Skip to main content

API Reference

MongoRAG Class​

Constructor​

Creates a new instance of the MongoRAG class.

const rag = new MongoRAG({
  mongoUrl: string,
  database: string,
  collection: string,
  embedding: {
    provider: 'openai',
    apiKey: string,
    model?: string,
    batchSize?: number,
    dimensions?: number
  },
  search?: {
    maxResults?: number,
    minScore?: number,
    similarityMetric?: 'cosine' | 'dotProduct' | 'euclidean'
  }
});

Parameters​

  • config.mongoUrl (string, required): MongoDB connection URI.
  • config.database (string, required): Default MongoDB database name.
  • config.collection (string, required): Default MongoDB collection name.
  • config.embedding (object, required):
    • provider (string, required): Embedding provider (openai, ollama, or voyage are supported).
    • apiKey (string, required): API key for the embedding provider (not required for ollama).
    • model (string, optional): Model name. Defaults depend on provider:
      • OpenAI: 'text-embedding-3-small'
      • Voyage: 'voyage-3' (other options: voyage-3-large, voyage-3-lite, voyage-code-3, voyage-finance-2, voyage-law-2)
      • Ollama: requires model specification
    • baseUrl (string, optional): Base URL for Ollama API (default: 'http://localhost:11434').
    • batchSize (number, optional): Batch size for embedding generation (default: 100).
    • dimensions (number, optional): Number of dimensions in the embedding space (default: 1536).
  • config.search (object, optional):
    • maxResults (number, optional): Maximum number of results to return (default: 5).
    • minScore (number, optional): Minimum similarity score threshold (default: 0.7).
    • similarityMetric (string, optional): Similarity function for search (cosine, dotProduct, euclidean). Defaults to 'cosine'.

Methods​

connect()​

Establishes connection to MongoDB.

await rag.connect();

disconnect()​

Closes the MongoDB connection.

await rag.disconnect();

ingestBatch()​

Ingests a batch of documents into the vector store.

await rag.ingestBatch(documents, {
  batchSize?: number,
  preprocessor?: Function
});

Parameters:

  • documents (array, required): Array of documents to ingest
  • options (object, optional):
    • batchSize: Override default batch size
    • preprocessor: Custom preprocessor for this batch

Returns: Promise<void>

Performs a vector search for similar documents.

const results = await rag.search(query, {
  database?: string,
  collection?: string,
  maxResults?: number,
  minScore?: number
});

Parameters:

  • query (string, required): Search query
  • options (object, optional):
    • maxResults: Maximum number of results
    • minScore: Minimum similarity score
    • filter: MongoDB filter for metadata

Returns: Promise<Array<SearchResult>>

Type Definitions​

interface SearchResult {
  content: string;
  documentId: string;
  metadata?: Record<string, any>;
  score: number;
}

Configuration Examples​

Basic Configuration​

const rag = new MongoRAG({
  mongoUrl: 'mongodb+srv://your-connection-string',
  database: 'ragdb',
  collection: 'documents',
  embedding: {
    provider: 'openai',
    apiKey: process.env.EMBEDDING_API_KEY
  }
});

Advanced Configuration​

const rag = new MongoRAG({
  mongoUrl: 'mongodb+srv://your-connection-string',
  database: 'ragdb',
  collection: 'documents',
  embedding: {
    provider: 'openai',
    apiKey: process.env.EMBEDDING_API_KEY,
    model: 'text-embedding-ada-002',
    batchSize: 100,
    dimensions: 1536
  },
  search: {
    maxResults: 10,
    minScore: 0.8,
    similarityMetric: 'dotProduct'
  }
});

Error Handling​

The library provides specific error types:

try {
  await rag.search('query');
} catch (error) {
  console.error('An error occurred:', error.message);
  // Handle the error appropriately
}

For more detailed examples and use cases, refer to: