🛡️ SlimArmor CLI (No JS)

Fallback interface when the interactive CLI is blocked. Submits requests server-side. Write tests require auth unless ALLOW_WRITE_TESTS_NOAUTH=1 is temporarily set.

\n\n

\n \n \n \n \n \n

\n\n \n\n ${error ? `

${error}

` : \"\"}\n ${response ? `

${response}

` : \"\"}\n\n`;\n}\n\nfunction serveCLIFallback(params: {\n baseUrl: string;\n method?: string | null;\n path?: string | null;\n body?: string | null;\n token?: string | null;\n response?: string;\n error?: string;\n}): Response {\n const html = renderCLIFallback({\n baseUrl: params.baseUrl,\n method: params.method ?? undefined,\n path: params.path ?? undefined,\n body: params.body ?? undefined,\n token: params.token ?? undefined,\n response: params.response,\n error: params.error,\n });\n return new Response(html, {\n headers: { \"Content-Type\": \"text/html; charset=utf-8\" },\n });\n}\n\nasync function handleCLIFallback(req: Request): Promise {\n const url = new URL(req.url);\n const form = await req.formData();\n const method = String(form.get(\"method\") || \"GET\").toUpperCase();\n const path = String(form.get(\"path\") || \"/stats\");\n const token = String(form.get(\"token\") || \"\");\n const body = String(form.get(\"body\") || \"\");\n\n const targetUrl = new URL(path, url.origin).toString();\n const headers: Record = { Accept: \"application/json\" };\n if (token) headers.Authorization = `Bearer ${token}`;\n\n try {\n const init: RequestInit = { method, headers };\n if (method === \"POST\") {\n headers[\"Content-Type\"] = \"application/json\";\n init.body = body || \"{}\";\n }\n const res = await fetch(targetUrl, init);\n const text = await res.text();\n\n return serveCLIFallback({\n baseUrl: url.origin,\n method,\n path,\n body,\n token,\n response: text,\n });\n } catch (err) {\n return serveCLIFallback({\n baseUrl: url.origin,\n method,\n path,\n body,\n token,\n error: err instanceof Error ? err.message : String(err),\n });\n }\n}\n",[],[213],{"_105":221,"_97":222,"_163":223,"_165":-5,"_166":148,"_167":224,"_169":225,"_107":226,"_101":187,"_172":227,"_174":228,"_176":22,"_177":-5,"_178":229,"_100":230},"3559cc28-0098-11f1-9fea-42dde27851f2","ui.ts","c8ff15e8-ffe7-11f0-9667-42dde27851f2",63,true,["D",1770078915492],"script","/**\n * SlimArmor Browser CLI\n * A terminal-style interface for the vector database\n */\n\nconst CLI_HTML = `\n\n\n \n \n SlimArmor CLI\n \n\n\n \n \n

\n \n

\n ❯\n \n

\n\n \n\n`;\n\nconst CLI_JS = `\n const API_BASE = window.location.origin;\n let adminToken = sessionStorage.getItem('adminToken') || '';\n let serverAuthRequired = null;\n let commandHistory = JSON.parse(sessionStorage.getItem('commandHistory') || '[]');\n let historyIndex = -1;\n \n const output = document.getElementById('output');\n const input = document.getElementById('input');\n const authStatus = document.getElementById('auth-status');\n\n if (input) {\n input.focus();\n document.addEventListener('click', () => input.focus());\n }\n \n // Threshold presets\n const THRESHOLDS = {\n tight: 0.5,\n balanced: 0.64,\n loose: 0.7\n };\n \n // Update auth status display\n function updateAuthStatus() {\n if (adminToken) {\n authStatus.textContent = '🔓 authenticated';\n authStatus.className = 'status authed';\n return;\n }\n \n if (serverAuthRequired === false) {\n authStatus.textContent = '🟢 open mode';\n authStatus.className = 'status authed';\n } else {\n authStatus.textContent = '🔒 read-only';\n authStatus.className = 'status';\n }\n }\n\n async function refreshAuthStatus() {\n try {\n const data = await api('GET', '/auth');\n if (typeof data.auth_required === 'boolean') {\n serverAuthRequired = data.auth_required;\n }\n } catch {\n // ignore\n }\n updateAuthStatus();\n }\n \n // Print line to output\n function print(text, className = '') {\n const line = document.createElement('div');\n line.className = 'line ' + className;\n line.innerHTML = text;\n output.appendChild(line);\n output.scrollTop = output.scrollHeight;\n }\n \n // Print raw HTML\n function printHTML(html) {\n const container = document.createElement('div');\n container.innerHTML = html;\n output.appendChild(container);\n output.scrollTop = output.scrollHeight;\n }\n \n // API call helper\n async function api(method, path, body = null) {\n const headers = { 'Content-Type': 'application/json' };\n if (adminToken) {\n headers['Authorization'] = 'Bearer ' + adminToken;\n }\n \n const opts = { method, headers };\n if (body) opts.body = JSON.stringify(body);\n \n const res = await fetch(API_BASE + path, opts);\n return res.json();\n }\n \n // Parse command into tokens (respects quotes)\n function parseCommand(input) {\n const tokens = [];\n let current = '';\n let inQuotes = false;\n let quoteChar = '';\n \n for (let i = 0; i < input.length; i++) {\n const c = input[i];\n \n if ((c === '\"' || c === \"'\") && !inQuotes) {\n inQuotes = true;\n quoteChar = c;\n } else if (c === quoteChar && inQuotes) {\n inQuotes = false;\n quoteChar = '';\n } else if (c === ' ' && !inQuotes) {\n if (current) tokens.push(current);\n current = '';\n } else {\n current += c;\n }\n }\n if (current) tokens.push(current);\n \n return tokens;\n }\n \n // Parse flags from tokens\n function parseFlags(tokens) {\n const args = [];\n const flags = {};\n \n for (let i = 0; i < tokens.length; i++) {\n const t = tokens[i];\n if (t.startsWith('--')) {\n const key = t.slice(2);\n const next = tokens[i + 1];\n if (next && !next.startsWith('-')) {\n flags[key] = next;\n i++;\n } else {\n flags[key] = true;\n }\n } else if (t.startsWith('-') && t.length === 2) {\n const key = t.slice(1);\n const next = tokens[i + 1];\n if (next && !next.startsWith('-')) {\n flags[key] = next;\n i++;\n } else {\n flags[key] = true;\n }\n } else {\n args.push(t);\n }\n }\n \n return { args, flags };\n }\n\n function coerceValue(value) {\n if (value === 'true') return true;\n if (value === 'false') return false;\n const num = Number(value);\n if (!Number.isNaN(num) && value.trim() !== '') return num;\n return value;\n }\n \n // Make ID clickable\n function clickableId(id) {\n return '' + escapeHtml(id) + '';\n }\n \n // Escape HTML\n function escapeHtml(str) {\n return String(str).replace(/&/g, '&').replace(//g, '>');\n }\n \n // Commands\n const commands = {\n async help() {\n printHTML(\\`\n

📖 Core Commands

search \"query\"Semantic search

-k 10Max results (default: 10)

--mode balancedtight|balanced|loose

--max 0.64Max distance threshold

--offset 0Pagination offset

--filter key=valueMeta filters (comma-separated)

--hybridKeyword + vector boost

--alpha 0.25Hybrid weight (0..1)

get <id>Show record details

listList all record IDs

--limit 20Max records to show

--prefix seed-Filter by ID prefix

--offset 0Pagination offset

statsStorage & index stats

calibrate \"query\"Suggest thresholds

✏️ Write Commands (requires auth)

upsert <id> \"text\"Create/update record

--meta '{\"tag\":\"notes\"}'Optional metadata

del <id>Delete record

seed 100Seed synthetic data

clear --yesDelete ALL records

reindexRecreate vector index

🔐 Auth

auth <token>Set admin token

logoutClear auth token

💡 Tips

↑/↓Navigate command history

Click IDsAuto-fill get command

\n \\`);\n },\n \n async stats() {\n print('Fetching stats...', 'info');\n const data = await api('GET', '/stats');\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n printHTML(\\`\n

Records\\${data.count.toLocaleString()}

Avg Text Length\\${data.avg_text_length} chars

Text Storage\\${data.storage.text_mb} MB

Embedding Storage\\${data.storage.embedding_mb} MB

Total Estimated\\${data.storage.estimated_total_mb} MB

Capacity Used\\${data.capacity.current_pct_of_1gb}% of 1GB

Est. Max Records~\\${data.capacity.estimated_max_records.toLocaleString()}

Index Metric\\${data.index_settings.metric}

Index Compression\\${data.index_settings.compress_neighbors}

\n \\`);\n },\n \n async search(args, flags) {\n if (args.length < 1) {\n print('Usage: search \"query\" [-k 10] [--mode balanced] [--max 0.64] [--offset 0] [--filter key=value] [--hybrid --alpha 0.25]', 'error');\n return;\n }\n \n const query = args[0];\n const k = parseInt(flags.k || '10');\n const offset = parseInt(flags.offset || '0');\n \n // Determine maxDistance from mode or explicit --max\n let maxDistance;\n if (flags.max) {\n maxDistance = parseFloat(flags.max);\n } else if (flags.mode && THRESHOLDS[flags.mode]) {\n maxDistance = THRESHOLDS[flags.mode];\n }\n\n // Parse filters\n let filters;\n if (flags.filter) {\n filters = {};\n const pairs = String(flags.filter).split(',');\n pairs.forEach(p => {\n const [rawKey, rawValue] = p.split('=');\n if (!rawKey || rawValue === undefined) return;\n const key = rawKey.trim();\n let value = rawValue.trim();\n\n // Array values via pipe: key=a|b|c\n if (value.includes('|')) {\n const vals = value.split('|').map(v => v.trim()).filter(Boolean).map(coerceValue);\n if (vals.length > 0) filters[key] = vals;\n return;\n }\n\n filters[key] = coerceValue(value);\n });\n }\n\n const hybrid = !!flags.hybrid || typeof flags.alpha !== 'undefined';\n const alpha = typeof flags.alpha !== 'undefined' ? parseFloat(flags.alpha) : undefined;\n \n print(\\`Searching for \"\\${query}\"...\\`, 'info');\n \n const data = await api('POST', '/search', { \n query, \n k, \n maxDistance, \n offset,\n filters,\n hybrid: hybrid ? { enabled: true, alpha } : undefined,\n });\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n print(\\`Found \\${data.count} results (k=\\${k}, offset=\\${offset}, maxDistance=\\${maxDistance || 'none'})\\`, 'success');\n \n if (data.results.length === 0) {\n print('No matching results.', 'info');\n return;\n }\n \n let html = '';\n data.results.forEach((r, i) => {\n const text = r.text.length > 60 ? r.text.slice(0, 60) + '...' : r.text;\n html += \\`\n \n \n \n \n \\`;\n });\n html += '

#	ID	Distance	Text
\\${i + 1}	\\${clickableId(r.id)}	\\${r.distance.toFixed(4)}	\\${escapeHtml(text)}

';\n printHTML(html);\n },\n \n async get(args) {\n if (args.length < 1) {\n print('Usage: get ', 'error');\n return;\n }\n \n const id = args[0];\n const data = await api('GET', '/get?id=' + encodeURIComponent(id));\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n const r = data.record;\n printHTML(\\`\n

ID: \\${escapeHtml(r.id)}

Text: \\${escapeHtml(r.text)}

\n \\${r.meta ? '

Meta: ' + escapeHtml(JSON.stringify(r.meta)) + '

' : ''}\n

Updated: \\${new Date(r.updated_at).toLocaleString()}

\n \\`);\n print(\\`💡 del \\${id} | upsert \\${id} \"new text\"\\`, 'info');\n },\n \n async list(args, flags) {\n const limit = parseInt(flags.limit || '20');\n const offset = parseInt(flags.offset || '0');\n const prefix = flags.prefix ? String(flags.prefix) : '';\n const query = new URLSearchParams({ limit, offset });\n if (prefix) query.set('prefix', prefix);\n const data = await api('GET', '/list?' + query.toString());\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n let ids = data.ids;\n \n print(\\`Found \\${ids.length} record(s)\\${flags.prefix ? ' matching prefix \"' + flags.prefix + '\"' : ''}\\`, 'success');\n \n if (ids.length === 0) return;\n \n let html = '';\n ids.forEach((id, i) => {\n html += \\`\n \n \n \n \\`;\n });\n html += '

#	ID	Actions
\\${i + 1}	\\${clickableId(id)}	del

';\n printHTML(html);\n },\n \n async calibrate(args) {\n if (args.length < 1) {\n print('Usage: calibrate \"query\"', 'error');\n return;\n }\n \n const query = args[0];\n print(\\`Calibrating for \"\\${query}\"...\\`, 'info');\n \n const data = await api('GET', '/calibrate?q=' + encodeURIComponent(query));\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n printHTML(\\`\n

Query\"\\${escapeHtml(data.query)}\"

Results Found\\${data.total_results}

Distance Min\\${data.distance_distribution.min}

Distance Median\\${data.distance_distribution.median}

Distance Max\\${data.distance_distribution.max}

Suggested Thresholds

🎯 Tight (top 3)\\${data.suggested_thresholds.tight.value}

⚖️ Balanced (top 10)\\${data.suggested_thresholds.balanced.value}

📦 Loose (all)\\${data.suggested_thresholds.loose.value}

\n \\`);\n \n print(\\`💡 Try: search \"\\${query}\" --mode tight\\`, 'info');\n },\n \n async upsert(args, flags) {\n if (!adminToken) {\n print('❌ Authentication required. Use: auth ', 'error');\n return;\n }\n \n if (args.length < 2) {\n print('Usage: upsert \"text...\" [--meta \\'{\"key\":\"value\"}\\']', 'error');\n return;\n }\n \n const id = args[0];\n const text = args.slice(1).join(' ');\n let meta;\n \n if (flags.meta) {\n try {\n meta = JSON.parse(flags.meta);\n } catch {\n print('Invalid --meta JSON. Example: --meta \\'{\"tag\":\"notes\"}\\'', 'error');\n return;\n }\n }\n \n print(\\`Upserting \"\\${id}\"...\\`, 'info');\n \n const data = await api('POST', '/upsert', { id, text, meta });\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n print(\\`✅ \\${data.embedded ? 'Embedded and saved' : 'Updated (text unchanged)'}: \\${id}\\`, 'success');\n },\n \n async del(args) {\n if (!adminToken) {\n print('❌ Authentication required. Use: auth ', 'error');\n return;\n }\n \n if (args.length < 1) {\n print('Usage: del ', 'error');\n return;\n }\n \n const id = args[0];\n const data = await api('POST', '/delete', { id });\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n print(\\`✅ Deleted: \\${id} (deleted: \\${data.deleted})\\`, 'success');\n },\n \n async seed(args) {\n if (!adminToken) {\n print('❌ Authentication required. Use: auth ', 'error');\n return;\n }\n \n const n = parseInt(args[0] || '100');\n print(\\`Seeding \\${n} records (this may take a while)...\\`, 'info');\n \n const data = await api('GET', '/seed?n=' + n);\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n printHTML(\\`\n

Seeded\\${data.seeded}

Embedded\\${data.embedded}

Skipped\\${data.skipped}

Time\\${(data.elapsed_ms / 1000).toFixed(1)}s

Avg per record\\${data.avg_ms_per_record}ms

\n \\`);\n },\n \n async clear(args, flags) {\n if (!adminToken) {\n print('❌ Authentication required. Use: auth ', 'error');\n return;\n }\n \n if (!flags.yes) {\n print('⚠️ This will DELETE ALL records! Use: clear --yes', 'error');\n return;\n }\n \n print('Clearing database...', 'info');\n const data = await api('POST', '/clear?confirm=yes');\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n print('✅ All records deleted', 'success');\n },\n \n async reindex() {\n if (!adminToken) {\n print('❌ Authentication required. Use: auth ', 'error');\n return;\n }\n \n print('Recreating index...', 'info');\n const data = await api('POST', '/reindex');\n \n if (data.error) {\n print('Error: ' + data.error, 'error');\n return;\n }\n \n print('✅ Index recreated', 'success');\n },\n \n auth(args) {\n if (args.length < 1) {\n print('Usage: auth ', 'error');\n return;\n }\n \n adminToken = args[0];\n sessionStorage.setItem('adminToken', adminToken);\n updateAuthStatus();\n print('✅ Token saved (stored in session)', 'success');\n },\n \n logout() {\n adminToken = '';\n sessionStorage.removeItem('adminToken');\n updateAuthStatus();\n print('✅ Logged out', 'success');\n },\n \n async ping() {\n const data = await api('GET', '/ping');\n print('🏓 Pong! Server time: ' + data.timestamp, 'success');\n },\n \n cls() {\n output.innerHTML = '';\n },\n \n clear_output() {\n output.innerHTML = '';\n }\n };\n \n // Aliases\n commands.delete = commands.del;\n commands['?'] = commands.help;\n commands.ls = commands.list;\n \n // Run a command\n async function runCommand(cmdStr) {\n // Add to history\n if (cmdStr && cmdStr !== commandHistory[0]) {\n commandHistory.unshift(cmdStr);\n if (commandHistory.length > 50) commandHistory.pop();\n sessionStorage.setItem('commandHistory', JSON.stringify(commandHistory));\n }\n historyIndex = -1;\n \n print(cmdStr, 'command');\n \n const tokens = parseCommand(cmdStr);\n if (tokens.length === 0) return;\n \n const cmd = tokens[0].toLowerCase();\n const { args, flags } = parseFlags(tokens.slice(1));\n \n if (commands[cmd]) {\n try {\n await commands[cmd](args, flags);\n } catch (e) {\n print('Error: ' + e.message, 'error');\n }\n } else {\n print(\\`Unknown command: \\${cmd}. Type 'help' for available commands.\\`, 'error');\n }\n }\n \n // Make runCommand available globally for click handlers\n window.runCommand = runCommand;\n \n // Input handling\n input.addEventListener('keydown', async (e) => {\n if (e.key === 'Enter') {\n const cmd = input.value.trim();\n input.value = '';\n if (cmd) await runCommand(cmd);\n } else if (e.key === 'ArrowUp') {\n e.preventDefault();\n if (historyIndex < commandHistory.length - 1) {\n historyIndex++;\n input.value = commandHistory[historyIndex];\n }\n } else if (e.key === 'ArrowDown') {\n e.preventDefault();\n if (historyIndex > 0) {\n historyIndex--;\n input.value = commandHistory[historyIndex];\n } else {\n historyIndex = -1;\n input.value = '';\n }\n }\n });\n \n // Initial state\n updateAuthStatus();\n refreshAuthStatus();\n \n // Welcome message\n print('🛡️ SlimArmor Vector DB CLI', 'highlight');\n print(\"Type 'help' for available commands\", 'info');\n print('', '');\n `;\n\nexport function serveUI(): Response {\n return new Response(CLI_HTML, {\n headers: { \"Content-Type\": \"text/html; charset=utf-8\" },\n });\n}\n\nexport function serveUIJS(): Response {\n return new Response(CLI_JS, {\n headers: {\n \"Content-Type\": \"application/javascript; charset=utf-8\",\n \"Cache-Control\": \"no-store\",\n },\n });\n}\n",[],[222],{"_105":232,"_97":233,"_163":234,"_165":-5,"_166":148,"_167":102,"_169":225,"_107":235,"_101":197,"_172":227,"_174":236,"_176":22,"_177":-5,"_178":237,"_100":238},"440ce362-15d4-11f1-8b72-42dde27851f2","vectordb.ts","85b83e64-ffe1-11f0-a01c-42dde27851f2",["D",1772413684396],"/**\n * Mini Vector DB for Val Town (v3 - Multi-Provider)\n * \n * Supports any OpenAI-compatible embedding provider:\n * - Nebius (default)\n * - OpenAI\n * - OpenRouter\n * - Azure OpenAI\n * - Local (Ollama, LM Studio)\n * - Any OpenAI-compatible API\n * \n * Configure via environment variables:\n * - EMBEDDING_API_URL (default: Nebius)\n * - EMBEDDING_API_KEY (required)\n * - EMBEDDING_MODEL (default: Qwen/Qwen3-Embedding-8B)\n * - EMBEDDING_DIM (default: 4096)\n */\n\nimport sqlite from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// ============================================================\n// CONFIGURATION (via environment variables)\n// ============================================================\n\n// Provider presets\nconst PROVIDERS = {\n nebius: {\n url: \"https://api.tokenfactory.nebius.com/v1/embeddings\",\n keyEnv: \"NEBIUS_API_KEY\",\n defaultModel: \"Qwen/Qwen3-Embedding-8B\",\n defaultDim: 4096,\n },\n openai: {\n url: \"https://api.openai.com/v1/embeddings\",\n keyEnv: \"OPENAI_API_KEY\",\n defaultModel: \"text-embedding-3-small\",\n defaultDim: 1536,\n },\n openrouter: {\n url: \"https://openrouter.ai/api/v1/embeddings\",\n keyEnv: \"OPENROUTER_API_KEY\",\n defaultModel: \"openai/text-embedding-3-small\",\n defaultDim: 1536,\n },\n} as const;\n\ntype ProviderName = keyof typeof PROVIDERS;\n\n// Table/index names\nconst TABLE_NAME = \"vectordb\";\nconst INDEX_NAME = \"vectordb_embedding_idx\";\nconst FTS_TABLE = \"vectordb_fts\";\nconst META_TABLE = \"vectordb_meta\";\n\nlet RESOLVED_DIM: number | null = null;\nlet FTS_AVAILABLE: boolean | null = null;\n\n// Fix #2: Module-level setup guard — skips redundant SQLite round-trips on warm requests\nlet _setupDone = false;\n\n// Get configuration from environment\nfunction getConfig() {\n // Check for explicit provider setting\n const providerName = (Deno.env.get(\"EMBEDDING_PROVIDER\") || \"nebius\") as ProviderName;\n const provider = PROVIDERS[providerName] || PROVIDERS.nebius;\n \n // Allow overrides via env vars\n const url = Deno.env.get(\"EMBEDDING_API_URL\") || provider.url;\n const model = Deno.env.get(\"EMBEDDING_MODEL\") || provider.defaultModel;\n const rawDim = Deno.env.get(\"EMBEDDING_DIM\");\n const dimAuto = (rawDim || \"\").toLowerCase() === \"auto\";\n const parsedDim = parseInt(rawDim || String(provider.defaultDim), 10);\n const dim = RESOLVED_DIM ?? (Number.isFinite(parsedDim) && parsedDim > 0 ? parsedDim : provider.defaultDim);\n \n // Get API key (check provider-specific first, then generic)\n const apiKey = Deno.env.get(provider.keyEnv) || Deno.env.get(\"EMBEDDING_API_KEY\");\n \n return { url, model, dim, dimAuto, apiKey, providerName };\n}\n\nfunction getIndexConfig() {\n const metricRaw = (Deno.env.get(\"INDEX_METRIC\") || \"\").toLowerCase();\n const metric = metricRaw === \"l2\" ? \"l2\" : \"cosine\";\n const maxNeighborsRaw = parseInt(Deno.env.get(\"INDEX_MAX_NEIGHBORS\") || \"64\", 10);\n const maxNeighbors = Number.isFinite(maxNeighborsRaw) && maxNeighborsRaw >= 8 && maxNeighborsRaw <= 256\n ? maxNeighborsRaw\n : 64;\n const compressRaw = (Deno.env.get(\"INDEX_COMPRESS_NEIGHBORS\") || \"\").toLowerCase();\n const compressNeighbors = (\n compressRaw === \"none\" ||\n compressRaw === \"float1bit\" ||\n compressRaw === \"float8\" ||\n compressRaw === \"float16\" ||\n compressRaw === \"floatb16\" ||\n compressRaw === \"float32\"\n )\n ? compressRaw\n : \"float8\";\n\n const alphaRaw = Deno.env.get(\"INDEX_ALPHA\");\n const alphaParsed = alphaRaw ? parseFloat(alphaRaw) : 1.2;\n const alpha = Number.isFinite(alphaParsed) && alphaParsed >= 1 ? alphaParsed : 1.2;\n\n const searchLRaw = Deno.env.get(\"INDEX_SEARCH_L\");\n const searchLParsed = searchLRaw ? parseInt(searchLRaw, 10) : 200;\n const searchL = Number.isFinite(searchLParsed) && searchLParsed > 0 ? searchLParsed : 200;\n\n const insertLRaw = Deno.env.get(\"INDEX_INSERT_L\");\n const insertLParsed = insertLRaw ? parseInt(insertLRaw, 10) : 70;\n const insertL = Number.isFinite(insertLParsed) && insertLParsed > 0 ? insertLParsed : 70;\n\n return {\n metric,\n maxNeighbors,\n compressNeighbors,\n alpha,\n searchL,\n insertL,\n hasAlpha: !!alphaRaw,\n hasSearchL: !!searchLRaw,\n hasInsertL: !!insertLRaw,\n };\n}\n\n// ============================================================\n// TYPES\n// ============================================================\n\nexport interface VectorRecord {\n id: string;\n text: string;\n meta?: Record;\n updated_at: number;\n}\n\nexport interface SearchResult {\n id: string;\n text: string;\n meta?: Record;\n distance: number;\n score?: number;\n keyword_score?: number;\n}\n\nexport interface Stats {\n count: number;\n estimated_storage_mb: number;\n}\n\nexport interface ProviderInfo {\n provider: string;\n url: string;\n model: string;\n dimensions: number;\n dim_auto: boolean;\n}\n\nexport interface IndexInfo {\n metric: string;\n max_neighbors: number;\n compress_neighbors: string;\n alpha: number;\n search_l: number;\n insert_l: number;\n}\n\nexport interface EmbeddingAuthInfo {\n required_env: string;\n has_key: boolean;\n}\n\nexport type FilterValue = string | number | boolean | null | Array;\n\nexport interface SearchOptions {\n filters?: Record;\n offset?: number;\n hybrid?: {\n enabled?: boolean;\n alpha?: number; // 0..1, keyword weight\n keyword_k?: number;\n };\n}\n\nexport interface UpsertInput {\n id: string;\n text: string;\n meta?: Record;\n}\n\nexport interface ChunkOptions {\n size?: number; // approx chars per chunk\n overlap?: number; // approx chars overlap\n}\n\n// ============================================================\n// GUARDRAILS\n// ============================================================\n\nfunction assertDim(vec: number[], expected: number): void {\n if (vec.length !== expected) {\n throw new Error(`Embedding dim mismatch: got ${vec.length}, expected ${expected}`);\n }\n}\n\nasync function hashText(text: string): Promise {\n const encoder = new TextEncoder();\n const data = encoder.encode(text);\n const hashBuffer = await crypto.subtle.digest(\"SHA-256\", data);\n const hashArray = Array.from(new Uint8Array(hashBuffer));\n return hashArray.map(b => b.toString(16).padStart(2, \"0\")).join(\"\");\n}\n\n// ============================================================\n// EMBEDDING CLIENT (OpenAI-Compatible)\n// ============================================================\n\ninterface EmbeddingResponse {\n data: Array<{ embedding: number[]; index: number }>;\n model: string;\n usage?: { prompt_tokens: number; total_tokens: number };\n}\n\nfunction getExpectedDim(config: ReturnType): number | null {\n if (RESOLVED_DIM) return RESOLVED_DIM;\n if (config.dimAuto) return null;\n return config.dim;\n}\n\nasync function embedManyRaw(inputs: string[], config = getConfig()): Promise {\n if (!config.apiKey) {\n throw new Error(`Missing API key. Set ${PROVIDERS[config.providerName]?.keyEnv || \"EMBEDDING_API_KEY\"} env var`);\n }\n\n const controller = new AbortController();\n const timeoutId = setTimeout(() => controller.abort(), 30000);\n\n try {\n const res = await fetch(config.url, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${config.apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({ \n model: config.model, \n input: inputs,\n }),\n signal: controller.signal,\n });\n\n if (!res.ok) {\n const errText = await res.text();\n throw new Error(`Embedding API error ${res.status}: ${errText}`);\n }\n\n const json = (await res.json()) as EmbeddingResponse;\n const embeddings: Array = new Array(inputs.length);\n for (const item of json.data) {\n embeddings[item.index] = item.embedding;\n }\n\n if (embeddings.some((e) => !e)) {\n throw new Error(\"Embedding API returned incomplete data\");\n }\n\n return embeddings as number[][];\n } finally {\n clearTimeout(timeoutId);\n }\n}\n\nasync function embedMany(inputs: string[]): Promise {\n const config = getConfig();\n const expectedDim = getExpectedDim(config);\n const batchSize = 96;\n const result: number[][] = [];\n\n for (let i = 0; i < inputs.length; i += batchSize) {\n const batch = inputs.slice(i, i + batchSize);\n const embeddings = await embedManyRaw(batch, config);\n if (expectedDim !== null) {\n for (const emb of embeddings) assertDim(emb, expectedDim);\n }\n result.push(...embeddings);\n }\n\n return result;\n}\n\nasync function embed(input: string): Promise {\n const [embedding] = await embedMany([input]);\n return embedding;\n}\n\n// ============================================================\n// VECTOR DB API\n// ============================================================\n\nasync function ensureFtsTable(): Promise {\n if (FTS_AVAILABLE !== null) return;\n try {\n await sqlite.execute(`CREATE VIRTUAL TABLE IF NOT EXISTS ${FTS_TABLE} USING fts5(id, text)`);\n FTS_AVAILABLE = true;\n } catch {\n FTS_AVAILABLE = false;\n }\n}\n\nasync function resolveDimForSetup(): Promise {\n const config = getConfig();\n if (!config.dimAuto) return config.dim;\n if (RESOLVED_DIM) return RESOLVED_DIM;\n\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${META_TABLE} (\n key TEXT PRIMARY KEY,\n value TEXT\n )\n `);\n\n const existing = await sqlite.execute({\n sql: `SELECT value FROM ${META_TABLE} WHERE key = ?`,\n args: [\"dim\"],\n });\n\n const stored = (existing.rows?.[0] as { value?: string } | undefined)?.value;\n const parsed = stored ? parseInt(stored, 10) : NaN;\n if (Number.isFinite(parsed) && parsed > 0) {\n RESOLVED_DIM = parsed;\n return parsed;\n }\n\n // Try to read dimension from existing table schema (if present)\n const tableInfo = await sqlite.execute(`PRAGMA table_info(${TABLE_NAME})`);\n const embeddingCol = (tableInfo.rows as Array<{ name: string; type: string }>).find(r => r.name === \"embedding\");\n if (embeddingCol && typeof embeddingCol.type === \"string\") {\n const match = embeddingCol.type.match(/F32_BLOB\$(\\d+)\$/i);\n if (match) {\n const detected = parseInt(match[1], 10);\n if (Number.isFinite(detected) && detected > 0) {\n RESOLVED_DIM = detected;\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO ${META_TABLE} (key, value) VALUES (?, ?)`,\n args: [\"dim\", String(RESOLVED_DIM)],\n });\n return RESOLVED_DIM;\n }\n }\n }\n\n const [embedding] = await embedManyRaw([\"dimension probe\"], config);\n RESOLVED_DIM = embedding.length;\n\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO ${META_TABLE} (key, value) VALUES (?, ?)`,\n args: [\"dim\", String(RESOLVED_DIM)],\n });\n\n return RESOLVED_DIM;\n}\n\n// Fix #5: syncFtsUpsert uses a single batch instead of 2 sequential queries\nasync function syncFtsUpsert(id: string, text: string): Promise {\n if (FTS_AVAILABLE === null) await ensureFtsTable();\n if (!FTS_AVAILABLE) return;\n try {\n await sqlite.batch([\n { sql: `DELETE FROM ${FTS_TABLE} WHERE id = ?`, args: [id] },\n { sql: `INSERT INTO ${FTS_TABLE} (id, text) VALUES (?, ?)`, args: [id, text] },\n ]);\n } catch {\n // ignore FTS failures to keep core functionality working\n }\n}\n\nasync function syncFtsDelete(id: string): Promise {\n if (FTS_AVAILABLE === null) await ensureFtsTable();\n if (!FTS_AVAILABLE) return;\n try {\n await sqlite.execute({\n sql: `DELETE FROM ${FTS_TABLE} WHERE id = ?`,\n args: [id],\n });\n } catch {\n // ignore FTS failures to keep core functionality working\n }\n}\n\n/**\n * Get current provider configuration\n */\nexport function getProviderInfo(): ProviderInfo {\n const config = getConfig();\n return {\n provider: config.providerName,\n url: config.url,\n model: config.model,\n dimensions: config.dim,\n dim_auto: config.dimAuto,\n };\n}\n\nexport function getIndexInfo(): IndexInfo {\n const cfg = getIndexConfig();\n return {\n metric: cfg.metric,\n max_neighbors: cfg.maxNeighbors,\n compress_neighbors: cfg.compressNeighbors,\n alpha: cfg.alpha,\n search_l: cfg.searchL,\n insert_l: cfg.insertL,\n };\n}\n\nexport function getEmbeddingAuthInfo(): EmbeddingAuthInfo {\n const config = getConfig();\n const requiredEnv = PROVIDERS[config.providerName]?.keyEnv || \"EMBEDDING_API_KEY\";\n return {\n required_env: requiredEnv,\n has_key: !!config.apiKey,\n };\n}\n\n/**\n * Setup: Create table and optimized index\n * Automatically uses the configured embedding dimension.\n * Fix #2: Skips all SQLite work on subsequent warm calls via _setupDone guard.\n */\nexport async function setup(): Promise {\n if (_setupDone) return;\n\n const indexConfig = getIndexConfig();\n const dim = await resolveDimForSetup();\n\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${META_TABLE} (\n key TEXT PRIMARY KEY,\n value TEXT\n )\n `);\n \n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${TABLE_NAME} (\n id TEXT PRIMARY KEY,\n text TEXT NOT NULL,\n text_hash TEXT NOT NULL,\n embedding F32_BLOB(${dim}),\n meta_json TEXT,\n updated_at INTEGER NOT NULL\n )\n `);\n\n await ensureFtsTable();\n\n try {\n const idxArgs = [\n `metric=${indexConfig.metric}`,\n `max_neighbors=${indexConfig.maxNeighbors}`,\n ];\n if (indexConfig.compressNeighbors !== \"none\") {\n idxArgs.push(`compress_neighbors=${indexConfig.compressNeighbors}`);\n }\n if (indexConfig.hasAlpha) idxArgs.push(`alpha=${indexConfig.alpha}`);\n if (indexConfig.hasSearchL) idxArgs.push(`search_l=${indexConfig.searchL}`);\n if (indexConfig.hasInsertL) idxArgs.push(`insert_l=${indexConfig.insertL}`);\n const idxArgsSql = idxArgs.map((s) => `'${s}'`).join(\", \");\n\n await sqlite.execute(`\n CREATE INDEX ${INDEX_NAME} \n ON ${TABLE_NAME} (libsql_vector_idx(embedding, ${idxArgsSql}))\n `);\n } catch (e) {\n const msg = e instanceof Error ? e.message : String(e);\n if (!msg.includes(\"already exists\")) {\n throw e;\n }\n }\n\n _setupDone = true;\n}\n\n/**\n * Recreate index with optimized settings\n */\nexport async function reindex(): Promise<{ success: boolean; message: string }> {\n try {\n const indexConfig = getIndexConfig();\n await sqlite.execute(`DROP INDEX IF EXISTS ${INDEX_NAME}`);\n\n const idxArgs = [\n `metric=${indexConfig.metric}`,\n `max_neighbors=${indexConfig.maxNeighbors}`,\n ];\n if (indexConfig.compressNeighbors !== \"none\") {\n idxArgs.push(`compress_neighbors=${indexConfig.compressNeighbors}`);\n }\n if (indexConfig.hasAlpha) idxArgs.push(`alpha=${indexConfig.alpha}`);\n if (indexConfig.hasSearchL) idxArgs.push(`search_l=${indexConfig.searchL}`);\n if (indexConfig.hasInsertL) idxArgs.push(`insert_l=${indexConfig.insertL}`);\n const idxArgsSql = idxArgs.map((s) => `'${s}'`).join(\", \");\n\n await sqlite.execute(`\n CREATE INDEX ${INDEX_NAME} \n ON ${TABLE_NAME} (libsql_vector_idx(embedding, ${idxArgsSql}))\n `);\n return { success: true, message: \"Index recreated with optimized settings\" };\n } catch (e) {\n const msg = e instanceof Error ? e.message : String(e);\n return { success: false, message: msg };\n }\n}\n\n/**\n * Upsert: Insert or update a record\n */\nexport async function upsert(\n id: string, \n text: string, \n meta?: Record\n): Promise<{ embedded: boolean }> {\n const newHash = await hashText(text);\n const metaJson = meta ? JSON.stringify(meta) : null;\n const now = Date.now();\n\n const existing = await sqlite.execute({\n sql: `SELECT text_hash FROM ${TABLE_NAME} WHERE id = ?`,\n args: [id],\n });\n\n const existingHash = (existing.rows?.[0] as { text_hash: string } | undefined)?.text_hash;\n\n if (existingHash === newHash) {\n await sqlite.execute({\n sql: `UPDATE ${TABLE_NAME} SET meta_json = ?, updated_at = ? WHERE id = ?`,\n args: [metaJson, now, id],\n });\n return { embedded: false };\n }\n\n const embedding = await embed(text);\n\n await sqlite.execute({\n sql: `\n INSERT INTO ${TABLE_NAME} (id, text, text_hash, embedding, meta_json, updated_at)\n VALUES (?, ?, ?, vector32(?), ?, ?)\n ON CONFLICT(id) DO UPDATE SET\n text = excluded.text,\n text_hash = excluded.text_hash,\n embedding = excluded.embedding,\n meta_json = excluded.meta_json,\n updated_at = excluded.updated_at\n `,\n args: [id, text, newHash, JSON.stringify(embedding), metaJson, now],\n });\n\n await syncFtsUpsert(id, text);\n\n return { embedded: true };\n}\n\n/**\n * Batch upsert: Insert or update many records\n * Fix #1: Uses sqlite.batch() instead of per-record loops for both embed and skip paths.\n * Fix #5: FTS syncs are batched alongside main upserts in one round-trip.\n */\nexport async function upsertMany(\n records: UpsertInput[]\n): Promise<{ embedded: number; skipped: number; results: Array<{ id: string; embedded: boolean }> }> {\n if (records.length === 0) return { embedded: 0, skipped: 0, results: [] };\n\n const ids = records.map((r) => r.id);\n const placeholders = ids.map(() => \"?\").join(\",\");\n const existing = await sqlite.execute({\n sql: `SELECT id, text_hash FROM ${TABLE_NAME} WHERE id IN (${placeholders})`,\n args: ids,\n });\n\n const existingMap = new Map();\n for (const row of existing.rows as Array<{ id: string; text_hash: string }>) {\n existingMap.set(row.id, row.text_hash);\n }\n\n const hashes = await Promise.all(records.map((r) => hashText(r.text)));\n const toEmbed: Array<{ record: UpsertInput; hash: string }> = [];\n const toSkip: Array<{ record: UpsertInput; hash: string }> = [];\n\n records.forEach((record, idx) => {\n const newHash = hashes[idx];\n const existingHash = existingMap.get(record.id);\n if (existingHash === newHash) {\n toSkip.push({ record, hash: newHash });\n } else {\n toEmbed.push({ record, hash: newHash });\n }\n });\n\n const results: Array<{ id: string; embedded: boolean }> = [];\n const now = Date.now();\n\n // Fix #1a: Batch all meta-only updates in one round-trip\n if (toSkip.length > 0) {\n const skipStatements = toSkip.map(({ record }) => ({\n sql: `UPDATE ${TABLE_NAME} SET meta_json = ?, updated_at = ? WHERE id = ?`,\n args: [record.meta ? JSON.stringify(record.meta) : null, now, record.id],\n }));\n await sqlite.batch(skipStatements);\n for (const { record } of toSkip) {\n results.push({ id: record.id, embedded: false });\n }\n }\n\n // Fix #1b: Batch all embed upserts + FTS syncs in one round-trip\n if (toEmbed.length > 0) {\n const texts = toEmbed.map((item) => item.record.text);\n const embeddings = await embedMany(texts);\n\n const upsertStatements = toEmbed.map(({ record, hash }, i) => ({\n sql: `\n INSERT INTO ${TABLE_NAME} (id, text, text_hash, embedding, meta_json, updated_at)\n VALUES (?, ?, ?, vector32(?), ?, ?)\n ON CONFLICT(id) DO UPDATE SET\n text = excluded.text,\n text_hash = excluded.text_hash,\n embedding = excluded.embedding,\n meta_json = excluded.meta_json,\n updated_at = excluded.updated_at\n `,\n args: [\n record.id,\n record.text,\n hash,\n JSON.stringify(embeddings[i]),\n record.meta ? JSON.stringify(record.meta) : null,\n now,\n ],\n }));\n\n // FTS sync statements batched alongside upserts (Fix #5)\n if (FTS_AVAILABLE === null) await ensureFtsTable();\n const ftsStatements = FTS_AVAILABLE\n ? toEmbed.flatMap(({ record }) => [\n { sql: `DELETE FROM ${FTS_TABLE} WHERE id = ?`, args: [record.id] },\n { sql: `INSERT INTO ${FTS_TABLE} (id, text) VALUES (?, ?)`, args: [record.id, record.text] },\n ])\n : [];\n\n await sqlite.batch([...upsertStatements, ...ftsStatements]);\n\n for (const { record } of toEmbed) {\n results.push({ id: record.id, embedded: true });\n }\n }\n\n return {\n embedded: toEmbed.length,\n skipped: toSkip.length,\n results,\n };\n}\n\n/**\n * Simple chunking helper (approx chars)\n */\nexport function chunkText(text: string, options: ChunkOptions = {}): string[] {\n const size = Math.max(200, options.size ?? 800);\n const overlap = Math.max(0, options.overlap ?? 100);\n if (text.length <= size) return [text];\n\n const chunks: string[] = [];\n let start = 0;\n while (start < text.length) {\n const end = Math.min(text.length, start + size);\n let chunk = text.slice(start, end);\n\n // Try to break on whitespace for nicer chunks\n const lastSpace = chunk.lastIndexOf(\" \");\n if (lastSpace > size * 0.6) {\n chunk = chunk.slice(0, lastSpace);\n }\n\n chunks.push(chunk.trim());\n start += Math.max(chunk.length - overlap, 1);\n }\n\n return chunks.filter((c) => c.length > 0);\n}\n\n/**\n * Chunked upsert: splits text and upserts as id::chunkN\n */\nexport async function upsertChunked(\n id: string,\n text: string,\n meta?: Record,\n options: ChunkOptions = {}\n): Promise<{ chunks: number; embedded: number; skipped: number }> {\n const chunks = chunkText(text, options);\n const records: UpsertInput[] = chunks.map((chunk, i) => ({\n id: `${id}::chunk${i + 1}`,\n text: chunk,\n meta: {\n ...(meta || {}),\n parent_id: id,\n chunk_index: i + 1,\n chunk_total: chunks.length,\n },\n }));\n\n const result = await upsertMany(records);\n return { chunks: chunks.length, embedded: result.embedded, skipped: result.skipped };\n}\n\n/**\n * Search: Semantic search using vector similarity\n */\nfunction buildFilterClause(filters?: Record): { clause: string; args: Array } {\n if (!filters || Object.keys(filters).length === 0) return { clause: \"\", args: [] };\n\n const clauses: string[] = [];\n const args: Array = [];\n\n for (const [key, value] of Object.entries(filters)) {\n const safeKey = key.replace(/[^a-zA-Z0-9_]/g, \"\");\n if (!safeKey || safeKey !== key) {\n throw new Error(`Invalid filter key: ${key}`);\n }\n\n const path = `$.${safeKey}`;\n\n if (Array.isArray(value)) {\n if (value.length === 0) continue;\n const placeholders = value.map(() => \"?\").join(\",\");\n clauses.push(`json_extract(t.meta_json, ?) IN (${placeholders})`);\n args.push(path, ...value);\n } else if (value === null) {\n clauses.push(`json_extract(t.meta_json, ?) IS NULL`);\n args.push(path);\n } else {\n clauses.push(`json_extract(t.meta_json, ?) = ?`);\n args.push(path, value);\n }\n }\n\n if (clauses.length === 0) return { clause: \"\", args: [] };\n return { clause: `WHERE ${clauses.join(\" AND \")}`, args };\n}\n\nasync function getKeywordScores(query: string, limit: number): Promise> {\n if (FTS_AVAILABLE === null) await ensureFtsTable();\n if (!FTS_AVAILABLE) return new Map();\n\n try {\n const res = await sqlite.execute({\n sql: `SELECT id, bm25(${FTS_TABLE}) as score FROM ${FTS_TABLE} WHERE ${FTS_TABLE} MATCH ? 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JSON.parse(row.meta_json) : undefined,\n distance: row.distance,\n }));\n\n if (maxDistance !== undefined) {\n results = results.filter(r => r.distance <= maxDistance);\n }\n\n const hybridEnabled = !!options.hybrid?.enabled || typeof options.hybrid?.alpha === \"number\";\n if (hybridEnabled) {\n const alpha = Math.min(1, Math.max(0, options.hybrid?.alpha ?? 0.25));\n const keywordK = Math.max(20, options.hybrid?.keyword_k ?? (k + offset) * 2);\n const keywordScores = await getKeywordScores(query, keywordK);\n\n results = results.map((r) => {\n const vecScore = Math.max(0, 1 - r.distance);\n const kwScore = keywordScores.get(r.id) ?? 0;\n const combined = (1 - alpha) * vecScore + alpha * kwScore;\n return { ...r, score: combined, keyword_score: kwScore };\n });\n\n results.sort((a, b) => (b.score ?? 0) - (a.score ?? 0));\n }\n\n return results.slice(offset, offset + k);\n}\n\n/**\n * Delete: Remove a record by ID\n */\nexport async function remove(id: string): Promise<{ deleted: boolean }> {\n const result = await sqlite.execute({\n sql: `DELETE FROM ${TABLE_NAME} WHERE id = ?`,\n args: [id],\n });\n if ((result.rowsAffected ?? 0) > 0) {\n await syncFtsDelete(id);\n }\n return { deleted: (result.rowsAffected ?? 0) > 0 };\n}\n\n/**\n * Stats: Get record count and storage estimate\n * Fix #6: Uses RESOLVED_DIM when available so auto-dim mode reports correctly.\n */\nexport async function stats(): Promise {\n const config = getConfig();\n const result = await sqlite.execute(`SELECT COUNT(*) as count FROM ${TABLE_NAME}`);\n const count = Number((result.rows[0] as { count: number }).count);\n \n // Fix #6: prefer RESOLVED_DIM over config.dim for accurate estimate in auto-dim mode\n const dim = RESOLVED_DIM ?? config.dim;\n const bytesPerRecord = (dim * 4) + 6000;\n const estimated_storage_mb = Math.round((count * bytesPerRecord) / 1024 / 1024 * 100) / 100;\n\n return { count, estimated_storage_mb };\n}\n\n/**\n * Get a single record by ID\n */\nexport async function get(id: string): Promise {\n const result = await sqlite.execute({\n sql: `SELECT id, text, meta_json, updated_at FROM ${TABLE_NAME} WHERE id = ?`,\n args: [id],\n });\n\n if (!result.rows || result.rows.length === 0) return null;\n\n const row = result.rows[0] as { id: string; text: string; meta_json: string | null; updated_at: number };\n return {\n id: row.id,\n text: row.text,\n meta: row.meta_json ? 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kamenxrider

slimarmor