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// needed for deno run; not req for smallweb or valtown\nimport { performSearch, summarizeUrl, buildSearchPrompt, routeQueryMode } from \"./chatCompletion.js\";\nimport { extractOrRepairJsonResults, extractResultsFromToolOutputs, sanitizeResults, synthesizeResultsFromContext, synthesizeTextFromContext } from \"./jsonUtils.js\";\nimport { streamChatAndAggregate } from \"./chatStreaming.js\";\n\nconst app = new Hono();\nconst serverApiKey = null; // Deno.env.get(\"GROQ_API_KEY\");\n\n// Settings configuration\nconst settings = {\n model: 'openai/gpt-oss-120b',\n stream: false,\n reasoningEffort: 'low'\n};\n\n\n// Helper: Collect readable text from tool records for URL-grounded extraction\nfunction collectToolTextFromArray(toolArray) {\n if (!Array.isArray(toolArray)) return '';\n const parts = [];\n for (const t of toolArray) {\n try {\n const name = t?.name || t?.tool || t?.function?.name || t?.id || '';\n const args = t?.arguments || t?.input || t?.params || null;\n const output = t?.output || t?.result || t?.content || null;\n const searchResults = t?.search_results || t?.results || null;\n if (name) parts.push('name: ' + String(name));\n if (args) parts.push('arguments: ' + (typeof args === 'string' ? args : JSON.stringify(args)));\n if (output) parts.push('output: ' + (typeof output === 'string' ? output : JSON.stringify(output)));\n if (searchResults) parts.push('search_results: ' + (typeof searchResults === 'string' ? searchResults : JSON.stringify(searchResults)));\n } catch (_) {\n // ignore malformed tool objects\n }\n }\n return parts.join('\\n');\n}\n\n\n\n// Check if server has API key\napp.get('/api/check-key', (c) => {\n return c.json({ hasServerKey: !!serverApiKey });\n});\n\n// Environment-aware file reading function\nasync function readFileContent(filePath) {\n // Check if we're in Val Town environment\n if (typeof Deno !== \"undefined\" && Deno.env.get(\"valtown\")) {\n // Use Val Town's readFile\n return await readFile(filePath, import.meta.url);\n } else {\n // Use Deno's native readTextFile for local development\n return await Deno.readTextFile(filePath);\n }\n}\n\n// Serve root with HTML content\napp.get('/', async (c) => {\n try {\n const htmlContent = await readFileContent('index.html');\n return c.html(htmlContent);\n } catch (error) {\n console.error('Error reading HTML file:', error);\n return c.text('Error loading page', 500);\n }\n});\n\n// Serve frontend modules (no static middleware)\napp.get('/frontend/researchApp.js', async (c) => {\n try {\n const js = await readFileContent('frontend/researchApp.js');\n return new Response(js, {\n headers: { 'Content-Type': 'application/javascript; charset=utf-8' },\n });\n } catch (error) {\n console.error('Error reading researchApp.js:', error);\n return c.text('Not found', 404);\n }\n});\n\napp.get('/frontend/init.js', async (c) => {\n try {\n const js = await readFileContent('frontend/init.js');\n return new Response(js, {\n headers: { 'Content-Type': 'application/javascript; charset=utf-8' },\n });\n } catch (error) {\n console.error('Error reading init.js:', error);\n return c.text('Not found', 404);\n }\n});\n\n\napp.post('/api/search', async (c) => {\n try {\n const body = await c.req.json();\n const { query, dateRange, source, language, userApiKey, offset = 0, url, title } = body || {};\n\n const apiKey = serverApiKey || userApiKey;\n if (!apiKey) {\n return c.json({ error: 'No Groq API key available. Please provide your own key.' }, 400);\n }\n\n // Unified handler: if URL present, run summary; otherwise require query and run search\n if (url && typeof url === 'string' && url.trim()) {\n const { summary, summaryTime } = await summarizeUrl({\n apiKey,\n url,\n title,\n language,\n model: settings.model,\n stream: settings.stream,\n reasoningEffort: settings.reasoningEffort,\n });\n return c.json({ summary, summaryTime, url, title, language });\n }\n\n if (!query || !query.trim()) {\n return c.json({ error: 'No search query provided' }, 400);\n }\n\n const { results, searchTime, content: assistantContent, full: fullResponse, route } = await performSearch({\n apiKey,\n query,\n dateRange,\n source,\n language,\n offset,\n model: settings.model,\n stream: settings.stream,\n reasoningEffort: settings.reasoningEffort,\n // Force links mode when a specific source is chosen\n forceLinksRoute: !!source,\n });\n\n // Server-side debug logging of the full model response and assistant content\n try {\n console.log('>> [performSearch] fullResponse:', JSON.stringify(fullResponse));\n } catch (_) {\n console.log('>> [performSearch] fullResponse: ');\n }\n try {\n console.log('>> [performSearch] assistantContent:', typeof assistantContent === 'string' ? assistantContent : JSON.stringify(assistantContent));\n } catch (_) {\n console.log('>> [performSearch] assistantContent: ');\n }\n let outResults = sanitizeResults(results);\n let outAssistant = assistantContent;\n // Fallback synthesis: if route is links and results empty, synthesize from available context\n if ((route !== 'text') && (!outResults || outResults.length === 0)) {\n try {\n const toolText = ''; // non-stream path has no tool logs; rely on assistant text\n const synthesized = await synthesizeResultsFromContext({\n query,\n language,\n toolText,\n assistantText: String(assistantContent || ''),\n reasoningText: '',\n existingResults: outResults || []\n }, apiKey);\n if (Array.isArray(synthesized) && synthesized.length > 0) {\n outResults = sanitizeResults(synthesized);\n }\n } catch (_) { /* ignore */ }\n }\n // If router chose text but model returned nothing, synthesize concise text\n if (route === 'text' && (!outAssistant || !String(outAssistant).trim())) {\n try {\n const synthesizedText = await synthesizeTextFromContext({\n query,\n language,\n toolText: '',\n assistantText: '',\n reasoningText: ''\n }, apiKey);\n if (synthesizedText) outAssistant = synthesizedText;\n } catch (_) { /* ignore */ }\n }\n console.log('>> [performSearch] results (sanitized):', outResults);\n return c.json({\n results: outResults,\n searchTime,\n assistantContent: outAssistant,\n route,\n query,\n dateRange,\n source,\n language,\n offset,\n });\n } catch (error) {\n console.error('Server error:', error);\n return c.json({ error: 'Server error: ' + error.message }, 500);\n }\n});\n\n// API: Raw SSE passthrough for chat completions stream (for @test_chat_stream.js)\napp.post('/api/chatStream', async (c) => {\n try {\n let body = {};\n try {\n body = await c.req.json();\n } catch (_) {\n body = {};\n }\n const {\n userApiKey,\n messages,\n query,\n dateRange,\n source,\n language,\n offset = 0,\n model = 'openai/gpt-oss-120b',\n reasoning_effort = 'medium',\n tools = [{ type: 'browser_search' }],\n temperature,\n top_p,\n stop,\n } = body || {};\n\n const apiKey = serverApiKey || userApiKey || Deno.env.get(\"GROQ_API_KEY\");\n if (!apiKey) {\n return c.json({ error: 'No Groq API key available. Please provide your own key.' }, 400);\n }\n\n let finalMessages = Array.isArray(messages) && messages.length > 0\n ? messages\n : (typeof query === 'string' && query.trim().length > 0\n ? [\n {\n role: 'system',\n content: 'Return ONLY JSON matching the schema; no markdown or commentary. For every result, url MUST be a direct deep link to the specific item/resource (not a homepage, category page, or search results page). Prefer canonical targets: external article for HN listings, PubMed PMID page or publisher DOI for papers, arXiv abs page, DOI resolver, exact thread permalinks for discussions, and precise GitHub paths. Never output root domains as url. ALWAYS conduct searches in English; if the query is not in English, translate to English for search and present outputs in the requested language.'\n },\n { role: 'user', content: buildSearchPrompt(query, source, dateRange, language, offset) }\n ]\n : null);\n // Route logic: allow plain-text answers for explanatory queries\n try {\n if (typeof query === 'string' && query.trim()) {\n const route = await routeQueryMode(apiKey, query);\n console.log('>> [/api/chatStream] route:', route);\n // If a specific source is chosen, force list mode\n if (source) {\n finalMessages = [\n {\n role: 'system',\n content: 'Return ONLY JSON matching the schema; no markdown or commentary. For every result, url MUST be a direct deep link to the specific item/resource (not a homepage, category page, or search results page). Prefer canonical targets: external article for HN listings, PubMed PMID page or publisher DOI for papers, arXiv abs page, DOI resolver, exact thread permalinks for discussions, and precise GitHub paths. Never output root domains as url. ALWAYS conduct searches in English; if the query is not in English, translate to English for search and present outputs in the requested language.'\n },\n { role: 'user', content: buildSearchPrompt(query, source, dateRange, language, offset) }\n ];\n console.log('>> [/api/chatStream] forced links mode due to selected source');\n } else if (route === 'text') {\n finalMessages = [\n { role: 'system', content: 'If the user is asking for an explanation or summary rather than a list of links, respond with a concise plain-text answer in the requested language (no JSON). Do not use markdown fences.' },\n { role: 'user', content: query }\n ];\n console.log('>> [/api/chatStream] router selected text mode');\n }\n }\n } catch (_) { /* ignore */ }\n if (!finalMessages) {\n finalMessages = [{\n role: 'user',\n content: 'go to https://news.ycombinator.com/ and give me the first 5 stories'\n }];\n }\n\n const payload = {\n model,\n messages: finalMessages,\n stream: true,\n reasoning_effort,\n tools,\n ...(temperature !== undefined ? { temperature } : {}),\n ...(top_p !== undefined ? { top_p } : {}),\n ...(stop !== undefined ? { stop } : {}),\n };\n\n const aggregates = await streamChatAndAggregate(apiKey, payload, { logSse: false });\n let results = [];\n // Hoist variables so we can safely reference them after try/catch for logging\n let textContent = '';\n let reasoningText = '';\n let toolText = '';\n try {\n textContent = aggregates?.collapsed?.content || '';\n reasoningText = aggregates?.collapsed?.reasoning || '';\n toolText = collectToolTextFromArray(aggregates?.collapsed?.tools || []);\n if (toolText && toolText.trim()) {\n // Prefer URLs grounded in actual tool outputs\n results = await extractResultsFromToolOutputs(toolText, apiKey, language);\n }\n if ((!results || results.length === 0) && textContent && textContent.trim()) {\n // Fallback to general JSON repair on model text\n results = await extractOrRepairJsonResults(textContent, apiKey, language);\n }\n if ((!results || results.length === 0) && reasoningText && reasoningText.trim()) {\n // Final fallback: use reasoning channel content\n results = await extractOrRepairJsonResults(reasoningText, apiKey, language);\n }\n // Last-resort synthesis if still empty\n if ((!results || results.length === 0)) {\n const synthesized = await synthesizeResultsFromContext({\n query,\n language,\n toolText,\n assistantText: String(textContent || ''),\n reasoningText: String(reasoningText || ''),\n existingResults: []\n }, apiKey);\n if (Array.isArray(synthesized) && synthesized.length > 0) {\n results = synthesized;\n }\n }\n } catch (_e) {\n // best-effort parsing; ignore errors\n }\n console.log('>> [chatStream] textContent:', textContent);\n return c.json({ model, ...aggregates, results: sanitizeResults(results) });\n } catch (error) {\n console.error('chatStream error:', error);\n return c.json({ error: 'chatStream error: ' + error.message }, 500);\n }\n});\n\n// API: True SSE passthrough for chat completions stream (browser consumes incrementally)\napp.post('/api/chatStreamSSE', async (c) => {\n try {\n let body = {};\n try {\n body = await c.req.json();\n } catch (_) {\n body = {};\n }\n\n const {\n userApiKey,\n messages,\n query,\n dateRange,\n source,\n language,\n offset = 0,\n model = 'openai/gpt-oss-120b',\n reasoning_effort = 'medium',\n tools = [{ type: 'browser_search' }],\n temperature,\n top_p,\n stop,\n } = body || {};\n\n const apiKey = serverApiKey || userApiKey || Deno.env.get(\"GROQ_API_KEY\");\n if (!apiKey) {\n return c.json({ error: 'No Groq API key available. Please provide your own key.' }, 400);\n }\n\n let finalMessages = Array.isArray(messages) && messages.length > 0\n ? messages\n : (typeof query === 'string' && query.trim().length > 0\n ? [\n {\n role: 'system',\n content: 'Return ONLY JSON matching the schema; no markdown or commentary. For every result, url MUST be a direct deep link to the specific item/resource (not a homepage, category page, or search results page). Prefer canonical targets: external article for HN listings, PubMed PMID page or publisher DOI for papers, arXiv abs page, DOI resolver, exact thread permalinks for discussions, and precise GitHub paths. Never output root domains as url. ALWAYS conduct searches in English; if the query is not in English, translate to English for search and present outputs in the requested language.'\n },\n { role: 'user', content: buildSearchPrompt(query, source, dateRange, language, offset) }\n ]\n : [{ role: 'user', content: 'go to https://news.ycombinator.com/ and give me the first 5 stories' }]);\n\n // Route logic for SSE streaming as well\n let selectedRoute = 'links';\n try {\n if (typeof query === 'string' && query.trim()) {\n const route = await routeQueryMode(apiKey, query);\n selectedRoute = source ? 'links' : (route || 'links');\n if (source) {\n // Force links prompt\n finalMessages = [\n {\n role: 'system',\n content: 'Return ONLY JSON matching the schema; no markdown or commentary. For every result, url MUST be a direct deep link to the specific item/resource (not a homepage, category page, or search results page). Prefer canonical targets: external article for HN listings, PubMed PMID page or publisher DOI for papers, arXiv abs page, DOI resolver, exact thread permalinks for discussions, and precise GitHub paths. Never output root domains as url. ALWAYS conduct searches in English; if the query is not in English, translate to English for search and present outputs in the requested language.'\n },\n { role: 'user', content: buildSearchPrompt(query, source, dateRange, language, offset) }\n ];\n console.log('>> [/api/chatStreamSSE] forced links mode due to selected source');\n } else if (selectedRoute === 'text') {\n finalMessages = [\n { role: 'system', content: 'If the user is asking for an explanation or summary rather than a list of links, respond with a concise plain-text answer in the requested language (no JSON). Do not use markdown fences.' },\n { role: 'user', content: query }\n ];\n console.log('>> [/api/chatStreamSSE] router selected text mode');\n }\n }\n } catch (_) { /* ignore */ }\n\n const payload = {\n model,\n messages: finalMessages,\n stream: true,\n reasoning_effort,\n tools,\n ...(temperature !== undefined ? { temperature } : {}),\n ...(top_p !== undefined ? { top_p } : {}),\n ...(stop !== undefined ? { stop } : {}),\n };\n\n const encoder = new TextEncoder();\n const stream = new ReadableStream({\n start: async (controller) => {\n try {\n const upstream = await fetch(\"https://api.groq.com/openai/v1/chat/completions\", {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': 'Bearer ' + apiKey,\n },\n body: JSON.stringify(payload),\n });\n\n if (!upstream.ok || !upstream.body) {\n let txt = '';\n try { txt = await upstream.text(); } catch (_) {}\n controller.enqueue(encoder.encode('event: error\\n'));\n controller.enqueue(encoder.encode('data: ' + JSON.stringify({ status: upstream.status, message: txt }) + '\\n\\n'));\n controller.enqueue(encoder.encode('data: [DONE]\\n\\n'));\n controller.close();\n return;\n }\n\n const reader = upstream.body.getReader();\n const decoder = new TextDecoder();\n let buffer = '';\n let collapsedContent = '';\n let collapsedReasoning = '';\n let collectedTools = [];\n\n // Initial comment to establish stream\n try { controller.enqueue(encoder.encode(': connected\\n\\n')); } catch (_) {}\n // Send route selection to client\n try {\n controller.enqueue(encoder.encode('event: route\\n'));\n controller.enqueue(encoder.encode('data: ' + JSON.stringify({ route: selectedRoute }) + '\\n\\n'));\n } catch (_) {}\n\n // Keep-alive pings\n const keepAliveId = setInterval(() => {\n try { controller.enqueue(encoder.encode(': keep-alive\\n\\n')); } catch (_) {}\n }, 15000);\n\n try {\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n buffer += decoder.decode(value, { stream: true });\n const events = buffer.split('\\n\\n');\n buffer = events.pop() || '';\n for (const evt of events) {\n if (!evt.trim()) continue;\n const lines = evt.split('\\n');\n let currentEvent = 'message';\n for (const line of lines) {\n if (line.startsWith('event: ')) {\n currentEvent = line.slice(7).trim();\n continue;\n }\n if (!line.startsWith('data: ')) continue;\n const data = line.slice(6);\n // Passthrough Groq SSE data lines\n controller.enqueue(encoder.encode('data: ' + data + '\\n\\n'));\n // Accumulate collapsed content to produce JSON results at end\n try {\n const parsed = JSON.parse(data);\n const choice = Array.isArray(parsed.choices) ? parsed.choices[0] : null;\n const delta = choice?.delta || {};\n if (delta.reasoning) collapsedReasoning += delta.reasoning;\n if (delta.content) collapsedContent += delta.content;\n if (Array.isArray(delta.executed_tools)) {\n collectedTools.push(...delta.executed_tools);\n }\n if (Array.isArray(delta.tool_calls)) {\n collectedTools.push(...delta.tool_calls);\n }\n } catch (_) {\n // ignore non-JSON payloads such as [DONE]\n }\n }\n }\n }\n // Signal end of stream – emit final structured results using LLM fixer\n try {\n let results = [];\n if (selectedRoute !== 'text') {\n try {\n const toolText = collectToolTextFromArray(collectedTools);\n const textForExtraction = (collapsedContent && collapsedContent.trim())\n ? collapsedContent\n : ((collapsedReasoning && collapsedReasoning.trim()) ? collapsedReasoning : '');\n if (toolText && toolText.trim()) {\n results = await extractResultsFromToolOutputs(toolText, apiKey, language);\n }\n if ((!results || results.length === 0) && textForExtraction) {\n results = await extractOrRepairJsonResults(textForExtraction, apiKey, language);\n }\n // Last-resort synthesis from all accumulated context\n if ((!results || results.length === 0)) {\n const synthesized = await synthesizeResultsFromContext({\n query,\n language,\n toolText,\n assistantText: String(collapsedContent || ''),\n reasoningText: String(collapsedReasoning || ''),\n existingResults: []\n }, apiKey);\n if (Array.isArray(synthesized) && synthesized.length > 0) {\n results = synthesized;\n }\n }\n } catch (_) {}\n }\n controller.enqueue(encoder.encode('event: results\\n'));\n controller.enqueue(encoder.encode('data: ' + JSON.stringify({ route: selectedRoute, results: sanitizeResults(results) }) + '\\n\\n'));\n } catch (_) {}\n controller.enqueue(encoder.encode('data: [DONE]\\n\\n'));\n } catch (err) {\n controller.enqueue(encoder.encode('event: error\\n'));\n controller.enqueue(encoder.encode('data: ' + JSON.stringify({ message: err?.message || String(err) }) + '\\n\\n'));\n } finally {\n clearInterval(keepAliveId);\n try { controller.close(); } catch (_) {}\n }\n } catch (outerErr) {\n try {\n controller.enqueue(encoder.encode('event: error\\n'));\n controller.enqueue(encoder.encode('data: ' + JSON.stringify({ message: outerErr?.message || String(outerErr) }) + '\\n\\n'));\n controller.enqueue(encoder.encode('data: [DONE]\\n\\n'));\n } catch (_) {}\n try { controller.close(); } catch (_) {}\n }\n }\n });\n\n return new Response(stream, {\n headers: {\n 'Content-Type': 'text/event-stream',\n 'Cache-Control': 'no-cache, no-transform',\n 'Connection': 'keep-alive',\n },\n });\n } catch (error) {\n console.error('chatStreamSSE error:', error);\n return c.json({ error: 'chatStreamSSE error: ' + error.message }, 500);\n }\n});\n\n// API endpoint for paper summarization\n// Removed legacy /api/summarize; summaries are handled by /api/search when a URL is provided\n\n// Export app.fetch for Val Town, otherwise export app — this is only for hono apps\nexport default (typeof Deno !== \"undefined\" && Deno.env.get(\"valtown\")) ? app.fetch : app;",[150],"f77b21ce-7616-11f0-8f0c-0224a6c84d84","jsonUtils.js","f77b22e6-7616-11f0-8f0c-0224a6c84d84",15,["D",1754850144846],"// General JSON extraction/repair helper using Groq Chat Completions\n// Converts arbitrary text into a strict JSON object and returns the `results` array\nexport async function extractOrRepairJsonResults(rawText, apiKey, language = 'english') {\n const response = await fetch('https://api.groq.com/openai/v1/chat/completions', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`,\n },\n body: JSON.stringify({\n model: 'openai/gpt-oss-120b',\n messages: [\n {\n role: 'system',\n content:\n 'You convert arbitrary text into a strict JSON object with a top-level `results` array. Return ONLY JSON, no markdown or comments. Output must already be sanitized: descriptions must be clean plain text with no citations (e.g., [1], (2)), no line numbers (e.g., L0:), no dagger symbols (†), no HTML, no code blocks, and no section headers. Descriptions must add new information beyond the title — never repeat or mirror the title. If a description would equal or start with the title, remove the title portion and ensure the remaining text adds value; if nothing remains, set description to null. Keep prose concise and readable.'\n },\n {\n role: 'user',\n content:\n 'Convert the following into ONLY valid JSON that matches the schema. If fields are missing, use null. Do not include markdown fences. In description, output plain text only (no citations, numbers-in-brackets, line numbers, or HTML). Present titles and descriptions in language: ' + String(language || 'english') + '\\n---\\n' + rawText\n }\n ],\n response_format: {\n type: 'json_schema',\n json_schema: {\n name: 'search_results',\n schema: {\n type: 'object',\n properties: {\n results: {\n type: 'array',\n items: {\n type: 'object',\n properties: {\n title: { type: 'string' },\n url: { type: 'string' },\n description: { type: ['string', 'null'] },\n source: { type: ['string', 'null'] },\n published_at: { type: ['string', 'null'] },\n comments: { type: ['number', 'null'] },\n pmid: { type: ['string', 'null'] },\n doi: { type: ['string', 'null'] }\n },\n required: ['title', 'url'],\n additionalProperties: false\n }\n }\n },\n required: ['results'],\n additionalProperties: false\n }\n }\n },\n stream: false\n }),\n });\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Groq API error: ${response.status} ${text}`);\n }\n const data = await response.json();\n const jsonText = data.choices?.[0]?.message?.content || '{}';\n try {\n const parsed = JSON.parse(jsonText);\n let results = Array.isArray(parsed?.results) ? parsed.results : [];\n return results;\n } catch (_) {\n return [];\n }\n}\n\n// Extract results strictly from tool outputs (ground truth URLs only)\n// The tool logs may include lines like \"URL: ...\", markdown lists, tables, or raw text.\n// This helper instructs the model to ONLY use URLs that appear in the provided text and never invent new ones.\nexport async function extractResultsFromToolOutputs(toolText, apiKey, language = 'english') {\n const response = await fetch('https://api.groq.com/openai/v1/chat/completions', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`,\n },\n body: JSON.stringify({\n model: 'openai/gpt-oss-120b',\n messages: [\n {\n role: 'system',\n content:\n 'You will receive raw logs from browser tool executions that include titles and URLs. Critically: ONLY use URLs that literally appear in the logs. Do not fabricate or guess any URL. Produce a strict JSON object with a top-level `results` array. Each result should map a meaningful title to one of the URLs present in the logs. Prefer canonical deep links by rule: Hacker News → exact item permalink or the external article if explicitly listed; PubMed → PMID page; arXiv → /abs/; DOI → https://doi.org/...; Reddit/forums → exact thread permalink; GitHub → exact path to repo/issue/file/release. If a suitable title is not obvious, derive a concise one from nearby text. If description can be inferred, include a short clean sentence with no citations or artifacts; otherwise set description to null. Never include any URL not present in the logs.'\n },\n {\n role: 'user',\n content:\n 'From the following tool logs, extract results using ONLY URLs that occur in the text. Return ONLY valid JSON matching the schema. If fields are missing, use null. No markdown fences. Present titles and descriptions in language: ' + String(language || 'english') + '\\n---\\n' + toolText\n }\n ],\n response_format: {\n type: 'json_schema',\n json_schema: {\n name: 'search_results_from_tools',\n schema: {\n type: 'object',\n properties: {\n results: {\n type: 'array',\n items: {\n type: 'object',\n properties: {\n title: { type: 'string' },\n url: { type: 'string' },\n description: { type: ['string', 'null'] },\n source: { type: ['string', 'null'] },\n published_at: { type: ['string', 'null'] },\n comments: { type: ['number', 'null'] },\n pmid: { type: ['string', 'null'] },\n doi: { type: ['string', 'null'] }\n },\n required: ['title', 'url'],\n additionalProperties: false\n }\n }\n },\n required: ['results'],\n additionalProperties: false\n }\n }\n },\n stream: false\n }),\n });\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Groq API error: ${response.status} ${text}`);\n }\n const data = await response.json();\n const jsonText = data.choices?.[0]?.message?.content || '{}';\n try {\n const parsed = JSON.parse(jsonText);\n const results = Array.isArray(parsed?.results) ? parsed.results : [];\n return results;\n } catch (_) {\n return [];\n }\n}\n\n// Remove null/undefined optional fields so UI never shows pmid: null or doi: null\nexport function sanitizeResults(results) {\n if (!Array.isArray(results)) return [];\n return results.map((r) => {\n const cleaned = { ...r };\n for (const key of ['pmid', 'doi', 'description', 'source', 'published_at', 'comments']) {\n if (cleaned[key] === null || cleaned[key] === undefined) {\n delete cleaned[key];\n }\n }\n return cleaned;\n });\n}\n\n\n\n// Structured summary extraction to eliminate citation artifacts and enforce clean fields\nexport async function extractStructuredSummary(rawText, apiKey) {\n const response = await fetch('https://api.groq.com/openai/v1/chat/completions', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`,\n },\n body: JSON.stringify({\n model: 'openai/gpt-oss-120b',\n messages: [\n {\n role: 'system',\n content:\n 'You convert arbitrary summary-like text into a strict JSON object describing the content. Return ONLY JSON, no markdown. Remove all citation artifacts (e.g., [1], (2), †), line numbers (e.g., L0:), and scraped UI fragments. Keep clean prose. Choose one type: research or general. For research, populate research_question, methods_and_findings, significance. For general, populate what_it_is, key_points (short bullet strings), and details. If any field would repeat or mirror the title or adds no value, set it to null. Keep text concise.'\n },\n {\n role: 'user',\n content:\n 'Normalize the following text into ONLY valid JSON matching the schema. No markdown fences, no HTML. Remove citations, numbers-in-brackets, line numbers, and artifacts.\\n---\\n' + rawText\n }\n ],\n response_format: {\n type: 'json_schema',\n json_schema: {\n name: 'structured_summary',\n schema: {\n type: 'object',\n properties: {\n type: { type: 'string', enum: ['general', 'research'] },\n general: {\n type: 'object',\n properties: {\n what_it_is: { type: 'string' },\n key_points: { type: 'array', items: { type: 'string' } },\n details: { type: 'string' }\n },\n additionalProperties: false\n },\n research: {\n type: 'object',\n properties: {\n research_question: { type: 'string' },\n methods_and_findings: { type: 'string' },\n significance: { type: 'string' }\n },\n additionalProperties: false\n }\n },\n required: ['type'],\n additionalProperties: false\n }\n }\n },\n stream: false\n }),\n });\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Groq API error: ${response.status} ${text}`);\n }\n const data = await response.json();\n const jsonText = data.choices?.[0]?.message?.content || '{}';\n try {\n const parsed = JSON.parse(jsonText);\n return parsed && typeof parsed === 'object' ? parsed : null;\n } catch (_) {\n return null;\n }\n}\n\nfunction escapeHtml(str) {\n if (!str) return '';\n return String(str)\n .replace(/&/g, '&')\n .replace(//g, '>')\n .replace(/\"/g, '"')\n .replace(/'/g, ''');\n}\n\n// Deterministic HTML renderer for structured summary\nexport function renderStructuredSummaryToHtml(structured) {\n if (!structured || typeof structured !== 'object') return '';\n const type = structured.type;\n if (type === 'research' && structured.research) {\n const rq = escapeHtml(structured.research.research_question || '');\n const mf = escapeHtml(structured.research.methods_and_findings || '');\n const sg = escapeHtml(structured.research.significance || '');\n let html = '';\n if (rq) html += '

Research Question: ' + rq + '

';\n if (mf) html += '

Methods & Findings: ' + mf + '

';\n if (sg) html += '

Significance: ' + sg + '

';\n return html;\n }\n // default to general\n const general = structured.general || {};\n const what = escapeHtml(general.what_it_is || '');\n const details = escapeHtml(general.details || '');\n const points = Array.isArray(general.key_points) ? general.key_points : [];\n let html = '';\n if (what) html += '

What it is: ' + what + '

';\n if (points.length > 0) {\n html += '

Key points:

' + escapeHtml(p) + '

';\n }\n if (details) html += '

Details: ' + details + '

';\n return html;\n}\n\n\n// Synthesis fallback: produce results[] from mixed context (tool logs, assistant text, reasoning, existing results)\n// Only used when earlier parsing/repair yielded nothing or timed out\nexport async function synthesizeResultsFromContext(context, apiKey) {\n try {\n const {\n query = '',\n language = 'english',\n toolText = '',\n assistantText = '',\n reasoningText = '',\n existingResults = []\n } = context || {};\n\n const parts = [];\n if (query) parts.push('User query: ' + String(query));\n parts.push('Present titles and descriptions in language: ' + String(language || 'english'));\n if (Array.isArray(existingResults) && existingResults.length > 0) {\n parts.push('Existing results JSON (may be empty or partial): ' + JSON.stringify({ results: existingResults }));\n }\n if (toolText && String(toolText).trim()) parts.push('Tool logs:\\n' + String(toolText));\n if (assistantText && String(assistantText).trim()) parts.push('Assistant text:\\n' + String(assistantText));\n if (reasoningText && String(reasoningText).trim()) parts.push('Reasoning text:\\n' + String(reasoningText));\n\n const response = await fetch('https://api.groq.com/openai/v1/chat/completions', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`,\n },\n body: JSON.stringify({\n model: 'openai/gpt-oss-120b',\n messages: [\n {\n role: 'system',\n content: 'Synthesize a strict JSON search results list from provided context. CRITICAL: Prefer URLs that literally occur in tool logs or assistant text; avoid fabricating. Apply canonical link rules (HN item or external, PubMed PMID, arXiv /abs, DOI resolver, exact thread permalinks, precise GitHub paths). Descriptions must be clean plain text without citations (no [1], (2), L0:, †), HTML, or code fences, and must add info beyond the title. Present titles and descriptions in the requested language.'\n },\n {\n role: 'user',\n content: parts.join('\\n\\n')\n }\n ],\n response_format: {\n type: 'json_schema',\n json_schema: {\n name: 'search_results_synthesis',\n schema: {\n type: 'object',\n properties: {\n results: {\n type: 'array',\n items: {\n type: 'object',\n properties: {\n title: { type: 'string' },\n url: { type: 'string' },\n description: { type: ['string', 'null'] },\n source: { type: ['string', 'null'] },\n published_at: { type: ['string', 'null'] },\n comments: { type: ['number', 'null'] },\n pmid: { type: ['string', 'null'] },\n doi: { type: ['string', 'null'] }\n },\n required: ['title', 'url'],\n additionalProperties: false\n }\n }\n },\n required: ['results'],\n additionalProperties: false\n }\n }\n },\n stream: false\n })\n });\n if (!response.ok) {\n try { console.error('synthesizeResultsFromContext error:', await response.text()); } catch (_) {}\n return [];\n }\n const data = await response.json();\n const jsonText = data.choices?.[0]?.message?.content || '{}';\n try {\n const parsed = JSON.parse(jsonText);\n return Array.isArray(parsed?.results) ? parsed.results : [];\n } catch (_) {\n return [];\n }\n } catch (_err) {\n return [];\n }\n}\n\n// Synthesis fallback: produce concise plain-text answer from context (used when router selects text or lists fail)\nexport async function synthesizeTextFromContext(context, apiKey) {\n try {\n const {\n query = '',\n language = 'english',\n toolText = '',\n assistantText = '',\n reasoningText = ''\n } = context || {};\n\n const parts = [];\n if (query) parts.push('User query: ' + String(query));\n parts.push('Respond in language: ' + String(language || 'english'));\n if (toolText && String(toolText).trim()) parts.push('Tool logs:\\n' + String(toolText));\n if (assistantText && String(assistantText).trim()) parts.push('Assistant text:\\n' + String(assistantText));\n if (reasoningText && String(reasoningText).trim()) parts.push('Reasoning text:\\n' + String(reasoningText));\n\n const response = await fetch('https://api.groq.com/openai/v1/chat/completions', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`,\n },\n body: JSON.stringify({\n model: 'openai/gpt-oss-120b',\n messages: [\n {\n role: 'system',\n content: 'Answer the user clearly and concisely using only the provided context. Return plain text only (no markdown fences). Remove any citations, numbers-in-brackets, line numbers, and artifacts. If unsure, say so briefly.'\n },\n { role: 'user', content: parts.join('\\n\\n') }\n ],\n stream: false\n })\n });\n if (!response.ok) {\n try { console.error('synthesizeTextFromContext error:', await response.text()); } catch (_) {}\n return '';\n }\n const data = await response.json();\n const text = data.choices?.[0]?.message?.content || '';\n return String(text || '').trim();\n } catch (_err) {\n return '';\n }\n}\n",[158],"6a718c52-761e-11f0-999f-0224a6c84d84","index.html","f67361ba-7616-11f0-baaa-0224a6c84d84",19,["D",1754853345534],3,"\n\n\n \n \n Chatter\n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n\n \n\n\n

\n \n

\n Chatter\n \n

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\n\n \n \n\n

\n \n

\n \n \n

Chatter

General-purpose AI assistant

\n\n \n

\n \n \n

\n Get your free key here\n

\n\n \n

\n API Key:\n \n

\n \n \n

\n\n \n

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\"https://esm.sh/preact@10.27.0/jsx-runtime\": \"f33f69315b770515e473b14403cef9c49f3427ff61016041220e36505ab8cc68\",\n \"https://esm.town/v/std/utils@85-main/file/index.ts\": \"f7b4d1f95fb1ef1cbcb3bc0d2fe83194f18403c7d35115a9f066cba4ef6b84d0\",\n \"https://esm.town/v/std/utils@85-main/index.ts\": \"3d060a58ccd352517da960077ffd78b80c1051ca2117cb37adbff0365cb45efb\",\n \"https://esm.town/v/std/utils@85-main/parseImportMeta/project.ts\": \"1a47e75e21faf22b38fb07e6ead284b6a381a48970a39490e585507a8bb47bbf\",\n \"https://esm.town/v/std/utils@85-main/serve-file/index.ts\": \"1386c1fe12e8e9a41e3561b0a1e55f332bc559fe98fa81f99434b4f642275cd4\",\n \"https://esm.town/v/std/utils@85-main/test/index.tsx\": \"41fc8ccb29945e8cf65263dc61a6840decf9f62fb035be8d0651267ac84b573d\"\n }\n}\n",[173],"f74d217a-7616-11f0-8a59-0224a6c84d84","deno.json","f74d226a-7616-11f0-8a59-0224a6c84d84",12,["D",1754850144578],"{\n \"tasks\": {\n \"serve\": \"deno serve --allow-read --allow-import --allow-env --allow-write 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test with SSE parsing and aggregation\n// - Reads GROQ_API_KEY from environment (when run as CLI)\n// - Exports streamChatAndAggregate(apiKey, payload, { logSse }) to allow server reuse\n// - CLI: logs raw SSE lines and prints aggregated JSON at the end\n\nimport \"jsr:@std/dotenv/load\";\n\nconst API_URL = \"https://api.groq.com/openai/v1/chat/completions\";\n\nfunction getApiKey() {\n const key = (typeof Deno !== \"undefined\") ? Deno.env.get(\"GROQ_API_KEY\") : null;\n if (!key) {\n console.error(\"Missing GROQ_API_KEY in environment.\");\n }\n return key;\n}\n\nfunction buildPayload(userQuery) {\n const prompt = typeof userQuery === \"string\" && userQuery.trim().length > 0\n ? userQuery\n : \"Stream test: please emit a short response and demonstrate reasoning and any tool usage.\";\n\n return {\n messages: [\n {\n role: \"user\",\n content: prompt,\n },\n ],\n model: \"openai/gpt-oss-120b\",\n stream: true,\n reasoning_effort: \"medium\",\n tools: [\n {\n type: \"browser_search\",\n },\n ],\n };\n}\n\n// Utility: Parse one Groq SSE JSON line (string without the leading \"data: \")\nfunction parseGroqSseJsonLine(jsonStr) {\n try {\n const obj = JSON.parse(jsonStr);\n return obj;\n } catch (_err) {\n return null;\n }\n}\n\n// Utility: Normalize a parsed chunk into events and collapsed accumulators\nfunction applyChunkToAggregates(parsed, aggregates) {\n const { events, collapsed } = aggregates;\n\n const timestamp = Date.now();\n const baseInfo = {\n id: parsed.id,\n model: parsed.model,\n created: parsed.created,\n instance_id: parsed.instance_id,\n system_fingerprint: parsed.system_fingerprint,\n timestamp,\n };\n\n if (Array.isArray(parsed.choices) && parsed.choices.length > 0) {\n const choice = parsed.choices[0];\n const delta = choice?.delta || {};\n\n if (delta.role) {\n events.push({ type: \"role\", role: delta.role, ...baseInfo });\n }\n\n if (delta.reasoning) {\n events.push({ type: \"reasoning\", content: delta.reasoning, channel: delta.channel, ...baseInfo });\n collapsed.reasoning += delta.reasoning;\n }\n\n if (delta.content) {\n events.push({ type: \"content\", content: delta.content, ...baseInfo });\n collapsed.content += delta.content;\n }\n\n if (delta.executed_tools) {\n events.push({ type: \"executed_tools\", executed_tools: delta.executed_tools, ...baseInfo });\n for (const t of delta.executed_tools) {\n collapsed.tools.push({ kind: \"executed\", ...t, timestamp });\n }\n }\n\n if (delta.tool_calls) {\n events.push({ type: \"tool_calls\", tool_calls: delta.tool_calls, ...baseInfo });\n for (const t of delta.tool_calls) {\n collapsed.tools.push({ kind: \"call\", ...t, timestamp });\n }\n }\n }\n}\n\nexport async function streamChatAndAggregate(apiKey, payload, options = {}) {\n const { logSse = false } = options;\n\n const response = await fetch(API_URL, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: `Bearer ${apiKey}`,\n },\n body: JSON.stringify(payload),\n });\n\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`HTTP ${response.status} — ${text}`);\n }\n\n const reader = response.body?.getReader();\n const aggregates = {\n events: [],\n collapsed: { reasoning: \"\", content: \"\", tools: [] },\n };\n\n if (!reader) {\n try {\n const nonStream = await response.json();\n const content = nonStream?.choices?.[0]?.message?.content || \"\";\n if (content) {\n aggregates.events.push({ type: \"content\", content, timestamp: Date.now() });\n aggregates.collapsed.content = content;\n }\n } catch (_e) {\n // ignore\n }\n console.log(\"No readable stream; returning aggregates:\");\n console.log(JSON.stringify(aggregates, null, 2));\n return;\n }\n\n const decoder = new TextDecoder();\n let buffer = \"\";\n if (logSse) console.log(\"--- BEGIN SSE ---\");\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n buffer += decoder.decode(value, { stream: true });\n\n // Split Server-Sent Events by double newlines\n const events = buffer.split(\"\\n\\n\");\n buffer = events.pop() || \"\";\n\n for (const evt of events) {\n if (!evt.trim()) continue;\n // Extract `data:` line(s)\n const lines = evt.split(\"\\n\");\n for (const line of lines) {\n if (line.startsWith(\"data: \")) {\n const data = line.slice(6);\n if (data === \"[DONE]\") {\n if (logSse) console.log(\"[DONE]\");\n break;\n }\n if (logSse) {\n // Log raw SSE packet\n console.log(\"data:\", data);\n }\n // Parse and aggregate\n const parsed = parseGroqSseJsonLine(data);\n if (!parsed) continue;\n applyChunkToAggregates(parsed, aggregates);\n }\n }\n }\n }\n if (logSse) {\n console.log(\"--- END SSE ---\");\n console.log(\"Aggregates:\");\n console.log(JSON.stringify(aggregates, null, 2));\n }\n return aggregates;\n}\n\n// Entry point\nif (import.meta.main) {\n const apiKey = getApiKey();\n if (!apiKey) Deno.exit(1);\n\n const userQuery = Deno.args?.[0] || \"\";\n const payload = buildPayload(userQuery);\n\n streamChatAndAggregate(apiKey, payload, { logSse: true })\n .then(() => console.log(\"Stream complete.\"))\n .catch((err) => {\n console.error(\"Stream failed:\", err);\n Deno.exit(1);\n });\n}\n\n\n",[187],"f6661564-7616-11f0-b3f9-0224a6c84d84","chatCompletion.js","f6661672-7616-11f0-b3f9-0224a6c84d84",["D",1754850144369],"// Minimal Groq Chat Completions client using fetch\nimport { extractOrRepairJsonResults, extractStructuredSummary, renderStructuredSummaryToHtml } from \"./jsonUtils.js\";\nexport async function groqChatCompletion(apiKey, payload) {\n console.log('>>> [groqChatCompletion] Payload:', payload);\n const response = await fetch('https://api.groq.com/openai/v1/chat/completions', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${apiKey}`,\n },\n body: JSON.stringify(payload),\n });\n if (!response.ok) {\n const text = await response.text();\n throw new Error(`Groq API error: ${response.status} ${text}`);\n }\n console.log('>>> [groqChatCompletion] Response:', response);\n return await response.json();\n}\n\n// Use LLM to normalize any freeform output into strict JSON that matches our schema.\n// extractOrRepairJsonResults moved to ./jsonUtils.js\n\nexport function buildSearchPrompt(query, source, dateRange, language, offset) {\n // Always search in English; present in the selected language\n const presentationLanguage = (typeof language === 'string' && language.trim()) ? language : 'english';\n const languageInstruction = `IMPORTANT: Always conduct searches in English. Workflow: 1) If the user query is not in English, translate \"${query}\" into English search terms and use ONLY the English translation for searching. 2) After finding results, present all titles and descriptions in ${presentationLanguage}. Always search in English but present results in ${presentationLanguage}.`;\n\n const base = source && source.trim()\n ? `Search ${source} for: \"${query}\"`\n : `\"${query}\"`;\n\n let dateRangeText = '';\n if (dateRange) {\n dateRangeText = ({\n '1month': 'in the last month',\n '3months': 'in the last 3 months',\n '6months': 'in the last 6 months',\n '1year': 'in the last year',\n })[dateRange] || '';\n }\n\n const startResult = offset + 1;\n const endResult = offset + 5;\n const offsetInstruction = offset > 0 ? ` Skip the first ${offset} results and` : '';\n\n const searchPrompt = `${base}${dateRangeText ? ' ' + dateRangeText : ''}.${offsetInstruction} Return results ${startResult}-${endResult} (max 5 new results). Return ONLY JSON with a top-level \\\\\"results\\\\\" array. Each result must include: title and url. Optionally include: description (one concise sentence only if confidently grounded by the target page). Do NOT add or guess fields like source, published_at, comments, pmid, or doi — omit anything unknown.\\n\\nURL RULES (critical):\\n- Always provide a direct deep link to the specific item/resource — never a homepage, category page, or search results page.\\n- If the listing points to another resource, set url to the canonical content page, not the aggregator:\\n - Hacker News: prefer the external article URL if present; otherwise use the exact item permalink (https://news.ycombinator.com/item?id=...). Do not invent dates or comment counts.\\n - PubMed: use the PMID page (https://pubmed.ncbi.nlm.nih.gov/PMID/) or the publisher DOI landing page if available. Do not link to the PubMed homepage.\\n - arXiv: use https://arxiv.org/abs/ (or the PDF if explicitly asked for PDFs).\\n - DOI: use the https://doi.org/... resolver link.\\n - Reddit/Forums: use the exact thread permalink if the discussion is the result.\\n - GitHub: link to the exact repository, release, issue, or file path, not github.com root.\\n- Never return root domains (e.g., https://pubmed.ncbi.nlm.nih.gov/, https://news.ycombinator.com/). Ensure url resolves to the specific item. ${languageInstruction}`;\n return searchPrompt;\n}\n\n// Lightweight router: decide between 'links' (JSON list) and 'text' (short answer)\nexport async function routeQueryMode(apiKey, query) {\n try {\n const res = await groqChatCompletion(apiKey, {\n model: 'openai/gpt-oss-120b',\n messages: [\n { role: 'system', content: 'Classify the user request as either links or text. Respond with a single token: links or text. Use links if the user appears to want a search results list of sources; use text if the user is asking for an explanation/summary/definition.' },\n { role: 'user', content: String(query || '') }\n ],\n stream: false,\n });\n const out = (res?.choices?.[0]?.message?.content || '').trim().toLowerCase();\n if (out.includes('links')) return 'links';\n if (out.includes('text')) return 'text';\n } catch (_) { /* fall back to heuristic */ }\n // Heuristic fallback\n const q = String(query || '').trim().toLowerCase();\n const chatStarts = ['explain', 'what is', 'what are', 'how does', 'how do', 'summarize', 'overview', 'define', 'in simple terms', 'briefly', 'why is', 'compare', 'contrast'];\n if (chatStarts.some((s) => q.startsWith(s))) return 'text';\n if (q.endsWith('?') && q.length <= 200) return 'text';\n return 'links';\n}\n\nexport async function performSearch({ apiKey, query, dateRange, source, language, offset = 0, model, stream, reasoningEffort, forceLinksRoute = false }) {\n const prompt = buildSearchPrompt(query, source, dateRange, language, offset);\n const start = Date.now();\n const route = forceLinksRoute ? 'links' : await routeQueryMode(apiKey, query);\n const response = await groqChatCompletion(apiKey, {\n model,\n messages: route === 'text'\n ? [\n {\n role: 'system',\n content: `If the user is asking for an explanation or summary rather than a list of links, respond with a concise plain-text answer in ${language || 'english'} (no JSON). If the request appears to be a search for sources, return ONLY JSON with a top-level results array as previously specified. ALWAYS conduct searches in English when needed.`\n },\n { role: 'user', content: query }\n ]\n : [\n {\n role: 'system',\n content: `Return ONLY JSON matching the schema; no markdown or commentary. For every result, url MUST be a direct deep link to the specific item/resource (not a homepage, category page, or search results page). Prefer canonical targets: external article for HN listings, PubMed PMID page or publisher DOI for papers, arXiv abs page, DOI resolver, exact thread permalinks for discussions, and precise GitHub paths. Never output root domains as url. Additionally, ALWAYS conduct searches in English. If the user query is not in English, translate it to English first and use ONLY the English translation for searching. Present titles and descriptions in ${language || 'english'}.`\n },\n { role: 'user', content: prompt },\n ],\n stream: false,\n reasoning_effort: reasoningEffort,\n tools: [ { type: 'browser_search' } ],\n });\n const end = Date.now();\n const searchTime = (end - start) / 1000;\n const contentText = response.choices?.[0]?.message?.content || '';\n\n console.log('>> contentText', contentText);\n let results = [];\n if (route === 'links') {\n try {\n // Try direct parse first (in case the model already returned valid JSON)\n const stripped = String(contentText).replace(/^```json\\s*/i, '').replace(/```\\s*$/i, '');\n const parsed = JSON.parse(stripped);\n if (Array.isArray(parsed?.results)) results = parsed.results;\n } catch (_) {\n // as a fallback, attempt repair pass\n results = await extractOrRepairJsonResults(contentText, apiKey, language);\n }\n } else {\n // text mode → do not attempt to coerce content into results\n results = [];\n }\n // Also return assistant content and full response for upstream logging / fallback display\n return { results, searchTime, content: contentText, full: response, route };\n}\n\nimport { marked } from \"npm:marked\";\n\nfunction buildSummaryPrompt(url, title, language) {\n const languageInstruction = language && language !== 'english'\n ? `Please provide the summary in ${language}. `\n : '';\n\n return `Visit this URL: ${url}\n \nTitle: \"${title}\"\n\nCRITICAL INSTRUCTIONS:\n- Read the page content and produce a clean, professional summary.\n- Ignore navigation, comments, ads, headers/footers, and numerical artifacts.\n- If this is a research paper, use the RESEARCH format below.\n- If this is a news/blog/listing/discussion page (e.g., HN, Reddit, news site), use the GENERAL format below.\n- DO NOT include any citations, reference numbers, or raw scraping artifacts.\n- Write in clear prose without formatting artifacts.\n- The opening line of the summary must not simply repeat the title; avoid mirroring the title.\n\nReturn only one of the two structures below in markdown (choose based on page type). ${languageInstruction}\n\nRESEARCH FORMAT:\n

\n**Research Question:** [brief description of what the study investigates]\n\n**Methods & Findings:** [1-2 paragraphs covering methodology and key results - NO CITATIONS]\n\n**Significance:** [why this research matters and potential applications - NO CITATIONS]\n

\n\nGENERAL FORMAT:\n

\n**What it is:** [brief description of the page]\n\n**Key points:** [bullet list or short paragraphs with the main takeaways]\n\n**Details:** [1–2 short paragraphs with notable specifics]\n

`;\n}\n\nexport async function summarizeUrl({ apiKey, url, title, language, model, stream, reasoningEffort }) {\n const summaryPrompt = buildSummaryPrompt(url, title, language);\n const start = Date.now();\n const response = await groqChatCompletion(apiKey, {\n model,\n messages: [\n {\n role: 'system',\n content: 'You are a concise web content summarization expert. CRITICAL REQUIREMENTS: 1) Return ONLY the summary content within

tags - no other text, explanations, or metadata. 2) Use clean, professional markdown formatting. 3) NEVER include citations, reference numbers, line numbers, or any numerical markers like [1†, L0:, L1:, etc. 4) Write in clear prose without any formatting artifacts from source documents. 5) Adapt structure to page type and, if it is a research paper, strictly use the sections: Research Question, Methods & Findings, Significance; otherwise use the GENERAL structure.'\n },\n {\n role: 'user',\n content: summaryPrompt\n }\n ],\n stream,\n reasoning_effort: reasoningEffort,\n tools: [ { type: 'browser_search' } ],\n });\n const end = Date.now();\n const summaryTime = (end - start) / 1000;\n const responseText = response.choices[0]?.message?.content || '';\n\n // Prefer structured, JSON-cleaned summary to avoid leaking citation junk\n let cleanedHtml = '';\n try {\n const inner = (responseText.match(/

([\\s\\S]*?)<\\/summary>/) || [null, responseText])[1];\n const structured = await extractStructuredSummary(inner, apiKey);\n if (structured) {\n cleanedHtml = renderStructuredSummaryToHtml(structured);\n }\n } catch (_) {\n // ignore and fallback\n }\n if (!cleanedHtml) {\n // Final fallback: render markdown, but still try to strip outer

tags\n const match = responseText.match(/

([\\s\\S]*?)<\\/summary>/);\n const raw = match ? match[1].trim() : responseText;\n cleanedHtml = marked(raw);\n }\n\n return { summary: cleanedHtml, summaryTime 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default function researchApp() {\n return {\n searchQuery: '',\n dateRange: '',\n selectedSource: '',\n customSource: '',\n selectedLanguage: 'english',\n results: [],\n assistantContent: '',\n route: 'links',\n isSearching: false,\n isLoadingMore: false,\n hasSearched: false,\n currentOffset: 0,\n hasMoreResults: true,\n errorMessage: '',\n summaryStates: {},\n hasServerKey: true,\n userApiKey: '',\n apiKeyInput: '',\n // Stream test state\n streamPrompt: '',\n isStreaming: false,\n streamError: '',\n streamAggregates: null,\n streamOpen: true,\n eventsOpen: false,\n // Trace log state\n traceEvents: [],\n traceExpanded: false,\n expandedToolMap: {},\n copyEventsText: 'copy json',\n copyEventsTimeoutId: null,\n \n // Timer state\n timerStartMs: null,\n timerIntervalId: null,\n timerFrozenText: '',\n timerElapsedMs: 0,\n \n // Chat state\n messages: [],\n chatInput: '',\n // Streaming reasoning state per assistant message\n messageReasoningMap: {},\n expandedReasoningMap: {},\n \n // Typewriter effect variables\n hasUserInteracted: false,\n typewriterActive: false,\n typewriterTimeouts: [],\n currentLanguageIndex: 0,\n currentCharIndex: 0,\n isTyping: false,\n lastTypewriterText: '',\n \n // Testing override - if set to a string, will replace typewriter effect\n testOverride: '', // Set to null or '' to disable override\n\n // Sidebar + chats (Dexie-backed)\n sidebarOpen: false, // mobile overlay\n sidebarCollapsed: false, // desktop collapse\n sidebarTransitioning: false,\n sidebarAnimMs: 300,\n activeSearchId: null,\n chats: [], // { id, title, createdAt }\n currentChatId: null,\n db: null,\n editingChatId: null,\n editingChatTitle: '',\n\n async init() {\n console.log('🔧 Chat app initializing...');\n \n // Load API key from localStorage\n const storedApiKey = localStorage.getItem('groq_api_key');\n if (storedApiKey) {\n this.userApiKey = storedApiKey;\n }\n \n // Check if server has API key\n await this.checkServerKey();\n \n // Initialize Dexie\n try {\n this.db = new window.Dexie('chat_db');\n // v1: initial store (kept for upgrades from very early versions)\n this.db.version(1).stores({\n chats: '++id,title,createdAt'\n });\n // v2: ensure messages store exists and chatId index is present\n this.db.version(2).stores({\n chats: '++id,title,createdAt',\n messages: '++id,chatId,createdAt,role,content'\n });\n } catch (e) {\n console.error('Dexie init error', e);\n }\n await this.loadChats();\n // Always start with a fresh ephemeral chat on page load (not persisted until first message)\n this.currentChatId = null;\n this.activeSearchId = null;\n this.messages = [{ role: 'assistant', content: 'Hi! How can I help you today?' }];\n this.sidebarOpen = false;\n this.$nextTick(() => this.scrollToBottom());\n console.log('✅ Chat app initialized');\n },\n\n // Sidebar helpers\n toggleSidebar() { this.sidebarOpen = !this.sidebarOpen; },\n collapseSidebar() {\n // Trigger width animation immediately but delay the expand-control reveal\n this.sidebarTransitioning = true;\n this.sidebarCollapsed = true;\n setTimeout(() => { this.sidebarTransitioning = false; }, this.sidebarAnimMs);\n },\n expandSidebar() {\n // Trigger width animation immediately and hide expand-control during transition\n this.sidebarTransitioning = true;\n this.sidebarCollapsed = false;\n setTimeout(() => { this.sidebarTransitioning = false; }, this.sidebarAnimMs);\n },\n async loadChats() {\n try {\n const rows = await this.db.chats.orderBy('createdAt').reverse().toArray();\n this.chats = rows.map(r => ({ id: r.id, title: r.title, createdAt: r.createdAt }));\n } catch (_) { this.chats = []; }\n },\n async createNewChat() {\n const base = (this.chatInput && this.chatInput.trim()) || 'New chat';\n const title = base.substring(0, 60);\n const createdAt = Date.now();\n const id = await this.db.chats.add({ title, createdAt });\n await this.loadChats();\n await this.selectChat(id);\n },\n async selectChat(id) {\n this.currentChatId = id;\n this.activeSearchId = id; // reuse for CSS highlight\n // Load messages for chat\n try {\n const rows = await this.db.messages.where('chatId').equals(id).sortBy('createdAt');\n this.messages = rows.map(r => ({ role: r.role, content: r.content, reasoning: r.reasoning }));\n } catch (_) {\n this.messages = [];\n }\n // Add welcome if empty\n if (!this.messages.length) {\n this.messages = [{ role: 'assistant', content: 'Hi! How can I help you today?' }];\n }\n this.sidebarOpen = false;\n this.$nextTick(() => this.scrollToBottom());\n },\n startEditingChat(id, currentTitle) {\n this.editingChatId = id;\n this.editingChatTitle = currentTitle || '';\n // Focus the input on next tick\n try {\n setTimeout(() => {\n const el = document.querySelector('input[data-chat-id=\"' + id + '\"]');\n if (el) { el.focus(); el.select(); }\n }, 50);\n } catch (_) {}\n },\n async commitEditingChat() {\n const rawId = this.editingChatId;\n const id = typeof rawId === 'string' ? Number(rawId) : rawId;\n if (!id) return this.cancelEditingChat();\n const title = (this.editingChatTitle || '').trim() || 'New chat';\n try {\n if (this.db) {\n const prev = await this.db.chats.get(id);\n await this.db.chats.put({ id, title, createdAt: prev?.createdAt || Date.now() });\n }\n await this.loadChats();\n this.activeSearchId = id;\n } catch (_) {\n // no-op\n }\n this.editingChatId = null;\n this.editingChatTitle = '';\n },\n cancelEditingChat() {\n this.editingChatId = null;\n this.editingChatTitle = '';\n },\n async deleteChat(id) {\n try {\n if (!this.db) return;\n const chatId = typeof id === 'string' ? Number(id) : id;\n // Try deleting messages by indexed query first; fall back to filter if index is missing\n try {\n await this.db.messages.where('chatId').equals(chatId).delete();\n } catch (_) {\n try { await this.db.messages.filter(m => m.chatId === chatId).delete(); } catch (_) { /* ignore */ }\n }\n // Delete the chat row even if message deletion failed\n try { await this.db.chats.delete(chatId); } catch (_) { /* ignore */ }\n await this.loadChats();\n // If deleting current chat, select the next available\n if (this.currentChatId === chatId) {\n const next = this.chats[0]?.id;\n if (next) {\n await this.selectChat(next);\n } else {\n await this.createNewChat();\n }\n }\n } catch (_) { /* ignore */ }\n },\n async onDeleteChat(id) {\n try {\n console.log('onDeleteChat', id);\n const chatId = typeof id === 'string' ? Number(id) : id;\n await this.deleteChat(chatId);\n } catch (_) { /* ignore */ }\n },\n\n async checkServerKey() {\n try {\n const response = await fetch('/api/check-key');\n const data = await response.json();\n this.hasServerKey = data.hasServerKey;\n } catch (error) {\n console.error('Error checking server key:', error);\n this.hasServerKey = false;\n }\n },\n\n setApiKey() {\n if (this.apiKeyInput.trim()) {\n this.userApiKey = this.apiKeyInput.trim();\n localStorage.setItem('groq_api_key', this.userApiKey);\n this.apiKeyInput = '';\n }\n },\n\n changeApiKey() {\n this.userApiKey = '';\n },\n\n clearApiKey() {\n this.userApiKey = '';\n localStorage.removeItem('groq_api_key');\n },\n\n get maskedApiKey() {\n if (!this.userApiKey) return '';\n const key = this.userApiKey;\n return key.substring(0, 8) + '*'.repeat(Math.max(0, key.length - 12)) + key.substring(key.length - 4);\n },\n\n get displaySourceName() {\n const sourceMap = {\n 'pubmed': 'PubMed',\n 'biorxiv': 'bioRxiv',\n 'arxiv': 'arXiv',\n 'medrxiv': 'medRxiv',\n 'researchgate': 'ResearchGate',\n 'google scholar': 'Google Scholar',\n 'hacker news': 'Hacker News',\n 'reddit': 'Reddit',\n 'news': 'News'\n };\n \n if (this.selectedSource === 'custom' && this.customSource) {\n return this.customSource;\n }\n \n return sourceMap[this.selectedSource] || this.selectedSource;\n },\n\n // Internationalization system\n get i18n() {\n const translations = {\n 'english': {\n // Main headers\n title: '(re)Search',\n subtitle: 'Search the web and academic sources with multilingual summaries',\n \n // API Key section\n apiKey: 'API Key:',\n change: 'Change',\n clear: 'Clear',\n getKeyHere: 'Get your free key here',\n \n // Form labels\n language: 'language',\n researchQuery: 'search query',\n dateRange: 'Choose a date range (optional)',\n source: 'Choose a source (optional)',\n \n // Form placeholders\n enterTopic: 'Enter your research topic...',\n customSource: 'e.g., Nature, Science, Cell, Hacker News...',\n customSourceOption: 'Custom source...',\n chooseDateRange: 'Choose a date range (optional)',\n chooseSource: 'Choose a source (optional)',\n \n // Date range options\n lastMonth: 'Last month',\n last3Months: 'Last 3 months',\n last6Months: 'Last 6 months',\n lastYear: 'Last year',\n allTime: 'All time',\n \n // Buttons\n search: 'Search',\n searching: 'Searching',\n streaming: 'Streaming...',\n searchMore: '(Re)(Re)Search',\n summarize: 'Summarize',\n analyzing: 'Analyzing...',\n openPaper: 'Open',\n \n // Messages\n foundResults: 'Found',\n results: 'results',\n loading: 'Searching',\n loadingMore: 'Loading more results',\n aiAnalyzing: 'AI is analyzing the paper...',\n analysisTime: 'This may take 10-30 seconds depending on paper length',\n noResultsOrError: 'No results found or a server error occurred.',\n \n // Loading message templates\n searchingTemplate: (source, query, dateRange) => {\n let msg = `Searching ${source}`;\n if (query) msg += ` for \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n loadingMoreTemplate: (source, query, dateRange) => {\n let msg = `Loading more results from ${source}`;\n if (query) msg += ` for \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n \n // Error messages\n pleaseAddApiKey: 'please add api key',\n pleaseEnterQuery: 'please enter a search query',\n \n // Summary\n summary: 'Summary:'\n },\n 'french': {\n title: '(re)Recherche',\n subtitle: 'Rechercher dans plusieurs bases de données académiques avec des résumés multilingues',\n \n apiKey: 'Clé API :',\n change: 'Modifier',\n clear: 'Effacer',\n getKeyHere: 'Obtenez votre clé gratuite ici',\n \n language: 'langue',\n researchQuery: 'requête de recherche',\n dateRange: 'Choisir une plage de dates (facultative)',\n source: 'Choisir une source (facultative)',\n \n enterTopic: 'Entrez votre sujet de recherche...',\n customSource: 'ex. Nature, Science, Cell...',\n customSourceOption: 'Source personnalisée...',\n chooseDateRange: 'Choisir une plage de dates (facultative)',\n chooseSource: 'Choisir une source (facultative)',\n \n lastMonth: 'Le mois dernier',\n last3Months: 'Les 3 derniers mois',\n last6Months: 'Les 6 derniers mois',\n lastYear: 'La dernière année',\n allTime: 'Tout le temps',\n \n search: '(re)Rechercher',\n searching: '(re)Recherche',\n streaming: 'Diffusion...',\n searchMore: '(Re)(Re)Recherche',\n summarize: 'Résumer',\n analyzing: 'Analyse...',\n openPaper: 'Ouvrir',\n \n foundResults: 'Trouvé',\n results: 'résultats',\n loading: 'Recherche',\n loadingMore: 'Chargement de plus de résultats',\n aiAnalyzing: 'L\\'IA analyse l\\'article...',\n analysisTime: 'Cela peut prendre 10 à 30 secondes selon la longueur de l\\'article',\n noResultsOrError: 'Aucun résultat trouvé ou une erreur du serveur s\\'est produite.',\n \n // Loading message templates\n searchingTemplate: (source, query, dateRange) => {\n let msg = `Recherche sur ${source}`;\n if (query) msg += ` pour \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n loadingMoreTemplate: (source, query, dateRange) => {\n let msg = `Chargement de plus de résultats de ${source}`;\n if (query) msg += ` pour \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n \n pleaseAddApiKey: 'veuillez ajouter la clé api',\n pleaseEnterQuery: 'veuillez entrer une requête de recherche',\n \n summary: 'Résumé :'\n },\n 'spanish': {\n title: '(re)Buscar',\n subtitle: 'Buscar en múltiples bases de datos académicas con resúmenes multilingües',\n \n apiKey: 'Clave API:',\n change: 'Cambiar',\n clear: 'Limpiar',\n getKeyHere: 'Obtén tu clave gratuita aquí',\n \n language: 'idioma',\n researchQuery: 'consulta de investigación',\n dateRange: 'Elegir un rango de fechas (opcional)',\n source: 'Elegir una fuente (opcional)',\n \n enterTopic: 'Ingresa tu tema de investigación...',\n customSource: 'ej. Nature, Science, Cell...',\n customSourceOption: 'Fuente personalizada...',\n chooseDateRange: 'Elegir un rango de fechas (opcional)',\n chooseSource: 'Elegir una fuente (opcional)',\n \n lastMonth: 'Último mes',\n last3Months: 'Últimos 3 meses',\n last6Months: 'Últimos 6 meses',\n lastYear: 'Último año',\n allTime: 'Todo el tiempo',\n \n search: '(re)Buscar',\n searching: '(re)Buscando',\n streaming: 'Transmitiendo...',\n searchMore: '(Re)(Re)Buscar',\n summarize: 'Resumir',\n analyzing: 'Analizando...',\n openPaper: 'Abrir',\n \n foundResults: 'Encontrado',\n results: 'resultados',\n loading: 'Buscando',\n loadingMore: 'Cargando más resultados',\n aiAnalyzing: 'La IA está analizando el artículo...',\n analysisTime: 'Esto puede tomar 10-30 segundos dependiendo de la longitud del artículo',\n noResultsOrError: 'No se encontraron resultados o ocurrió un error del servidor.',\n \n // Loading message templates\n searchingTemplate: (source, query, dateRange) => {\n let msg = `Buscando en ${source}`;\n if (query) msg += ` por \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n loadingMoreTemplate: (source, query, dateRange) => {\n let msg = `Cargando más resultados de ${source}`;\n if (query) msg += ` por \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n \n pleaseAddApiKey: 'por favor agrega clave api',\n pleaseEnterQuery: 'por favor ingresa una consulta de búsqueda',\n \n summary: 'Resumen:'\n },\n 'german': {\n title: '(re)Suchen',\n subtitle: 'Durchsuchen Sie mehrere akademische Datenbanken mit mehrsprachigen Zusammenfassungen',\n \n apiKey: 'API-Schlüssel:',\n change: 'Ändern',\n clear: 'Löschen',\n getKeyHere: 'Holen Sie sich Ihren kostenlosen Schlüssel hier',\n \n language: 'sprache',\n researchQuery: 'suchanfrage',\n dateRange: 'Datumsbereich wählen (optional)',\n source: 'Quelle wählen (optional)',\n \n enterTopic: 'Geben Sie Ihr Forschungsthema ein...',\n customSource: 'z.B. Nature, Science, Cell...',\n customSourceOption: 'Benutzerdefinierte Quelle...',\n chooseDateRange: 'Datumsbereich wählen (optional)',\n chooseSource: 'Quelle wählen (optional)',\n \n lastMonth: 'Letzter Monat',\n last3Months: 'Letzte 3 Monate',\n last6Months: 'Letzte 6 Monate',\n lastYear: 'Letztes Jahr',\n allTime: 'Alle Zeit',\n \n search: '(re)Suchen',\n searching: '(re)Suchen',\n streaming: 'Streaming...',\n searchMore: '(Re)(Re)Suchen',\n summarize: 'Zusammenfassen',\n analyzing: 'Analysiere...',\n openPaper: 'Öffnen',\n \n foundResults: 'Gefunden',\n results: 'ergebnisse',\n loading: 'Suchen',\n loadingMore: 'Lade weitere Ergebnisse',\n aiAnalyzing: 'KI analysiert den Artikel...',\n analysisTime: 'Dies kann 10-30 Sekunden dauern, abhängig von der Artikellänge',\n noResultsOrError: 'Keine Ergebnisse gefunden oder ein Serverfehler ist aufgetreten.',\n \n // Loading message templates\n searchingTemplate: (source, query, dateRange) => {\n let msg = `Suche in ${source}`;\n if (query) msg += ` nach \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n loadingMoreTemplate: (source, query, dateRange) => {\n let msg = `Lade weitere Ergebnisse von ${source}`;\n if (query) msg += ` nach \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n \n pleaseAddApiKey: 'bitte api-schlüssel hinzufügen',\n pleaseEnterQuery: 'bitte suchanfrage eingeben',\n \n summary: 'Zusammenfassung:'\n },\n 'japanese': {\n title: '(re)検索',\n subtitle: '多言語要約付きの複数の学術データベースを検索',\n \n apiKey: 'APIキー:',\n change: '変更',\n clear: 'クリア',\n getKeyHere: '無料キーを取得する',\n \n language: '言語',\n researchQuery: '研究クエリ',\n dateRange: '日付範囲を選択 (任意)',\n source: 'ソースを選択 (任意)',\n \n enterTopic: '研究トピックを入力...',\n customSource: '例：Nature、Science、Cell...',\n customSourceOption: 'カスタムソース...',\n chooseDateRange: '日付範囲を選択 (任意)',\n chooseSource: 'ソースを選択 (任意)',\n \n lastMonth: '先月',\n last3Months: '過去3ヶ月',\n last6Months: '過去6ヶ月',\n lastYear: '昨年',\n allTime: '全期間',\n \n search: '(re)検索',\n searching: '(re)検索中',\n streaming: 'ストリーミング中...',\n searchMore: '(再)(再)検索',\n summarize: '要約',\n analyzing: '分析中...',\n openPaper: '開く',\n \n foundResults: '見つかった',\n results: '件の結果',\n loading: '検索中',\n loadingMore: 'より多くの結果を読み込み中',\n aiAnalyzing: 'AIが論文を分析中...',\n analysisTime: '論文の長さによって10〜30秒かかることがあります',\n noResultsOrError: '結果が見つからないか、サーバーエラーが発生しました。',\n \n // Loading message templates\n searchingTemplate: (source, query, dateRange) => {\n let msg = `${source}で検索中`;\n if (query) msg += `「${query}」`;\n if (dateRange) msg += `(${dateRange})`;\n return msg + '...';\n },\n loadingMoreTemplate: (source, query, dateRange) => {\n let msg = `${source}からさらに結果を読み込み中`;\n if (query) msg += `「${query}」`;\n if (dateRange) msg += `(${dateRange})`;\n return msg + '...';\n },\n \n pleaseAddApiKey: 'apiキーを追加してください',\n pleaseEnterQuery: '検索クエリを入力してください',\n \n summary: '要約:'\n },\n 'korean': {\n title: '(re)검색',\n subtitle: '다국어 요약과 함께 여러 학술 데이터베이스 검색',\n \n apiKey: 'API 키:',\n change: '변경',\n clear: '지우기',\n getKeyHere: '여기서 무료 키 받기',\n \n language: '언어',\n researchQuery: '연구 쿼리',\n dateRange: '날짜 범위 선택 (선택 사항)',\n source: '소스 선택 (선택 사항)',\n \n enterTopic: '연구 주제를 입력하세요...',\n customSource: '예: Nature, Science, Cell...',\n customSourceOption: '사용자 정의 소스...',\n chooseDateRange: '날짜 범위 선택 (선택 사항)',\n chooseSource: '소스 선택 (선택 사항)',\n \n lastMonth: '지난 달',\n last3Months: '지난 3개월',\n last6Months: '지난 6개월',\n lastYear: '작년',\n allTime: '전체 기간',\n \n search: '(re)검색',\n searching: '(re)검색 중',\n streaming: '스트리밍 중...',\n searchMore: '(재)(재)검색',\n summarize: '요약',\n analyzing: '분석 중...',\n openPaper: '열기',\n \n foundResults: '발견된',\n results: '개 결과',\n loading: '검색 중',\n loadingMore: '더 많은 결과 로딩 중',\n aiAnalyzing: 'AI가 논문을 분석 중...',\n analysisTime: '논문 길이에 따라 10-30초가 걸릴 수 있습니다',\n noResultsOrError: '결과가 없거나 서버 오류가 발생했습니다.',\n \n // Loading message templates\n searchingTemplate: (source, query, dateRange) => {\n let msg = `${source}에서 검색 중`;\n if (query) msg += ` \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n loadingMoreTemplate: (source, query, dateRange) => {\n let msg = `${source}에서 더 많은 결과 로딩 중`;\n if (query) msg += ` \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n \n pleaseAddApiKey: 'api 키를 추가해 주세요',\n pleaseEnterQuery: '검색 쿼리를 입력해 주세요',\n \n summary: '요약:'\n },\n 'chinese': {\n title: '(re)搜索',\n subtitle: '搜索多个学术数据库并提供多语言摘要',\n \n apiKey: 'API密钥:',\n change: '更改',\n clear: '清除',\n getKeyHere: '在此获取免费密钥',\n \n language: '语言',\n researchQuery: '研究查询',\n dateRange: '选择日期范围 (可选)',\n source: '选择来源 (可选)',\n \n enterTopic: '输入您的研究主题...',\n customSource: '例如：Nature、Science、Cell...',\n customSourceOption: '自定义来源...',\n chooseDateRange: '选择日期范围 (可选)',\n chooseSource: '选择来源 (可选)',\n \n lastMonth: '上个月',\n last3Months: '最近3个月',\n last6Months: '最近6个月',\n lastYear: '去年',\n allTime: '全部时间',\n \n search: '(re)搜索',\n searching: '(re)搜索中',\n streaming: '流式传输中...',\n searchMore: '(再)(再)搜索',\n summarize: '总结',\n analyzing: '分析中...',\n openPaper: '打开',\n \n foundResults: '找到',\n results: '个结果',\n loading: '搜索中',\n loadingMore: '加载更多结果',\n aiAnalyzing: 'AI正在分析论文...',\n analysisTime: '根据论文长度，这可能需要10-30秒',\n noResultsOrError: '未找到结果或发生服务器错误。',\n \n // Loading message templates\n searchingTemplate: (source, query, dateRange) => {\n let msg = `正在${source}中搜索`;\n if (query) msg += `“${query}”`;\n if (dateRange) msg += `(${dateRange})`;\n return msg + '...';\n },\n loadingMoreTemplate: (source, query, dateRange) => {\n let msg = `正在从${source}加载更多结果`;\n if (query) msg += `“${query}”`;\n if (dateRange) msg += `(${dateRange})`;\n return msg + '...';\n },\n \n pleaseAddApiKey: '请添加api密钥',\n pleaseEnterQuery: '请输入搜索查询',\n \n summary: '摘要:'\n },\n 'portuguese': {\n title: '(re)Pesquisar',\n subtitle: 'Pesquise em múltiplas bases de dados acadêmicas com resumos multilíngues',\n \n apiKey: 'Chave API:',\n change: 'Alterar',\n clear: 'Limpar',\n getKeyHere: 'Obtenha sua chave gratuita aqui',\n \n language: 'idioma',\n researchQuery: 'consulta de pesquisa',\n dateRange: 'Escolher intervalo de datas (opcional)',\n source: 'Escolher fonte (opcional)',\n \n enterTopic: 'Digite seu tópico de pesquisa...',\n customSource: 'ex. Nature, Science, Cell...',\n customSourceOption: 'Fonte personalizada...',\n chooseDateRange: 'Escolher intervalo de datas (opcional)',\n chooseSource: 'Escolher fonte (opcional)',\n \n lastMonth: 'Último mês',\n last3Months: 'Últimos 3 meses',\n last6Months: 'Últimos 6 meses',\n lastYear: 'Último ano',\n allTime: 'Todo o tempo',\n \n search: '(re)Pesquisar',\n searching: '(re)Pesquisando',\n streaming: 'Transmitindo...',\n searchMore: '(Re)(Re)Pesquisar',\n summarize: 'Resumir',\n analyzing: 'Analisando...',\n openPaper: 'Abrir',\n \n foundResults: 'Encontrado',\n results: 'resultados',\n loading: 'Pesquisando',\n loadingMore: 'Carregando mais resultados',\n aiAnalyzing: 'IA está analisando o artigo...',\n analysisTime: 'Isso pode levar 10-30 segundos dependendo do comprimento do artigo',\n noResultsOrError: 'Nenhum resultado encontrado ou ocorreu um erro no servidor.',\n \n // Loading message templates\n searchingTemplate: (source, query, dateRange) => {\n let msg = `Pesquisando no ${source}`;\n if (query) msg += ` por \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n loadingMoreTemplate: (source, query, dateRange) => {\n let msg = `Carregando mais resultados do ${source}`;\n if (query) msg += ` por \"${query}\"`;\n if (dateRange) msg += ` (${dateRange})`;\n return msg + '...';\n },\n \n pleaseAddApiKey: 'por favor adicione chave api',\n pleaseEnterQuery: 'por favor digite uma consulta de pesquisa',\n \n summary: 'Resumo:'\n }\n };\n \n return translations[this.selectedLanguage] || translations['english'];\n },\n\n getSummaryTitle() {\n return this.i18n.summary;\n },\n\n getSearchButtonText() {\n return `${this.i18n.search} ${this.displaySourceName}`;\n },\n\n getSearchingButtonText() {\n return `${this.i18n.searching} ${this.displaySourceName}...`;\n },\n\n get loadingMessage() {\n // Use the last typewriter text if available, otherwise fall back to current searchQuery\n const queryText = this.lastTypewriterText || this.searchQuery;\n const dateRangeText = this.dateRange ? this.getDateRangeText() : '';\n \n // Get the actual source being searched (matches backend logic)\n const actualSource = this.getActualSearchSourceName();\n \n // Use language-specific template\n const template = this.isLoadingMore ? this.i18n.loadingMoreTemplate : this.i18n.searchingTemplate;\n return template(actualSource, queryText, dateRangeText);\n },\n\n // Timer computed display\n get displayTimerText() {\n if (this.timerFrozenText) return this.timerFrozenText;\n if (!this.timerStartMs) return '';\n const elapsed = this.timerElapsedMs / 1000;\n const seconds = Math.floor(elapsed);\n const hundredths = Math.floor((elapsed - seconds) * 100);\n const s = String(seconds).padStart(2, '0');\n const h = String(hundredths).padStart(2, '0');\n return `${s}.${h}s`;\n },\n\n getDateRangeText() {\n const dateRangeMap = {\n '1month': this.i18n.lastMonth.toLowerCase(),\n '3months': this.i18n.last3Months.toLowerCase(),\n '6months': this.i18n.last6Months.toLowerCase(),\n '1year': this.i18n.lastYear.toLowerCase()\n };\n return dateRangeMap[this.dateRange] || '';\n },\n\n getActualSearchSource() {\n if (this.selectedSource === 'custom' && this.customSource) {\n return `on ${this.customSource}`;\n } else if (this.selectedSource === 'pubmed') {\n return 'PubMed';\n } else if (this.selectedSource === 'biorxiv') {\n return 'bioRxiv';\n } else if (this.selectedSource === 'arxiv') {\n return 'arXiv';\n } else if (this.selectedSource === 'medrxiv') {\n return 'medRxiv';\n } else if (this.selectedSource === 'researchgate') {\n return 'ResearchGate';\n } else if (this.selectedSource === 'google scholar') {\n return 'Google Scholar';\n } else {\n return '';\n }\n },\n\n getActualSearchSourceName() {\n if (this.selectedSource === 'custom' && this.customSource) {\n return this.customSource;\n } else if (this.selectedSource === 'pubmed') {\n return 'PubMed';\n } else if (this.selectedSource === 'biorxiv') {\n return 'bioRxiv';\n } else if (this.selectedSource === 'arxiv') {\n return 'arXiv';\n } else if (this.selectedSource === 'medrxiv') {\n return 'medRxiv';\n } else if (this.selectedSource === 'researchgate') {\n return 'ResearchGate';\n } else if (this.selectedSource === 'google scholar') {\n return 'Google Scholar';\n } else if (this.selectedSource === 'hacker news') {\n return 'Hacker News';\n } else if (this.selectedSource === 'reddit') {\n return 'Reddit';\n } else if (this.selectedSource === 'news') {\n return 'News';\n } else {\n return '';\n }\n },\n\n handleSourceChange() {},\n\n startTypewriter() {},\n\n typewriterCycle() {},\n\n typeText() {},\n\n clearAllTimeouts() {\n this.typewriterTimeouts.forEach(timeout => clearTimeout(timeout));\n this.typewriterTimeouts = [];\n },\n\n stopTypewriter() {},\n\n // Timer controls\n startTimer() {\n this.stopTimer();\n this.timerFrozenText = '';\n this.timerStartMs = Date.now();\n this.timerElapsedMs = 0;\n // Update every 30ms for smooth hundredths\n this.timerIntervalId = setInterval(() => {\n this.timerElapsedMs = Date.now() - this.timerStartMs;\n }, 30);\n },\n stopTimer() {\n if (this.timerIntervalId) {\n clearInterval(this.timerIntervalId);\n this.timerIntervalId = null;\n }\n if (this.timerStartMs) {\n const elapsed = (this.timerElapsedMs || (Date.now() - this.timerStartMs)) / 1000;\n const seconds = Math.floor(elapsed);\n const hundredths = Math.floor((elapsed - seconds) * 100);\n const s = String(seconds).padStart(2, '0');\n const h = String(hundredths).padStart(2, '0');\n this.timerFrozenText = `${s}.${h}s...done`;\n }\n this.timerStartMs = null;\n this.timerElapsedMs = 0;\n },\n\n handleSearchClick() {},\n \n async searchResearch() {},\n\n async summarizePaper(url, title, index) {\n console.log('📄 Summary triggered for:', title, 'at index:', index);\n // Initialize summary state for this paper\n if (!this.summaryStates[index]) {\n this.summaryStates[index] = {};\n }\n \n this.summaryStates[index].loading = true;\n this.summaryStates[index].summary = '';\n\n try {\n const response = await fetch('/api/search', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n url,\n title,\n language: this.selectedLanguage,\n userApiKey: this.userApiKey\n })\n });\n\n if (!response.ok) throw new Error('Summary failed: ' + response.statusText);\n \n const data = await response.json();\n \n if (data.error) {\n this.summaryStates[index].summary = 'Error: ' + data.error;\n } else {\n this.summaryStates[index].summary = data.summary;\n }\n } catch (error) {\n this.summaryStates[index].summary = 'Error analyzing paper: ' + error.message;\n } finally {\n this.summaryStates[index].loading = false;\n }\n },\n async runStreamTest() {},\n // Pretty-print assistant content if it's valid JSON, else return as-is\n formatAssistantContent() {\n const text = this.assistantContent || '';\n if (!text) return '';\n const trimmed = text.trim();\n if ((trimmed.startsWith('{') || trimmed.startsWith('['))) {\n try {\n const obj = JSON.parse(trimmed);\n return JSON.stringify(obj, null, 2);\n } catch (_) {\n return text;\n }\n }\n return text;\n },\n // Render assistant content as HTML using marked + DOMPurify (safe markdown rendering)\n renderAssistantContent() {\n const raw = this.formatAssistantContent();\n try {\n // If it is JSON (from formatAssistantContent), keep as a code block\n if (raw && (raw.trim().startsWith('{') || raw.trim().startsWith('['))) {\n const escaped = raw.replace(/&/g, '&').replace(//g, '>');\n return `

${escaped}

`;\n }\n const html = (window.marked ? window.marked.parse(raw) : raw);\n return (window.DOMPurify ? window.DOMPurify.sanitize(html) : html);\n } catch (_) {\n return raw;\n }\n },\n // Trace helpers\n computeTraceMaxHeight() {\n return this.traceExpanded ? '' : 'max-height: 100px';\n },\n toggleTool(idx) {\n this.expandedToolMap[idx] = !this.expandedToolMap[idx];\n this.queueScrollToBottom();\n },\n toggleReasoning(idx) {\n this.expandedReasoningMap[idx] = !this.expandedReasoningMap[idx];\n this.queueScrollReasoning(idx);\n },\n prettify(obj) {\n try { return JSON.stringify(obj, null, 2); } catch (_) { return String(obj); }\n },\n async copyEventsJson() {\n try {\n const json = JSON.stringify(this.traceEvents || [], null, 2);\n await navigator.clipboard.writeText(json);\n this.copyEventsText = 'copied';\n if (this.copyEventsTimeoutId) clearTimeout(this.copyEventsTimeoutId);\n this.copyEventsTimeoutId = setTimeout(() => {\n this.copyEventsText = 'copy json';\n this.copyEventsTimeoutId = null;\n }, 3000);\n } catch (_) {\n // no-op\n }\n },\n // Smoothly ensure the trace scroller is at bottom after DOM updates\n queueScrollToBottom() {\n try {\n const el = this.$refs && this.$refs.traceContainer;\n if (!el) return;\n const doScroll = () => { el.scrollTop = el.scrollHeight; };\n if (typeof this.$nextTick === 'function') {\n this.$nextTick(doScroll);\n } else {\n requestAnimationFrame(doScroll);\n }\n } catch (_) {\n // no-op\n }\n },\n // Smoothly keep the per-message reasoning panel scrolled to bottom\n queueScrollReasoning(idx) {\n try {\n const el = document.querySelector('[data-reasoning-idx=\"' + idx + '\"]');\n if (!el) return;\n const doScroll = () => { el.scrollTop = el.scrollHeight; };\n if (typeof this.$nextTick === 'function') {\n this.$nextTick(doScroll);\n } else {\n requestAnimationFrame(doScroll);\n }\n } catch (_) { /* ignore */ }\n },\n\n // Chat helpers\n renderMarkdown(text) {\n const raw = String(text || '');\n try {\n const html = (window.marked ? window.marked.parse(raw) : raw);\n return (window.DOMPurify ? window.DOMPurify.sanitize(html) : html);\n } catch (_) { return raw; }\n },\n autoResize(e) {\n try {\n const el = e && e.target; if (!el) return;\n el.style.height = 'auto';\n el.style.height = Math.min(el.scrollHeight, 320) + 'px';\n } catch (_) {}\n },\n maybeSendOnEnter(e) {\n if (e.shiftKey) {\n try {\n const el = e.target;\n const start = el.selectionStart;\n const end = el.selectionEnd;\n const value = el.value;\n el.value = value.slice(0, start) + '\\n' + value.slice(end);\n this.chatInput = el.value;\n el.selectionStart = el.selectionEnd = start + 1;\n this.$nextTick(() => this.autoResize({ target: el }));\n } catch (_) {}\n return;\n }\n this.sendMessage();\n },\n scrollToBottom() {\n try {\n const el = this.$refs && this.$refs.messagesContainer; if (!el) return;\n const doScroll = () => { el.scrollTop = el.scrollHeight; };\n if (typeof this.$nextTick === 'function') this.$nextTick(doScroll); else requestAnimationFrame(doScroll);\n } catch (_) {}\n },\n async sendMessage() {\n const text = (this.chatInput || '').trim();\n if (!text) return;\n this.errorMessage = '';\n if (!this.hasServerKey && !this.userApiKey) {\n this.errorMessage = 'please add api key';\n return;\n }\n // If no persisted chat exists yet (ephemeral session), create and select one now\n if (!this.currentChatId) {\n try {\n const base = (this.chatInput && this.chatInput.trim()) || 'New chat';\n const title = base.substring(0, 60);\n const createdAt = Date.now();\n const id = await this.db.chats.add({ title, createdAt });\n await this.loadChats();\n this.currentChatId = id;\n this.activeSearchId = id;\n } catch (_) { /* ignore */ }\n }\n this.messages.push({ role: 'user', content: text });\n try {\n if (this.currentChatId && this.db) {\n await this.db.messages.add({ chatId: this.currentChatId, createdAt: Date.now(), role: 'user', content: text });\n }\n } catch (_) {}\n this.chatInput = '';\n try { const ta = document.querySelector('textarea'); if (ta) { ta.style.height = 'auto'; ta.rows = 1; } } catch (_) {}\n this.scrollToBottom();\n await this.streamAssistant();\n },\n async streamAssistant() {\n if (this.isStreaming) return;\n this.isStreaming = true;\n const assistantIndex = this.messages.push({ role: 'assistant', content: '' }) - 1;\n let pendingAssistantBuffer = '';\n let pendingReasoningBuffer = '';\n // Initialize reasoning buffers for this message\n this.messageReasoningMap[assistantIndex] = this.messageReasoningMap[assistantIndex] || '';\n this.expandedReasoningMap[assistantIndex] = this.expandedReasoningMap[assistantIndex] || false;\n try {\n const payload = {\n userApiKey: this.userApiKey,\n messages: this.messages.map(m => ({ role: m.role, content: m.content }))\n };\n const response = await fetch('/api/chatStreamSSE', {\n method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(payload)\n });\n if (!response.ok || !response.body) {\n let txt = ''; try { txt = await response.text(); } catch (_) {}\n throw new Error(txt || response.statusText);\n }\n const reader = response.body.getReader();\n const decoder = new TextDecoder();\n let buffer = '';\n while (true) {\n const { done, value } = await reader.read(); if (done) break;\n buffer += decoder.decode(value, { stream: true });\n const chunks = buffer.split('\\n\\n'); buffer = chunks.pop() || '';\n for (const chunk of chunks) {\n if (!chunk.trim()) continue;\n const lines = chunk.split('\\n'); let currentEvent = 'message';\n for (const line of lines) {\n if (line.startsWith('event: ')) { currentEvent = line.slice(7).trim(); continue; }\n if (!line.startsWith('data: ')) continue;\n const data = line.slice(6);\n if (data === '[DONE]') continue;\n let parsed = null; try { parsed = JSON.parse(data); } catch (_) { parsed = null; }\n if (!parsed) continue;\n if (currentEvent === 'results') continue;\n const choice = Array.isArray(parsed.choices) ? parsed.choices[0] : null;\n const delta = choice && choice.delta ? choice.delta : {};\n if (delta.content) {\n this.messages[assistantIndex].content += delta.content;\n pendingAssistantBuffer += delta.content;\n this.scrollToBottom();\n }\n if (delta.reasoning) {\n const prev = this.messageReasoningMap[assistantIndex] || '';\n const addition = String(delta.reasoning || '');\n this.messageReasoningMap[assistantIndex] = prev + addition;\n pendingReasoningBuffer += addition;\n this.queueScrollReasoning(assistantIndex);\n }\n }\n }\n }\n } catch (e) {\n this.errorMessage = e.message || String(e);\n } finally {\n this.isStreaming = false;\n // persist assistant message once at end\n try {\n if (this.currentChatId && this.db && pendingAssistantBuffer.trim()) {\n await this.db.messages.add({ chatId: this.currentChatId, createdAt: Date.now(), role: 'assistant', content: pendingAssistantBuffer, reasoning: pendingReasoningBuffer || '' });\n }\n } catch (_) {}\n // If chat has default title, synthesize one using model\n try {\n const chatMeta = this.currentChatId ? 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Diff · v23