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Version: V1.1.0

Simple example

This example uses the server mode of seekdb to demonstrate the basic operations of embedding functions, helping you understand how to use them quickly.

  1. Connect to seekdb.
  2. Create a collection with embedding functions.
  3. Add data using documents (vectors will be automatically generated).
  4. Query using query texts (vectors will be automatically generated).
  5. Print the query results.

Example​

import { SeekdbClient } from "seekdb";

async function main() {
  // ==================== Step 1: Create Client Connection ====================
  // Server mode (connecting to seekdb server or OceanBase)
  const client = new SeekdbClient({
    host: "127.0.0.1",
    port: 2881,
    database: "test",
    user: "root",
    password: "",
  });

  // ==================== Step 2: Create a Collection with Embedding Function ====================
  // A collection is like a table that stores documents with vector embeddings
  const collectionName = "my_simple_collection";

  // Create collection with default embedding function
  // The embedding function will automatically convert documents to embeddings

  // Default embedding function is used if no embedding function is provided
  const collection = await client.createCollection({
    name: collectionName,
  });

  console.log(
    `Created collection '${collection.name}' with dimension: ${collection.dimension}`,
  );
  console.log(`Embedding function: ${collection.embeddingFunction?.name}`);

  // ==================== Step 3: Add Data to Collection ====================
  // With embedding function, you can add documents directly without providing embeddings
  // The embedding function will automatically generate embeddings from documents

  const documents = [
    "Machine learning is a subset of artificial intelligence",
    "Python is a popular programming language",
    "Vector databases enable semantic search",
    "Neural networks are inspired by the human brain",
    "Natural language processing helps computers understand text",
  ];

  const ids = ["id1", "id2", "id3", "id4", "id5"];

  // Add data with documents only - embeddings will be auto-generated by embedding function
  await collection.add({
    ids,
    documents, // embeddings will be automatically generated
    metadatas: [
      { category: "AI", index: 0 },
      { category: "Programming", index: 1 },
      { category: "Database", index: 2 },
      { category: "AI", index: 3 },
      { category: "NLP", index: 4 },
    ],
  });

  console.log(`\nAdded ${documents.length} documents to collection`);
  console.log(
    "Note: Embeddings were automatically generated from documents using the embedding function",
  );

  // ==================== Step 4: Query the Collection ====================
  // With embedding function, you can query using text directly
  // The embedding function will automatically convert query text to query vector

  // Query using text - query vector will be auto-generated by embedding function
  const queryText = "artificial intelligence and machine learning";

  const results = await collection.query({
    queryTexts: queryText, // Query text - will be embedded automatically
    nResults: 3, // Return top 3 most similar documents
    include: ["documents", "metadatas", "distances"],
  });

  console.log(`\nQuery: '${queryText}'`);
  console.log(`Query results: ${results.ids[0].length} items found`);

  // ==================== Step 5: Print Query Results ====================
  for (let i = 0; i < results.ids[0].length; i++) {
    console.log(`\nResult ${i + 1}:`);
    console.log(`  ID: ${results.ids[0][i]}`);
    console.log(`  Distance: ${results.distances?.[0][i]?.toFixed(4)}`);
    if (results.documents) {
      console.log(`  Document: ${results.documents[0][i]}`);
    }
    if (results.metadatas) {
      console.log(`  Metadata: ${JSON.stringify(results.metadatas[0][i])}`);
    }
  }

  // ==================== Step 6: Cleanup ====================
  await client.deleteCollection(collectionName);
  console.log(`\nDeleted collection '${collectionName}'`);

  await client.close();
}

main().catch(console.error);

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