Configure mongot for MongoDB Vector Search Automated Embedding

What Automated Embedding Does

Without Automated Embedding, your application must:

1. Generate an embedding for each document that you insert or update.

2. Store that embedding alongside the document in your collection.

3. Generate an embedding for every query, using the same model, at query-time.

4. Submit the query embedding to $vectorSearch.

With Automated Embedding, mongot handles steps 1, 2, and 3. Your application:

1. Configures a MongoDB Vector Search index with the autoEmbed field type for the text field.

2. Inserts and updates documents normally.

   mongot reads the text field, generates the embedding through the configured embedding model, and stores it.

3. Issues $vectorSearch queries with the query text, not a pre-computed embedding.

   mongot generates the embedding for the query text.

Supported Embedding Providers

Self-managed mongot Automated Embedding integrates with Voyage AI embedding models.

You incur costs for embedding generation. To learn more, see Manage Billing for Automated Embedding.

전제 조건

You must have the following prerequisites to use Automated Embedding:

• mongot 1.70.1 or later on a self-managed deployment (Community tarball or container, Local Development, and Enterprise Edition through Kubernetes Operator).

• MongoDB 8.2 이상.

• Two Voyage AI API keys.

  Use two separate keys, one for index-time embedding generation and one for query-time. Separate keys isolate query workload from indexing workload and allow independent rate-limit accounting. To learn more, see Rate Limits.

• Outbound network access from mongot to the embedding endpoint.

  The default endpoint is https://ai.mongodb.com/v1/embeddings. This endpoint proxies Voyage AI with MongoDB-managed billing, for keys generated using the Atlas UI. You can also use https://api.voyageai.com/v1/embeddings for direct Voyage AI access if you generated the API key directly from Voyage AI.

절차

docker run \
   -e VOYAGE_API_KEY=<your-voyage-api-key> \
   -p 27017:27017 \
   mongodb/mongodb-atlas-local:preview

docker run \
   -e VOYAGE_API_KEY=<your-voyage-api-key> \
   -e EMBEDDING_PROVIDER_ENDPOINT=https://api.voyageai.com/v1/embeddings \
   -p 27017:27017 \
   mongodb/mongodb-atlas-local:preview

embedding:
  queryKeyFile: /etc/mongot/secrets/voyage-api-query-key
  indexingKeyFile: /etc/mongot/secrets/voyage-api-indexing-key
  providerEndpoint: https://ai.mongodb.com/v1/embeddings

db.movies.createSearchIndex(
  "movie-semantic-index",
  "vectorSearch",
    {
      "fields": [
        {
          "type": "autoEmbed",
          "modality": "text",
          "path": "plot",
          "model": "voyage-4",
          "numDimensions": 1024,
          "similarity": "cosine",
          "indexingMethod": "hnsw",
          "hnswOptions": {
            "maxEdges": 16,
            "numEdgeCandidates": 50
          },
          "quantization": "scalar"
        },
        {
          "type": "filter",
          "path": "genre"
        }
      ]
    }
)

db.movies.aggregate([
  {
    "$vectorSearch": {
      "index": "movie-semantic-index",
      "path":  "plot",
      "query": "a heist gone wrong in a rainy city",
      "model": "voyage-4",
      "numCandidates": 100,
      "limit": 10
    }
  },
  {
    "$project": {
      "_id":   0,
      "title": 1,
      "score": { $meta: "vectorSearchScore" }
    }
  }
])

Embedding Storage

mongot persists embeddings in a dedicated internal database on the cluster. mongot does not store embeddings in your source collection. mongot can regenerate embeddings from the source text, and you incur charges for embedding generation.

Changes to the embedding model, output dimensions, or quantization trigger a full re-embedding of the affected index, for which you incur charges for embedding generation. See When mongot Regenerates Embeddings.

To learn more, see Generated Embeddings Collection.

When mongot Regenerates Embeddings

mongot regenerates embeddings for an autoEmbed field when any of the following occur:

• You insert a document.

• You change the embedded field in a document. mongot detects the change through change streams.

• You change the embedding model in the index definition.

• You change the embedding output dimension or data type.

• You change the text field path.

If you change any of the last three items, mongot rebuilds the entire index from scratch. The rebuild can be costly for large collections. Plan model changes deliberately.

Cost and Rate Limiting

MongoDB charges per million tokens at model-specific rates. Indexing cost is proportional to the total text volume of the indexed field across the collection. Query cost is proportional to the volume of query text.

If you use the Voyage AI API key that you created using your Atlas account, you can view API key usage and rate limits in the Atlas UI. To learn more, see Manage Billing for the Embedding and Reranking API. If you use keys created directly from Voyage AI, you must monitor your usage and rate limits in the Voyage AI dashboard.

Voyage AI-side rate-limit errors appear in mongot logs and as failed-document indicators on the affected index. These errors do not disable the index, but they can delay indexing of new documents.

Failure Modes

제한 사항

The following limitations apply to Automated Embedding:

• Preview status. Configuration shape, supported models, and default endpoints might change before Automated Embedding is generally available.

• One embedding model per field. Mixed-model indexes are not supported.

• No embedding model failover. If the Voyage AI endpoint is unreachable, indexing of new documents stalls.

다음 단계

• Automated Embedding Overview for the cross-platform conceptual overview.

• Configure mongot for the full mongot configuration reference.