[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_153":154,"_155":156},"routes/_app.x.$handle.$name",{"_96":95,"_33":28,"_104":86,"_27":105,"_13":14,"_106":-5,"_11":107,"_108":109,"_110":111,"_112":113,"_114":115,"_20":-5,"_116":-5,"_117":-5,"_118":25,"_119":120,"_121":25,"_122":-5,"_123":-5,"_124":125},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_44":45,"_33":28,"_39":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":-5,"_18":55,"_56":-5,"_57":25,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":68,"_69":70,"_71":70,"_72":73,"_74":73,"_75":73,"_76":70,"_77":70,"_78":79,"_80":81,"_82":83,"_84":25},"version",72,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":30},"file",{"_31":32,"_33":28,"_34":35,"_36":38,"_39":40,"_41":14,"_42":43,"_24":25},"previous",{"_31":32,"_33":28,"_34":35,"_36":37},"deleted",false,"deletedHere","path","PineconeIndex","latestPath","main.tsx","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\n\n// export 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// options:null\n// pc: null\n// _index: null\n// _namespace:null\n \n// }\n\n\n\n/**\n * Options passed to the constructor.\n */\nexport 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 */\nexport 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 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}","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\n// export 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// options:null\n// pc: null\n// _index: null\n// _namespace:null\n \n// }\n\n\n\n/**\n * Options passed to the constructor.\n */\nexport 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 */\nexport 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: 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Diff · v72