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{ sqlite } from \"https://esm.town/v/stevekrouse/sqlite?v=12\";\nimport emojis from \"npm:emojis-list\";\nimport * as nodeEmoji from \"npm:node-emoji\";\nimport { OpenAI } from \"npm:openai\";\n\n// Initialize OpenAI client\nconst openai = new OpenAI();\n\nawait sqlite.batch([\n // \"DROP TABLE IF EXISTS emojis\",\n \"CREATE TABLE IF NOT EXISTS emojis (emoji varchar primary key, emb F32_BLOB(1536))\",\n `CREATE INDEX IF NOT EXISTS movies_idx ON emojis (\n libsql_vector_idx(emb, 'metric=cosine')\n )`,\n]);\n// Get embedding for a single emoji\nasync function getEmbedding(emoji: string): Promise {\n const result = await openai.embeddings.create({\n input: emoji,\n model: \"text-embedding-3-small\",\n });\n return result.data[0].embedding;\n}\n\nasync function loadEmoji(e: string) {\n const result = await sqlite.execute(\"SELECT 1 FROM emojis WHERE emoji = ?\", [e]);\n if (result.rows.length !== 0) {\n console.log(e, \"already loaded\");\n return;\n }\n const emojiData = nodeEmoji.find(e);\n if (!emojiData) {\n console.log(e, \"not found\");\n return;\n }\n const targetEmbedding = await getEmbedding(`${e} ${emojiData.key}`);\n try {\n await sqlite.execute(\"INSERT INTO emojis VALUES (?, vector(?))\", [e, `[${targetEmbedding.join(\",\")}]`]);\n console.log(e, \"saved\", emojiData);\n } catch (e) {\n console.error(e);\n }\n}\n\nasync function loadAllEmojis() {\n const batchSize = 20;\n for (let i = 0; i < emojis.length; i += batchSize) {\n const batch = emojis.slice(i, i + batchSize);\n const batchPromises = batch.map(loadEmoji);\n await Promise.all(batchPromises);\n }\n}\n\n// await loadAllEmojis();\n\nexport async function searchEmojis(query: string): Promise {\n return (await sqlite.execute(\n `SELECT emoji\n FROM vector_top_k('movies_idx', vector(?), 15)\n JOIN emojis\n ON emojis.rowid = id;`,\n [`[${(await getEmbedding(query)).join(\",\")}]`],\n )).rows.map(r => r.emoji as string);\n}\n\n// // Type definitions\n// type EmojiEmbedding = {\n// emoji: string;\n// embedding: number[];\n// };\n\n// // Function to get all emojis (this is a subset for demonstration)\n\n// // Calculate cosine similarity between two vectors\n// function cosineSimilarity(vecA: number[], vecB: number[]): number {\n// const dotProduct = vecA.reduce((sum, a, i) => sum + a * vecB[i], 0);\n// const magnitudeA = Math.sqrt(vecA.reduce((sum, a) => sum + a * a, 0));\n// const magnitudeB = Math.sqrt(vecB.reduce((sum, b) => sum + b * b, 0));\n// return dotProduct / (magnitudeA * magnitudeB);\n// }\n\n// // Get embedding for a single emoji\n// async function getEmbedding(emoji: string): Promise {\n// const result = await openai.embeddings.create({\n// input: emoji,\n// model: \"text-embedding-3-small\",\n// });\n// return result.data[0].embedding;\n// }\n\n// // Get embeddings for all emojis\n// async function getAllEmbeddings(): Promise {\n// const emojis = getAllEmojis();\n// const embeddings: EmojiEmbedding[] = [];\n\n// // Process emojis in batches to avoid rate limits\n// const batchSize = 5;\n// for (let i = 0; i < emojis.length; i += batchSize) {\n// const batch = emojis.slice(i, i + batchSize);\n// const batchPromises = batch.map(async (emoji) => {\n// const embedding = await getEmbedding(emoji);\n// return { emoji, embedding };\n// });\n\n// const batchResults = await Promise.all(batchPromises);\n// embeddings.push(...batchResults);\n\n// // Add a small delay between batches\n// if (i + batchSize < emojis.length) {\n// await new Promise(resolve => setTimeout(resolve, 1000));\n// }\n// }\n\n// return embeddings;\n// }\n\n// // Find nearest neighbors for a given emoji\n// function findNearestNeighbors(\n// targetEmbedding: number[],\n// allEmbeddings: EmojiEmbedding[],\n// k: number = 5,\n// ): { emoji: string; similarity: number }[] {\n// return allEmbeddings\n// .map(entry => ({\n// emoji: entry.emoji,\n// similarity: cosineSimilarity(targetEmbedding, entry.embedding),\n// }))\n// .sort((a, b) => b.similarity - a.similarity)\n// .slice(0, k);\n// }\n\n// // Main function to demonstrate usage\n// async function main() {\n// try {\n// console.log(\"Getting embeddings for all emojis...\");\n// const allEmbeddings = await getAllEmbeddings();\n\n// // Example: Find nearest neighbors for 😆\n// const targetEmoji = \"😆\";\n// const targetEmbedding = await getEmbedding(targetEmoji);\n\n// console.log(`Finding nearest neighbors for ${targetEmoji}...`);\n// const neighbors = findNearestNeighbors(targetEmbedding, allEmbeddings);\n\n// console.log(\"\\nNearest neighbors:\");\n// neighbors.forEach(({ emoji, similarity }) => {\n// console.log(`${emoji}: ${similarity.toFixed(4)}`);\n// });\n// } catch (error) {\n// console.error(\"Error:\", error);\n// }\n// }\n\n// // Run the program\n// main();","import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite?v=12\";\nimport emojis from \"npm:emojis-list\";\nimport * as nodeEmoji from \"npm:node-emoji\";\nimport { OpenAI } from \"npm:openai\";\n\n// Initialize OpenAI client\nconst openai = new OpenAI();\n\nawait sqlite.batch([\n // \"DROP TABLE IF EXISTS emojis\",\n \"CREATE TABLE IF NOT EXISTS emojis (emoji varchar primary key, emb F32_BLOB(1536))\",\n `CREATE INDEX IF NOT EXISTS movies_idx ON emojis (\n libsql_vector_idx(emb, 'metric=cosine')\n )`,\n]);\n// Get embedding for a single emoji\nasync function getEmbedding(emoji: string): Promise {\n const result = await openai.embeddings.create({\n input: emoji,\n model: \"text-embedding-3-small\",\n });\n return result.data[0].embedding;\n}\n\nasync function loadEmoji(e: string) {\n const result = await sqlite.execute(\"SELECT 1 FROM emojis WHERE emoji = ?\", [e]);\n if (result.rows.length !== 0) {\n console.log(e, \"already loaded\");\n return;\n }\n const emojiData = nodeEmoji.find(e);\n if (!emojiData) {\n console.log(e, \"not found\");\n return;\n }\n const targetEmbedding = await getEmbedding(`${e} ${emojiData.key}`);\n try {\n await sqlite.execute(\"INSERT INTO emojis VALUES (?, vector(?))\", [e, `[${targetEmbedding.join(\",\")}]`]);\n console.log(e, \"saved\", emojiData);\n } catch (e) {\n console.error(e);\n }\n}\n\nasync function loadAllEmojis() {\n const batchSize = 20;\n for (let i = 0; i < emojis.length; i += batchSize) {\n const batch = emojis.slice(i, i + batchSize);\n const batchPromises = batch.map(loadEmoji);\n await Promise.all(batchPromises);\n }\n}\n\n// await loadAllEmojis();\n\nexport async function searchEmojis(query: string): Promise {\n return (await sqlite.execute(\n `SELECT emoji\n FROM vector_top_k('movies_idx', vector(?), 50)\n JOIN emojis\n ON emojis.rowid = id;`,\n [`[${(await getEmbedding(query)).join(\",\")}]`],\n )).rows.map(r => r.emoji as string);\n}\n\n// // Type definitions\n// type EmojiEmbedding = {\n// emoji: string;\n// embedding: number[];\n// };\n\n// // Function to get all emojis (this is a subset for demonstration)\n\n// // Calculate cosine similarity between two vectors\n// function cosineSimilarity(vecA: number[], vecB: number[]): 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getEmbedding(emoji);\n// return { emoji, embedding };\n// });\n\n// const batchResults = await Promise.all(batchPromises);\n// embeddings.push(...batchResults);\n\n// // Add a small delay between batches\n// if (i + batchSize < emojis.length) {\n// await new Promise(resolve => setTimeout(resolve, 1000));\n// }\n// }\n\n// return embeddings;\n// }\n\n// // Find nearest neighbors for a given emoji\n// function findNearestNeighbors(\n// targetEmbedding: number[],\n// allEmbeddings: EmojiEmbedding[],\n// k: number = 5,\n// ): { emoji: string; similarity: number }[] {\n// return allEmbeddings\n// .map(entry => ({\n// emoji: entry.emoji,\n// similarity: cosineSimilarity(targetEmbedding, entry.embedding),\n// }))\n// .sort((a, b) => b.similarity - a.similarity)\n// .slice(0, k);\n// }\n\n// // Main function to demonstrate usage\n// async function main() {\n// try {\n// console.log(\"Getting embeddings for all emojis...\");\n// const allEmbeddings = await getAllEmbeddings();\n\n// // Example: Find nearest neighbors for 😆\n// const targetEmoji = \"😆\";\n// const targetEmbedding = await getEmbedding(targetEmoji);\n\n// console.log(`Finding nearest neighbors for ${targetEmoji}...`);\n// const neighbors = findNearestNeighbors(targetEmbedding, allEmbeddings);\n\n// console.log(\"\\nNearest neighbors:\");\n// neighbors.forEach(({ emoji, similarity }) => {\n// console.log(`${emoji}: ${similarity.toFixed(4)}`);\n// });\n// } catch (error) {\n// console.error(\"Error:\", error);\n// }\n// }\n\n// // Run the program\n// 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