tinyvecdb

TinyVecDB Node.js API Documentation

This document provides a comprehensive overview of the TinyVecDB Node.js API.

Table of Contents

• Installation
• Core Concepts
• Basic Usage
• API Reference
  • TinyVecClient
  • Supporting Types

Installation

npm install tinyvecdb

Core Concepts

TinyVecDB is an embedded vector database that emphasizes speed, low memory usage, and simplicity. The core of TinyVecDB is written in C, and this library provides a Node.js binding to that engine. The key concepts are:

• Embeddings: Fixed-dimension float vectors (e.g., 512 dimensions)
• Metadata: JSON-serializable data associated with each vector
• Similarity Search: Finding the nearest neighbors to a query vector using cosine similarity
• Filtering: Query vectors based on metadata attributes

Basic Usage

import {
  TinyVecClient,
  type TinyVecInsertion,
  type TinyVecSearchOptions,
} from "tinyvecdb";

async function example() {
  // Connect to database (will create the file if it doesn't exist)
  const client = TinyVecClient.connect("./vectors.db", { dimensions: 512 });

  // Create sample vectors
  const insertions: TinyVecInsertion[] = [];
  for (let i = 0; i < 50; i++) {
    // Using Float32Array (more efficient)
    const vec = new Float32Array(512);
    for (let j = 0; j < 512; j++) {
      vec[j] = Math.random();
    }

    // Or using standard JavaScript arrays
    // const vec = Array.from({ length: 512 }, () => Math.random());

    insertions.push({
      vector: vec,
      metadata: { name: `item-${i}`, category: "example" },
    });
  }

  // Insert vectors
  const inserted = await client.insert(insertions);
  console.log("Inserted:", inserted);

  // Create a query vector
  const queryVector = new Float32Array(512);
  for (let i = 0; i < 512; i++) {
    queryVector[i] = Math.random();
  }

  // Search for similar vectors (without filtering)
  const results = await client.search(queryVector, 5);
  // Example results:
  /*
  [
    {
      id: 8,
      similarity: 0.801587700843811,
      metadata: { name: 'item-8', category: 'example' }
    },
    {
      id: 16,
      similarity: 0.7834401726722717,
      metadata: { name: 'item-16', category: 'example' }
    },
    {
      id: 5,
      similarity: 0.7815409898757935,
      metadata: { name: 'item-5', category: 'example' }
    },
    ...
  ]
  */

  // Search with filtering
  const searchOptions: TinyVecSearchOptions = {
    filter: { category: { $eq: "example" } },
  };
  const filteredResults = await client.search(queryVector, 5, searchOptions);

  // Delete items by ID
  const deleteByIdResult = await client.deleteByIds([1, 2, 3]);
  console.log("Delete by ID result:", deleteByIdResult);
  // Example output: Delete by ID result: { deletedCount: 3, success: true }

  // Delete by metadata filter
  const deleteByFilterResult = await client.deleteByFilter({
    filter: { category: { $eq: "example" } },
  });
  console.log("Delete by filter result:", deleteByFilterResult);
  // Example output: Delete by filter result: { deletedCount: 47, success: true }

  // Get database statistics
  const stats = await client.getIndexStats();
  console.log(
    `Database has ${stats.vectors} vectors with ${stats.dimensions} dimensions`
  );
}

API Reference

TinyVecClient

The main class you'll interact with is TinyVecClient. It provides all methods for managing the vector database.

Static Methods

TinyVecClient.connect(path, config?)

Creates and connects to a TinyVecDB database.

Parameters:

• path: string - Path to the database file
• config?: TinyVecConfig (optional) - Configuration options

Example:

const client = TinyVecClient.connect("./vectors.db", { dimensions: 512 });

Instance Methods

async insert(vectors)

Inserts vectors with metadata into the database. Each metadata item must be a JSON-serializable object.

Parameters:

• vectors: TinyVecInsertion[] - Array of vectors to insert

Returns:

• Promise<number> - The number of vectors successfully inserted

Example:

const vector = new Float32Array(512).fill(0.1);
const count = await client.insert([
  {
    vector,
    metadata: {
      documentId: "doc1",
      title: "Example Document",
      category: "reference",
    },
  },
]);

async search(queryVector, topK, searchOptions?)

Searches for the most similar vectors to the query vector, with optional filtering.

Parameters:

• queryVector: NumericArray
  A query vector to search for, which can be any of the following types:

  type NumericArray =
    | number[]
    | Float32Array
    | Float64Array
    | Int8Array
    | Int16Array
    | Int32Array
    | Uint8Array
    | Uint16Array
    | Uint32Array;
  

  Internally, it will be converted to a Float32Array for similarity calculations.
• topK: number - Number of results to return
• searchOptions?: TinyVecSearchOptions (optional) - Contains filter criteria for the search

Returns:

• Promise<TinyVecResult[]> - Array of search results

Example:

// Search without filtering
const results = await client.search(queryVector, 10);

// Search with filtering
const searchOptions: TinyVecSearchOptions = {
  filter: { year: { $eq: 2024 } },
};
const filteredResults = await client.search(queryVector, 10, searchOptions);

async deleteByIds(ids)

Deletes vectors by their IDs.

Parameters:

• ids: number[] - Array of vector IDs to delete

Returns:

• Promise<TinyVecDeletionResult> - Object containing deletion count and success status

Example:

const result = await client.deleteByIds([1, 2, 3]);
console.log(
  `Deleted ${result.deletedCount} vectors. Success: ${result.success}`
);
// Example output: Deleted 3 vectors. Success: true

async deleteByFilter(searchOptions)

Deletes vectors that match the given filter criteria.

Parameters:

• searchOptions: TinyVecSearchOptions - Contains filter criteria for deletion

Returns:

• Promise<TinyVecDeletionResult> - Object containing deletion count and success status

Example:

const searchOptions: TinyVecSearchOptions = {
  filter: { year: { $eq: 2024 } },
};
const result = await client.deleteByFilter(searchOptions);
console.log(
  `Deleted ${result.deletedCount} vectors. Success: ${result.success}`
);

async updateById(items)

Updates existing database entries with new metadata and/or vector values.

Parameters:

• items: Array<UpdateItem> - Array of items to update, each containing ID and optional metadata/vector data

Returns:

• UpdateResult - Object containing update count and success status

Example:

import { TinyVecClient, type UpdateItem } from "tinyvecdb";

// Create update items
const updateItems: UpdateItem[] = [
  {
    id: 1,
    vector: new Float32Array(
      Array(128)
        .fill()
        .map(() => Math.random())
    ),
    metadata: { category: "electronics", inStock: true },
  },
  {
    id: 42,
    metadata: { price: 99.99, featured: true },
  },
  {
    id: 75,
    vector: new Float32Array(
      Array(128)
        .fill()
        .map(() => Math.random())
    ),
  },
];

// Update entries in the database
const result = await client.updateById(updateItems);
console.log(
  `Updated ${result.updatedCount} entries. Success: ${result.success}`
);

async getPaginated(config)

Retrieves database entries with pagination support.

Parameters:

• config: PaginationConfig - Configuration object containing skip and limit parameters for pagination

Returns:

• Array<PaginationItem> - Array of paginated items, each containing ID, metadata, and vector data

Example:

import { TinyVecClient, type PaginationConfig } from "tinyvecdb";

// Create a pagination configuration
const config: PaginationConfig = {
  skip: 0,
  limit: 10,
};

// Retrieve paginated results
const paginatedItems = await client.getPaginated(config);
console.log(`Retrieved ${paginatedItems.length} items`);

// Process the results
for (const item of paginatedItems) {
  console.log(`ID: ${item.id}, Metadata:`, item.metadata);
}

// Get the next page
const nextPageConfig: PaginationConfig = {
  skip: 10,
  limit: 10,
};
const nextPage = await client.getPaginated(nextPageConfig);
console.log(`Next page has ${nextPage.length} items`);

The PaginationConfig type requires two properties:

• skip: Number of items to skip (must be non-negative)
• limit: Maximum number of items to return (must be positive)

Each returned pagination item contains:

• id: Unique identifier for the vector
• metadata: Associated metadata (as a JSON-serializable value)
• vector: The actual vector data as an array

async getIndexStats()

Retrieves statistics about the database.

Parameters:

• None

Returns:

• Promise<TinyVecStats> - Database statistics

Example:

const stats = await client.getIndexStats();
console.log(
  `Database has ${stats.vectors} vectors with ${stats.dimensions} dimensions`
);

Supporting Types

TinyVecDeletionResult

Result from delete operations.

Properties:

• deletedCount: number - Number of vectors deleted
• success: boolean - Whether the operation was successful

Example:

const result = await client.deleteByIds([1, 2, 3]);
if (result.success) {
  console.log(`Successfully deleted ${result.deletedCount} vectors`);
} else {
  console.log("Deletion operation failed");
}

TinyVecConfig

Configuration for the vector database.

Properties:

• dimensions: number - The dimensionality of vectors to be stored

Example:

const config: TinyVecConfig = { dimensions: 512 };

TinyVecInsertion

Interface representing a vector to be inserted.

Properties:

• vector: NumericArray - The vector data
• metadata: Record<string, any> - JSON-serializable metadata associated with the vector

Example:

const insertion: TinyVecInsertion = {
  vector: new Float32Array(512).fill(0.1),
  metadata: { category: "example" },
};

TinyVecSearchOptions

Options for search queries, including filtering.

Properties:

• filter: object - Filter criteria in MongoDB-like query syntax

Available filter operators:

• $eq: Matches values equal to a specified value
• $gt: Matches values greater than a specified value
• $gte: Matches values greater than or equal to a specified value
• $in: Matches any values specified in an array
• $lt: Matches values less than a specified value
• $lte: Matches values less than or equal to a specified value
• $ne: Matches values not equal to a specified value
• $nin: Matches none of the values specified in an array

Filters can be nested for complex queries.

Example:

// Simple filter
const searchOptions: TinyVecSearchOptions = {
  filter: { make: { $eq: "Toyota" } },
};

// Complex nested filter
const complexSearchOptions: TinyVecSearchOptions = {
  filter: {
    category: {
      subcategory: {
        value: { $gt: 200 },
      },
    },
    tags: { $in: ["premium", "featured"] },
  },
};

TinyVecResult

Interface representing a search result.

Properties:

• similarity: number - Cosine similarity score
• index: number - ID of the matched vector
• metadata: Record<string, any> | null - Metadata associated with the matched vector

Example:

// Results from a search query
for (const result of results) {
  console.log(
    `ID: ${result.index}, Similarity: ${result.similarity}, Metadata:`,
    result.metadata
  );
}
// Example output:
// ID: 34, Similarity: 0.8109967303276062, Metadata: { category: 'example', name: 'item-34' }
// ID: 46, Similarity: 0.789353609085083, Metadata: { category: 'example', name: 'item-46' }
// ID: 22, Similarity: 0.7870827913284302, Metadata: { category: 'example', name: 'item-22' }

TinyVecStats

Interface representing database statistics.

Properties:

• vectors: number - Number of vectors in the database
• dimensions: number - Dimensionality of the vectors

Example:

const stats = await client.getIndexStats();
console.log(`Vector count: ${stats.vectors}, Dimensions: ${stats.dimensions}`);

Using TinyVecDB with Next.js

If you're using TinyVecDB in a Next.js application, you'll need to add the following configuration to your next.config.js file to ensure proper functionality:

/** @type {import('next').NextConfig} */
const nextConfig = {
  // This prevents Next.js from bundling TinyVecDB and allows it to work correctly on the server
  serverExternalPackages: ["tinyvecdb"],

  // Your other Next.js configuration options...
};

module.exports = nextConfig;

This configuration is required because TinyVecDB uses native Node.js addons which cannot be bundled by Next.js. The serverExternalPackages option tells Next.js to load TinyVecDB directly from the Node.js environment on the server instead of trying to bundle it.

Important Notes:

• TinyVecDB can only be used in server-side code
• For older Next.js versions, you may need to use experimental.serverComponentsExternalPackages instead