Como implementar o Vector Search do Edge SQL

import { OpenAIEmbeddings } from "@langchain/openai";
import { useExecute, useQuery, createDatabase } from "azion/sql";

export default async function vectorSearch() {

  // // Create the database
   const {data:createDatabaseData, error:createDatabaseError} = await createDatabase('vectorDatabase',{debug:true})

   if (createDatabaseError) {
     return Response.json({ error: createDatabaseError.message }, { status: 500 });
   }

  // // Setup the database
  // // Create the embeddings column according to the dimension of the embeddings model
  // // In this case, the text-embedding-3-small model has 1536 dimensions
  // // You should also create the index, which makes the search faster!
  const setupStatements = [
    `CREATE TABLE documents (
      id INTEGER PRIMARY KEY AUTOINCREMENT,
      content TEXT NOT NULL,
      embedding F32_BLOB(1536)
    );`,
    `CREATE INDEX documents_idx ON documents (
      libsql_vector_idx(embedding, 'metric=cosine')
    );`
  ];

  // Here, use useExecute, which supports create and insert statements
  const {data:setupData, error:setupError} = await useExecute('vectorDatabase',setupStatements)

  if (setupError) {
    return Response.json({ error: setupError.message }, { status: 500 });
  }

  // Create the embeddings model
  const embeddings = new OpenAIEmbeddings({
    model: "text-embedding-3-small",
    verbose: false,
    apiKey: process.env.OPENAI_API_KEY
  });

  // Sample documents about France, England, and Brazil
  const documents = [
    "Paris is the capital of France",
    "The Eiffel Tower is a French landmark",
    "London is the capital of England",
    "Big Ben is located in London",
    "Brasilia is the capital of Brazil",
    "The Amazon rainforest is in Brazil",
    "French cuisine is world-famous",
    "Tea is popular in England",
    "The English Channel separates Britain and France"
  ];

  // Generate embeddings for all documents and prepare statements
  const insertStatements = [];
  for (const doc of documents) {
    const embedding = await embeddings.embedQuery(doc);
    insertStatements.push(
      `INSERT INTO documents (content, embedding)
       VALUES ('${doc}', vector('[${embedding}]'));`
    );
  }

  // Here, use useExecute, which supports insert statements
  const {data:insertData, error:insertError} = await useExecute('vectorDatabase',insertStatements,{debug:true})

  if (insertError) {
    return Response.json({ error: insertError.message }, { status: 500 });
  }

  // Search for the most similar document to the query
  // The same embedding model is used to generate the query embedding
  const query = "What is the capital of Brazil?";
  const queryEmbedding = await embeddings.embedQuery(query);

  // Prepare the search statement
  // The vector_top_k function is used to find the most similar documents to the query
  const searchStatements = [
    `
      SELECT id, content
      FROM documents
      WHERE rowid IN vector_top_k('documents_idx', vector('[${queryEmbedding}]'), 2)
    `
  ];

  const {data:searchData, error:searchError} = await useQuery('vectorDatabase',searchStatements)

  if (searchError) {
    return Response.json({ error: searchError.message }, { status: 500 });
  }

  return Response.json({ data: searchData });
}