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`PINECONE_KEY`: Your Pinecone API key\n - `OPENAI_KEY`: Your OpenAI API key\n\n2. **Use the API**\n ```bash\n # Search your documents\n curl -X POST https://your-val-url.web.val.run/query/ \\\n -H \"Content-Type: application/json\" \\\n -d '\"machine learning applications\"'\n ```\n\n## API Reference\n\n### Search Documents\n\nFind the most relevant documents for your query.\n\n```http\nPOST /query/\n```\n\n**Request Body:**\n\n```json\n\"your search query here\"\n```\n\nor\n\n```json\n{\n \"query\": \"your search query here\"\n}\n```\n\n**Response:**\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### Add Documents\n\nUpload new documents to your index.\n\n```http\nPOST /upsert/\n```\n\n**Request Body:**\n\n```json\n{\n \"texts\": [\n \"First document content\",\n \"Second document content\"\n ]\n}\n```\n\n**Response:**\n\n```json\n{\n \"success\": true,\n \"count\": 2\n}\n```\n\n### Clear Index\n\nRemove all documents from your index.\n\n```http\nPOST /empty/\n```\n\n**Response:**\n\n```json\n{\n \"success\": true,\n \"message\": \"Index cleared\"\n}\n```\n\n### Health Check\n\nCheck if the service is running and configured properly.\n\n```http\nGET /health/\n```\n\n**Response:**\n\n```json\n{\n \"status\": \"healthy\",\n \"timestamp\": \"2025-12-20T19:21:17.737Z\",\n \"config\": {\n \"pineconeKey\": \"✓ Set\",\n \"openaiKey\": \"✓ Set\"\n }\n}\n```\n\n## Usage Examples\n\n### JavaScript\n\n```javascript\nconst API_URL = \"https://your-val-url.web.val.run\";\n\n// Search for documents\nasync function search(query) {\n const response = await fetch(`${API_URL}/query/`, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify(query),\n });\n return response.json();\n}\n\n// Add new documents\nasync function addDocuments(texts) {\n const response = await fetch(`${API_URL}/upsert/`, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ texts }),\n });\n return response.json();\n}\n\n// Example usage\nconst results = await search(\"artificial intelligence trends\");\nawait addDocuments([\n \"AI is revolutionizing healthcare\",\n \"Machine learning in finance\",\n]);\n```\n\n### Python\n\n```python\nimport requests\n\nAPI_URL = 'https://your-val-url.web.val.run'\n\n# Search documents\ndef search(query):\n response = requests.post(f'{API_URL}/query/', json=query)\n return response.json()\n\n# Add documents\ndef add_documents(texts):\n response = requests.post(f'{API_URL}/upsert/', json={'texts': texts})\n return response.json()\n\n# Example usage\nresults = search('climate change solutions')\nadd_documents(['Solar energy is becoming more efficient', 'Wind power costs are decreasing'])\n```\n\n### cURL\n\n```bash\n# Search\ncurl -X POST https://your-val-url.web.val.run/query/ \\\n -H \"Content-Type: application/json\" \\\n -d '\"renewable energy technologies\"'\n\n# Add documents\ncurl -X POST https://your-val-url.web.val.run/upsert/ \\\n -H \"Content-Type: application/json\" \\\n -d '{\"texts\": [\"Document 1\", \"Document 2\"]}'\n\n# Clear index\ncurl -X POST https://your-val-url.web.val.run/empty/\n\n# Health check\ncurl https://your-val-url.web.val.run/health/\n```\n\n## Error Handling\n\nThe API returns standard HTTP status codes:\n\n- `200` - Success\n- `400` - Bad Request (missing required fields)\n- `405` - Method Not Allowed (wrong HTTP method)\n- `500` - Server Error (API key issues, service problems)\n\nError responses include helpful messages:\n\n```json\n{\n \"error\": \"Query text is required\",\n \"hint\": \"Make sure PINECONE_KEY and OPENAI_KEY environment variables are set\"\n}\n```\n\n## Configuration\n\nThe service uses these default settings:\n\n- **Index**: `wikipedia-simple-text-embedding-ada-002-250k`\n- **Model**: `text-embedding-ada-002`\n- **Dimensions**: 1536\n- **Results**: Top 10 matches per query\n\nTo customize these settings, modify the PineconeIndex configuration in the code.","# PineconeIndex HTTP API\n\nA simple HTTP interface for querying and managing your Pinecone vector database.\nSearch through your documents using natural language queries via REST API\nendpoints.\n\n## Creating Your Own Instance\n\nTo use PineconeIndex in your own Val Town project:\n\n```typescript\nimport PineconeIndex from \"https://esm.town/v/peterqliu/PineconeIndex/PineconeIndex\";\n\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// query index with string for nearest neighbors\nexport default async function (req: Request): Promise {\n return await index.handleRequest(req);\n}\n```\n\n## API Reference\n\n### Search Documents\n\nFind the most relevant documents for your query.\n\n```http\nPOST /query/\n```\n\n**Request Body:**\n\n```json\n\"your search query here\"\n```\n\n**Response:**\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### Add Documents\n\nUpload new documents to your index.\n\n```http\nPOST /upsert/\n```\n\n**Request Body:**\n\n```json\n{\n \"texts\": [\n \"First document content\",\n \"Second document content\"\n ]\n}\n```\n\n**Response:**\n\n```json\n{\n \"success\": true,\n \"count\": 2\n}\n```\n\n### Clear Index\n\nRemove all documents from your index.\n\n```http\nPOST /empty/\n```\n\n**Response:**\n\n```json\n{\n \"success\": true,\n \"message\": \"Index cleared\"\n}\n```\n\n### Health Check\n\nCheck if the service is running and configured properly.\n\n```http\nGET /health/\n```\n\n**Response:**\n\n```json\n{\n \"status\": \"healthy\",\n \"timestamp\": \"2025-12-20T19:21:17.737Z\",\n \"config\": {\n \"pineconeKey\": \"✓ Set\",\n \"openaiKey\": \"✓ Set\"\n }\n}\n```\n\n## Usage Examples\n\n### JavaScript\n\n```javascript\nconst API_URL = \"https://your-val-url.web.val.run\";\n\n// Search for documents\nasync function search(query) {\n const response = await fetch(`${API_URL}/query/`, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify(query),\n });\n return response.json();\n}\n\n// Add new documents\nasync function addDocuments(texts) {\n const response = await fetch(`${API_URL}/upsert/`, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ texts }),\n });\n return response.json();\n}\n\n// Example usage\nconst results = await search(\"artificial intelligence trends\");\nawait addDocuments([\n \"AI is revolutionizing healthcare\",\n \"Machine learning in finance\",\n]);\n```\n\n### Python\n\n```python\nimport requests\n\nAPI_URL = 'https://your-val-url.web.val.run'\n\n# Search documents\ndef search(query):\n response = requests.post(f'{API_URL}/query/', json=query)\n return response.json()\n\n# Add documents\ndef add_documents(texts):\n response = requests.post(f'{API_URL}/upsert/', json={'texts': texts})\n return response.json()\n\n# Example usage\nresults = search('climate change solutions')\nadd_documents(['Solar energy is becoming more efficient', 'Wind power costs are decreasing'])\n```\n\n### cURL\n\n```bash\n# Search\ncurl -X POST https://your-val-url.web.val.run/query/ \\\n -H \"Content-Type: application/json\" \\\n -d '\"renewable energy technologies\"'\n\n# Add documents\ncurl -X POST https://your-val-url.web.val.run/upsert/ \\\n -H \"Content-Type: application/json\" \\\n -d '{\"texts\": [\"Document 1\", \"Document 2\"]}'\n\n# Clear index\ncurl -X POST https://your-val-url.web.val.run/empty/\n\n# Health check\ncurl https://your-val-url.web.val.run/health/\n```\n\n## Error Handling\n\nThe API returns standard HTTP status codes:\n\n- `200` - Success\n- `400` - Bad Request (missing required fields)\n- `405` - Method Not Allowed (wrong HTTP method)\n- `500` - Server Error (API key issues, service problems)\n\nError responses include helpful messages:\n\n```json\n{\n \"error\": \"Query text is required\",\n \"hint\": \"Make sure PINECONE_KEY and OPENAI_KEY environment variables are set\"\n}\n```\n\n## Configuration\n\nThe service uses these default settings:\n\n- **Index**: `wikipedia-simple-text-embedding-ada-002-250k`\n- **Model**: `text-embedding-ada-002`\n- **Dimensions**: 1536\n- **Results**: Top 10 matches per query\n\nTo customize these settings, modify the PineconeIndex configuration in the code.","createdAt",["D",1766260223472],"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",{"_95":94,"_102":98},"org",{"_95":94,"_96":-5,"_97":98,"_18":54,"_99":100,"_101":25},"branch",{"_13":84,"_32":85,"_42":41,"_86":43,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":44,"_93":94},"isOwned",162,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","pql","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoicHJveHkiLCJzcmMiOiJodHRwczovL2ltYWdlcy5jbGVyay5kZXYvdXBsb2FkZWQvaW1nXzJrYUVVM1VyRHRhckNnNGxidGEwUUdaSFRDYSJ9","tier","pro","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_42":43,"_32":44,"_37":176,"_46":-5,"_47":25,"_48":49,"_50":49,"_51":52,"_53":-5,"_18":54,"_55":-5,"_56":25,"_57":-5,"_58":-5,"_59":60,"_61":62,"_63":-5,"_64":-5,"_65":-5,"_66":67,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":177,"_79":178,"_81":179,"_83":25},"tree",[163,164,165],"files",[163,164,165],"counts",{"_153":60,"_154":67,"_155":60,"_156":157,"_158":159},"contents",[126,127,128],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles",true,"grants","orgs","customDomains",{},{"_42":145,"_32":44,"_30":146,"_132":-5,"_40":41,"_39":147,"_134":25,"_37":148,"_13":149,"_33":150,"_34":151,"_24":25,"_139":-5,"_27":152},{"_42":141,"_32":28,"_30":31,"_132":-5,"_40":41,"_39":14,"_134":25,"_37":142,"_13":143,"_33":20,"_34":36,"_24":25,"_139":-5,"_27":144},{"_42":129,"_32":130,"_30":131,"_132":-5,"_40":41,"_39":133,"_134":25,"_37":135,"_13":136,"_33":137,"_34":138,"_24":25,"_139":-5,"_27":140},"e883470a-dddb-11f0-bea7-42dde27851f2","sample_server","00980d1a-ddd9-11f0-b789-42dde27851f2","parentId",88,"latest",["D",1766259701676],3,"http","import PineconeIndex from \"https://esm.town/v/peterqliu/PineconeIndex/PineconeIndex\";\n\nlet wpIndex: PineconeIndex | null = null;\n\nfunction getIndex(): PineconeIndex {\n if (!wpIndex) {\n const pineconeKey = Deno.env.get(\"PINECONE_KEY\");\n const modelToken = Deno.env.get(\"OPENAI_KEY\");\n\n if (!pineconeKey) {\n throw new Error(\"PINECONE_KEY environment variable is required\");\n }\n if (!modelToken) {\n throw new Error(\"OPENAI_KEY environment variable is required\");\n }\n\n wpIndex = new PineconeIndex({\n name: \"wikipedia-simple-text-embedding-ada-002-250k\",\n model: \"text-embedding-ada-002\",\n dimensions: 1536,\n pineconeKey,\n modelToken,\n });\n }\n return wpIndex;\n}\n\n/**\n * HTTP handler that routes requests to the PineconeIndex\n * Supports routes like:\n * - POST /query/ - Query the index with text\n * - POST /upsert/ - Add texts to the index\n * - POST /empty/ - Clear the index\n * - GET /health/ - Health check\n */\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const pathSegments = url.pathname.split('/').filter(segment => segment.length > 0);\n const operation = pathSegments[0];\n\n // Handle health check without requiring API keys\n if (operation === 'health') {\n const pineconeKey = Deno.env.get(\"PINECONE_KEY\");\n const modelToken = Deno.env.get(\"OPENAI_KEY\");\n \n return Response.json({\n status: 'healthy',\n timestamp: new Date().toISOString(),\n config: {\n pineconeKey: pineconeKey ? '✓ Set' : '✗ Missing',\n openaiKey: modelToken ? '✓ Set' : '✗ Missing',\n }\n });\n }\n\n try {\n const index = getIndex();\n return await index.handleRequest(req);\n } catch (error) {\n return Response.json(\n { \n error: error.message,\n hint: \"Make sure PINECONE_KEY and OPENAI_KEY environment variables are set\"\n },\n { status: 500 }\n );\n }\n}","label",[130],"1f859c66-dddd-11f0-a978-42dde27851f2",["D",1766260223472],6,[28],"57641b66-ddd9-11f0-ac67-42dde27851f2","550e6a1c-6d75-11f0-9956-0224a6c84d84",87,["D",1766258599221],82,"script","import { Pinecone } from \"npm:@pinecone-database/pinecone\";\nimport axios from \"npm:axios\";\nimport { v4 as uuidv4 } from \"npm:uuid\"; // for generating unique IDs\n\n\nexport default class PineconeIndex {\n \n constructor(options) {\n const {name, dimensions, pineconeIndex, pineconeKey, modelToken, model} = options;\n\n this.options = options;\n this.pc = new Pinecone({ apiKey: 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,\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 info = await this.pc.describeIndex(name);\n const host = info.host;\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 info = await this.pc.describeIndex(name);\n const host = info.host;\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 \n async upsertRecords(texts) {\n const idx = await this.getIndex();\n \n // Process texts one at a time to avoid message size limits\n for (const text of texts) {\n const values = await this.embed(text);\n const vector = {\n id: uuidv4(), // generate unique ID per text\n values,\n metadata: { text }, // keep original text in metadata\n };\n \n await idx.upsert([vector]);\n }\n }\n\n async query(text:string){\n const idx = await this.getIndex();\n const vector = await this.embed(text);\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} = this.options;\n const response = await axios.post(\n \"https://api.openai.com/v1/embeddings\",\n {model, input: text }, \n {\n headers: {\n \"Authorization\": `Bearer ${modelToken}`,\n },\n }\n );\n \n return response.data.data[0].embedding;\n }\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 => segment.length > 0);\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' ? body : (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 || (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.upsertRecords(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({ status: 'healthy', timestamp: new Date().toISOString() });\n \n default:\n return Response.json(\n { error: `Unknown operation: ${operation}. Supported operations: query, upsert, empty, health` },\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') ? 400 : 500;\n \n return Response.json({ error: error.message }, { status });\n }\n }\n options:null\n pc: null\n _index: null\n _namespace:null\n \n}\n\n\n\n// /**\n// * Options passed to the constructor.\n// */\n// export interface PineconeIndexOptions {\n// /** Name of the Pinecone index */\n// name: string;\n// /** Number of dimensions the vectors will have */\n// dimensions: number;\n// /** Optional – a custom index name that you might want to use later */\n// pineconeIndex?: string;\n// /** Pinecone API key */\n// pineconeKey: string;\n// /** OpenAI API key used for embeddings */\n// modelToken: string;\n// /** OpenAI model name (e.g. “text-embedding-ada-002”) */\n// model: string;\n// /** Optional namespace inside the index */\n// namespace?: string;\n// }\n\n// /**\n// * A helper class that wraps a Pinecone index.\n// */\n// export default class PineconeIndex {\n// /** Raw constructor options */\n// private readonly options: PineconeIndexOptions;\n// /** The Pinecone client instance */\n// private readonly pc: PineconeClient;\n// /** Cached reference to the index (or namespace) */\n// private _index: Index | null = null;\n// /** Namespace that will be used if supplied */\n// private readonly _namespace: string;\n\n// constructor(options: PineconeIndexOptions) {\n// this.options = options;\n// this.pc = new Pinecone({ apiKey: options.pineconeKey });\n// this._namespace = options.namespace ?? '';\n// }\n\n// /* Creates the Pinecone index if it does not already exist. */\n// async create(): Promise {\n// const { name, dimensions } = this.options;\n// await this.pc.createIndex({\n// name,\n// dimension: dimensions,\n// metric: 'cosine',\n// spec: { serverless: { cloud: 'aws', region: 'us-east-1' } },\n// deletionProtection: 'disabled',\n// waitUntilReady: true,\n// });\n\n// // Immediately capture the index host and create a local reference\n// const info = await this.pc.describeIndex(name);\n// const host = info.host;\n// this._index = this.pc.Index(host);\n// }\n\n// /**\n// * Lazily resolves the index (or the namespace inside it).\n// */\n// async getIndex(): Promise {\n// if (this._index) {\n// return this._index;\n// }\n\n// const { name } = this.options;\n// const info = await this.pc.describeIndex(name);\n// const host = info.host;\n// const idx = this.pc.index(name, host);\n\n// this._index = this._namespace ? idx.namespace(this._namespace) : idx;\n// return this._index;\n// }\n\n// /**\n// * Upsets an array of text records. Each record is embedded and stored\n// * with a UUID and its original text as metadata.\n// */\n// async upsertRecords(texts: string[]): Promise {\n// const idx = await this.getIndex();\n\n// for (const text of texts) {\n// const values = await this.embed(text);\n// const vector = {\n// id: uuidv4(),\n// values,\n// metadata: { text },\n// };\n// await idx.upsert([vector]);\n// }\n// }\n\n// /**\n// * Queries the index for the 10 most similar vectors to the supplied text.\n// */\n// async query(text: string): Promise {\n// const idx = await this.getIndex();\n// const vector = await this.embed(text);\n// console.log(vector)\n// return idx.query({\n// vector,\n// topK: 10,\n// includeValues: false,\n// includeMetadata: true,\n// });\n// }\n\n// /**\n// * Returns an embedding for a single string using the configured model.\n// */\n// async embed(text: string): Promise {\n// const { model, modelToken } = this.options;\n// const response = await axios.post(\n// 'https://api.openai.com/v1/embeddings',\n// { model, input: text },\n// {\n// headers: {\n// Authorization: `Bearer ${modelToken}`,\n// },\n// }\n// );\n\n// // response.data.data[0].embedding is guaranteed to be a number[]\n// return response.data.data[0].embedding;\n// }\n\n// /**\n// * Deletes all vectors in the current namespace (or the whole index if no\n// * namespace is set).\n// */\n// async empty(): Promise {\n// const idx = await this.getIndex();\n// await idx.deleteAll();\n// }\n// }",[44],"pulls","branches","forks","envVars",{"_160":60,"_162":60},"connections",{"_160":60,"_161":60},"count","unfilledCount","emptyCount",{"_42":145,"_32":44,"_30":146,"_132":-5,"_40":41,"_39":147,"_134":25,"_37":173,"_13":149,"_33":150,"_34":151,"_24":25,"_139":-5,"_167":174,"_27":175},{"_42":141,"_32":28,"_30":31,"_132":-5,"_40":41,"_39":14,"_134":25,"_37":170,"_13":143,"_33":20,"_34":36,"_24":25,"_139":-5,"_167":171,"_27":172},{"_42":129,"_32":130,"_30":131,"_132":-5,"_40":41,"_39":133,"_134":25,"_37":166,"_13":136,"_33":137,"_34":138,"_24":25,"_139":-5,"_167":168,"_27":169},["D",1766259701676],"children",[],[130],["D",1766260223472],[],[28],["D",1766258599221],[],[44],["D",1753901115385],{"_95":94,"_102":98},{"_95":94,"_96":-5,"_97":98,"_18":54,"_99":100,"_101":25},{"_13":84,"_32":85,"_42":41,"_86":43,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":44,"_93":94},"ctx",{"_77":-5,"_123":213,"_214":215,"_216":217,"_218":25,"_219":25,"_220":25,"_221":121,"_222":-5,"_223":-7,"_224":-7,"_225":60,"_226":25,"_227":25,"_228":-5,"_229":25,"_230":25,"_231":25,"_232":-5},"ENV",{"_184":185,"_186":187,"_188":85,"_189":190,"_191":192,"_193":194,"_195":196,"_197":198,"_199":200,"_201":202,"_203":204,"_205":206,"_207":208,"_209":210,"_211":212},"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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