Principia | Prerequisite Tree Generator

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_153":154,"_155":156},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":104,"_13":14,"_105":-5,"_11":106,"_107":108,"_109":110,"_111":112,"_113":114,"_20":-5,"_115":-5,"_116":-5,"_117":25,"_118":119,"_120":57,"_121":-5,"_122":-5,"_123":124},"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",7,"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.ts","latestPath","fileId","42bf035e-a28a-11f0-96b3-0224a6c84d84","name","fileType","http","content","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n// @ts-ignore\nimport { blob } from \"https://esm.town/v/std/blob?v=11\";\n// @ts-ignore\nimport { nanoid } from \"npm:nanoid\";\n\n// --- AI BEHAVIORAL GUIDELINES ---\nconst GENERATION_STYLE_GUIDE =\n \"As the AI logician 'Principia', you must adopt the persona of a rigorous epistemologist. Your deconstruction of facts must be precise, methodical, and devoid of speculation. Employ a formal, academic tone and vocabulary. The objective is to produce a logically sound breakdown of knowledge, as if preparing a formal proof, not a casual explanation.\";\n\nconst DECONSTRUCT_FACT_PROMPT = `\nYou are 'Principia', an expert logician and epistemologist. Your task is to perform a rigorous deconstruction of the {Target Fact}. Your final output must be a single, minified JSON object. Do not include any explanations or conversational text outside of the JSON.\n\nThe structure MUST be:\n{\n \"prerequisites\": [\n { \"fact\": \"A critical, immediate prerequisite fact.\", \"justification\": \"A brief explanation of why this fact is necessary to understand the target.\" }\n ]\n}\n`;\n\nconst AUDITOR_PROMPT = `\nYou are 'Meta-Principia', an epistemological auditor. The logician 'Principia' has concluded that the following statement is a foundational, atomic fact: \"{Target Fact}\".\n\nYour task is to challenge this conclusion with extreme skepticism. Scrutinize the statement for any unstated assumptions, hidden dependencies, or more fundamental concepts upon which it relies.\n\nIs this fact truly self-evident, or can it be deconstructed further? If it can, provide the more fundamental prerequisite facts using the same JSON format as Principia.\n\nIf you agree the fact is atomic and cannot be deconstructed, you MUST return an empty \"prerequisites\" array.\n\n--- EXAMPLE ---\nUSER: {Target Fact}: \"1 + 1 = 2.\"\nASSISTANT:\n{\n \"prerequisites\": [\n { \"fact\": \"The number '1' is defined as the successor to '0'.\", \"justification\": \"The statement relies on the definition of the number 1.\" },\n { \"fact\": \"The operation '+' (addition) is defined.\", \"justification\": \"The symbol '+' must have a defined operational meaning.\" }\n ]\n}\n--- END EXAMPLE ---\n`;\n\n// --- Type Definitions ---\ninterface Prerequisite {\n fact: string;\n justification: string;\n}\n\ninterface FactNode {\n id: string;\n fact: string;\n justification?: string;\n children: FactNode[];\n depth: number;\n}\n\ninterface JobState {\n status: \"processing\" | \"complete\" | \"error\";\n error?: string;\n tree: FactNode | null;\n}\n\n// --- CORE AI & TREE LOGIC ---\nconst MAX_DEPTH = 4;\nconst AUDIT_DEPTH_INTERVAL = 2;\nconst openai = new OpenAI();\n\nasync function callAI(fact: string, prompt: string): Promise {\n try {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: GENERATION_STYLE_GUIDE },\n { role: \"user\", content: prompt.replace(\"{Target Fact}\", fact) },\n ],\n response_format: { type: \"json_object\" },\n });\n const content = completion.choices[0].message.content;\n if (!content) return [];\n\n const parsed = JSON.parse(content);\n // Handle cases where the AI might return an empty object or not the array\n if (parsed && Array.isArray(parsed.prerequisites)) {\n return parsed.prerequisites;\n }\n return [];\n } catch (error) {\n console.error(`AI call failed for fact \"${fact}\":`, error);\n // Don't throw, return empty to allow the process to continue\n return [];\n }\n}\n\n/**\n * The \"Transfer Function\": Builds the dependency tree in the background using a\n * robust, breadth-first (level-by-level) approach to prevent race conditions.\n */\nasync function buildTreeInBackground(jobId: string, rootNode: FactNode) {\n try {\n let currentLevelNodes = [rootNode];\n\n for (let depth = 0; depth < MAX_DEPTH; depth++) {\n if (currentLevelNodes.length === 0) break; // No more nodes to process\n\n // 1. In parallel, get prerequisites for all nodes on the current level.\n const childPromises = currentLevelNodes.map(async (node) => {\n let prerequisites = await callAI(node.fact, DECONSTRUCT_FACT_PROMPT);\n\n // Auditor Intervention Logic: If the main AI finds nothing, or at\n // specific depths, the auditor challenges the result.\n if (\n prerequisites.length === 0 ||\n (node.depth > 0 && node.depth % AUDIT_DEPTH_INTERVAL === 0)\n ) {\n const deeperPrerequisites = await callAI(node.fact, AUDITOR_PROMPT);\n if (deeperPrerequisites.length > 0) {\n prerequisites = deeperPrerequisites;\n }\n }\n\n // Filter for valid prerequisites to prevent errors from malformed AI responses\n const validPrerequisites = prerequisites.filter((p) =>\n p && typeof p.fact === \"string\" && p.fact.trim() !== \"\"\n );\n\n node.children = validPrerequisites.map((p) => ({\n id: nanoid(),\n fact: p.fact,\n justification: p.justification,\n children: [],\n depth: node.depth + 1,\n }));\n\n return node.children;\n });\n\n const nextLevelNodesNested = await Promise.all(childPromises);\n\n // 2. After the whole level is processed, save the updated tree state once.\n await blob.setJSON(jobId, { status: \"processing\", tree: rootNode });\n\n // 3. Set up the next level for the next loop iteration.\n currentLevelNodes = nextLevelNodesNested.flat();\n }\n\n // 4. Finally, mark the job as complete.\n await blob.setJSON(jobId, { status: \"complete\", tree: rootNode });\n } catch (error) {\n console.error(\"Tree building error:\", error);\n await blob.setJSON(jobId, {\n status: \"error\",\n tree: rootNode, // Send partial tree on error\n error: error.message,\n });\n }\n}\n\nfunction generateHtml(sourceUrl: string): string {\n // The complete HTML/CSS/React frontend code is returned here.\n // It has been significantly enhanced for UI/UX.\n return `\n\n\n \n \n Principia | Prerequisite Tree Generator\n 🌳\">\n \n \n \n \n \n \n\n\n\n

\n\n\n`;\n}\n\n// --- HTTP REQUEST HANDLER ---\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const path = url.pathname;\n const headers = { \"Content-Type\": \"application/json\" };\n\n try {\n // === POST /start : Start a new tree generation job ===\n if (req.method === \"POST\" && path.endsWith(\"/start\")) {\n const { fact } = await req.json();\n if (!fact || typeof fact !== \"string\" || fact.trim().length === 0) {\n throw new Error(\"A valid, non-empty 'fact' string is required.\");\n }\n\n const jobId = nanoid();\n const rootNode: FactNode = {\n id: nanoid(),\n fact: fact,\n children: [],\n depth: 0,\n };\n\n // Set the initial state before starting the background process.\n await blob.setJSON(jobId, { status: \"processing\", tree: rootNode });\n\n // Execute the long-running process without awaiting it.\n buildTreeInBackground(jobId, rootNode);\n\n return new Response(JSON.stringify({ jobId }), { headers });\n }\n\n // === GET /status : Poll for the status of a job ===\n if (req.method === \"GET\" && path.endsWith(\"/status\")) {\n const jobId = url.searchParams.get(\"jobId\");\n if (!jobId) throw new Error(\"A 'jobId' query parameter is required.\");\n\n const state = await blob.getJSON(jobId);\n if (!state) {\n return new Response(\n JSON.stringify({ status: \"error\", error: \"Job not found.\" }),\n { status: 404, headers },\n );\n }\n return new Response(JSON.stringify(state), { headers });\n }\n\n // === GET / : Serve the frontend HTML ===\n if (req.method === \"GET\" && (path === \"/\" || path.endsWith(\"/ui\"))) {\n const sourceUrl = `https://${url.hostname}${\n url.pathname.substring(0, url.pathname.lastIndexOf(\"/\"))\n }`;\n return new Response(generateHtml(sourceUrl), {\n headers: { \"Content-Type\": \"text/html\" },\n });\n }\n\n // Default 404\n return new Response(JSON.stringify({ error: \"Not Found\" }), {\n status: 404,\n headers,\n });\n } catch (error) {\n const errorMessage = error instanceof Error\n ? error.message\n : \"An unknown server error occurred.\";\n return new Response(JSON.stringify({ error: errorMessage }), {\n status: 400,\n headers,\n });\n }\n}","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n// @ts-ignore\nimport { blob } from \"https://esm.town/v/std/blob?v=11\";\n// @ts-ignore\nimport { nanoid } from \"npm:nanoid\";\n\n// --- AI BEHAVIORAL GUIDELINES ---\nconst GENERATION_STYLE_GUIDE =\n \"As the AI logician 'Principia', you must adopt the persona of a rigorous epistemologist. Your deconstruction of facts must be precise, methodical, and devoid of speculation. Employ a formal, academic tone and vocabulary. The objective is to produce a logically sound breakdown of knowledge, as if preparing a formal proof, not a casual explanation.\";\n\nconst DECONSTRUCT_FACT_PROMPT = `\nYou are 'Principia', an expert logician and epistemologist. Your task is to perform a rigorous deconstruction of the {Target Fact}. Your final output must be a single, minified JSON object. Do not include any explanations or conversational text outside of the JSON.\n\nThe structure MUST be:\n{\n \"prerequisites\": [\n { \"fact\": \"A critical, immediate prerequisite fact.\", \"justification\": \"A brief explanation of why this fact is necessary to understand the target.\" }\n ]\n}\n`;\n\nconst AUDITOR_PROMPT = `\nYou are 'Meta-Principia', an epistemological auditor. The logician 'Principia' has concluded that the following statement is a foundational, atomic fact: \"{Target Fact}\".\n\nYour task is to challenge this conclusion with extreme skepticism. Scrutinize the statement for any unstated assumptions, hidden dependencies, or more fundamental concepts upon which it relies.\n\nIs this fact truly self-evident, or can it be deconstructed further? If it can, provide the more fundamental prerequisite facts using the same JSON format as Principia.\n\nIf you agree the fact is atomic and cannot be deconstructed, you MUST return an empty \"prerequisites\" array.\n\n--- EXAMPLE ---\nUSER: {Target Fact}: \"1 + 1 = 2.\"\nASSISTANT:\n{\n \"prerequisites\": [\n { \"fact\": \"The number '1' is defined as the successor to '0'.\", \"justification\": \"The statement relies on the definition of the number 1.\" },\n { \"fact\": \"The operation '+' (addition) is defined.\", \"justification\": \"The symbol '+' must have a defined operational meaning.\" }\n ]\n}\n--- END EXAMPLE ---\n`;\n\nconst TOPIC_ANALYSIS_PROMPT = `\nYou are 'The Cartographer', an AI that maps knowledge domains. Your task is to analyze a list of factual statements and assign a single, concise, high-level topic (1-3 words) to each. Group related concepts under a consistent topic name.\n\nThe input will be a JSON array of strings. The output MUST be a single, minified JSON object mapping each fact to its topic.\n\n--- EXAMPLE ---\nUSER: [\"The Earth is a planet.\", \"Gravity is a force of attraction between two masses.\", \"A year is the time it takes for Earth to orbit the Sun.\"]\nASSISTANT:\n{\n \"The Earth is a planet.\": \"Astronomy\",\n \"Gravity is a force of attraction between two masses.\": \"Physics\",\n \"A year is the time it takes for Earth to orbit the Sun.\": \"Astronomy\"\n}\n--- END EXAMPLE ---\n\nAnalyze the following facts:\n{Facts}\n`;\n\n// --- Type Definitions ---\ninterface Prerequisite {\n fact: string;\n justification: string;\n}\n\ninterface FactNode {\n id: string;\n fact: string;\n justification?: string;\n children: FactNode[];\n depth: number;\n topic?: string; // New grouping element\n}\n\ninterface JobState {\n status: \"processing\" | \"complete\" | \"error\";\n error?: string;\n tree: FactNode | null;\n}\n\n// --- CORE AI & TREE LOGIC ---\nconst MAX_DEPTH = 4;\nconst AUDIT_DEPTH_INTERVAL = 2;\nconst openai = new OpenAI();\n\nasync function callAI(content: string, promptTemplate: string): Promise {\n try {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: GENERATION_STYLE_GUIDE },\n {\n role: \"user\",\n content: promptTemplate.replace(/\\{(Target Fact|Facts)\\}/, content),\n },\n ],\n response_format: { type: \"json_object\" },\n });\n const responseContent = completion.choices[0].message.content;\n return responseContent ? JSON.parse(responseContent) : null;\n } catch (error) {\n console.error(\n `AI call failed for prompt targeting \"${content.substring(0, 50)}...\":`,\n error,\n );\n return null; // Return null on failure for graceful handling\n }\n}\n\nasync function analyzeTopics(facts: string[]): Promise> {\n if (facts.length === 0) return {};\n const result = await callAI(JSON.stringify(facts), TOPIC_ANALYSIS_PROMPT);\n return result || {};\n}\n\n/**\n * The \"Transfer Function\": Builds the dependency tree in the background using a\n * robust, breadth-first (level-by-level) approach to prevent race conditions.\n */\nasync function buildTreeInBackground(jobId: string, rootNode: FactNode) {\n try {\n let currentLevelNodes = [rootNode];\n\n // Analyze the root node's topic first\n const rootTopicMap = await analyzeTopics([rootNode.fact]);\n rootNode.topic = rootTopicMap[rootNode.fact] || \"Core Concept\";\n await blob.setJSON(jobId, { status: \"processing\", tree: rootNode });\n\n for (let depth = 0; depth < MAX_DEPTH; depth++) {\n if (currentLevelNodes.length === 0) break;\n\n const childPromises = currentLevelNodes.map(async (node) => {\n const deconstructResult = await callAI(\n node.fact,\n DECONSTRUCT_FACT_PROMPT,\n );\n let prerequisites =\n (deconstructResult && deconstructResult.prerequisites) || [];\n\n if (\n prerequisites.length === 0 ||\n (node.depth > 0 && node.depth % AUDIT_DEPTH_INTERVAL === 0)\n ) {\n const auditResult = await callAI(node.fact, AUDITOR_PROMPT);\n const deeperPrerequisites =\n (auditResult && auditResult.prerequisites) || [];\n if (deeperPrerequisites.length > 0) {\n prerequisites = deeperPrerequisites;\n }\n }\n\n const validPrerequisites = prerequisites.filter((p) =>\n p && typeof p.fact === \"string\" && p.fact.trim() !== \"\"\n );\n\n node.children = validPrerequisites.map((p) => ({\n id: nanoid(),\n fact: p.fact,\n justification: p.justification,\n children: [],\n depth: node.depth + 1,\n }));\n\n return node.children;\n });\n\n const nextLevelNodesNested = await Promise.all(childPromises);\n const nextLevelNodes = nextLevelNodesNested.flat();\n\n // Analyze topics for the newly generated level\n const factsToAnalyze = nextLevelNodes.map((node) => node.fact);\n const topicMap = await analyzeTopics(factsToAnalyze);\n\n // Assign topics to the nodes\n nextLevelNodes.forEach((node) => {\n node.topic = topicMap[node.fact] || \"Uncategorized\";\n });\n\n await blob.setJSON(jobId, { status: \"processing\", tree: rootNode });\n currentLevelNodes = nextLevelNodes;\n }\n\n await blob.setJSON(jobId, { status: \"complete\", tree: rootNode });\n } catch (error) {\n console.error(\"Tree building error:\", error);\n await blob.setJSON(jobId, {\n status: \"error\",\n tree: rootNode,\n error: error.message,\n });\n }\n}\n\nfunction generateHtml(sourceUrl: string): string {\n // Enhanced HTML with network visualization libraries and UI controls\n return `\n\n\n \n \n Principia | Knowledge Deconstructor\n 🧠\">\n \n \n \n \n \n \n \n \n\n\n\n

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