Insert a document (HTTP)

For details about additional languages, see:

Inserts a single document into a collection.

Documents are stored in collections. They represent a single row or record of data in Astra DB Serverless databases. For more information, see About collections with the Data API (HTTP).

If the collection is vector-enabled, pregenerated vector embeddings can be included by using the reserved $vector field. If the collection has vectorize enabled, vector embeddings can be automatically generated from text specified in the reserved $vectorize field. You can later use the $vector or $vectorize field to perform a vector search or hybrid search.

If the collection has lexical enabled, use the reserved $lexical field to store a string to index for lexicographical matching and the lexical search component of hybrid search.

Alternatively, you can use the $hybrid shorthand to populate the $vectorize and $lexical fields.

Ready to write code? See the examples for this method to get started.

Result

Inserts the specified document and returns a JSON object that includes the ID of the inserted document.

The ID value depends on the ID type. For more information, see Document IDs (HTTP).

Example response:

{
  "status": {
    "insertedIds": [
      "3f557bef-fd53-47ea-957b-effd53c7eaec"
    ]
  }
}

Signature

Use the insertOne command.

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertOne": {
    "document": DOCUMENT_JSON_OBJECT
  }
}'

Parameters

Name Type Summary

document

object

A JSON object describing the document to insert.

A document can contain user-defined and reserved fields.

User-defined field names can be any non-empty sequence of Unicode characters, with the following exceptions:

  • Field names cannot start with $.

  • Field names cannot be exactly *.

  • If a field name includes & or ., you must escape those characters when you use the field in a filter, sort, projection, or update. For more information, see Work with . and & in field names (HTTP).

Reserved fields are tied to specific functionality. Include the following reserved fields in your documents, if applicable:

  • _id: An optional unique identifier for the document. If _id is omitted, it is created automatically based on the collection’s ID type. For more information, see Document IDs (HTTP).

  • $vector: An optional array of numbers representing a vector embedding for vector search. The $vector field is only supported for vector-enabled collections. A document cannot contain both a $vector and a $vectorize field. For more information, see $vector in collections (HTTP).

  • $vectorize: An optional string from which to generate vector embeddings for vector search. The $vectorize field is only supported for collections that have an embedding provider integration. A document cannot contain both a $vector and a $vectorize field. For more information, see $vectorize in collections (HTTP).

  • $lexical: An optional string to make the document searchable for lexicographical matching and the lexical search component of hybrid search. The $lexical field is only supported for collections that have lexical search enabled. For more information, see $lexical in collections (HTTP).

  • $hybrid: An optional string that populates both $vectorize and $lexical. The $hybrid shorthand is only supported for collections that have vectorize and lexical search enabled. If a document uses $hybrid, it cannot contain a root-level $vectorize or $lexical field. For more information, see $hybrid in collections (HTTP).

For examples, see Examples.

Examples

The following examples demonstrate how to insert a document into a collection.

Insert a document

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "title": "Hidden Shadows of the Past",
      "genres": ["Biography", "Graphic Novel", "Dystopian", "Drama"],
      "metadata": {
          "isbn": "978-1-905585-40-3",
          "language": "French",
          "edition": "Anniversary Edition"
      },
      "number_of_pages": 245
    }
  }
}'

Insert a document with vector embeddings

Use the reserved $vector field to insert a document with pregenerated vector embeddings.

You can later use this field to perform a vector search.

All embeddings in the collection should use the same provider, model, and dimensions. Mismatched embeddings can cause inaccurate vector searches.

The $vector field is only supported for vector-enabled collections. For more information, see Create a collection that can store vector embeddings and $vector in collections (HTTP).

You can provide the vector embeddings as an array of floats, or you can use $binary to provide the vector embeddings as a Base64-encoded string. $binary can be more performant.

For more information about how to convert an array of floats to a Base64-encoded string, see Binary encoding of vector embeddings.

Array of floats
curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "$vector": [0.08, -0.62, 0.39]
    }
  }
}'
$binary
curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "$vector": {"$binary": "PaPXCr8euFI+x64U"}
    }
  }
}'

Insert a document and generate vector embeddings

Use the reserved $vectorize field to generate a vector embedding automatically. The value of $vectorize can be any string.

You can later use this field to perform a vector search.

The $vectorize field is only supported for collections that have vectorize enabled. For more information, see Create a collection that can automatically generate vector embeddings and $vectorize in collections (HTTP).

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "$vectorize": "Text to vectorize"
    }
  }
}'

If you plan to use hybrid search to find this document, the document must have both the $lexical field and the $vector field populated.

Example specifying the $vector and $lexical fields:

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "$vector": [0.08, -0.62, 0.39],
      "$lexical": "An athlete who loves biking, hiking, running, and swimming in the outdoors"
    }
  }
}'

Example specifying the $vectorize and $lexical fields:

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "$vectorize": "An athlete who loves biking, hiking, running, and swimming in the outdoors",
      "$lexical": "She shares her love of triathlons by coaching kids after school"
    }
  }
}'

Example using the $hybrid shorthand, which populates the $lexical and $vectorize field:

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "$hybrid": "An athlete who loves biking, hiking, running, and swimming in the outdoors"
    }
  }
}'

Insert a document for retrieval with lexicographical matching

If you plan to use lexicographical matching to find this document, the document must have the $lexical field populated.

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "$lexical": "An active hiker, runner, and triathlete who loves the outdoors."
    }
  }
}'

Insert a document and specify the ID

You can specify the _id field directly, or you can use the objectId, uuid, uuidv6, or uuidv7 types.

Example specifying an integer:

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "_id": 1
    }
  }
}'

Example using the objectId type:

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "name": "Jane Doe",
      "_id": { "$objectId": "6672e1cbd7fabb4e5493916f" }
    }
  }
}'

Insert a document with a binary field

You can insert binary data as a Base64-encoded string with $binary.

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "exampleBinary": {"$binary": "PaPXCr8euFI+x64U"}
    }
  }
}'

Insert a document with nested fields

Although you can use dot notation in a filter to find a document, you cannot use dot notation to insert a document. To specify nested fields in the inserted document, you must build a map, list, or set.

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertOne": {
    "document": {
      "title": "Hidden Shadows of the Past",
      "genres": ["Biography", "Graphic Novel", "Dystopian", "Drama"],
      "metadata": {
        "isbn": "978-1-905585-40-3",
        "language": "French",
        "edition": "Anniversary Edition"
      }
    }
  }
}'

Was this helpful?

Give Feedback

How can we improve the documentation?

© Copyright IBM Corporation 2026 | Privacy policy | Terms of use |  Manage Privacy Choices

Apache, Apache Cassandra, Cassandra, Apache Tomcat, Tomcat, Apache Lucene, Apache Solr, Apache Hadoop, Hadoop, Apache Pulsar, Pulsar, Apache Spark, Spark, Apache TinkerPop, TinkerPop, Apache Kafka and Kafka are either registered trademarks or trademarks of the Apache Software Foundation or its subsidiaries in Canada, the United States and/or other countries. Kubernetes is the registered trademark of the Linux Foundation.

General Inquiries: Contact IBM