Emoji Vibes Search

\n Use plain language to search for emojis. Get great results.\n
\n Built on Val Town with sqlite vector search and openai embeddings.\n
\n Fork the source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",[],[169],"files",[147,167],"counts",{"_180":33,"_181":155,"_182":155,"_183":33},"pulls","branches","forks","envVars","contents",[186,189],{"_106":148,"_99":149,"_150":151,"_152":-5,"_153":138,"_154":155,"_156":112,"_108":187,"_103":33,"_158":159,"_160":161,"_162":27,"_163":-5,"_102":188},["D",1733674411247],[149],{"_106":168,"_99":169,"_150":119,"_152":-5,"_153":138,"_154":137,"_156":112,"_108":190,"_103":171,"_158":172,"_160":173,"_162":27,"_163":-5,"_102":191},["D",1741035698129],[169],"upstream",{"_106":194,"_99":195,"_115":-5,"_116":-5,"_113":114,"_21":196},"2876093c-252d-11f0-8a67-569c3dd06744","simpleWikipediaInstantSearch",{"_97":98,"_133":134},"screenshots","emptyEnvVars","routes/_app.x.$handle.$name.branch.$branch.history",{"_97":98,"_99":100,"_101":43,"_104":201,"_205":206,"_339":33,"_340":27},{"_106":107,"_99":100,"_108":202,"_110":-5,"_111":112,"_113":114,"_115":-5,"_116":-5,"_12":117,"_118":119,"_120":112,"_121":-5,"_122":94,"_123":124,"_125":124,"_126":127,"_128":127,"_129":127,"_130":124,"_131":27,"_21":203,"_135":204},["D",1745953793997],{"_97":98,"_133":134},{"_103":137,"_99":43,"_106":138,"_139":107,"_140":141,"_142":33,"_143":114,"_144":117},"history",[207,212,220,228,236,244,252,260,267,275,283,291,299,307,315,322,329],{"_150":119,"_103":137,"_108":208,"_153":138,"_176":209},["D",1741035698129],[210],{"_106":168,"_153":138,"_150":119,"_99":169,"_160":173,"_158":172,"_103":171,"_152":-5,"_108":208,"_154":137,"_163":-5,"_156":112,"_162":27,"_102":211},[169],{"_150":119,"_103":171,"_108":213,"_153":138,"_176":214},["D",1738007985874],[215],{"_106":216,"_153":138,"_150":119,"_99":169,"_160":217,"_158":172,"_103":218,"_152":-5,"_108":213,"_154":171,"_163":-5,"_156":27,"_162":27,"_102":219},"88a8242a-252d-11f0-8a67-569c3dd06744","import { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nimport { Hono } from \"npm:hono@3.2.7\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Vibes Search\n \n \n \n\n\n

Emoji Vibes Search

\n Use plain language to search for emojis. Get great results.\n
\n Built on Val Town with sqlite vector search and openai embeddings.\n
\n Fork the source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",15,[169],{"_150":119,"_103":218,"_108":221,"_153":138,"_176":222},["D",1737752160069],[223],{"_106":224,"_153":138,"_150":119,"_99":169,"_160":225,"_158":172,"_103":226,"_152":-5,"_108":221,"_154":218,"_163":-5,"_156":27,"_162":27,"_102":227},"88a359ea-252d-11f0-8a67-569c3dd06744","import { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nimport { Hono } from \"npm:hono@3.2.7\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Emoji Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",14,[169],{"_150":119,"_103":226,"_108":229,"_153":138,"_176":230},["D",1737752152202],[231],{"_106":232,"_153":138,"_150":119,"_99":169,"_160":233,"_158":172,"_103":234,"_152":-5,"_108":229,"_154":226,"_163":-5,"_156":27,"_162":27,"_102":235},"889e189a-252d-11f0-8a67-569c3dd06744","import { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Emoji Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",13,[169],{"_150":119,"_103":234,"_108":237,"_153":138,"_176":238},["D",1733753711664],[239],{"_106":240,"_153":138,"_150":119,"_99":169,"_160":241,"_158":172,"_103":242,"_152":-5,"_108":237,"_154":234,"_163":-5,"_156":27,"_162":27,"_102":243},"88995b02-252d-11f0-8a67-569c3dd06744","import { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Emoji Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",12,[169],{"_150":119,"_103":242,"_108":245,"_153":138,"_176":246},["D",1733753581646],[247],{"_106":248,"_153":138,"_150":119,"_99":169,"_160":249,"_158":172,"_103":250,"_152":-5,"_108":245,"_154":242,"_163":-5,"_156":27,"_162":27,"_102":251},"88948aaa-252d-11f0-8a67-569c3dd06744","import { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Emoji Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",11,[169],{"_150":119,"_103":250,"_108":253,"_153":138,"_176":254},["D",1733753502818],[255],{"_106":256,"_153":138,"_150":119,"_99":169,"_160":257,"_158":172,"_103":258,"_152":-5,"_108":253,"_154":250,"_163":-5,"_156":27,"_162":27,"_102":259},"888f9964-252d-11f0-8a67-569c3dd06744","import { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Emoji Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",10,[169],{"_150":119,"_103":258,"_108":261,"_153":138,"_176":262},["D",1733753490574],[263],{"_106":264,"_153":138,"_150":119,"_99":169,"_160":257,"_158":172,"_103":265,"_152":-5,"_108":261,"_154":258,"_163":-5,"_156":27,"_162":27,"_102":266},"888ae504-252d-11f0-8a67-569c3dd06744",9,[169],{"_150":119,"_103":265,"_108":268,"_153":138,"_176":269},["D",1733675710859],[270],{"_106":271,"_153":138,"_150":119,"_99":169,"_160":272,"_158":172,"_103":273,"_152":-5,"_108":268,"_154":265,"_163":-5,"_156":27,"_162":27,"_102":274},"8885dc6c-252d-11f0-8a67-569c3dd06744","import { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Emoji Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",8,[169],{"_150":119,"_103":273,"_108":276,"_153":138,"_176":277},["D",1733674681287],[278],{"_106":279,"_153":138,"_150":119,"_99":169,"_160":280,"_158":172,"_103":281,"_152":-5,"_108":276,"_154":273,"_163":-5,"_156":27,"_162":27,"_102":282},"8880f81e-252d-11f0-8a67-569c3dd06744","import { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Emoji Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",7,[169],{"_150":119,"_103":281,"_108":284,"_153":138,"_176":285},["D",1733674508072],[286],{"_106":287,"_153":138,"_150":119,"_99":169,"_160":288,"_158":172,"_103":289,"_152":-5,"_108":284,"_154":281,"_163":-5,"_156":27,"_162":27,"_102":290},"887c25f0-252d-11f0-8a67-569c3dd06744","import { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Simple Wikipedia Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",6,[169],{"_150":119,"_103":289,"_108":292,"_153":138,"_176":293},["D",1733674502630],[294],{"_106":295,"_153":138,"_150":119,"_99":169,"_160":296,"_158":172,"_103":297,"_152":-5,"_108":292,"_154":289,"_163":-5,"_156":27,"_162":27,"_102":298},"887746d4-252d-11f0-8a67-569c3dd06744","import Fuse from \"npm:fuse.js\";\nimport { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Simple Wikipedia Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",5,[169],{"_150":119,"_103":297,"_108":300,"_153":138,"_176":301},["D",1733674495410],[302],{"_106":303,"_153":138,"_150":119,"_99":169,"_160":304,"_158":172,"_103":305,"_152":-5,"_108":300,"_154":297,"_163":-5,"_156":27,"_162":27,"_102":306},"88727f28-252d-11f0-8a67-569c3dd06744","import Fuse from \"npm:fuse.js\";\nimport { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\nconst fuse = new Fuse(titlesFile.titles, fuseOptions);\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Emoji Instant Search\n \n \n \n\n\n

Simple Wikipedia Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",4,[169],{"_150":119,"_103":305,"_108":308,"_153":138,"_176":309},["D",1733674477988],[310],{"_106":311,"_153":138,"_150":119,"_99":169,"_160":312,"_158":172,"_103":313,"_152":-5,"_108":308,"_154":305,"_163":-5,"_156":27,"_162":27,"_102":314},"886dad90-252d-11f0-8a67-569c3dd06744","import Fuse from \"npm:fuse.js\";\nimport { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\nconst fuse = new Fuse(titlesFile.titles, fuseOptions);\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = await searchEmojis(query);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Simple Wikipedia Instant Search\n \n \n \n\n\n

Simple Wikipedia Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",3,[169],{"_150":119,"_103":313,"_108":316,"_153":138,"_176":317},["D",1733674459763],[318],{"_106":319,"_153":138,"_150":119,"_99":169,"_160":320,"_158":172,"_103":94,"_152":-5,"_108":316,"_154":313,"_163":-5,"_156":27,"_162":27,"_102":321},"8868d612-252d-11f0-8a67-569c3dd06744","import Fuse from \"npm:fuse.js\";\nimport { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\nconst fuse = new Fuse(titlesFile.titles, fuseOptions);\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n\n return c.json(await searchEmojis(query), 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Simple Wikipedia Instant Search\n \n \n \n\n\n

Simple Wikipedia Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",[169],{"_150":119,"_103":94,"_108":323,"_153":138,"_176":324},["D",1733674457389],[325],{"_106":326,"_153":138,"_150":119,"_99":169,"_160":327,"_158":172,"_103":155,"_152":-5,"_108":323,"_154":94,"_163":-5,"_156":27,"_162":27,"_102":328},"8864141a-252d-11f0-8a67-569c3dd06744","import Fuse from \"npm:fuse.js\";\nimport { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nimport { searchEmojis } from \"https://esm.town/v/maxm/emojiVectorEmbeddings\";\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\nconst fuse = new Fuse(titlesFile.titles, fuseOptions);\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n \n return c.json(await searchEmojis(query);, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Simple Wikipedia Instant Search\n \n \n \n\n\n

Simple Wikipedia Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",[169],{"_150":151,"_103":155,"_108":330,"_153":138,"_176":331},["D",1733674411247],[332,334],{"_106":148,"_153":138,"_150":151,"_99":149,"_160":161,"_158":159,"_103":33,"_152":-5,"_108":330,"_154":155,"_163":-5,"_156":112,"_162":27,"_102":333},[149],{"_106":335,"_153":138,"_150":119,"_99":169,"_160":336,"_158":172,"_103":33,"_152":-5,"_108":337,"_154":155,"_163":-5,"_156":27,"_162":27,"_102":338},"885ecbd6-252d-11f0-8a67-569c3dd06744","import Fuse from \"npm:fuse.js\";\nimport { Hono } from \"npm:hono@3.2.7\";\nimport _ from \"npm:lodash\";\n// https://gist.github.com/maxmcd/b170e57d53cb942642c35b135e2d52f1 (takes a bit of time to load)\nimport { extractValInfo } from \"https://esm.town/v/pomdtr/extractValInfo\";\nimport titlesFile from \"https://gist.githubusercontent.com/maxmcd/b170e57d53cb942642c35b135e2d52f1/raw/fa475f970726ef4ad0e3d258f8df1b95c05f3775/simple_wiki_titles.json\" with {\n type: \"json\",\n};\nconst app = new Hono();\n\nconst { htmlUrl } = extractValInfo(import.meta.url);\n\nconst fuseOptions = {\n // Only search for prefixes to make things faster\n location: 0,\n threshold: 0,\n distance: 0,\n};\n\nconst fuse = new Fuse(titlesFile.titles, fuseOptions);\n\n// Search endpoint\napp.get(\"/search\", async (c) => {\n let query = c.req.query(\"q\");\n if (typeof query !== \"string\") {\n return c.text(\"Invalid query\", 400);\n }\n query = query.trim();\n const t0 = performance.now();\n const results = fuse.search(query).slice(0, 10).map((doc) => doc.item);\n return c.json(results, 200, { \"Time\": String(performance.now() - t0) });\n});\n\n// Serve the frontend\napp.get(\"/\", (c) => {\n return c.html(html);\n});\n\nconst html = `\n\n\n\n \n \n Simple Wikipedia Instant Search\n \n \n \n\n\n

Simple Wikipedia Instant Search

\n Just a little demo to demonstrate instant search on Val Town.\n
\n Check out the\n source and build your own!\n

\n search\n \n

\n View Source\n\n \n\n\n`;\n\nexport default app.fetch.bind(app);",["D",1733674411247],[169],"page","hasNext","routes/_app","actionData","errors"]

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