MongoDB Atlas Vector Store Provider

The MongoDB Atlas provider implements the vectorstore.VectorStore interface using MongoDB Atlas Vector Search via the HTTP Data API. It supports cosine similarity search with metadata filtering through the $vectorSearch aggregation stage.

Choose MongoDB Atlas when your application already uses MongoDB and you want to add vector search alongside your existing document data. Atlas Vector Search integrates with MongoDB’s aggregation pipeline, allowing you to combine vector similarity with traditional document queries without managing a separate vector database.

Installation

go get github.com/lookatitude/beluga-ai/rag/vectorstore/providers/mongodb

Quick Start

package main

import (
    "context"
    "log"
    "os"

    "github.com/lookatitude/beluga-ai/config"
    "github.com/lookatitude/beluga-ai/rag/vectorstore"
    "github.com/lookatitude/beluga-ai/schema"
    _ "github.com/lookatitude/beluga-ai/rag/vectorstore/providers/mongodb"
)

func main() {
    store, err := vectorstore.New("mongodb", config.ProviderConfig{
        BaseURL: os.Getenv("MONGODB_DATA_API_URL"),
        APIKey:  os.Getenv("MONGODB_API_KEY"),
        Options: map[string]any{
            "database":   "my_db",
            "collection": "documents",
            "index":      "vector_index",
        },
    })
    if err != nil {
        log.Fatal(err)
    }

    ctx := context.Background()

    docs := []schema.Document{
        {ID: "doc1", Content: "Go is a statically typed language", Metadata: map[string]any{"lang": "en"}},
    }
    embeddings := [][]float32{make([]float32, 1536)}

    err = store.Add(ctx, docs, embeddings)
    if err != nil {
        log.Fatal(err)
    }
}

Configuration

Parameter	Type	Default	Description
BaseURL	string	(required)	MongoDB Data API endpoint
APIKey	string	(optional)	Data API key
Options["database"]	string	beluga	Database name
Options["collection"]	string	documents	Collection name
Options["index"]	string	vector_index	Atlas Vector Search index name

Prerequisites

1. Create a MongoDB Atlas cluster
2. Enable the Data API for your cluster
3. Create an Atlas Vector Search index on the embedding field:

{
  "fields": [
    {
      "type": "vector",
      "path": "embedding",
      "numDimensions": 1536,
      "similarity": "cosine"
    }
  ]
}

Direct Construction

import (
    mongostore "github.com/lookatitude/beluga-ai/rag/vectorstore/providers/mongodb"
)

store := mongostore.New(
    os.Getenv("MONGODB_DATA_API_URL"),
    mongostore.WithDatabase("production"),
    mongostore.WithCollection("docs"),
    mongostore.WithIndex("prod_vector_index"),
    mongostore.WithAPIKey(os.Getenv("MONGODB_API_KEY")),
)

Metadata Filtering

Metadata filters are applied to the metadata field path:

results, err := store.Search(ctx, queryVec, 10,
    vectorstore.WithFilter(map[string]any{
        "lang": "en",
    }),
)

This translates to a MongoDB filter on metadata.lang.

Search with Threshold

results, err := store.Search(ctx, queryVec, 10,
    vectorstore.WithThreshold(0.7),
)
for _, doc := range results {
    fmt.Printf("ID: %s, Score: %.4f, Content: %s\n", doc.ID, doc.Score, doc.Content)
}

Document Structure

Documents are stored with the following structure:

{
  "_id": "doc1",
  "content": "Document text content",
  "embedding": [0.1, 0.2, ...],
  "metadata": {
    "lang": "en",
    "category": "technical"
  }
}

Custom HTTP Client

store := mongostore.New(
    os.Getenv("MONGODB_DATA_API_URL"),
    mongostore.WithHTTPClient(customClient),
    mongostore.WithDatabase("my_db"),
)