automated-embedding

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Automated Embedding Overview

Atlas

You can configure MongoDB Vector Search to automatically generate and manage vector embeddings for the text data in your Atlas cluster. When you enable Automated Embedding, MongoDB Vector Search automatically generates embeddings using the specified Voyage AI embedding model at index-time for the specified text field in your collection and at query-time for the text string in your query.

Automated Embedding simplifies the process of building semantic search. You don't need to generate, store, or manage vector embeddings yourself. Atlas handles embedding generation, updates, and querying natively.

Note:

When you use Automated Embedding, MongoDB Vector Search stores generated vector embeddings on your cluster.

When you use Automated Embedding on dedicated clusters (M10+), you must enable storage auto-scaling. If your cluster runs out of disk space, MongoDB Vector Search pauses embedding generation and the index transitions to Stale state. Once the disk space is freed up, MongoDB Vector Search resumes embedding generation.

Enable and Use Automated Embedding

Atlas

To enable Automated Embedding, you create a MongoDB Vector Search index using the autoEmbed type. The autoEmbed type specifies the field for which you want to enable Automated Embedding and the embedding model that you want to use. You can also include one or more fields to pre-filter your data using the filter type.

{
  "fields": [
    {
      "type": "autoEmbed",
      "modality": "text",
      "path": "<field-to-index>",
      "model": "<embedding-model>"
    },
    {
      "type": "filter",
      "path": "<field-to-index>"
    },
    ...
  ]
}

To learn more about the index syntax and fields, see [How to Index Fields for Vector Search.](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-avs-types-vector-search

MongoDB Vector Search automatically generates embeddings for existing and new documents that you insert or update.

Once you've created the index, you can run your queries. MongoDB Vector Search automatically generates embeddings for your query text using the same embedding model that you specified in the index. You can optionally specify a different embedding model using the model option in the [$vectorSearch](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch pipeline stage, but the specified embedding model must be compatible with the embedding model used at index-time.

[
  {
    "$vectorSearch": {
      "index": "<index-name>",
      "path": "<field-to-index>",
      "query": "<query-text>",
      "model": "<embedding-model>"
    }
  },
  {
    "$project": {
      "_id": 0,
      "<field-to-index>": 1,
      "score": { "$meta": "vectorSearchScore" }
    }
  }
]

To learn more, see [Run Vector Search ANN and ENN Queries.](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#std-label-return-vector-search-results

Embeddings Storage

Automated embedding indexes generate vector embeddings asynchronously and persist to your MongoDB cluster on a separate reserve database. Each Automated Embedding index has exactly one corresponding generated embeddings collection. The generated embeddings collection is stored in a dedicated internal database on the same cluster.

To learn more, see [Generated Embeddings Collection.](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/overview.md#std-label-auto-embed-materialized-views

Available Models

MongoDB Vector Search integrates with Voyage AI's state-of-the-art embedding models, each optimized for specific use-case:

Note:

voyage-code-4 requires an Atlas deployment. On self-managed deployments, use voyage-code-3 for code search and technical documentation.

To learn more, see [Models for Automated Embedding.](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/models.md#std-label-avs-auto-embeddings-model-ecosystem

Key Concepts

embedding model

Embedding models are algorithms that convert data into vector embeddings that capture your data's semantic, or underlying, meaning. These vectors enable vector search.

To learn more about the embedding models for Automated Embedding, see [Available Models.](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding.md#std-label-auto-embed-supported-models

vector embeddings

A vector embedding is an array of numbers, with each dimension representing a different feature or attribute of your data. Vectors can be used to represent any type of data, from text, images, and video to unstructured data. You create vector embeddings by passing your data through an embedding model, and you can store these embeddings in a database that supports vector embeddings like MongoDB.

To learn more about the embeddings storage for Automated Embedding, see [Generated Embeddings Collection.](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/overview.md#std-label-auto-embed-materialized-views

tokens

In the context of embedding models and LLMs, tokens are the fundamental units of text, such as words, subwords, or characters that the model processes to create embeddings or generate text. Tokens are how you are billed for usage of embedding models and LLMs.

To learn more about tokens for Automated Embedding, see [Manage Billing for Automated Embedding.](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/billing.md#std-label-auto-embed-billing

rate limits

Rate limits are restrictions imposed by API providers on the number of requests a user can make within a specific time frame, often measured in tokens per minute (TPM) or requests per minute (RPM). These limits ensure fair usage, prevent abuse, and maintain the stability and performance of the service for all users.

To learn more about rate limits for Automated Embedding, see [Rate Limits.](https://www.mongodb.com/docs/vector-search/crud-embeddings/automated-embedding/models.md#std-label-auto-embed-rate-limits

quantization

Quantization reduces the precision of vector embeddings to decrease memory and storage usage, with trade-offs in search accuracy. For Automated Embedding, MongoDB Vector Search supports the following quantization types:

To learn more about the quantization for Automated Embedding, see [About Quantization.](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-mdb-vs-quantization-auto

number of dimensions

The number of dimensions specifies the length of the embedding vector for each document (how many numbers are in the array). Higher dimensions capture more semantic detail and generally improve retrieval accuracy, but increase storage and compute costs (index size, RAM usage, and sometimes latency).

To learn more, see [numCandidates Selection.](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#std-label-avs-num-candidates

similarity

The similarity function is used to measure the similarity between two vectors or the closeness of a query vector to the vectors in the index. MongoDB Vector Search supports the following similarity functions:

• cosine - measures similarity based on the angle between vectors. We recommend this similarity function for unnormalized vectors like scalar quantization.

• dotProduct - measures similarity like cosine, but takes into account the magnitude of the vector. We recommend this similarity function for normalized vectors like float (full fidelity) quantization.

• euclidean - measures the distance between ends of vectors. We recommend this similarity function for binary or binaryNoRescore quantization, where vectors are compressed and distance in Hamming or Euclidean space is the right signal.

To learn more about the similarity functions for Automated Embedding, see [About the Similarity Functions.](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-mdb-vs-similarity-functions-auto