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\"\";\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._handleQuery(req);\n \n case 'upsert':\n return await this._handleUpsert(req);\n \n case 'empty':\n return await this._handleEmpty(req);\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 * Handle query requests - expects JSON body with query text\n */\n private async _handleQuery(req: Request): Promise {\n if (req.method !== 'POST') {\n return Response.json({ error: 'Query endpoint only accepts POST requests' }, { status: 405 });\n }\n\n const body = await req.json();\n \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 return Response.json({ error: 'Query text is required' }, { status: 400 });\n }\n\n const results = await this.query(queryText);\n return Response.json(results);\n }\n\n /**\n * Handle upsert requests - expects JSON body with texts array\n */\n private async _handleUpsert(req: Request): Promise {\n if (req.method !== 'POST') {\n return Response.json({ error: 'Upsert endpoint only accepts POST requests' }, { status: 405 });\n }\n\n const body = await req.json();\n const texts = body.texts || body.data || (Array.isArray(body) ? body : [body]);\n \n if (!Array.isArray(texts) || texts.length === 0) {\n return Response.json({ error: 'Texts array is required' }, { status: 400 });\n }\n\n await this.upsertRecords(texts);\n return Response.json({ success: true, count: texts.length });\n }\n\n /**\n * Handle empty requests - clears the index/namespace\n */\n private async _handleEmpty(req: Request): Promise {\n if (req.method !== 'POST') {\n return Response.json({ error: 'Empty endpoint only accepts POST requests' }, { status: 405 });\n }\n\n await this.empty();\n return Response.json({ success: true, message: 'Index cleared' });\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",{"_148":60,"_150":60},"connections",{"_148":60,"_149":60},"count","unfilledCount","emptyCount",{"_42":133,"_33":44,"_31":134,"_128":-5,"_40":41,"_39":135,"_129":26,"_37":157,"_13":137,"_34":138,"_35":139,"_23":26,"_131":-5,"_154":158,"_27":159},{"_42":127,"_33":28,"_31":32,"_128":-5,"_40":41,"_39":14,"_129":26,"_37":153,"_13":60,"_34":20,"_35":36,"_23":26,"_131":-5,"_154":155,"_27":156},["D",1766258453597],"children",[],[28],["D",1766258444566],[],[44],["D",1753901115385],{"_95":94,"_102":98},{"_95":94,"_96":-5,"_97":98,"_18":54,"_99":100,"_101":26},{"_13":84,"_33":85,"_42":41,"_86":43,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":44,"_93":94},"ctx",{"_77":-5,"_122":197,"_198":199,"_200":201,"_202":26,"_203":26,"_204":26,"_205":24,"_206":-5,"_207":-7,"_208":-7,"_209":60,"_210":26,"_211":26,"_212":-5,"_213":26,"_214":26,"_215":26,"_216":-5},"ENV",{"_168":169,"_170":171,"_172":85,"_173":174,"_175":176,"_177":178,"_179":180,"_181":182,"_183":184,"_185":186,"_187":188,"_189":190,"_191":192,"_193":194,"_195":196},"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"]

Diff · v83