Principia | Knowledge Deconstructor

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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}\". Your 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. If it can be deconstructed further, provide the more fundamental prerequisite facts using the same JSON format as Principia. If you agree the fact is atomic, you MUST return an empty \"prerequisites\" array.\n`;\n\n// --- ENHANCED PROMPT FOR BETTER GROUPING ---\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.\n\nCRITICAL INSTRUCTION: Be granular and specific in your categorization. When possible, categorize the facts into **at least 3-5 distinct topics** to reveal more detailed conceptual relationships. Avoid assigning a single, overly broad topic like \"Science\" or \"Physics\" to all facts if more specific sub-domains like \"Optics,\" \"Kinematics,\" or \"Cosmology\" are applicable.\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 speed of light is constant in a vacuum.\", \"A lens focuses light by refraction.\", \"An object in motion stays in motion.\", \"Gravity causes apples to fall.\"]\nASSISTANT:\n{\n \"The speed of light is constant in a vacuum.\": \"Relativity\",\n \"A lens focuses light by refraction.\": \"Optics\",\n \"An object in motion stays in motion.\": \"Newtonian Mechanics\",\n \"Gravity causes apples to fall.\": \"Gravitational Theory\"\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;\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;\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\nasync function buildTreeInBackground(jobId: string, rootNode: FactNode) {\n try {\n let currentLevelNodes = [rootNode];\n\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 if (nextLevelNodes.length === 0) {\n await blob.setJSON(jobId, { status: \"processing\", tree: rootNode });\n break;\n }\n\n const factsToAnalyze = [\n ...new Set(nextLevelNodes.map((node) => node.fact)),\n ];\n const topicMap = await analyzeTopics(factsToAnalyze);\n\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 return `\n\n\n \n \n Principia | Knowledge Deconstructor\n 🧠\">\n \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 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 const jobId = nanoid();\n const rootNode: FactNode = {\n id: nanoid(),\n fact: fact,\n children: [],\n depth: 0,\n };\n await blob.setJSON(jobId, { status: \"processing\", tree: rootNode });\n buildTreeInBackground(jobId, rootNode);\n return new Response(JSON.stringify({ jobId }), { headers });\n }\n\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 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 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 return new Response(JSON.stringify({ error: \"Not Found\" }), {\n status: 404,\n headers,\n });\n } catch (error) {\n const errorMessage = error instanceof Error\n ? 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