[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_157":158,"_159":160},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_119":-5,"_20":120,"_22":121,"_122":123,"_124":125,"_25":126,"_24":-5,"_127":-5,"_128":-5,"_129":53,"_130":131,"_132":44,"_133":-5,"_134":-5,"_135":136},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":-7,"_25":26,"_27":-5,"_28":-5,"_29":-5,"_30":-5,"_31":-5},"handle","swdu","name","novelai-oai-proxy","branchName","main","version","path","","project",{"_33":61,"_13":14,"_45":62,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":69,"_70":-5,"_71":72,"_73":-5,"_74":53,"_75":-5,"_76":-5,"_77":78,"_79":80,"_81":82,"_83":84,"_85":86,"_87":-5,"_88":89,"_90":89,"_91":92,"_93":92,"_94":92,"_95":89,"_96":89,"_97":98,"_99":100,"_101":102,"_103":53},"tree",[56],"file","contents",[32],"upstream","pull","ahead","behind","envVars",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_18":55},"id","8ed0ded8-5a28-11f1-8022-ee650bb23af1","main.ts","fileId","b9a5b966-5a06-11f1-9151-ee650bb23af1","parentId","branchId","b9a1d10c-5a06-11f1-9151-ee650bb23af1","branchVersion",22,"latest",true,"createdAt",["D",1779926566609],21,"fileType","http","content","/**\n * NovelAI -> OpenAI compatible proxy\n *\n * Env vars (set them in Val Town -> Settings -> Environment Variables):\n * NAI_TOKEN : NovelAI Persistent API token (required)\n * PROXY_KEY : optional shared secret to protect this endpoint\n *\n * Endpoints:\n * GET /v1/models -> list models (no auth needed, so client dropdowns work)\n * POST /v1/chat/completions -> OAI chat (streaming & non-streaming)\n * POST /v1/completions -> OAI text completion (NAI is natively a completion model)\n *\n * /v1 prefix is optional; /models, /chat/completions, /completions also work.\n */\n\n// @ts-ignore - npm shim\nimport llama3Tokenizer from \"npm:llama3-tokenizer-js@1.2.0\";\n\nconst NAI_TEXT_API = \"https://text.novelai.net\";\nconst NAI_LEGACY_API = \"https://api.novelai.net\";\n\ntype ModelInfo = {\n nai: string;\n tokenizer: \"llama3\" | \"nerdstash\";\n bytesPerToken: 2 | 4;\n baseUrl: string;\n};\n\nconst MODEL_MAP: Record = {\n erato: { nai: \"llama-3-erato-v1\", tokenizer: \"llama3\", bytesPerToken: 4, baseUrl: NAI_TEXT_API },\n kayra: { nai: \"kayra-v1\", tokenizer: \"nerdstash\", bytesPerToken: 2, baseUrl: NAI_TEXT_API },\n clio: { nai: \"clio-v1\", tokenizer: \"nerdstash\", bytesPerToken: 2, baseUrl: NAI_LEGACY_API },\n \"llama-3-erato-v1\": { nai: \"llama-3-erato-v1\", tokenizer: \"llama3\", bytesPerToken: 4, baseUrl: NAI_TEXT_API },\n \"kayra-v1\": { nai: \"kayra-v1\", tokenizer: \"nerdstash\", bytesPerToken: 2, baseUrl: NAI_TEXT_API },\n \"clio-v1\": { nai: \"clio-v1\", tokenizer: \"nerdstash\", bytesPerToken: 2, baseUrl: NAI_LEGACY_API },\n};\n\nconst MODELS_LIST = [\n { id: \"erato\", object: \"model\", created: 1727740800, owned_by: \"novelai\" },\n { id: \"kayra\", object: \"model\", created: 1692835200, owned_by: \"novelai\" },\n { id: \"clio\", object: \"model\", created: 1684800000, owned_by: \"novelai\" },\n];\n\nconst CORS = {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Headers\": \"Authorization, Content-Type, x-api-key\",\n \"Access-Control-Allow-Methods\": \"GET, POST, OPTIONS\",\n};\n\n// ---------- helpers ----------\n\nfunction tokensToB64(tokens: number[], bytesPerToken: number): string {\n const buf = new Uint8Array(tokens.length * bytesPerToken);\n const view = new DataView(buf.buffer);\n for (let i = 0; i < tokens.length; i++) {\n if (bytesPerToken === 4) view.setUint32(i * 4, tokens[i], true);\n else view.setUint16(i * 2, tokens[i], true);\n }\n let s = \"\";\n for (let i = 0; i < buf.length; i++) s += String.fromCharCode(buf[i]);\n return btoa(s);\n}\n\nfunction b64ToTokens(b64: string, bytesPerToken: number): number[] {\n const bin = atob(b64);\n const buf = new Uint8Array(bin.length);\n for (let i = 0; i < bin.length; i++) buf[i] = bin.charCodeAt(i);\n const view = new DataView(buf.buffer);\n const tokens: number[] = [];\n for (let i = 0; i < buf.length; i += bytesPerToken) {\n tokens.push(bytesPerToken === 4 ? view.getUint32(i, true) : view.getUint16(i, true));\n }\n return tokens;\n}\n\nfunction encodeForModel(prompt: string, info: ModelInfo): number[] {\n if (info.tokenizer === \"llama3\") {\n // llama3-tokenizer-js prepends BOS (128000). For prompts NAI handles\n // the BOS server-side; we keep tokens raw and strip only when needed.\n return llama3Tokenizer.encode(prompt);\n }\n throw new Error(`This proxy currently only supports the llama3 tokenizer (model 'erato'). Requested model needs the NerdStash tokenizer.`);\n}\n\nfunction decodeForModel(tokens: number[], info: ModelInfo): string {\n if (info.tokenizer === \"llama3\") return llama3Tokenizer.decode(tokens);\n return \"\";\n}\n\nfunction messagesToPrompt(messages: Array<{ role: string; content: string }>): { prompt: string; userName: string; botName: string } {\n // NAI's official chat format (https://docs.novelai.net/en/text/chatformat):\n // memory_information\n // ***\n // [ Style: chat ]\n //\n // User: hi\n // Assistant: hello!\n // User: ...\n // Assistant: \n // We stop at \"\\nUser:\" so the model only emits one assistant turn.\n const userName = \"User\";\n const botName = \"Assistant\";\n\n const systemBits: string[] = [];\n const turnBits: string[] = [];\n for (const msg of messages) {\n const c = (msg.content ?? \"\").toString().trim();\n if (!c) continue;\n if (msg.role === \"system\") systemBits.push(c);\n else if (msg.role === \"user\") turnBits.push(`${userName}: ${c}`);\n else if (msg.role === \"assistant\") turnBits.push(`${botName}: ${c}`);\n }\n const memory = systemBits.length\n ? `${systemBits.join(\"\\n\")}\\n***\\n[ Style: chat ]\\n\\n`\n : `[ Style: chat ]\\n\\n`;\n const conversation = turnBits.join(\"\\n\");\n // trailing space after \"Assistant:\" — nudges BPE so the model continues\n // with content rather than re-emitting \"Assistant:\".\n const prompt = `${memory}${conversation}\\n${botName}: `;\n return { prompt, userName, botName };\n}\n\n// Post-process Erato's chat output: strip prompt echo, leading \"----\",\n// truncate at the next user turn, etc.\nfunction cleanChatOutput(raw: string): string {\n let text = raw;\n // Erato sometimes emits a literal \"----\\n\" before the actual content.\n text = text.replace(/^-{2,}\\r?\\n?/, \"\");\n // Truncate at next user turn / separator / EOS marker first.\n const stopStrings = [\"\\nUser:\", \"\\n\\nUser:\", \"\\n***\", \"<|end_of_text|>\"];\n let cut = text.length;\n for (const s of stopStrings) {\n const i = text.indexOf(s);\n if (i !== -1 && i < cut) cut = i;\n }\n text = text.slice(0, cut);\n // If the (now-truncated) output still contains a prompt-echo \"Assistant:\",\n // take only what's AFTER the FIRST such marker — that's the model's\n // actual first reply turn.\n const aIdx = text.indexOf(\"Assistant:\");\n if (aIdx !== -1) text = text.slice(aIdx + \"Assistant:\".length);\n return text.trim();\n}\n\nfunction jsonResp(data: unknown, status = 200): Response {\n return new Response(JSON.stringify(data), {\n status,\n headers: { \"Content-Type\": \"application/json\", ...CORS },\n });\n}\n\nfunction oaiError(message: string, status = 400, type = \"invalid_request_error\"): Response {\n return jsonResp({ error: { message, type, code: null } }, status);\n}\n\n// ---------- handler ----------\n\nexport default async function handler(req: Request): Promise {\n if (req.method === \"OPTIONS\") return new Response(null, { headers: CORS });\n\n const url = new URL(req.url);\n // strip /v1 prefix if present\n const pathname = url.pathname.replace(/^\\/v1\\b/, \"\") || \"/\";\n\n // ----- GET /models : no auth, so dropdown population works -----\n if (req.method === \"GET\" && pathname === \"/models\") {\n return jsonResp({ object: \"list\", data: MODELS_LIST });\n }\n\n // Root info page\n if (req.method === \"GET\" && (pathname === \"/\" || pathname === \"\")) {\n return jsonResp({\n name: \"novelai-oai-proxy\",\n endpoints: [\"GET /v1/models\", \"POST /v1/chat/completions\", \"POST /v1/completions\"],\n models: MODELS_LIST.map((m) => m.id),\n note: \"Set NAI_TOKEN env var on the proxy. Use 'erato' for best results (only Llama3 tokenizer is bundled).\",\n });\n }\n\n // ----- everything below requires auth -----\n const proxyKey = Deno.env.get(\"PROXY_KEY\");\n if (proxyKey) {\n const auth = req.headers.get(\"Authorization\") ?? \"\";\n const tok = auth.replace(/^Bearer\\s+/i, \"\");\n if (tok !== proxyKey) return oaiError(\"Incorrect API key provided.\", 401, \"invalid_request_error\");\n }\n\n const naiToken = Deno.env.get(\"NAI_TOKEN\");\n if (!naiToken) return oaiError(\"NAI_TOKEN env var is not configured on the proxy.\", 500, \"server_error\");\n\n // ----- POST /chat/completions or /completions -----\n if (req.method === \"POST\" && (pathname === \"/chat/completions\" || pathname === \"/completions\")) {\n const isChat = pathname === \"/chat/completions\";\n let body: any;\n try { body = await req.json(); } catch { return oaiError(\"Invalid JSON body.\"); }\n\n const requestedModel = (body.model ?? \"erato\").toString();\n const info = MODEL_MAP[requestedModel];\n if (!info) return oaiError(`Unknown model '${requestedModel}'. Available: ${Object.keys(MODEL_MAP).join(\", \")}`);\n if (info.tokenizer !== \"llama3\") {\n return oaiError(\n `Model '${requestedModel}' uses NerdStash tokenizer which isn't bundled in this proxy yet. Use 'erato' instead.`,\n 400,\n );\n }\n\n const promptStr = isChat\n ? messagesToPrompt(body.messages ?? []).prompt\n : (body.prompt ?? \"\").toString();\n if (!promptStr) return oaiError(\"Empty prompt.\");\n\n let promptTokens: number[];\n try { promptTokens = encodeForModel(promptStr, info); } catch (e) { return oaiError(String(e), 500); }\n\n // We rely on client-side string-level stop trimming (below) because\n // NAI's token-level stop sequences are unreliable due to context-dependent BPE.\n\n const stream = !!body.stream;\n const maxTokens = Math.min(Math.max(body.max_tokens ?? 200, 1), 2048);\n const temperature = body.temperature ?? 1.0;\n const topP = body.top_p ?? 0.95;\n\n const naiBody: any = {\n input: tokensToB64(promptTokens, info.bytesPerToken),\n model: info.nai,\n parameters: {\n use_string: false,\n temperature,\n max_length: maxTokens,\n min_length: 1,\n top_p: topP,\n top_k: 0,\n top_a: 1.0,\n typical_p: 1.0,\n tail_free_sampling: 1.0,\n repetition_penalty: 1.0,\n repetition_penalty_range: 2048,\n repetition_penalty_slope: 0.0,\n repetition_penalty_frequency: 0.0,\n repetition_penalty_presence: 0.0,\n generate_until_sentence: true,\n use_cache: false,\n return_full_text: false,\n prefix: \"vanilla\",\n },\n };\n\n const endpoint = `${info.baseUrl}/ai/generate${stream ? \"-stream\" : \"\"}`;\n const naiRes = await fetch(endpoint, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${naiToken}`,\n \"Content-Type\": \"application/json\",\n \"Accept\": stream ? \"text/event-stream\" : \"application/json\",\n \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36\",\n \"Origin\": \"https://novelai.net\",\n \"Referer\": \"https://novelai.net/\",\n },\n body: JSON.stringify(naiBody),\n });\n\n if (!naiRes.ok) {\n const txt = await naiRes.text();\n return oaiError(`NovelAI upstream error (${naiRes.status}): ${txt}`, naiRes.status, \"upstream_error\");\n }\n\n const completionId = `chatcmpl-nai-${Date.now()}`;\n const created = Math.floor(Date.now() / 1000);\n\n if (stream) {\n return streamResponse(naiRes, requestedModel, info, completionId, created, isChat);\n } // non-stream\n const naiJson = await naiRes.json();\n const outTokens = b64ToTokens(naiJson.output ?? \"\", info.bytesPerToken);\n let text = decodeForModel(outTokens, info);\n if (isChat) text = cleanChatOutput(text);\n\n if (isChat) {\n return jsonResp({\n id: completionId, object: \"chat.completion\", created, model: requestedModel,\n choices: [{ index: 0, message: { role: \"assistant\", content: text }, finish_reason: \"stop\" }],\n usage: { prompt_tokens: promptTokens.length, completion_tokens: outTokens.length, total_tokens: promptTokens.length + outTokens.length },\n });\n } else {\n return jsonResp({\n id: completionId, object: \"text_completion\", created, model: requestedModel,\n choices: [{ text, index: 0, finish_reason: \"stop\", logprobs: null }],\n usage: { prompt_tokens: promptTokens.length, completion_tokens: outTokens.length, total_tokens: promptTokens.length + outTokens.length },\n });\n }\n }\n\n return oaiError(`No such endpoint: ${req.method} ${pathname}`, 404, \"not_found\");\n}\n\n// ---------- streaming ----------\n\nfunction streamResponse(\n naiRes: Response, modelId: string, info: ModelInfo, id: string, created: number, isChat: boolean,\n): Response {\n const { readable, writable } = new TransformStream();\n const writer = writable.getWriter();\n const encoder = new TextEncoder();\n const STOP_STRINGS = isChat ? [\"\\nUser:\", \"\\n\\nUser:\", \"\\n***\", \"<|end_of_text|>\"] : [];\n const maxStopLen = STOP_STRINGS.reduce((m, s) => Math.max(m, s.length), 0);\n // While in echo-detection mode, hold output until we can decide whether\n // Erato is echoing the prompt. If we see \"Assistant:\" within this many\n // characters, treat everything before it as echo; otherwise start sending.\n const ECHO_SCAN_MAX = 80;\n\n (async () => {\n const reader = naiRes.body!.getReader();\n const decoder = new TextDecoder();\n let buf = \"\";\n const allTokens: number[] = [];\n let lastSent = \"\"; // chars already SENT to client\n let echoOffset = 0; // chars at start of fullText to skip\n let echoResolved = !isChat; // text mode: no echo handling needed\n let stopped = false;\n\n const send = (obj: unknown) => writer.write(encoder.encode(`data: ${JSON.stringify(obj)}\\n\\n`));\n const emit = async (delta: string) => {\n if (!delta) return;\n const chunk = isChat\n ? { id, object: \"chat.completion.chunk\", created, model: modelId, choices: [{ index: 0, delta: { content: delta }, finish_reason: null }] }\n : { id, object: \"text_completion\", created, model: modelId, choices: [{ text: delta, index: 0, finish_reason: null, logprobs: null }] };\n await send(chunk);\n };\n\n try {\n if (isChat) {\n await send({\n id, object: \"chat.completion.chunk\", created, model: modelId,\n choices: [{ index: 0, delta: { role: \"assistant\" }, finish_reason: null }],\n });\n }\n\n while (!stopped) {\n const { done, value } = await reader.read();\n if (done) break;\n buf += decoder.decode(value, { stream: true });\n const lines = buf.split(\"\\n\");\n buf = lines.pop() ?? \"\";\n\n for (const raw of lines) {\n if (stopped) break;\n const line = raw.trim();\n if (!line.startsWith(\"data:\")) continue;\n const data = line.slice(5).trim();\n if (!data || data === \"[DONE]\") continue;\n try {\n const parsed = JSON.parse(data);\n const tokB64 = parsed.token;\n if (!tokB64) continue;\n const newToks = b64ToTokens(tokB64, info.bytesPerToken);\n allTokens.push(...newToks);\n let fullText = decodeForModel(allTokens, info);\n if (isChat) fullText = fullText.replace(/^-{2,}\\r?\\n?/, \"\");\n\n // ---- echo detection ----\n if (!echoResolved) {\n const aIdx = fullText.indexOf(\"Assistant:\");\n if (aIdx !== -1) {\n echoOffset = aIdx + \"Assistant:\".length;\n echoResolved = true;\n } else if (fullText.length >= ECHO_SCAN_MAX) {\n // long enough that we'd notice a \"Assistant:\" by now — assume no echo\n echoResolved = true;\n } else {\n // keep buffering, don't send anything yet\n continue;\n }\n }\n\n const visible = fullText.slice(echoOffset).replace(/^[ \\t]+/, \"\");\n // recompute fullText effective start (skipping the leading space we trimmed)\n const trimmedSpaces = fullText.length - echoOffset - visible.length;\n const effStart = echoOffset + trimmedSpaces;\n\n // find stop string within the visible portion\n let stopIdx = -1;\n for (const s of STOP_STRINGS) {\n const i = visible.indexOf(s);\n if (i !== -1 && (stopIdx === -1 || i < stopIdx)) stopIdx = i;\n }\n\n let safeUntil: number;\n if (stopIdx !== -1) {\n safeUntil = stopIdx;\n } else if (maxStopLen > 0) {\n safeUntil = Math.max(0, visible.length - (maxStopLen - 1));\n } else {\n safeUntil = visible.length;\n }\n const safeText = visible.slice(0, safeUntil);\n const delta = safeText.slice(lastSent.length);\n if (delta) {\n lastSent = safeText;\n await emit(delta);\n }\n if (stopIdx !== -1) { stopped = true; break; }\n // keep effStart used for future iterations (suppress unused warning)\n void effStart;\n } catch { /* skip malformed */ }\n }\n }\n\n // flush any remaining held text if no stop was hit\n if (!stopped) {\n let fullText = decodeForModel(allTokens, info);\n if (isChat) fullText = fullText.replace(/^-{2,}\\r?\\n?/, \"\");\n if (!echoResolved) {\n const aIdx = fullText.indexOf(\"Assistant:\");\n echoOffset = aIdx !== -1 ? aIdx + \"Assistant:\".length : 0;\n }\n const visible = fullText.slice(echoOffset).replace(/^[ \\t]+/, \"\");\n let stopIdx = -1;\n for (const s of STOP_STRINGS) {\n const i = visible.indexOf(s);\n if (i !== -1 && (stopIdx === -1 || i < stopIdx)) stopIdx = i;\n }\n const finalText = stopIdx === -1 ? visible : visible.slice(0, stopIdx);\n const delta = finalText.slice(lastSent.length);\n if (delta) await emit(delta);\n }\n\n const finishChunk = isChat\n ? { id, object: \"chat.completion.chunk\", created, model: modelId, choices: [{ index: 0, delta: {}, finish_reason: \"stop\" }] }\n : { id, object: \"text_completion\", created, model: modelId, choices: [{ text: \"\", index: 0, finish_reason: \"stop\", logprobs: null }] };\n await send(finishChunk);\n await writer.write(encoder.encode(\"data: [DONE]\\n\\n\"));\n } catch (e) {\n try { await send({ error: { message: String(e), type: \"stream_error\" } }); 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