[{"_1":2,"_174":-5,"_175":-5},"loaderData",{"_3":4,"_92":93,"_161":162,"_173":-5},"root",{"_5":6,"_33":34,"_59":60,"_90":91},"ctx",{"_7":8,"_18":19,"_20":-5,"_21":22,"_23":24,"_25":26,"_27":26,"_28":26,"_29":30,"_31":32},"auth",{"_9":-5,"_10":-5,"_11":-5,"_12":-5,"_13":-5,"_14":-5,"_15":-5,"_16":-5,"_17":-5},"sessionClaims","sessionId","userId","actor","orgId","orgRole","orgSlug","orgPermissions","factorVerificationAge","environmentVariableKeys",[],"user","preferences_keybindings","default","preferences_theme","light","preferences_fixers",false,"preferences_copilot","preferences_selection_ai","preferences_model","claude-3-5-haiku-20241022","notification_count",0,"ENV",{"_35":36,"_37":38,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","VAL_RUNTIME_NAME","prod","RENDER_GIT_COMMIT","7dcf896e9ab5f2c6fa603363ecb12735311d2edf","RENDER_GIT_BRANCH","main","CLERK_PUBLISHABLE_KEY","pk_live_Y2xlcmsudmFsLnRvd24k","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","API_URL","https://api.val.town","WORKER_FINGERPRINT","c7de7de11d2fa3884730f0fa9fd384f8","clerkState",{"_61":62},"__internal_clerk_state",{"_63":64,"_65":44,"_66":67,"_68":67,"_69":26,"_70":67,"_71":67,"_72":67,"_73":67,"_74":67,"_75":67,"_76":67,"_77":67,"_78":79,"_88":26,"_89":26},"__clerk_ssr_state",{"_9":-5,"_10":-5,"_11":-5,"_12":-5,"_13":-5,"_14":-5,"_15":-5,"_16":-5,"_17":-5},"__publishableKey","__proxyUrl","","__domain","__isSatellite","__signInUrl","__signUpUrl","__afterSignInUrl","__afterSignUpUrl","__signInForceRedirectUrl","__signUpForceRedirectUrl","__signInFallbackRedirectUrl","__signUpFallbackRedirectUrl","__clerk_debug",{"_80":26,"_81":67,"_82":83,"_84":67,"_85":44,"_86":26,"_87":67},"isSignedIn","proxyUrl","reason","session-token-and-uat-missing","message","publishableKey","isSatellite","domain","__telemetryDisabled","__telemetryDebug","remixVersion",2,"routes/_app.v.$handle.$name",{"_94":95,"_96":97,"_95":98,"_156":-5,"_157":-5,"_158":159,"_130":131,"_143":144,"_101":102,"_99":100,"_119":-7,"_51":52,"_160":26},"type","val","author","c350ec50-e00a-11ef-85af-ef9415b39d08",{"_99":100,"_96":97,"_101":102,"_103":-5,"_104":-5,"_105":106,"_107":108,"_109":110,"_111":112,"_113":26,"_114":112,"_115":116,"_117":118,"_119":91,"_120":26,"_121":32,"_122":32,"_123":32,"_124":125,"_126":32,"_127":32,"_128":-5,"_20":129,"_134":135,"_140":-7,"_141":142,"_143":144,"_145":146},"name","ai","valNameId","61ccb3be-e00b-11ef-93a5-e6cdfca9ef9f","parentFolderId","isOwned","privacy","public","readme","An http and class wrapper for Vercel's AI SDK\n\nUsage:\n- Groq: `https://yawnxyz-ai.web.val.run/generate?prompt=\"tell me a beer joke\"&provider=groq&model=llama3-8b-8192`\n- Perplexity: `https://yawnxyz-ai.web.val.run/generate?prompt=\"what's the latest phage directory capsid & tail article about?\"&provider=perplexity`\n- Mistral: `https://yawnxyz-ai.web.val.run/generate?prompt=\"tell me a joke?\"&provider=mistral&model=\"mistral-small-latest\"`\n\n\n```\nasync function calculateEmbeddings(text) {\n const url = `https://yawnxyz-ai.web.val.run/generate?embed=true&value=${encodeURIComponent(text)}`;\n \n try {\n const response = await fetch(url);\n const data = await response.json();\n return data;\n } catch (error) {\n console.error('Error calculating embeddings:', error);\n return null;\n }\n}\n```\n","valType","http","currentVersion","7e8f43e0-e00b-11ef-87c0-e6cdfca9ef9f","legacyPrivate","id","code","import \"jsr:@std/dotenv/load\"; // needed for deno run; not req for smallweb or valtown\nimport { createAnthropic } from \"npm:@ai-sdk/anthropic@0.0.48\";\nimport { createGoogleGenerativeAI, google } from \"npm:@ai-sdk/google\";\nimport { createMistral, mistral } from \"npm:@ai-sdk/mistral\";\nimport { createOpenAI } from \"npm:@ai-sdk/openai\";\nimport { embed, generateObject, generateText, streamText } from \"npm:ai\";\nimport { cors } from \"npm:hono/cors\";\nimport { Hono } from \"npm:hono@3\";\n// import { z } from \"npm:zod\";\n// import { nanoid } from 'npm:nanoid';\nimport stringHash from \"npm:string-hash\";\n\nimport { blobby } from \"https://esm.town/v/yawnxyz/blobby\";\n\nconst URL = \"https://yawnxyz-coversheetai.web.val.run\";\nconst taskPrefix = \"task::ai:\";\n\nconsole.log(\"henlo\");\nconst app = new Hono();\n\napp.use(\n \"*\",\n cors({\n origin: \"*\",\n allowMethods: [\"GET\", \"POST\"],\n allowHeaders: [\"Content-Type\"],\n }),\n);\n\nconst anthropic = createAnthropic({\n // apiKey = Deno.env.get(\"ANTHROPIC_API_KEY\");\n apiKey: Deno.env.get(\"ANTHROPIC_API_KEY_COVERSHEET\"),\n});\n\nconst openai = createOpenAI({\n // apiKey = Deno.env.get(\"OPENAI_API_KEY\");\n apiKey: Deno.env.get(\"OPENAI_API_KEY_COVERSHEET\"),\n});\n\nconst groq = createOpenAI({\n baseURL: \"https://api.groq.com/openai/v1\",\n apiKey: Deno.env.get(\"GROQ_API_KEY\"),\n});\n\nconst perplexity = createOpenAI({\n apiKey: Deno.env.get(\"PERPLEXITY_API_KEY\") ?? \"\",\n baseURL: \"https://api.perplexity.ai/\",\n});\n\nconst googleProvider = createGoogleGenerativeAI({\n apiKey: Deno.env.get(\"GOOGLE_GENERATIVE_AI_API_KEY\"),\n});\n\nclass ModelProvider {\n constructor(options = {}) {\n this.id = options.id || \"new-model-provider\";\n this.chars = options.chars || {};\n this.system = options.system || \"You are a helpful assistant\";\n this.memories = options.memories || [];\n this.messages = options.messages || [];\n this.defaultProvider = options.provider || \"openai\";\n this.defaultModel = options.model;\n this.defaultMaxTokens = options.maxTokens;\n this.defaultTemperature = options.temperature;\n this.defaultStreaming = options.streaming || false;\n this.defaultSchema = options.schema;\n this.defaultTools = options.tools;\n this.defaultUseMessages = options.useMessages || false;\n this.defaultUseMemory = options.useMemory || false;\n this.defaultUseChars = options.useChars || false;\n this.defaultGetUpdatedChars = options.getUpdatedChars || false;\n this.memoryPrompt = options.memoryPrompt\n || \"Summarize the key points from the current conversation, focusing on the most important and relevant details for future reference.\";\n this.charsPrompt = options.charsPrompt\n || \"Based on the current conversation, describe how the characters state, emotions, inventory, or ideas might change.\";\n }\n\n async gen({\n prompt,\n messages,\n memories,\n chars,\n provider = this.defaultProvider,\n model = this.defaultModel,\n maxTokens = this.defaultMaxTokens,\n temperature = this.defaultTemperature,\n streaming = this.defaultStreaming,\n schema = this.defaultSchema,\n system = this.system,\n tools = this.defaultTools,\n useMessages = this.defaultUseMessages,\n useChars = this.defaultUseChars,\n useMemory = this.defaultUseMemory,\n getUpdatedChars = this.defaultGetUpdatedChars,\n ...additionalSettings\n }) {\n if (useMessages) {\n this.useMessages = useMessages;\n if (messages && messages.length > 0) {\n this.messages.push(...messages);\n }\n if (prompt) {\n this.messages.push({ role: \"user\", content: prompt });\n prompt = null;\n }\n messages = this.messages;\n }\n\n if (useMemory) {\n this.useMemory = useMemory;\n if (memories && memories.length > 0) {\n this.memories.push(...memories);\n }\n system = system + \"\\n\\n\" + this.memories.join(\"\\n\\n\");\n }\n\n if (useChars) {\n this.useChars = useChars;\n if (chars) {\n this.chars = { ...this.chars, ...chars };\n }\n system = system + \"\\n\\n\" + JSON.stringify(this.chars);\n }\n\n let result;\n switch (provider) {\n case \"openai\":\n result = await this.generateOpenAIResponse({\n model,\n prompt,\n maxTokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n ...additionalSettings,\n });\n break;\n case \"anthropic\":\n result = await this.generateAnthropicResponse({\n model,\n prompt,\n maxTokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n ...additionalSettings,\n });\n break;\n case \"groq\":\n result = await this.generateGroqResponse({\n model,\n prompt,\n maxTokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n ...additionalSettings,\n });\n break;\n case \"perplexity\":\n result = await this.generatePerplexityResponse({\n model,\n prompt,\n maxTokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n ...additionalSettings,\n });\n break;\n case \"mistral\":\n result = await this.generateMistralResponse({\n model,\n prompt,\n maxTokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n ...additionalSettings,\n });\n break;\n case \"google\":\n result = await this.generateGoogleResponse({\n model,\n prompt,\n maxTokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n ...additionalSettings,\n });\n break;\n default:\n throw new Error(`Invalid provider ${provider}`);\n }\n\n const { text, toolResults } = result;\n\n if (this.useMessages) {\n this.messages.push({ role: \"assistant\", content: text });\n }\n if (this.useMemory) {\n const memory = await this.generateMemory({ text, messages: this.messages });\n this.memories.push(memory);\n }\n // if (this.useChars && this.getUpdatedChars) {\n // const charsUpdate = await this.updateChars({ messages: this.messages });\n // console.log('Character state updated:', charsUpdate);\n // }\n\n // todo: add logging here\n\n return {\n text,\n toolResults,\n finishReason: result.finishReason,\n usage: result.usage,\n model: result.modelId,\n embedding: result.embedding,\n textStream: result.textStream,\n };\n }\n\n async generateOpenAIResponse(\n {\n model,\n prompt,\n maxTokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n embed,\n value,\n dimensions,\n user,\n ...additionalSettings\n },\n ) {\n const modelId = model || \"gpt-3.5-turbo\";\n\n if (embed) {\n let result = await this.generateOpenAIEmbedding({ model, value, dimensions, user });\n // console.log('embed!', result)\n return result;\n }\n\n const options = {\n model: openai(modelId),\n system,\n temperature,\n max_tokens: maxTokens || 100,\n ...additionalSettings,\n };\n\n if (prompt && messages && messages.length > 0) {\n throw new Error(\"prompt and messages cannot be defined at the same time\");\n } else if (prompt) {\n options.prompt = prompt;\n } else if (messages && messages.length > 0) {\n options.messages = messages;\n }\n\n if (tools) {\n options.tools = tools;\n }\n\n if (schema) {\n const { object } = await generateObject({\n ...options,\n schema,\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText(options);\n return { textStream, model: modelId };\n } else {\n let { text, finishReason, usage, toolResults } = await generateText(options);\n\n if (toolResults && toolResults.length > 0) {\n // we have to do this bc the AI SDK doesn't seem to let you carry over function calling results,\n // so we pretend to be the user instead of using the official role: tool API, which breaks everything\n for (const toolResult of toolResults) {\n options.messages.push({\n role: \"user\",\n content: \": \" + toolResult.result + \"\",\n });\n }\n let res = await generateText(options);\n\n // Remove tool result names from options.messages\n this.messages = this.messages.filter(\n message => !toolResults.some(toolResult => message.content.includes(\": \")),\n );\n\n text = res.text;\n }\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateOpenAIEmbedding({ model, value, dimensions, user }) {\n const modelId = model || \"text-embedding-3-large\";\n const options = {\n model: openai.embedding(modelId, {\n dimensions,\n user,\n }),\n value,\n };\n\n const { embedding } = await embed(options);\n return { embedding, model: modelId };\n }\n\n async generateAnthropicResponse(\n { model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings },\n ) {\n const modelId = model || \"claude-3-5-sonnet-20240620\";\n if (schema) {\n const { object } = await generateObject({\n model: anthropic(modelId),\n system,\n prompt: prompt,\n schema,\n max_tokens: maxTokens || 100,\n temperature,\n messages: messages,\n ...additionalSettings,\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText({\n model: anthropic(modelId),\n system,\n prompt: prompt,\n max_tokens: maxTokens || 100,\n messages: messages,\n ...additionalSettings,\n });\n return { textStream, model: modelId };\n } else {\n const { text, finishReason, usage, toolResults } = await generateText({\n model: anthropic(modelId),\n system,\n prompt: prompt,\n max_tokens: maxTokens || 100,\n messages: messages || null,\n ...additionalSettings,\n });\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateGroqResponse(\n { model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings },\n ) {\n const modelId = model || \"llama3-8b-8192\";\n\n if (schema) {\n const { object } = await generateObject({\n model: groq(modelId),\n system: system || \"\",\n prompt: prompt,\n schema,\n temperature,\n max_tokens: maxTokens || 100,\n messages: messages,\n ...additionalSettings,\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText({\n model: groq(modelId),\n system,\n prompt: prompt,\n max_tokens: maxTokens || 100,\n temperature,\n messages: messages,\n ...additionalSettings,\n });\n return { textStream, model: modelId };\n } else {\n console.log(\"generateGroqResponse\", modelId, prompt, maxTokens, temperature, messages, system);\n const { text, finishReason, usage, toolResults } = await generateText({\n model: groq(modelId),\n system,\n prompt: prompt,\n max_tokens: maxTokens || 100,\n temperature,\n messages: messages,\n ...additionalSettings,\n });\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateMistralResponse(\n { model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings },\n ) {\n const modelId = model || \"mistral-large-latest\";\n\n const options = {\n model: mistral(modelId),\n system,\n max_tokens: maxTokens || 100,\n temperature,\n ...additionalSettings,\n };\n\n if (prompt) {\n options.prompt = prompt;\n } else if (messages && messages.length > 0) {\n options.messages = messages;\n }\n\n if (tools) {\n options.tools = tools;\n }\n\n if (schema) {\n const { object } = await generateObject({\n ...options,\n schema,\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText(options);\n return { textStream, model: modelId };\n } else {\n const { text, finishReason, usage, toolResults } = await generateText(options);\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generatePerplexityResponse(\n { model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings },\n ) {\n const modelId = model || \"llama-3.1-sonar-large-128k-online\";\n\n const options = {\n model: perplexity(modelId),\n system,\n max_tokens: maxTokens || 100,\n temperature,\n ...additionalSettings,\n };\n\n if (prompt && messages?.length > 0) {\n throw new Error(\"prompt and messages cannot be defined at the same time\");\n } else if (prompt) {\n options.prompt = prompt;\n } else if (messages && messages?.length > 0) {\n options.messages = messages;\n } else {\n throw new Error(\"Either prompt or messages must be provided\");\n }\n\n if (tools) {\n options.tools = tools;\n }\n\n if (schema) {\n const { object } = await generateObject({\n ...options,\n schema,\n });\n return { object };\n } else if (streaming) {\n const { textStream } = await streamText(options);\n return { textStream, model: modelId };\n } else {\n const { text, finishReason, usage, toolResults } = await generateText(options);\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateGoogleResponse(\n { model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings },\n ) {\n const modelId = model || \"models/gemini-1.5-flash-latest\";\n\n const options = {\n model: googleProvider(modelId),\n system,\n max_tokens: maxTokens || 100,\n temperature,\n ...additionalSettings,\n };\n\n if (prompt) {\n options.prompt = prompt;\n } else if (messages && messages.length > 0) {\n options.messages = messages;\n }\n\n if (tools) {\n options.tools = tools;\n }\n\n if (schema) {\n const { object } = await generateObject({\n ...options,\n schema,\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText(options);\n return { textStream, model: modelId };\n } else {\n const { text, finishReason, usage, toolResults } = await generateText(options);\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateMemory({ provider = this, text, messages, prompt = this.memoryPrompt }) {\n const conversationSummary = messages ? messages.map(msg => `${msg.role}: ${msg.content}`).join(\"\\n\") : \"\";\n let inputText = `Messages:\\n${conversationSummary}\\n----\\n\\n`;\n if (text) {\n inputText += `\\n\\nAdditional Context: ${text}`;\n }\n\n if (provider) {\n inputText += `Background:\\n-----\\n`;\n inputText += provider.system + \"\\n\";\n inputText += provider.memories.join(\"\\n\") + \"\\n\";\n inputText += JSON.stringify(provider.chars) + \"\\n\";\n inputText += `\\n-----\\n\\n\\n`;\n }\n\n const { text: memory } = await generateText({\n model: anthropic(this.memoryModel || \"claude-3-5-sonnet-20240620\"),\n prompt: `${prompt}\\n\\n${inputText}`,\n });\n\n return memory;\n }\n\n async updateChars({ provider = this, text, messages, prompt = this.charsPrompt }) {\n const conversationSummary = messages ? messages.map(msg => `${msg.role}: ${msg.content}`).join(\"\\n\") : \"\";\n let inputText = `Messages:\\n${conversationSummary}\\n----\\n\\n`;\n if (text) {\n inputText += `\\n\\nAdditional Context: ${text}`;\n }\n if (provider) {\n inputText += `Background:\\n-----\\n`;\n inputText += provider.system + \"\\n\";\n inputText += provider.memories.join(\"\\n\") + \"\\n\";\n inputText += \"\" + JSON.stringify(provider.chars) + \"\\n\";\n inputText += `\\n-----\\n\\n\\n`;\n }\n\n const { text: charsUpdate } = await generateText({\n model: anthropic(this.charsModel || \"claude-3-5-sonnet-20240620\"),\n prompt: `${prompt}\\n\\n${inputText}`,\n });\n\n const updatedChars = JSON.parse(charsUpdate);\n this.chars = { ...this.chars, ...updatedChars };\n return charsUpdate;\n }\n}\n\nconst modelProvider = new ModelProvider();\nconst gen = modelProvider.gen;\n\napp.get(\"/generate\", async (c) => {\n const provider = c.req.query(\"provider\") || \"openai\";\n const model = c.req.query(\"model\");\n const prompt = c.req.query(\"prompt\");\n const maxTokens = parseInt(c.req.query(\"max_tokens\")) || undefined;\n const temperature = parseFloat(c.req.query(\"temperature\")) || undefined;\n const streaming = c.req.query(\"streaming\") === \"true\";\n const schema = JSON.parse(c.req.query(\"schema\") || \"null\");\n const system = c.req.query(\"system\");\n const messages = JSON.parse(c.req.query(\"messages\") || \"null\");\n const tools = JSON.parse(c.req.query(\"tools\") || \"null\");\n const embed = JSON.parse(c.req.query(\"embed\") || \"null\");\n const value = c.req.query(\"value\");\n const dimensions = c.req.query(\"dimensions\") ? Number(c.req.query(\"dimensions\")) : null;\n\n try {\n console.log(\"get/generate:\", { mode: \"get/generate\", prompt, provider, model });\n const response = await modelProvider.gen({\n provider,\n model,\n prompt,\n maxTokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n embed,\n value,\n });\n console.log(\"response:\", response);\n return c.json(response);\n } catch (error) {\n console.error(\"API error:\", error);\n return c.text(\"Error occurred.\", 500);\n }\n});\n\napp.post(\"/generate\", async (c) => {\n const {\n provider,\n model,\n prompt,\n max_tokens,\n temperature,\n streaming,\n schema,\n system,\n messages,\n tools,\n embed,\n value,\n dimensions,\n user,\n ...additionalSettings\n } = await c.req.json();\n\n try {\n console.log(\"post/generate\", { mode: \"post/generate\", prompt, provider, model });\n const response = await modelProvider.gen({\n provider: provider || \"openai\",\n model,\n prompt,\n maxTokens: max_tokens,\n temperature,\n streaming: streaming || false,\n schema,\n system,\n messages,\n tools,\n embed,\n value,\n dimensions,\n user,\n ...additionalSettings,\n });\n console.log(response);\n return c.json(response);\n } catch (error) {\n console.error(\"API error:\", error);\n return c.text(\"Error occurred.\", 500);\n }\n});\n\n// this launches a task based on the setting / prompt, and returns the empty task object\n// this prevents the constant google sheets regeneration abuse\n// also lets long-running tasks to complete\napp.post(\"/task\", async (c) => {\n let { taskId, useCache = true, ...additionalSettings } = await c.req.json();\n\n // taskid is based on a simple stringhash of the settings, which doubles as a way to check if the task has already been done\n // this catches lots of duplicate requests from Google Sheets\n taskId = taskId || `${taskPrefix}${stringHash(JSON.stringify(additionalSettings))}`;\n\n let taskObj = await blobby.get(taskId);\n console.log(\">>>> /task:\", taskId, taskObj);\n if ((taskObj && taskObj.output && useCache) || (taskObj && taskObj.error)) {\n return c.json(taskObj);\n }\n\n taskObj = {\n taskId,\n dateAdded: new Date().toISOString(),\n input: additionalSettings,\n output: null,\n };\n\n // just launch it, don't wait for the result\n // const taskRunResponse = await fetch(`${URL}/taskrun`, {\n const taskRunResponse = fetch(`${URL}/taskrun`, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({ ...taskObj, ...additionalSettings }),\n });\n\n console.log(\"[task] task launched!, taskObj:\", taskObj);\n\n // return the empty task object\n return c.json(taskObj);\n});\n\napp.post(\"/taskrun\", async (c) => {\n let { taskId, ...settings } = await c.req.json();\n\n // Generate a unique taskId if not provided\n // DON'T DO THIS!!! This adds more settings so creates a new task lol\n // but thankfully the taskId should always be passed in\n // taskId = taskId || `${taskPrefix}${stringHash(JSON.stringify(settings))}`;\n if (!taskId) return c.error(\"No taskId provided\", 400);\n\n let taskObj = await blobby.get(taskId);\n console.log(\">>>> /taskrun:\", taskId, taskObj);\n if (taskObj && taskObj.output) {\n console.log(\"returning cached task:\", { taskId, settings });\n return c.json(taskObj);\n }\n\n console.log(\"running tasks with:\", { taskId, settings });\n\n try {\n // Run the modelProvider with the provided settings\n const output = await modelProvider.gen(settings);\n\n // Retrieve the task object from blobby\n let taskObj = await blobby.get(taskId);\n\n // Update the task object with the output\n taskObj = {\n taskId,\n ...taskObj,\n ...settings,\n output,\n dateCompleted: new Date().toISOString(),\n };\n\n // Store the updated task object in blobby\n await blobby.set(taskId, taskObj);\n\n console.log(\"[taskrun] Task run complete:\", taskObj);\n return c.json(taskObj);\n } catch (error) {\n console.error(\"Task run error:\", error);\n\n // Update the task object with the error\n taskObj = {\n taskId,\n ...taskObj,\n ...settings,\n dateCompleted: new Date().toISOString(),\n error: error,\n };\n\n // Store the updated task object in blobby\n await blobby.set(taskId, taskObj);\n\n let errorObj = await blobby.get(taskId);\n console.error(\"Errored task object:\", errorObj);\n\n return c.text(\"Error occurred during task run.\", 500);\n }\n});\n\n// very simple wrapper, mostly for testing / making sure things work\nconst ai = async (input, opts = {}) => {\n if (typeof input === \"string\") {\n let result = await modelProvider.gen({ prompt: input, ...opts });\n // console.log(\"[ai]:\" + result.text);\n return result.text;\n } else {\n return modelProvider.gen(input);\n }\n};\n\nfunction test() {\n console.log(\"test!\");\n}\n\nexport default app.fetch;\nexport { ai, ModelProvider, modelProvider, test };","createdAt",["D",1738352544420],"version","currentUserHasLiked","likeCount","forkCount","referenceCount","dependencyCount",1,"commentCount","prCount","mergedPr",{"_130":131,"_132":133},"handle","goode_bye","profileImageUrl","https://img.clerk.com/eyJ0eXBlIjoicHJveHkiLCJzcmMiOiJodHRwczovL2ltYWdlcy5jbGVyay5kZXYvb2F1dGhfZ2l0aHViL2ltZ18yc1BMTmhTekRZNnNGWDk2Q1BTb2lCM3R4RDEifQ","forkedVal",{"_114":136,"_130":137,"_99":100,"_119":138,"_139":-5},"98327fca-8984-11ef-843e-de64eea55b61","yawnxyz",251,"prDisabledHint","interval","slug","goode_bye/ai","description","An http and class wrapper for Vercel's AI SDK Usage: Groq: https://yawnxyz-ai.web.val.run/generate?prompt=\"tell me a beer joke\"&provider=groq&model=llama3-8b-8192 Perplexity: https://yawnxyz-ai.web.val.run/generate?prompt=\"what's the latest phage directory capsid & tail article about?\"&provider=perplexity Mistral: https://yawnxyz-ai.web.val.run/generate?prompt=\"tell me a joke?\"&provider=mistral&model=\"mistral-small-latest\" async function calculateEmbeddings(text) {\n const url = `https://yawnxyz-ai.web.val.run/generate?embed=true&value=${encodeURIComponent(text)}`;\n \n try {\n const response = await fetch(url);\n const data = await response.json();\n return data;\n } catch (error) {\n console.error('Error calculating embeddings:', error);\n return null;\n }\n}","versions",[147,150,153],{"_119":91,"_148":-5,"_117":149,"_114":112},"prId",["D",1738352544420],{"_119":125,"_148":-5,"_117":151,"_114":152},["D",1738352536431],"79cdb580-e00b-11ef-a941-e6cdfca9ef9f",{"_119":32,"_148":-5,"_117":154,"_114":155},["D",1738352496177],"61ebdfc8-e00b-11ef-93a5-e6cdfca9ef9f","recentThread","breadcrumbs","comments",[],"isOwner","routes/_app.v.$handle.$name.versions.$(version)",{"_94":163,"_164":165,"_95":166},"single","redirect","/v/goode_bye/ai/versions",{"_114":152,"_115":167,"_119":125,"_117":168,"_169":100,"_101":102,"_109":110,"_130":131,"_11":97,"_170":-5,"_171":-5,"_172":-5,"_160":26,"_141":142},"import \"jsr:@std/dotenv/load\"; // needed for deno run; not req for smallweb or valtown\nimport { Hono } from \"npm:hono@3\";\nimport { cors } from \"npm:hono/cors\";\nimport { createOpenAI } from \"npm:@ai-sdk/openai\";\nimport { createAnthropic } from \"npm:@ai-sdk/anthropic@0.0.48\";\nimport { google, createGoogleGenerativeAI } from 'npm:@ai-sdk/google';\nimport { mistral, createMistral } from \"npm:@ai-sdk/mistral\";\nimport { generateText, streamText, generateObject, embed } from \"npm:ai\";\n// import { z } from \"npm:zod\";\n// import { nanoid } from 'npm:nanoid';\nimport stringHash from 'npm:string-hash';\n\nimport { blobby } from \"https://esm.town/v/yawnxyz/blobby\";\n\nconst URL = \"https://yawnxyz-coversheetai.web.val.run\"\nconst taskPrefix = \"task::ai:\"\n\nconsole.log(\"henlo\"\")\nconst app = new Hono();\n\napp.use('*', cors({\n origin: '*',\n allowMethods: ['GET', 'POST'],\n allowHeaders: ['Content-Type'],\n}));\n\nconst anthropic = createAnthropic({\n // apiKey = Deno.env.get(\"ANTHROPIC_API_KEY\");\n apiKey: Deno.env.get(\"ANTHROPIC_API_KEY_COVERSHEET\")\n});\n\nconst openai = createOpenAI({\n // apiKey = Deno.env.get(\"OPENAI_API_KEY\");\n apiKey: Deno.env.get(\"OPENAI_API_KEY_COVERSHEET\")\n});\n\n\nconst groq = createOpenAI({\n baseURL: 'https://api.groq.com/openai/v1',\n apiKey: Deno.env.get(\"GROQ_API_KEY\"),\n});\n\nconst perplexity = createOpenAI({\n apiKey: Deno.env.get(\"PERPLEXITY_API_KEY\") ?? '',\n baseURL: 'https://api.perplexity.ai/',\n});\n\nconst googleProvider = createGoogleGenerativeAI({\n apiKey: Deno.env.get(\"GOOGLE_GENERATIVE_AI_API_KEY\"),\n});\n\nclass ModelProvider {\n constructor(options = {}) {\n this.id = options.id || 'new-model-provider';\n this.chars = options.chars || {};\n this.system = options.system || 'You are a helpful assistant';\n this.memories = options.memories || [];\n this.messages = options.messages || [];\n this.defaultProvider = options.provider || 'openai';\n this.defaultModel = options.model;\n this.defaultMaxTokens = options.maxTokens;\n this.defaultTemperature = options.temperature;\n this.defaultStreaming = options.streaming || false;\n this.defaultSchema = options.schema;\n this.defaultTools = options.tools;\n this.defaultUseMessages = options.useMessages || false;\n this.defaultUseMemory = options.useMemory || false;\n this.defaultUseChars = options.useChars || false;\n this.defaultGetUpdatedChars = options.getUpdatedChars || false;\n this.memoryPrompt = options.memoryPrompt || 'Summarize the key points from the current conversation, focusing on the most important and relevant details for future reference.';\n this.charsPrompt = options.charsPrompt || 'Based on the current conversation, describe how the characters state, emotions, inventory, or ideas might change.';\n }\n\n async gen({\n prompt,\n messages,\n memories,\n chars,\n provider = this.defaultProvider,\n model = this.defaultModel,\n maxTokens = this.defaultMaxTokens,\n temperature = this.defaultTemperature,\n streaming = this.defaultStreaming,\n schema = this.defaultSchema,\n system = this.system,\n tools = this.defaultTools,\n useMessages = this.defaultUseMessages,\n useChars = this.defaultUseChars,\n useMemory = this.defaultUseMemory,\n getUpdatedChars = this.defaultGetUpdatedChars,\n ...additionalSettings\n }) {\n\n if (useMessages) {\n this.useMessages = useMessages;\n if (messages && messages.length > 0) {\n this.messages.push(...messages);\n }\n if (prompt) {\n this.messages.push({ role: 'user', content: prompt });\n prompt = null;\n }\n messages = this.messages;\n }\n\n if (useMemory) {\n this.useMemory = useMemory;\n if (memories && memories.length > 0) {\n this.memories.push(...memories);\n }\n system = system + '\\n\\n' + this.memories.join('\\n\\n');\n }\n\n if (useChars) {\n this.useChars = useChars;\n if (chars) {\n this.chars = { ...this.chars, ...chars };\n }\n system = system + '\\n\\n' + JSON.stringify(this.chars);\n }\n\n let result;\n switch (provider) {\n case 'openai':\n result = await this.generateOpenAIResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings });\n break;\n case 'anthropic':\n result = await this.generateAnthropicResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings });\n break;\n case 'groq':\n result = await this.generateGroqResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings });\n break;\n case 'perplexity':\n result = await this.generatePerplexityResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings });\n break;\n case 'mistral':\n result = await this.generateMistralResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings });\n break;\n case 'google':\n result = await this.generateGoogleResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings });\n break;\n default:\n throw new Error(`Invalid provider ${provider}`);\n }\n\n const { text, toolResults } = result;\n\n if (this.useMessages) {\n this.messages.push({ role: 'assistant', content: text });\n }\n if (this.useMemory) {\n const memory = await this.generateMemory({ text, messages: this.messages });\n this.memories.push(memory);\n }\n // if (this.useChars && this.getUpdatedChars) {\n // const charsUpdate = await this.updateChars({ messages: this.messages });\n // console.log('Character state updated:', charsUpdate);\n // }\n\n // todo: add logging here\n\n return {\n text,\n toolResults,\n finishReason: result.finishReason,\n usage: result.usage,\n model: result.modelId,\n embedding: result.embedding,\n textStream: result.textStream\n };\n }\n\n async generateOpenAIResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, embed, value, dimensions, user, ...additionalSettings }) {\n const modelId = model || 'gpt-3.5-turbo';\n\n if (embed) {\n let result = await this.generateOpenAIEmbedding({ model, value, dimensions, user });\n // console.log('embed!', result)\n return result\n }\n\n const options = {\n model: openai(modelId),\n system,\n temperature,\n max_tokens: maxTokens || 100,\n ...additionalSettings\n };\n\n if (prompt && messages && messages.length > 0) {\n throw new Error('prompt and messages cannot be defined at the same time');\n } else if (prompt) {\n options.prompt = prompt;\n } else if (messages && messages.length > 0) {\n options.messages = messages;\n }\n\n if (tools) {\n options.tools = tools;\n }\n\n if (schema) {\n const { object } = await generateObject({\n ...options,\n schema,\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText(options);\n return { textStream, model: modelId };\n } else {\n let { text, finishReason, usage, toolResults } = await generateText(options);\n\n if (toolResults && toolResults.length > 0) {\n // we have to do this bc the AI SDK doesn't seem to let you carry over function calling results, \n // so we pretend to be the user instead of using the official role: tool API, which breaks everything\n for (const toolResult of toolResults) {\n options.messages.push({\n role: \"user\",\n content: \": \" + toolResult.result + \"\",\n })\n }\n let res = await generateText(options);\n\n\n // Remove tool result names from options.messages\n this.messages = this.messages.filter(\n message => !toolResults.some(toolResult => message.content.includes(\": \"))\n );\n\n text = res.text;\n }\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateOpenAIEmbedding({ model, value, dimensions, user }) {\n const modelId = model || 'text-embedding-3-large';\n const options = {\n model: openai.embedding(modelId, {\n dimensions,\n user,\n }),\n value,\n };\n\n const { embedding } = await embed(options);\n return { embedding, model: modelId };\n }\n\n async generateAnthropicResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings }) {\n const modelId = model || 'claude-3-5-sonnet-20240620';\n if (schema) {\n const { object } = await generateObject({\n model: anthropic(modelId),\n system,\n prompt: prompt,\n schema,\n max_tokens: maxTokens || 100,\n temperature,\n messages: messages,\n ...additionalSettings\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText({\n model: anthropic(modelId),\n system,\n prompt: prompt,\n max_tokens: maxTokens || 100,\n messages: messages,\n ...additionalSettings\n });\n return { textStream, model: modelId };\n } else {\n const { text, finishReason, usage, toolResults } = await generateText({\n model: anthropic(modelId),\n system,\n prompt: prompt,\n max_tokens: maxTokens || 100,\n messages: messages || null,\n ...additionalSettings\n });\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateGroqResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings }) {\n const modelId = model || 'llama3-8b-8192';\n\n if (schema) {\n const { object } = await generateObject({\n model: groq(modelId),\n system:system||'',\n prompt: prompt,\n schema,\n temperature,\n max_tokens: maxTokens || 100,\n messages: messages,\n ...additionalSettings\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText({\n model: groq(modelId),\n system,\n prompt: prompt,\n max_tokens: maxTokens || 100,\n temperature,\n messages: messages,\n ...additionalSettings\n });\n return { textStream, model: modelId };\n } else {\n console.log('generateGroqResponse', modelId, prompt, maxTokens, temperature, messages, system)\n const { text, finishReason, usage, toolResults } = await generateText({\n model: groq(modelId),\n system,\n prompt: prompt,\n max_tokens: maxTokens || 100,\n temperature,\n messages: messages,\n ...additionalSettings\n });\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateMistralResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings }) {\n const modelId = model || 'mistral-large-latest';\n\n const options = {\n model: mistral(modelId),\n system,\n max_tokens: maxTokens || 100,\n temperature,\n ...additionalSettings\n };\n\n if (prompt) {\n options.prompt = prompt;\n } else if (messages && messages.length > 0) {\n options.messages = messages;\n }\n\n if (tools) {\n options.tools = tools;\n }\n\n if (schema) {\n const { object } = await generateObject({\n ...options,\n schema,\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText(options);\n return { textStream, model: modelId };\n } else {\n const { text, finishReason, usage, toolResults } = await generateText(options);\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generatePerplexityResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings }) {\n const modelId = model || 'llama-3.1-sonar-large-128k-online';\n\n const options = {\n model: perplexity(modelId),\n system,\n max_tokens: maxTokens || 100,\n temperature,\n ...additionalSettings\n };\n\n if (prompt && messages?.length > 0) {\n throw new Error('prompt and messages cannot be defined at the same time');\n } else if (prompt) {\n options.prompt = prompt;\n } else if (messages && messages?.length > 0) {\n options.messages = messages;\n } else {\n throw new Error('Either prompt or messages must be provided');\n }\n\n if (tools) {\n options.tools = tools;\n }\n\n if (schema) {\n const { object } = await generateObject({\n ...options,\n schema,\n });\n return { object };\n } else if (streaming) {\n const { textStream } = await streamText(options);\n return { textStream, model: modelId };\n } else {\n const { text, finishReason, usage, toolResults } = await generateText(options);\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateGoogleResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, ...additionalSettings }) {\n const modelId = model || 'models/gemini-1.5-flash-latest';\n\n const options = {\n model: googleProvider(modelId),\n system,\n max_tokens: maxTokens || 100,\n temperature,\n ...additionalSettings\n };\n\n if (prompt) {\n options.prompt = prompt;\n } else if (messages && messages.length > 0) {\n options.messages = messages;\n }\n\n if (tools) {\n options.tools = tools;\n }\n\n if (schema) {\n const { object } = await generateObject({\n ...options,\n schema,\n });\n return { object, model: modelId };\n } else if (streaming) {\n const { textStream } = await streamText(options);\n return { textStream, model: modelId };\n } else {\n const { text, finishReason, usage, toolResults } = await generateText(options);\n return { text, finishReason, usage, toolResults, model: modelId };\n }\n }\n\n async generateMemory({ provider = this, text, messages, prompt = this.memoryPrompt }) {\n const conversationSummary = messages ? messages.map(msg => `${msg.role}: ${msg.content}`).join('\\n') : '';\n let inputText = `Messages:\\n${conversationSummary}\\n----\\n\\n`;\n if (text) {\n inputText += `\\n\\nAdditional Context: ${text}`;\n }\n\n if (provider) {\n inputText += `Background:\\n-----\\n`;\n inputText += provider.system + '\\n';\n inputText += provider.memories.join('\\n') + '\\n';\n inputText += JSON.stringify(provider.chars) + '\\n';\n inputText += `\\n-----\\n\\n\\n`;\n }\n\n const { text: memory } = await generateText({\n model: anthropic(this.memoryModel || 'claude-3-5-sonnet-20240620'),\n prompt: `${prompt}\\n\\n${inputText}`,\n });\n\n return memory;\n }\n\n async updateChars({ provider = this, text, messages, prompt = this.charsPrompt }) {\n const conversationSummary = messages ? messages.map(msg => `${msg.role}: ${msg.content}`).join('\\n') : '';\n let inputText = `Messages:\\n${conversationSummary}\\n----\\n\\n`;\n if (text) {\n inputText += `\\n\\nAdditional Context: ${text}`;\n }\n if (provider) {\n inputText += `Background:\\n-----\\n`;\n inputText += provider.system + '\\n';\n inputText += provider.memories.join('\\n') + '\\n';\n inputText += \"\" + JSON.stringify(provider.chars) + '\\n';\n inputText += `\\n-----\\n\\n\\n`;\n }\n\n const { text: charsUpdate } = await generateText({\n model: anthropic(this.charsModel || 'claude-3-5-sonnet-20240620'),\n prompt: `${prompt}\\n\\n${inputText}`,\n });\n\n const updatedChars = JSON.parse(charsUpdate);\n this.chars = { ...this.chars, ...updatedChars };\n return charsUpdate;\n }\n}\n\nconst modelProvider = new ModelProvider();\nconst gen = modelProvider.gen;\n\napp.get('/generate', async (c) => {\n const provider = c.req.query('provider') || 'openai';\n const model = c.req.query('model');\n const prompt = c.req.query('prompt');\n const maxTokens = parseInt(c.req.query('max_tokens')) || undefined;\n const temperature = parseFloat(c.req.query('temperature')) || undefined;\n const streaming = c.req.query('streaming') === 'true';\n const schema = JSON.parse(c.req.query('schema') || 'null');\n const system = c.req.query('system');\n const messages = JSON.parse(c.req.query('messages') || 'null');\n const tools = JSON.parse(c.req.query('tools') || 'null');\n const embed = JSON.parse(c.req.query('embed') || 'null');\n const value = c.req.query('value');\n const dimensions = c.req.query('dimensions') ? Number(c.req.query('dimensions')) : null;\n\n\n try {\n console.log(\"get/generate:\", { mode: 'get/generate', prompt, provider, model });\n const response = await modelProvider.gen({ provider, model, prompt, maxTokens, temperature, streaming, schema, system, messages, tools, embed, value });\n console.log('response:', response)\n return c.json(response);\n } catch (error) {\n console.error('API error:', error);\n return c.text('Error occurred.', 500);\n }\n});\n\napp.post('/generate', async (c) => {\n const { provider, model, prompt, max_tokens, temperature, streaming, schema, system, messages, tools, embed, value, dimensions, user, ...additionalSettings } = await c.req.json();\n\n try {\n console.log(\"post/generate\", { mode: 'post/generate', prompt, provider, model });\n const response = await modelProvider.gen({\n provider: provider || 'openai',\n model,\n prompt,\n maxTokens: max_tokens,\n temperature,\n streaming: streaming || false,\n schema,\n system,\n messages,\n tools,\n embed,\n value,\n dimensions,\n user,\n ...additionalSettings\n });\n console.log(response);\n return c.json(response);\n } catch (error) {\n console.error('API error:', error);\n return c.text('Error occurred.', 500);\n }\n});\n\n\n\n\n\n// this launches a task based on the setting / prompt, and returns the empty task object\n// this prevents the constant google sheets regeneration abuse\n// also lets long-running tasks to complete\napp.post('/task', async (c) => {\n\n let { taskId, useCache = true, ...additionalSettings } = await c.req.json();\n\n\n // taskid is based on a simple stringhash of the settings, which doubles as a way to check if the task has already been done\n // this catches lots of duplicate requests from Google Sheets\n taskId = taskId || `${taskPrefix}${stringHash(JSON.stringify(additionalSettings))}`;\n\n\n let taskObj = await blobby.get(taskId);\n console.log('>>>> /task:', taskId, taskObj)\n if ((taskObj && taskObj.output && useCache) || (taskObj && taskObj.error)) {\n return c.json(taskObj);\n }\n\n taskObj = {\n taskId,\n dateAdded: new Date().toISOString(),\n input: additionalSettings,\n output: null\n }\n\n // just launch it, don't wait for the result\n // const taskRunResponse = await fetch(`${URL}/taskrun`, {\n const taskRunResponse = fetch(`${URL}/taskrun`, {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json'\n },\n body: JSON.stringify({ ...taskObj, ...additionalSettings })\n });\n\n console.log('[task] task launched!, taskObj:', taskObj)\n\n // return the empty task object\n return c.json(taskObj);\n});\n\n\n\napp.post('/taskrun', async (c) => {\n let { taskId, ...settings } = await c.req.json();\n\n // Generate a unique taskId if not provided\n // DON'T DO THIS!!! This adds more settings so creates a new task lol\n // but thankfully the taskId should always be passed in\n // taskId = taskId || `${taskPrefix}${stringHash(JSON.stringify(settings))}`;\n if(!taskId) return c.error('No taskId provided', 400);\n\n let taskObj = await blobby.get(taskId);\n console.log('>>>> /taskrun:', taskId, taskObj)\n if (taskObj && taskObj.output) {\n console.log('returning cached task:', { taskId, settings })\n return c.json(taskObj);\n }\n\n\n console.log('running tasks with:', { taskId, settings })\n\n try {\n // Run the modelProvider with the provided settings\n const output = await modelProvider.gen(settings);\n\n // Retrieve the task object from blobby\n let taskObj = await blobby.get(taskId);\n\n // Update the task object with the output\n taskObj = {\n taskId,\n ...taskObj,\n ...settings,\n output,\n dateCompleted: new Date().toISOString()\n };\n\n // Store the updated task object in blobby\n await blobby.set(taskId, taskObj);\n\n console.log('[taskrun] Task run complete:', taskObj);\n return c.json(taskObj);\n } catch (error) {\n console.error('Task run error:', error);\n\n // Update the task object with the error\n taskObj = {\n taskId,\n ...taskObj,\n ...settings,\n dateCompleted: new Date().toISOString(),\n error: error,\n };\n\n // Store the updated task object in blobby\n await blobby.set(taskId, taskObj);\n\n let errorObj = await blobby.get(taskId);\n console.error('Errored task object:', errorObj);\n\n\n return c.text('Error occurred during task run.', 500);\n }\n});\n\n\n// very simple wrapper, mostly for testing / making sure things work\nconst ai = async (input, opts={}) => {\n if (typeof input === 'string') {\n let result = await modelProvider.gen({ prompt: input, ...opts });\n // console.log(\"[ai]:\" + result.text);\n return result.text;\n } else {\n return modelProvider.gen(input);\n }\n}\n\nfunction test() {\n console.log(\"test!\")\n}\n\nexport default app.fetch;\nexport { ModelProvider, modelProvider, ai, test };\n",["D",1738352536431],"valName","deletedAt","messageId","threadId","routes/_app","actionData","errors"]

ai

Version 1