Lightweight In-Memory Vector Database for Fast Similarity Search
npm install neuradbA lightweight, zero-dependency in-memory vector database for blazing fast similarity search with OpenAI integration.
NeuraDB enables high-performance vector similarity search in TypeScript/JavaScript environments, supporting multiple similarity methods, metadata filtering, memory-efficient storage, and automatic OpenAI embeddings.
- 🧠 Fast in-memory vector search
- 🔢 Cosine, Euclidean, Dot Product similarity
- 🏷️ Metadata filtering
- 📦 Zero dependencies (OpenAI optional)
- ⚡ Memory-efficient
- 🧪 TypeScript-first API
- 🤖 Automatic OpenAI embeddings
- 📈 Batch processing with progress tracking
- 🔄 Automatic embedding dimension validation
``bashUsing npm
npm install neuradb
🧰 Usage
$3
typescript
import { NeuraDB } from "neuradb";const db = new NeuraDB();
// Add a document with pre-computed embedding
db.addDocument({
id: "doc1",
content: "Hello world",
embedding: [0.1, 0.2, 0.3],
metadata: { category: "greeting" },
});
// Search for similar documents
const results = db.search([0.1, 0.2, 0.3], {
limit: 5,
threshold: 0.7,
similarityMethod: "cosine",
});
console.log(results);
`$3
typescript
import { NeuraDB } from "neuradb";
import OpenAI from "openai";const openai = new OpenAI({ apiKey: "your-api-key" });
const db = new NeuraDB({
openai,
embeddingModel: "text-embedding-3-small",
defaultBatchSize: 100,
batchDelay: 1000,
});
// Add document with automatic embedding generation
await db.addDocument(
{
id: "doc1",
content: "Hello world",
metadata: { category: "greeting" },
},
{ createEmbedding: true }
);
// Search with text query (automatic embedding)
const results = await db.search("Hello there", {
limit: 5,
threshold: 0.7,
similarityMethod: "cosine",
metadataFilter: { category: "greeting" },
});
// Batch add documents with progress tracking
await db.addDocuments(
[
{ id: "1", content: "Text 1" },
{ id: "2", content: "Text 2" },
],
{
createEmbedding: true,
batchSize: 50,
batchDelay: 500,
onProgress: (processed, total) => console.log(
${processed}/${total}),
}
);
`🔍 API Overview
$3
| Method | Description |
| ----------------------------------------------------------------------------------- | ------------------------------------------------------------ |
|
| Add a single document with optional embedding generation |
| addDocuments(documents: VectorDocument[], options?: AddDocumentsOptions) | Batch-add multiple documents with progress tracking |
| search(query: number[] \| string, options?: SearchOptions) | Find documents most similar to the query (embedding or text) |
| findMostSimilar(query: number[] \| string, similarityMethod?: SimilarityMethod) | Return the single most similar document |
| updateDocument(document: VectorDocument, options?: { createEmbedding?: boolean }) | Update an existing document by ID |
| getDocument(id: string) | Fetch a document by its ID |
| getDocumentsByMetadata(filter: Record | Retrieve documents matching metadata criteria |
| clear() | Clear the entire store |$3
| Method | Description |
| ---------------------------------------- | ----------------------------------------------- |
|
setEmbeddingModel(model: string) | Set the OpenAI embedding model |
| setDefaultBatchSize(batchSize: number) | Set default batch size for embedding operations |
| setDefaultBatchDelay(delay: number) | Set delay between batches in milliseconds |
| getStats() | Get store statistics |🧠 Similarity Methods
| Method | Description |
| ----------- | ------------------------------------- |
|
cosine | Measures cosine angle between vectors |
| euclidean | Measures Euclidean distance |
| dot | Dot product of two vectors |📊 Stats
`typescript
const stats = db.getStats();
console.log(stats);
// {
// documentCount: 100,
// embeddingDimensions: 384,
// estimatedMemoryUsage: 250000
// }
`📁 Types
typescript
/**
* Represents a document with its vector embedding and metadata
*/
interface VectorDocument {
/* Unique identifier for the document /
id: string; /* The text content of the document /
content: string;
/* Vector embedding representation of the document /
embedding: number[];
/* Optional metadata associated with the document /
metadata?: Record;
/* Optional timestamp when the document was created /
createdAt?: Date;
/* Optional timestamp when the document was last updated /
updatedAt?: Date;
}
/**
* Result of a vector similarity search
*/
interface SearchResult {
/* The document that matched the search /
document: VectorDocument;
/* Similarity score between 0 and 1 (1 being most similar) /
similarity: number;
}
/**
* Supported similarity calculation methods
*/
type SimilarityMethod = "cosine" | "euclidean" | "dot";
/**
* Configuration options for vector search
*/
interface SearchOptions {
/* Maximum number of results to return /
limit?: number;
/* Minimum similarity threshold (0-1) /
threshold?: number;
/* Similarity calculation method to use /
similarityMethod?: SimilarityMethod;
/* Metadata filters to apply /
metadataFilter?: Record;
}
/**
* Options for adding multiple documents
*/
interface AddDocumentsOptions {
/* Whether to generate embeddings for documents without them /
createEmbedding?: boolean;
/* Number of documents to process in each batch /
batchSize?: number;
/* Delay between batches in milliseconds /
batchDelay?: number;
/* Progress callback function /
onProgress?: (processed: number, total: number) => void;
}
/**
* Statistics about the vector store
*/
interface VectorStoreStats {
/* Total number of documents stored /
documentCount: number;
/* Dimensions of the vector embeddings /
embeddingDimensions: number | null;
/* Memory usage estimation in bytes /
estimatedMemoryUsage: number;
}
``MIT © Haider Nakara
Inspired by the simplicity of ChromaDB, Pinecone, and Faiss — with a developer-friendly twist.
PRs, feature requests, and issues are welcome. Let's build better vector tooling together!