[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_207":208,"_209":210},"routes/_app.x.$handle.$name",{"_101":100,"_38":51,"_109":91,"_33":110,"_13":14,"_111":-5,"_11":112,"_113":114,"_115":116,"_117":118,"_119":120,"_26":-5,"_121":-5,"_122":-5,"_123":31,"_124":125,"_126":31,"_127":-5,"_128":-5,"_129":130},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":18,"_19":-5},"project",{"_49":50,"_38":51,"_44":52,"_53":-5,"_54":31,"_55":56,"_57":56,"_58":59,"_60":-5,"_19":61,"_62":-5,"_63":31,"_64":-5,"_65":-5,"_66":67,"_68":69,"_70":-5,"_71":-5,"_72":-5,"_73":74,"_75":76,"_77":76,"_78":79,"_80":79,"_81":79,"_82":76,"_83":76,"_84":85,"_86":87,"_88":89,"_90":31},"version",162,"changes",[25],"aiChatInfo",{"_20":21,"_22":-5,"_23":-5,"_24":-5},"userId","aiChatMessageId","4caa681d-d417-4b32-9458-390b8148fe13","aiChatThreadId","aiChatThreadUserId","aiChatThreadTitle",{"_26":27,"_28":29,"_30":31,"_32":31,"_33":34,"_35":34},"file",{"_36":37,"_38":34,"_39":40,"_41":43,"_44":45,"_46":14,"_47":48,"_30":31},"previous",{"_36":37,"_38":34,"_39":40,"_41":42},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","550e6a1c-6d75-11f0-9956-0224a6c84d84","name","fileType","script","content","import { Pinecone } from \"npm:@pinecone-database/pinecone\";\nimport axios from \"npm:axios\";\nimport { v4 as uuidv4 } from \"npm:uuid\"; // for generating unique IDs\n\nexport interface PineconeIndexOptions {\n name: string;\n pineconeKey: string;\n modelToken: string;\n model: string;\n dimension?: number;\n namespace?: string;\n}\n\nexport type UpsertInput =\n | string\n | { text: string; id?: string; metadata?: Record; [key: string]: any };\n\nexport default class PineconeIndex {\n options: PineconeIndexOptions;\n pc: Pinecone;\n _index: any;\n _namespace: string;\n\n constructor(options: PineconeIndexOptions) {\n this.options = options;\n this.pc = new Pinecone({ apiKey: options.pineconeKey });\n this._index = null;\n this._namespace = options.namespace ?? \"\";\n }\n\n async create() {\n const { name, dimension } = this.options;\n await this.pc.createIndex({\n name,\n dimension: dimension!,\n metric: \"cosine\",\n spec: { serverless: { cloud: \"aws\", region: \"us-east-1\" } },\n deletionProtection: \"disabled\",\n waitUntilReady: true,\n });\n\n // Immediately capture index host\n const { host } = await this.pc.describeIndex(name);\n this._index = this.pc.Index(host);\n }\n\n async getIndex() {\n if (this._index) return this._index;\n\n const { name } = this.options;\n const { host } = await this.pc.describeIndex(name);\n const idx = this.pc.index(name, host);\n\n const ns = this._namespace;\n this._index = ns ? idx.namespace(ns) : idx;\n return this._index;\n }\n\n async upsert(\n inputs: UpsertInput[],\n onEmbedding?: Function,\n ): Promise {\n const idx = await this.getIndex();\n const output = [];\n // Process inputs one at a time to avoid message size limits\n for (const input of inputs) {\n const text = typeof input === \"string\" ? input : input.text;\n\n if (!text) {\n console.warn(\"Skipping input with no text:\", input);\n continue;\n }\n\n let values = await this.embed(text);\n if (onEmbedding) values = onEmbedding(values);\n // If input is string, metadata is just { text }\n // If input is object, metadata is the whole object\n let toUpsert;\n if (typeof input === \"string\") {\n const metadata = { text };\n toUpsert = {\n id: uuidv4(), // generate unique ID per text\n values,\n metadata, // keep original text/object in metadata\n };\n } else {\n const { id, ...metadata } = input;\n toUpsert = {\n id: id || uuidv4(),\n values,\n metadata,\n };\n }\n\n await idx.upsert({ records: [toUpsert] });\n output.push(toUpsert);\n }\n\n return output;\n }\n\n async query(text: string) {\n const vector = await this.embed(text);\n\n return this.queryVector(vector);\n }\n\n async queryVector(vector: number[]) {\n const idx = await this.getIndex();\n return idx.query({\n vector,\n topK: 10,\n includeValues: false,\n includeMetadata: true,\n });\n }\n\n async embed(text: string) {\n const { model, modelToken, dimension } = this.options;\n const payload: { model: string; input: string; dimensions?: number } = { model, input: text };\n // text-embedding-3-* models support truncating output via `dimensions`,\n // so the same dimension used to create the Pinecone index also shapes\n // the embedding request (keeps them in sync).\n if (dimension) payload.dimensions = dimension;\n\n const response = await axios.post(\n \"https://api.openai.com/v1/embeddings\",\n payload,\n {\n headers: {\n \"Authorization\": `Bearer ${modelToken}`,\n },\n },\n );\n\n return response.data.data[0].embedding;\n }\n\n async fetchByIDs(ids: string[]) {\n const idx = await this.getIndex();\n const response = await idx.fetch({ ids });\n\n return Object.values(response.records);\n }\n async empty() {\n const idx = await this.getIndex();\n\n // Delete all vectors in the current namespace\n // If no namespace is set, this will delete all vectors in the index\n await idx.deleteAll();\n }\n\n /**\n * HTTP handler that routes requests to appropriate methods\n * Supports routes like /query/, /upsert/, /empty/\n */\n async handleRequest(req: Request): Promise {\n const url = new URL(req.url);\n const pathSegments = url.pathname.split(\"/\").filter((segment) =>\n segment.length > 0\n );\n\n // Extract the operation from the path (e.g., \"query\" from \"/query/\")\n const operation = pathSegments[0];\n\n try {\n switch (operation) {\n case \"query\":\n return await this._handleOperation(req, \"POST\", async (body) => {\n // Support both direct string and object with query property\n const queryText = typeof body === \"string\"\n ? body\n : (body.query || body.text || JSON.stringify(body));\n\n if (!queryText) {\n throw new Error(\"Query text is required\");\n }\n\n const results = await this.query(queryText);\n return results;\n });\n\n case \"upsert\":\n return await this._handleOperation(req, \"POST\", async (body) => {\n const texts = body.texts || body.data ||\n (Array.isArray(body) ? body : [body]);\n\n if (!Array.isArray(texts) || texts.length === 0) {\n throw new Error(\"Texts array is required\");\n }\n\n await this.upsert(texts);\n return { success: true, count: texts.length };\n });\n\n case \"empty\":\n return await this._handleOperation(req, \"POST\", async () => {\n await this.empty();\n return { success: true, message: \"Index cleared\" };\n });\n\n case \"health\":\n return Response.json({\n status: \"healthy\",\n timestamp: new Date().toISOString(),\n });\n\n default:\n return Response.json(\n {\n error:\n `Unknown operation: ${operation}. Supported operations: query, upsert, empty, health`,\n },\n { status: 400 },\n );\n }\n } catch (error) {\n console.error(`Error in ${operation} operation:`, error);\n return Response.json(\n { error: error.message || \"Internal server error\" },\n { status: 500 },\n );\n }\n }\n\n /**\n * Generic handler for HTTP operations with common validation and error handling\n */\n private async _handleOperation(\n req: Request,\n expectedMethod: string,\n handler: (body: any) => Promise,\n ): Promise {\n // Method validation\n if (req.method !== expectedMethod) {\n return Response.json(\n { error: `This endpoint only accepts ${expectedMethod} requests` },\n { status: 405 },\n );\n }\n\n try {\n // Parse body for non-GET requests\n const body = expectedMethod !== \"GET\" ? await req.json() : null;\n\n // Execute the specific handler logic\n const result = await handler(body);\n\n return Response.json(result);\n } catch (error) {\n // Handle validation errors (400) vs server errors (500)\n const status = error.message.includes(\"required\") ||\n error.message.includes(\"array\") ||\n error.message.includes(\"text\")\n ? 400\n : 500;\n\n return Response.json({ error: error.message }, { status });\n }\n }\n}","import { Pinecone } from \"npm:@pinecone-database/pinecone\";\nimport axios from \"npm:axios\";\nimport { v4 as uuidv4 } from \"npm:uuid\"; // for generating unique IDs\n\nexport interface PineconeIndexOptions {\n name: string;\n pineconeKey: string;\n modelToken: string;\n model: string;\n dimension?: number;\n namespace?: string;\n}\n\nexport type UpsertInput =\n | string\n | { text: string; id?: string; metadata?: Record; [key: string]: any };\n\nexport default class PineconeIndex {\n options: PineconeIndexOptions;\n pc: Pinecone;\n _index: any;\n _namespace: string;\n\n constructor(options: PineconeIndexOptions) {\n this.options = options;\n this.pc = new Pinecone({ apiKey: options.pineconeKey });\n this._index = null;\n this._namespace = options.namespace ?? \"\";\n }\n\n async create() {\n const { name, dimension } = this.options;\n await this.pc.createIndex({\n name,\n dimension: dimension!,\n metric: \"cosine\",\n spec: { serverless: { cloud: \"aws\", region: \"us-east-1\" } },\n deletionProtection: \"disabled\",\n waitUntilReady: true,\n });\n\n // Immediately capture index host\n const { host } = await this.pc.describeIndex(name);\n this._index = this.pc.Index(host);\n }\n\n async getIndex() {\n if (this._index) return this._index;\n\n const { name } = this.options;\n const { host } = await this.pc.describeIndex(name);\n const idx = this.pc.index(name, host);\n\n const ns = this._namespace;\n this._index = ns ? idx.namespace(ns) : idx;\n return this._index;\n }\n\n async upsert(\n inputs: UpsertInput[],\n onEmbedding?: Function,\n ): Promise {\n const idx = await this.getIndex();\n const output = [];\n // Process inputs one at a time to avoid message size limits\n for (const input of inputs) {\n const text = typeof input === \"string\" ? input : input.text;\n\n if (!text) {\n console.warn(\"Skipping input with no text:\", input);\n continue;\n }\n\n let values = await this.embed(text);\n if (onEmbedding) values = onEmbedding(values);\n // If input is string, metadata is just { text }\n // If input is object and passes an explicit `metadata`, use that as-is\n // (lets callers keep large fields like `text` out of Pinecone).\n // Otherwise, fall back to storing the whole object (minus `id`) as\n // metadata, for backwards compatibility.\n let toUpsert;\n if (typeof input === \"string\") {\n const metadata = { text };\n toUpsert = {\n id: uuidv4(), // generate unique ID per text\n values,\n metadata, // keep original text/object in metadata\n };\n } else {\n const { id, metadata: explicitMetadata, ...rest } = input;\n toUpsert = {\n id: id || uuidv4(),\n values,\n metadata: explicitMetadata ?? rest,\n };\n }\n\n await idx.upsert({ records: [toUpsert] });\n output.push(toUpsert);\n }\n\n return output;\n }\n\n async query(text: string) {\n const vector = await this.embed(text);\n\n return this.queryVector(vector);\n }\n\n async queryVector(vector: number[]) {\n const idx = await this.getIndex();\n return idx.query({\n vector,\n topK: 10,\n includeValues: false,\n includeMetadata: true,\n });\n }\n\n async embed(text: string) {\n const { model, modelToken, dimension } = this.options;\n const payload: { model: string; input: string; dimensions?: number } = { model, input: text };\n // text-embedding-3-* models support truncating output via `dimensions`,\n // so the same dimension used to create the Pinecone index also shapes\n // the embedding request (keeps them in sync).\n if (dimension) payload.dimensions = dimension;\n\n const response = await axios.post(\n \"https://api.openai.com/v1/embeddings\",\n payload,\n {\n headers: {\n \"Authorization\": `Bearer ${modelToken}`,\n },\n },\n );\n\n return response.data.data[0].embedding;\n }\n\n async fetchByIDs(ids: string[]) {\n const idx = await this.getIndex();\n const response = await idx.fetch({ ids });\n\n return Object.values(response.records);\n }\n async empty() {\n const idx = await this.getIndex();\n\n // Delete all vectors in the current namespace\n // If no namespace is set, this will delete all vectors in the index\n await idx.deleteAll();\n }\n\n /**\n * HTTP handler that routes requests to appropriate methods\n * Supports routes like /query/, /upsert/, /empty/\n */\n async handleRequest(req: Request): Promise {\n const url = new URL(req.url);\n const pathSegments = url.pathname.split(\"/\").filter((segment) =>\n segment.length > 0\n );\n\n // Extract the operation from the path (e.g., \"query\" from \"/query/\")\n const operation = pathSegments[0];\n\n try {\n switch (operation) {\n case \"query\":\n return await this._handleOperation(req, \"POST\", async (body) => {\n // Support both direct string and object with query property\n const queryText = typeof body === \"string\"\n ? body\n : (body.query || body.text || JSON.stringify(body));\n\n if (!queryText) {\n throw new Error(\"Query text is required\");\n }\n\n const results = await this.query(queryText);\n return results;\n });\n\n case \"upsert\":\n return await this._handleOperation(req, \"POST\", async (body) => {\n const texts = body.texts || body.data ||\n (Array.isArray(body) ? body : [body]);\n\n if (!Array.isArray(texts) || texts.length === 0) {\n throw new Error(\"Texts array is required\");\n }\n\n await this.upsert(texts);\n return { success: true, count: texts.length };\n });\n\n case \"empty\":\n return await this._handleOperation(req, \"POST\", async () => {\n await this.empty();\n return { success: true, message: \"Index cleared\" };\n });\n\n case \"health\":\n return Response.json({\n status: \"healthy\",\n timestamp: new Date().toISOString(),\n });\n\n default:\n return Response.json(\n {\n error:\n `Unknown operation: ${operation}. Supported operations: query, upsert, empty, health`,\n },\n { status: 400 },\n );\n }\n } catch (error) {\n console.error(`Error in ${operation} operation:`, error);\n return Response.json(\n { error: error.message || \"Internal server error\" },\n { status: 500 },\n );\n }\n }\n\n /**\n * Generic handler for HTTP operations with common validation and error handling\n */\n private async _handleOperation(\n req: Request,\n expectedMethod: string,\n handler: (body: any) => Promise,\n ): Promise {\n // Method validation\n if (req.method !== expectedMethod) {\n return Response.json(\n { error: `This endpoint only accepts ${expectedMethod} requests` },\n { status: 405 },\n );\n }\n\n try {\n // Parse body for non-GET requests\n const body = expectedMethod !== \"GET\" ? await req.json() : null;\n\n // Execute the specific handler logic\n const result = await handler(body);\n\n return Response.json(result);\n } catch (error) {\n // Handle validation errors (400) vs server errors (500)\n const status = error.message.includes(\"required\") ||\n error.message.includes(\"array\") ||\n error.message.includes(\"text\")\n ? 400\n : 500;\n\n return Response.json({ error: error.message }, { status });\n }\n }\n}","createdAt",["D",1783027702653],"branchVersion","branchId","550bf07a-6d75-11f0-9956-0224a6c84d84","id","550b8766-6d75-11f0-9956-0224a6c84d84","PineconeIndex",["D",1753901115385],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","Vector db's on Pinecone, with OpenAI embeddings","imageUrl","f129b0f0-09bd-41ac-a51c-c16933d81d65","pinnedFileId","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","5cdb635f-04df-409e-bb23-93b87a60f6ca","tursoName","tursoHostname","tursoMeasuredAt","pinned",1,"userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_101":100,"_108":104},"org",{"_101":100,"_102":-5,"_103":104,"_19":61,"_105":106,"_107":31},"branch",{"_13":14,"_38":91,"_49":48,"_92":50,"_93":-5,"_94":-5,"_95":-5,"_96":-5,"_97":-5,"_98":51,"_99":100},"isOwned","main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","pql","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoicHJveHkiLCJzcmMiOiJodHRwczovL2ltYWdlcy5jbGVyay5kZXYvdXBsb2FkZWQvaW1nXzJrYUVVM1VyRHRhckNnNGxidGEwUUdaSFRDYSJ9","tier","pro","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_49":50,"_38":51,"_44":203,"_53":-5,"_54":31,"_55":56,"_57":56,"_58":59,"_60":-5,"_19":61,"_62":-5,"_63":31,"_64":-5,"_65":-5,"_66":67,"_68":69,"_70":-5,"_71":-5,"_72":-5,"_73":74,"_75":76,"_77":76,"_78":79,"_80":79,"_81":79,"_82":76,"_83":76,"_84":204,"_86":205,"_88":206,"_90":31},"tree",[182,183,184,185,186],"files",[182,183,184,185,186],"counts",{"_172":67,"_173":74,"_174":67,"_175":176,"_177":178},"contents",[131,132,133,134,135],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_49":165,"_38":166,"_36":167,"_137":-5,"_47":48,"_46":168,"_138":139,"_44":169,"_13":67,"_39":26,"_41":170,"_30":31,"_142":-5,"_33":171},{"_49":158,"_38":159,"_36":160,"_137":-5,"_47":48,"_46":161,"_138":139,"_44":162,"_13":67,"_39":26,"_41":163,"_30":31,"_142":-5,"_33":164},{"_49":150,"_38":151,"_36":152,"_137":-5,"_47":48,"_46":153,"_138":139,"_44":154,"_13":155,"_39":26,"_41":156,"_30":31,"_142":-5,"_33":157},{"_49":144,"_38":145,"_36":146,"_137":-5,"_47":48,"_46":141,"_138":139,"_44":147,"_13":67,"_39":26,"_41":148,"_30":31,"_142":-5,"_33":149},{"_49":136,"_38":34,"_36":37,"_137":-5,"_47":48,"_46":14,"_138":139,"_44":140,"_13":141,"_39":40,"_41":43,"_30":31,"_142":-5,"_33":143},"d5217cf6-7cc2-45dd-bd38-7cc1c1a75b18","parentId","latest",true,["D",1783027702653],116,"label",[34],"16223f06-ddfd-11f0-aea9-42dde27851f2","deno.json","16223fce-ddfd-11f0-aea9-42dde27851f2",["D",1766273951620],"{\n \"$schema\": \"https://raw.githubusercontent.com/denoland/deno/98f62cee78e85bfc47c62ed703777c6bc8794f1c/cli/schemas/config-file.v1.json\",\n \"lock\": false,\n \"compilerOptions\": {\n \"noImplicitAny\": false,\n \"strict\": true,\n \"types\": [\"https://www.val.town/types/valtown.d.ts\"],\n \"lib\": [\n \"dom\",\n \"dom.iterable\",\n \"dom.asynciterable\",\n \"deno.ns\",\n \"deno.unstable\"\n ]\n },\n \"lint\": {\n \"include\": [\"deno:/https/esm.town/**/*\"],\n \"rules\": {\n \"exclude\": [\"no-explicit-any\"]\n }\n },\n \"node_modules_dir\": false,\n \"experimental\": {\n \"unstable-node-globals\": true,\n \"unstable-temporal\": true,\n \"unstable-worker-options\": true,\n \"unstable-sloppy-imports\": true\n }\n}",[145],"06c41880-1675-11f1-83ce-42dde27851f2","README.md","199d9e06-ddd9-11f0-8de2-42dde27851f2",157,["D",1772482730493],36,"# PineconeIndex\n\nA simple interface for making and querying Pinecone vector databases. Use OpenAI\nembeddings to vectorize and search\n\n## Quickstart\n\nCreate keys for\n[Pinecone](https://docs.pinecone.io/guides/projects/manage-api-keys) and\n[OpenAI](https://platform.openai.com/docs/api-reference/project-api-keys), and\nstore then in your environment variables. Then,\n\n```typescript\nimport PineconeIndex from \"https://esm.town/v/peterqliu/PineconeIndex/main.tsx\";\n\n// set up your environment variables\nconst pineconeKey = Deno.env.get(\"PINECONE_KEY\");\nconst modelToken = Deno.env.get(\"OPENAI_KEY\");\n\nconst index = new PineconeIndex({\n name: \"2025-all-docs\",\n model: \"text-embedding-ada-002\",\n dimensions: 1536,\n pineconeKey,\n modelToken,\n});\n\n// run this only once\nawait index.create();\n\n// Using the methods directly (see bottom of README for HTTP access)\nawait index.upsert([\"mary had a little lamb\", \"twinkle twinkle little star\"]);\nconst results = await index.query(\"shine, shine you diminutive celestial body\");\n```\n\nThe options passed to the constructor are a thin abstraction over options for\nnew Pinecone().createIndex(). For more detail, consult\n[that documentation](https://github.com/pinecone-io/pinecone-ts-client?tab=readme-ov-file#indexes).\n\n## API Reference\n\n### index.query(string)\n\nFind the most relevant documents for your query.\n\n```typescript\nconst results = await index.query(\"your search text\");\n```\n\n**Returns:**\n\n```json\n{\n \"matches\": [\n {\n \"id\": \"doc-123\",\n \"score\": 0.95,\n \"metadata\": {\n \"text\": \"Machine learning is transforming...\"\n }\n }\n ]\n}\n```\n\n### index.upsertRecords([string])\n\nUpload new documents to your index as an array of strings.\n\n```typescript\nawait index.upsertRecords([\n \"First document content\",\n \"Second document content\",\n]);\n```\n\n### Clear Index\n\nRemove all documents from your index.\n\n```typescript\nawait index.empty();\n```\n\n### Create Index\n\nCreate a new Pinecone index (if it doesn't exist).\n\n```typescript\nawait index.create();\n```\n\n## Remote HTTP access\n\nPineconeIndex also provides `handleRequest` as a convenience method to access\nyour indices via HTTP. This is useful when accessing the index from other vals,\nespecially without sharing Pinecone and OpenAI credentials.\n\n### Server setup\n\nFirst instantiate your index:\n\n```typescript\nconst index = new PinconeIndex({...});\n\nexport default async function (req: Request): Promise {\n return await index.handleRequest(req);\n}\n```\n\n### Usage\n\nOperations are determined by the URL path. The first segment after the domain\nspecifies the operation to perform.\n\n```javascript\nconst API_URL = \"https://index-val-url.web.val.run\";\n\n// Search for documents: append the desired operation to the url\nconst searchResponse = await fetch(`${API_URL}/query/`, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: \"artificial intelligence trends\",\n});\n```",[151],"162d90ea-ddfd-11f0-80b2-42dde27851f2","AGENTS.md","162d91a8-ddfd-11f0-80b2-42dde27851f2",117,["D",1766273951694],"You are an advanced assistant specialized in generating Val Town code.\n\n## Core Guidelines\n\n- Ask clarifying questions when requirements are ambiguous\n- Provide complete, functional solutions rather than skeleton implementations\n- Test your logic against edge cases before presenting the final solution\n- Ensure all code follows Val Town's specific platform requirements\n- If a section of code that you're working on is getting too complex, consider refactoring it into subcomponents\n\n## Code Standards\n\n- Generate code in TypeScript or TSX\n- Add appropriate TypeScript types and interfaces for all data structures\n- Prefer official SDKs or libraries than writing API calls directly\n- Ask the user to supply API or library documentation if you are at all unsure about it\n- **Never bake in secrets into the code** - always use environment variables\n- Include comments explaining complex logic (avoid commenting obvious operations)\n- Follow modern ES6+ conventions and functional programming practices if possible\n\n## Types of triggers\n\n### 1. HTTP Trigger\n\n- Create web APIs and endpoints\n- Handle HTTP requests and responses\n- Example structure:\n\n```ts\nexport default async function (req: Request) {\n return new Response(\"Hello World\");\n}\n```\n\nFiles that are HTTP triggers have http in their name like `foobar.http.tsx`\n\n### 2. Cron Triggers\n\n- Run on a schedule\n- Use cron expressions for timing\n- Example structure:\n\n```ts\nexport default async function () {\n // Scheduled task code\n}\n```\n\nFiles that are Cron triggers have cron in their name like `foobar.cron.tsx`\n\n### 3. Email Triggers\n\n- Process incoming emails\n- Handle email-based workflows\n- Example structure:\n\n```ts\nexport default async function (email: Email) {\n // Process email\n}\n```\n\nFiles that are Email triggers have email in their name like `foobar.email.tsx`\n\n\n## Val Town Standard Libraries\n\nVal Town provides several hosted services and utility functions.\n\n### Blob Storage\n\n```ts\nimport { blob } from \"https://esm.town/v/std/blob\";\nawait blob.setJSON(\"myKey\", { hello: \"world\" });\nlet blobDemo = await blob.getJSON(\"myKey\");\nlet appKeys = await blob.list(\"app_\");\nawait blob.delete(\"myKey\");\n```\n\n### SQLite\n\n```ts\nimport { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nconst TABLE_NAME = 'todo_app_users_2';\n// Create table - do this before usage and change table name when modifying schema\nawait sqlite.execute(`CREATE TABLE IF NOT EXISTS ${TABLE_NAME} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL\n)`);\n// Query data\nconst result = await sqlite.execute(`SELECT * FROM ${TABLE_NAME} WHERE id = ?`, [1]);\n```\n\nNote: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.\n\n### OpenAI\n\n```ts\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nconst openai = new OpenAI();\nconst completion = await openai.chat.completions.create({\n messages: [\n { role: \"user\", content: \"Say hello in a creative way\" },\n ],\n model: \"gpt-4o-mini\",\n max_tokens: 30,\n});\n```\n\n### Email\n\n```ts\nimport { email } from \"https://esm.town/v/std/email\";\n// By default emails the owner of the val\nawait email({ \n subject: \"Hi\", \n text: \"Hi\", \n html: \"

Hi

\"\n});\n```\n\n## Val Town Utility Functions\n\nVal Town provides several utility functions to help with common project tasks.\n\n### Importing Utilities\n\nAlways import utilities with version pins to avoid breaking changes:\n\n```ts\nimport { parseProject, readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n```\n\n### Available Utilities\n\n\n#### **serveFile** - Serve project files with proper content types\n\nFor example, in Hono:\n\n```ts\n// serve all files in frontend/ and shared/\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n```\n\n#### **readFile** - Read files from within the project:\n\n```ts\n// Read a file from the project\nconst fileContent = await readFile(\"/frontend/index.html\", import.meta.url);\n```\n\n#### **listFiles** - List all files in the project\n\n```ts\nconst files = await listFiles(import.meta.url);\n```\n\n#### **parseProject** - Extract information about the current project from import.meta.url\n\nThis is useful for including info for linking back to a val, ie in \"view source\" urls:\n\n```ts\nconst projectVal = parseProject(import.meta.url);\nconsole.log(projectVal.username); // Owner of the project\nconsole.log(projectVal.name); // Project name\nconsole.log(projectVal.version); // Version number\nconsole.log(projectVal.branch); // Branch name\nconsole.log(projectVal.links.self.project); // URL to the project page\n```\n\nHowever, it's *extremely importing* to note that `parseProject` and other Standard Library utilities ONLY RUN ON THE SERVER.\nIf you need access to this data on the client, run it in the server and pass it to the client by splicing it into the HTML page\nor by making an API request for it.\n\n## Val Town Platform Specifics\n\n- **Redirects:** Use `return new Response(null, { status: 302, headers: { Location: \"/place/to/redirect\" }})` instead of `Response.redirect` which is broken\n- **Images:** Avoid external images or base64 images. Use emojis, unicode symbols, or icon fonts/libraries instead\n- **AI Image:** To inline generate an AI image use: `

`\n- **Storage:** DO NOT use the Deno KV module for storage\n- **Browser APIs:** DO NOT use the `alert()`, `prompt()`, or `confirm()` methods\n- **Weather Data:** Use open-meteo for weather data (doesn't require API keys) unless otherwise specified\n- **View Source:** Add a view source link by importing & using `import.meta.url.replace(\"ems.sh\", \"val.town)\"` (or passing this data to the client) and include `target=\"_top\"` attribute\n- **Error Debugging:** Add `` to HTML to capture client-side errors\n- **Error Handling:** Only use try...catch when there's a clear local resolution; Avoid catches that merely log or return 500s. Let errors bubble up with full context\n- **Environment Variables:** Use `Deno.env.get('keyname')` when you need to, but generally prefer APIs that don't require keys\n- **Imports:** Use `https://esm.sh` for npm and Deno dependencies to ensure compatibility on server and browser\n- **Storage Strategy:** Only use backend storage if explicitly required; prefer simple static client-side sites\n- **React Configuration:** When using React libraries, pin versions with `?deps=react@18.2.0,react-dom@18.2.0` and start the file with `/** @jsxImportSource https://esm.sh/react@18.2.0 */`\n- Ensure all React dependencies and sub-dependencies are pinned to the same version\n- **Styling:** Default to using TailwindCSS via `` unless otherwise specified\n\n## Project Structure and Design Patterns\n\n### Recommended Directory Structure\n```\n├── backend/\n│ ├── database/\n│ │ ├── migrations.ts # Schema definitions\n│ │ ├── queries.ts # DB query functions\n│ │ └── README.md\n│ └── routes/ # Route modules\n│ ├── [route].ts\n│ └── static.ts # Static file serving\n│ ├── index.ts # Main entry point\n│ └── README.md\n├── frontend/\n│ ├── components/\n│ │ ├── App.tsx\n│ │ └── [Component].tsx\n│ ├── favicon.svg\n│ ├── index.html # Main HTML template\n│ ├── index.tsx # Frontend JS entry point\n│ ├── README.md\n│ └── style.css\n├── README.md\n└── shared/\n ├── README.md\n └── utils.ts # Shared types and functions\n```\n\n### Backend (Hono) Best Practices\n\n- Hono is the recommended API framework\n- Main entry point should be `backend/index.ts`\n- **Static asset serving:** Use the utility functions to read and serve project files:\n ```ts\n import { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n \n // serve all files in frontend/ and shared/\n app.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\n app.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n \n // For index.html, often you'll want to bootstrap with initial data\n app.get(\"/\", async c => {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject data to avoid extra round-trips\n const initialData = await fetchInitialData();\n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n });\n ```\n- Create RESTful API routes for CRUD operations\n- Always include this snippet at the top-level Hono app to re-throwing errors to see full stack traces:\n ```ts\n // Unwrap Hono errors to see original error details\n app.onError((err, c) => {\n throw err;\n });\n ```\n\n### Database Patterns\n- Run migrations on startup or comment out for performance\n- Change table names when modifying schemas rather than altering\n- Export clear query functions with proper TypeScript typing\n\n## Common Gotchas and Solutions\n\n1. **Environment Limitations:** \n - Val Town runs on Deno in a serverless context, not Node.js\n - Code in `shared/` must work in both frontend and backend environments\n - Cannot use `Deno` keyword in shared code\n - Use `https://esm.sh` for imports that work in both environments\n\n2. **SQLite Peculiarities:**\n - Limited support for ALTER TABLE operations\n - Create new tables with updated schemas and copy data when needed\n - Always run table creation before querying\n\n3. **React Configuration:**\n - All React dependencies must be pinned to 18.2.0\n - Always include `@jsxImportSource https://esm.sh/react@18.2.0` at the top of React files\n - Rendering issues often come from mismatched React versions\n\n4. **File Handling:**\n - Val Town only supports text files, not binary\n - Use the provided utilities to read files across branches and forks\n - For files in the project, use `readFile` helpers\n\n5. **API Design:**\n - `fetch` handler is the entry point for HTTP vals\n - Run the Hono app with `export default app.fetch // This is the entry point for HTTP vals`\n\n",[159],"16370c42-ddfd-11f0-b88d-42dde27851f2",".vtignore","16370cf6-ddfd-11f0-b88d-42dde27851f2",118,["D",1766273951761],".git\n.vscode\n.cursorrules\n.DS_Store\nnode_modules\nvendor",[166],"pulls","branches","forks","envVars",{"_179":67,"_181":67},"connections",{"_179":67,"_180":67},"count","unfilledCount","emptyCount",{"_49":165,"_38":166,"_36":167,"_137":-5,"_47":48,"_46":168,"_138":139,"_44":200,"_13":67,"_39":26,"_41":170,"_30":31,"_142":-5,"_188":201,"_33":202},{"_49":158,"_38":159,"_36":160,"_137":-5,"_47":48,"_46":161,"_138":139,"_44":197,"_13":67,"_39":26,"_41":163,"_30":31,"_142":-5,"_188":198,"_33":199},{"_49":150,"_38":151,"_36":152,"_137":-5,"_47":48,"_46":153,"_138":139,"_44":194,"_13":155,"_39":26,"_41":156,"_30":31,"_142":-5,"_188":195,"_33":196},{"_49":144,"_38":145,"_36":146,"_137":-5,"_47":48,"_46":141,"_138":139,"_44":191,"_13":67,"_39":26,"_41":148,"_30":31,"_142":-5,"_188":192,"_33":193},{"_49":136,"_38":34,"_36":37,"_137":-5,"_47":48,"_46":14,"_138":139,"_44":187,"_13":141,"_39":40,"_41":43,"_30":31,"_142":-5,"_188":189,"_33":190},["D",1783027702653],"children",[],[34],["D",1766273951620],[],[145],["D",1772482730493],[],[151],["D",1766273951694],[],[159],["D",1766273951761],[],[166],["D",1753901115385],{"_101":100,"_108":104},{"_101":100,"_102":-5,"_103":104,"_19":61,"_105":106,"_107":31},{"_13":14,"_38":91,"_49":48,"_92":50,"_93":-5,"_94":-5,"_95":-5,"_96":-5,"_97":-5,"_98":51,"_99":100},"ctx",{"_84":-5,"_128":240,"_241":242,"_243":244,"_245":31,"_246":31,"_247":31,"_248":139,"_249":-5,"_250":-7,"_251":-7,"_252":67,"_253":31,"_254":31,"_255":-5,"_256":31,"_257":31,"_258":31,"_259":-5},"ENV",{"_211":212,"_213":214,"_215":91,"_216":217,"_218":219,"_220":221,"_222":223,"_224":225,"_226":227,"_228":229,"_230":231,"_232":233,"_234":235,"_236":237,"_238":239},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","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","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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