Software: Apache/2.4.41 (Ubuntu). PHP/8.0.30 

uname -a: Linux apirnd 5.4.0-204-generic #224-Ubuntu SMP Thu Dec 5 13:38:28 UTC 2024 x86_64 

uid=33(www-data) gid=33(www-data) groups=33(www-data) 

Safe-mode: OFF (not secure)

/var/www/html/openAi/   drwxr-xr-x
Free 13.04 GB of 57.97 GB (22.5%)
                            Encoder    Tools    Proc.    FTP brute    Sec.    SQL    PHP-code    Update    Self remove    Logout    

Viewing file:     search.js (1.23 KB)      -rw-r--r--

const OpenAI = require("openai");
const mongoose = require("mongoose");
require("dotenv").config(); 
console.log(process.env.OPENAI_API_KEY);

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const queryText = "affordable brown test pool tester";
async function connectDB() {
    await mongoose.connect(process.env.MONGO_URI);
}
async function searchItem(queryText) {
    const queryEmbedding = (
        await openai.embeddings.create({
            model: "text-embedding-3-small",
            input: queryText
        })
    ).data[0].embedding;

const results = await mongoose.connection.db
  .collection('ProductVectors')
  .aggregate([
    {
      $vectorSearch: {
        queryVector: queryEmbedding,
        index: "vector_index",
        path: "embedding",
        numCandidates: 200,
        limit: 10,
        // filter: {
        //   visibility: 1,
        //   is_archived: 0
        // }
      }
    },
    {
      $project: {
        product_id: 1,
        variant_id: 1,
        score: { $meta: "vectorSearchScore" }
      }
    }   
  ])
  .toArray();
  console.log(results);
  return results;
}
(async () => {
  await connectDB();
  await searchItem(queryText);
})();