financial metrics (Revenue, Net Income, EPS)\", \"Identify all forward-looking statements.\"\n- \"Risk Analysis\": \"Pinpoint any disclosed risks or uncertainties.\", \"Identify mentions of litigation or legal challenges.\"\n- \"Summarization\": \"Summarize the management's discussion and analysis (MD&A) section.\", \"Provide a high-level overview of the quarterly performance.\"\n\nDOCUMENT TEXT:\n==============\n%%DOCUMENT_TEXT%%\n`;\n\nconst docJocAIAnalysisSystemPromptTemplate = `\nAI System for Precise Document Information Extraction. Your goal is to perform the user-specified task(s) on the provided document text. Your output MUST be a single JSON object (no markdown formatting) that strictly follows the schema provided below. When identifying specific items, use a corresponding \"finding_type\" (e.g., \"Data Extraction\", \"Key Theme\", \"Entity Identification\", \"Section Analysis\"). For \"Section Analysis\", provide the section title in the description, a summary of its meaning in 'implications', and any relevant notes.\n\nTASK(S) / QUERY: %%TASK_QUERY%%\nDOCUMENT SOURCE: %%INPUT_SOURCE_DESCRIPTION%%\n\nAnalyze the document text provided in the user message. Meticulously map ALL findings to the JSON schema, presenting information objectively. If the text is insufficient or ambiguous for parts of the query, note this in the 'limitations_or_warnings' field.\n\nOutput JSON Schema:\n{\n \"request_details\": { \"operation_performed\": \"string\", \"input_source_description\": \"string\" },\n \"analysis_summary\": { \"overview\": \"string\", \"key_findings_count\": \"integer\" },\n \"detailed_findings\": [\n {\n \"finding_id\": \"string\", // e.g., \"FIN-001\"\n \"finding_type\": \"string\", // e.g., \"Data Extraction\", \"Key Theme\", \"Entity Identification\"\n \"description\": \"string\", // For Section Analysis, this is the section title.\n \"severity_or_importance\": \"string\", // e.g., \"High\", \"Medium\", \"Low\"\n \"relevant_input_excerpt\": \"string\",\n \"page_or_section_reference\": \"string\",\n \"implications\": \"string\", // For Section Analysis, this is the summary of the section.\n \"recommended_action\": \"string\", // e.g., \"Further review recommended\", \"N/A\"\n \"confidence_score\": \"float\" // 0.0 to 1.0\n }\n ],\n \"execution_metadata\": {\n \"status\": \"string\", // e.g., \"Completed\", \"Completed with warnings\"\n \"timestamp\": \"string\", // ISO 8601\n \"limitations_or_warnings\": \"string\",\n \"disclaimer\": \"string\"\n }\n}\n`;\n\n// --- Type Definitions ---\ninterface LogEntry {\n agent: string;\n message: string | object;\n type: \"step\" | \"result\" | \"error\" | \"input\" | \"final\" | \"info\";\n}\ninterface DocJocAIResponse {\n request_details: { operation_performed: string; input_source_description: string };\n analysis_summary: { overview: string; key_findings_count: number };\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 recommended_action?: string;\n confidence_score?: number;\n }>;\n execution_metadata: { status: string; timestamp: string; limitations_or_warnings: string; disclaimer: string };\n}\ninterface DocJocDocument {\n id: string;\n name: string;\n text: string;\n sourceType: \"file\";\n sourceValue: string;\n dateAdded: string;\n type: \"document\";\n}\ninterface DocJocAnalysis {\n id: string;\n docId: string;\n docName?: string;\n query: string;\n result: DocJocAIResponse;\n datePerformed: string;\n type: \"analysis\";\n}\ninterface GroupedSuggestions {\n [category: string]: { task: string; is_high_priority: boolean }[];\n}\n\nfunction generateHtmlShell(initialQuery, sourceUrl) {\n const escapedQuery = initialQuery.replace(//g, \">\");\n return `\n\n\n\n\n\nDocJoc - AI Document Analysis\n\n\n\n

\n\n

\n${\n [\"dashboard\", \"new-analysis\", \"history\", \"settings\", \"document-detail\", \"analysis-detail\"].map(v =>\n `

`\n ).join(\"\")\n }\n

\n\n\n`;\n}\n\nexport default async function(req: Request) {\n const { OpenAI } = await import(\"https://esm.town/v/std/openai\");\n const { PDFExtract } = await import(\"npm:pdf.js-extract\");\n\n const 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 if (req.method === \"OPTIONS\") return new Response(null, { status: 204, headers: CORS_HEADERS });\n\n const url = new URL(req.url);\n const format = url.searchParams.get(\"format\");\n const action = url.searchParams.get(\"action\");\n const sourceUrl = import.meta.url.replace(\"esm.town\", \"val.town\");\n const openai = new OpenAI();\n const MAX_TEXT_SUGGEST = 20000;\n const MAX_TEXT_ANALYZE = 30000;\n\n async function extractPdfText(data: ArrayBuffer, fileName: string, log: LogEntry[]): Promise {\n log.push({ agent: \"PDF\", type: \"step\", message: `Processing: ${fileName}` });\n try {\n const pdfExtract = new PDFExtract();\n const pdfData = await pdfExtract.extractBuffer(data, {});\n const text = pdfData.pages.map(page => page.content.map(item => item.str).join(\" \")).join(\"\\n\\n\");\n if (!text?.trim()) {\n log.push({ agent: \"PDF\", type: \"error\", message: \"Extraction yielded empty text.\" });\n return null;\n }\n log.push({ agent: \"PDF\", type: \"info\", message: `Extracted ~${text.length} chars.` });\n return text;\n } catch (error) {\n log.push({ agent: \"PDF\", type: \"error\", message: `Extraction error: ${error.message}` });\n console.error(\"PDF Extraction Error:\", error);\n return null;\n }\n }\n\n async function callAI(\n systemPrompt: string,\n userMessage: string,\n log: LogEntry[],\n agentName: string,\n ): Promise

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