Gunvir Dhesi - Submission

app/api/documents/search/route.ts

@@ -0,0 +1,29 @@
+import { NextRequest } from "next/server";
+import { createClient } from "@supabase/supabase-js";
+import { supabaseUrl, supabaseKey } from "../../../utils/env";
+import { getEmbedding } from "../../../utils/openai";
+
+export const runtime = "nodejs";
+
+export async function POST(req: NextRequest) {
+  try {
+    const { query } = await req.json();
+    if (!query) {
+      return new Response(JSON.stringify({ error: "Missing query" }), { status: 400 });
+    }
+    const embedding = await getEmbedding(query);
+    const supabase = createClient(supabaseUrl, supabaseKey);
+    // Call the match_pages function
+    const { data, error } = await supabase.rpc("match_pages", {
+      query_embedding: embedding,
+      match_threshold: 0.7,
+      match_count: 10,
+    });
+    if (error) {
+      return new Response(JSON.stringify({ error: error.message }), { status: 500 });
+    }
+    return new Response(JSON.stringify({ results: data }), { status: 200 });
+  } catch (e: any) {
+    return new Response(JSON.stringify({ error: e.message }), { status: 500 });
+  }
+} 

app/api/documents/upload/route.ts

@@ -0,0 +1,81 @@
+import { NextRequest } from "next/server";
+import { createClient } from "@supabase/supabase-js";
+import { supabaseUrl, supabaseKey } from "../../../utils/env";
+import { getEmbedding } from "../../../utils/openai";
+import * as pdfjs from "pdfjs-dist/legacy/build/pdf";
+
+export const runtime = "nodejs";
+
+export async function POST(req: NextRequest) {
+  try {
+    const formData = await req.formData();
+    const fileBlob = formData.get("file");
+    const title = formData.get("title") as string;
+    if (!fileBlob || !title) {
+      console.error("Missing file or title", { fileBlob, title });
+      return new Response(JSON.stringify({ error: "Missing file or title" }), { status: 400 });
+    }
+
+    // Try to get a name for the file, fallback if not present
+    const fileName = (fileBlob as any).name || `upload-${Date.now()}.pdf`;
+
+    let arrayBuffer, buffer;
+    try {
+      arrayBuffer = await fileBlob.arrayBuffer();
+      buffer = Buffer.from(arrayBuffer);
+    } catch (err) {
+      console.error("Error converting fileBlob to buffer", err);
+      return new Response(JSON.stringify({ error: "Failed to process file upload" }), { status: 500 });
+    }
+
+    // Upload PDF to Supabase Storage
+    const supabase = createClient(supabaseUrl, supabaseKey);
+    const storagePath = `pdfs/${Date.now()}-${fileName}`;
+    const { error: uploadError } = await supabase.storage.from("pdfs").upload(storagePath, buffer, { upsert: true });
+    if (uploadError) {
+      console.error("Supabase upload error", uploadError);
+      return new Response(JSON.stringify({ error: uploadError.message }), { status: 500 });
+    }
+
+    // Extract per-page text using pdfjs
+    let pdf;
+    try {
+      pdf = await pdfjs.getDocument({ data: arrayBuffer }).promise;
+    } catch (err) {
+      console.error("PDF.js failed to parse document", err);
+      return new Response(JSON.stringify({ error: "Failed to parse PDF document" }), { status: 500 });
+    }
+    const numPages = pdf.numPages;
+    const pages = [];
+    for (let i = 1; i <= numPages; i++) {
+      const page = await pdf.getPage(i);
+      const textContent = await page.getTextContent();
+      const text = textContent.items.map((item: any) => item.str).join(" ");
+      let embedding = null;
+      if (text.trim().length > 0) {
+        try {
+          embedding = await getEmbedding(text);
+        } catch (err) {
+          console.error(`OpenAI embedding failed for page ${i}`, err);
+        }
+      }
+      pages.push({ page_number: i, text, embedding });
+    }
+
+    // Insert document record into Supabase
+    const { error: insertError } = await supabase.from("documents").insert({
+      title,
+      storage_path: storagePath,
+      pages,
+    });
+    if (insertError) {
+      console.error("Supabase insert error", insertError);
+      return new Response(JSON.stringify({ error: insertError.message }), { status: 500 });
+    }
+
+    return new Response(JSON.stringify({ success: true }), { status: 200 });
+  } catch (e: any) {
+    console.error("/api/documents/upload uncaught error", e);
+    return new Response(JSON.stringify({ error: e.message, stack: e.stack }), { status: 500 });
+  }
+} 

app/utils/env.ts

@@ -6,3 +6,4 @@ export const googleClientSecret = process.env.GOOGLE_CLIENT_SECRET as string;
 export const storageMethod = process.env.STORAGE_METHOD
   ? (process.env.STORAGE_METHOD as "supabase" | "sqlite")
   : "sqlite";
+export const OPENAI_API_KEY = process.env.OPENAI_API_KEY as string;

app/utils/openai.ts

@@ -0,0 +1,20 @@
+import { OPENAI_API_KEY } from "./env";
+
+export async function getEmbedding(text: string): Promise<number[]> {
+  const response = await fetch("https://api.openai.com/v1/embeddings", {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${OPENAI_API_KEY}`,
+    },
+    body: JSON.stringify({
+      input: text,
+      model: "text-embedding-ada-002",
+    }),
+  });
+  if (!response.ok) {
+    throw new Error(`OpenAI API error: ${response.statusText}`);
+  }
+  const data = await response.json();
+  return data.data[0].embedding;
+}