Refactor

src/app/api/search/route.tsx

@@ -1,58 +1,33 @@
-import { getXataClient } from "@/lib/xata";
+import { vectorSearch } from "@/lib/search";
 import { NextRequest, NextResponse } from "next/server";
 
 export const runtime = "edge";
 
-const xata = getXataClient();
-
 export async function GET(request: NextRequest) {
-  const searchParams: string = decodeURIComponent(
-    request.nextUrl.searchParams.toString()
-  );
-  if (searchParams.length > 128) {
+  const searchParams: string | null = request.nextUrl.searchParams.get("query");
+  let query: string;
+  if (searchParams === null) {
+    query = "";
+  } else {
+    query = searchParams;
+  }
+  if (query.length > 128) {
     return NextResponse.json(
       { message: "Search entry is too long" },
       { status: 403 }
     );
   }
 
   try {
-    // const results = await xata.db.Books.search(searchParams, {
-    //   target: ["title"],
-    //   prefix: "phrase",
-    //   fuzziness: 0,
-    //   page: { size: 35, offset: 0 },
-    // });
-
-    // Generate embeddings
-    const embeddings = await fetch("https://api.embaas.io/v1/embeddings/", {
-      method: "POST",
-      body: JSON.stringify({
-        texts: ["query: " + searchParams],
-        model: "multilingual-e5-large",
-      }),
-      headers: {
-        "Content-Type": "application/json",
-        Authorization: "Bearer " + process.env.EMBAAS_API_KEY,
-      },
-    }).then((response) => response.json());
-
-    const results = await xata.db.Books.vectorSearch(
-      "embeddings",
-      embeddings["data"][0]["embedding"],
-      {
-        similarityFunction: "cosineSimilarity",
-        size: 35,
-      }
-    );
+    const results = await vectorSearch(query);
 
     // Reduce network payload size for users
     const records = JSON.parse(JSON.stringify(results))["records"];
     records.forEach((book: Record<string, any>) => {
       delete book.id;
       delete book.embeddings;
       //delete book.xata;
-      console.log(book);
+      //console.log(book);
     });
 
     return NextResponse.json({ records });

src/components/Card/index.js

@@ -1,2 +1,2 @@
-export * from './Card';
-export { default } from './Card';
+export * from "./Card";
+export { default } from "./Card";

src/components/NetworkShader/NetworkMaterial.ts

@@ -1,5 +1,5 @@
-import { NoBlending, ShaderMaterial, Vector2 } from "three";
 import { ShaderMaterialProps } from "@react-three/fiber";
+import { NoBlending, ShaderMaterial, Vector2 } from "three";
 
 declare global {
   namespace JSX {
@@ -25,7 +25,7 @@ const vertexShader = `
 const fragmentShader = `
   uniform float uTime;
   uniform vec2 uResolution;
-  
+
   varying vec2 vUv;
 
   // sine randomness function from The Book Of Shaders
@@ -47,9 +47,9 @@ const fragmentShader = `
   float pointSegmentDistance(vec2 p, vec2 a, vec2 b) {
     vec2 ab = b - a;
     vec2 ap = p - a;
-    
+
     float d = clamp(dot(ap, ab) / dot(ab, ab), 0.0, 1.0);
-    
+
     return length(ap - ab * d);
   }
 

src/components/NetworkShader/NetworkShader.tsx

@@ -1,7 +1,7 @@
 "use client";
-import { Suspense, useEffect, useRef } from "react";
 import { OrthographicCamera } from "@react-three/drei";
 import { Canvas, extend, useFrame } from "@react-three/fiber";
+import { Suspense, useEffect, useRef } from "react";
 import * as THREE from "three";
 
 import NetworkMaterial from "./NetworkMaterial";

src/components/SearchBar/SearchBar.tsx

@@ -1,6 +1,6 @@
 "use client";
-import { useState } from "react";
 import { motion } from "framer-motion";
+import { useState } from "react";
 
 import styles from "./SearchBar.module.css";
 

src/components/Spinner/Spinner.tsx

@@ -1,4 +1,3 @@
-import React from "react";
 import { Hexagon } from "react-feather";
 
 import styles from "./Spinner.module.css";

src/data/book-data.ts

@@ -5,69 +5,3 @@ export interface Book {
   size: number;
   extension: string;
 }
-
-export const BOOKS = [
-  {
-    id: "rec_clo13k3ebat5c83jnr90",
-    title: "Kusuriya to Honshin no Kagami",
-    sources: ["Nyaa"],
-    size: 36.1,
-    extension: "rar",
-  },
-  {
-    id: "rec_clo14jbebat5c83jnr9g",
-    title: "[白石定規]魔女の旅々３",
-    sources: ["TMW Collections"],
-    size: 1.52,
-    extension: "epub",
-  },
-  {
-    id: "rec_clo0quijp1p3khg20umg",
-    title: "魔女の旅々",
-    sources: ["Anna's Archive", "Nyaa", "PeepoHappyBooks", "TMW Collections"],
-    size: 1.8,
-    extension: "azw3",
-  },
-  {
-    id: "rec_clo18m3ebat5c83jolqg",
-    title: "俺、ツインテールになります。4.5",
-    sources: ["PeepoHappyBooks", "Nyaa"],
-    size: 12.3,
-    extension: "epub",
-  },
-  {
-    id: "rec_clo0t7bebat5c83jnqog",
-    title: "灼眼のシャナXII",
-    sources: ["Anna's Archive", "Nyaa", "TMW Collections"],
-    size: 24.2,
-    extension: "epub",
-  },
-  {
-    id: "rec_clo151hk6ckak4tfv7g0",
-    title: "魔女の旅々６",
-    sources: ["PeepoHappyBooks"],
-    size: 12.56,
-    extension: "rar",
-  },
-  {
-    id: "rec_clo158ijp1p3khg20ut0",
-    title: "魔女の旅々１０",
-    sources: ["Anna's Archive", "Nyaa", "PeepoHappyBooks", "TMW Collections"],
-    size: 2.62,
-    extension: "azw3",
-  },
-  {
-    id: "rec_clo15rijp1p3khg20utg",
-    title: "魔女の旅々８",
-    sources: ["Anna's Archive","Nyaa","PeepoHappyBooks","TMW Collections"],
-    size: 623.12,
-    extension: "rar",
-  },
-  {
-    id: "rec_clo1602jp1p3khg20uu0",
-    title: "魔女の旅々９",
-    sources: ["PeepoHappyBooks"],
-    size: 12.5,
-    extension: "kfx",
-  },
-];

src/lib/search.ts

@@ -0,0 +1,38 @@
+import { getXataClient } from "@/lib/xata";
+
+const xata = getXataClient();
+
+export async function search(query: string) {
+  return await xata.db.Books.search(query, {
+    target: ["title"],
+    prefix: "phrase",
+    fuzziness: 0,
+    page: { size: 35, offset: 0 },
+  });
+}
+
+export async function vectorSearch(query: string) {
+  // Is this how JavaScript experts write functions?
+  return await fetch("https://api.embaas.io/v1/embeddings/", {
+    method: "POST",
+    body: JSON.stringify({
+      texts: ["query: " + query],
+      model: "multilingual-e5-large",
+    }),
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: "Bearer " + process.env.EMBAAS_API_KEY,
+    },
+  })
+    .then((response) => response.json())
+    .then((embeddings) =>
+      xata.db.Books.vectorSearch(
+        "embeddings",
+        embeddings["data"][0]["embedding"],
+        {
+          similarityFunction: "cosineSimilarity",
+          size: 35,
+        }
+      )
+    );
+}