Insert documents (HTTP)

For details about additional languages, see:

Inserts multiple documents 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 for each document. If the collection has vectorize enabled, vector embeddings can be automatically generated from text specified in the reserved $vectorize field for each document. 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 documents and returns a JSON object that includes the IDs of the inserted documents.

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

Example response:

{
  "status": {
    "insertedIds": [
      "3f557bef-fd53-47ea-957b-effd53c7eaec",
      101,
      "132ffr343"
    ]
  }
}

Signature

Use the insertMany 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 '{
  "insertMany": {
    "documents": DOCUMENTS_JSON_ARRAY,
    "options": {
      "ordered": BOOLEAN,
    }
  }
}'

Parameters

Name Type Summary

documents

array

An array of JSON objects describing the documents 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.

options

object

Optional. The options for this operation. See Properties of options for more details.
Properties of options
Name Type Summary

ordered

boolean

Optional. Whether the insertions must be processed sequentially. If false, the documents may be inserted in an arbitrary order and possibly concurrently. If you don’t need ordered inserts, DataStax recommends setting this parameter to false for faster performance.

For an example, see Insert documents and specify insertion behavior.

Default: false

Examples

The following examples demonstrate how to insert multiple documents into a collection.

Insert documents

The documents can have different structures.

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42
      },
      {
        "nickname": "Bobby",
        "color": "blue",
        "foods": ["carrots", "chocolate"]
      }
    ]
  }
}'

Insert documents with vector embeddings

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

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 may also insert a mix of documents with and without the $vector field.

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 '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42,
        "$vector": [0.08, -0.62, 0.39]
      },
      {
        "nickname": "Bobby",
        "$vector": [0.12, 0.53, 0.32]
      }
    ]
  }
}'
$binary
curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42,
        "$vector": {"$binary": "PaPXCr8euFI+x64U"}
      },
      {
        "nickname": "Bobby",
        "$vector": {"$binary": "PfXCjz8HrhQ+o9cK"}
      }
    ]
  }
}'

Insert documents and generate vector embeddings

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

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).

You may also insert a mix of documents with and without the $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 '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42,
        "$vectorize": "Text to vectorize for this document"
      },
      {
        "nickname": "Bobby",
        "$vectorize": "Text to vectorize for this document"
      }
    ]
  }
}'

If you plan to use hybrid search to find documents, each document must have both the $lexical field and the $vector 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 '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "$vector": [0.08, -0.62, 0.39],
        "$lexical": "An author who writes SciFi and fantasy novels."
      },
      {
        "name": "Mary Day",
        "$vectorize": "An athlete who loves biking, hiking, running, and swimming in the outdoors",
        "$lexical": "She shares her love of triathlons by coaching kids after school."
      },
      {
        "name": "Bobby",
        "$hybrid": "A software developer who enjoys managing databases"
      }
    ]
  }
}'

Insert documents for retrieval with lexicographical matching

If you plan to use lexicographical matching to find documents, each 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 '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "$lexical": "An author who writes SciFi and fantasy novels."
      },
      {
        "name": "Mary Day",
        "$lexical": "An active hiker, runner, and triathlete who loves the outdoors."
      }
    ]
  }
}'

Insert documents and specify the IDs

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

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertMany": {
    "documents": [
      {
        "name": "Melissa",
        "_id": { "$objectId": "6672e1cbd7fabb4e5493916f" }
      },
      {
        "name": "Jess",
        "_id": { "$uuid": "1ef2e42c-1fdb-6ad6-aae4-e84679831739" }
      },
      {
        "name": "Jane",
        "_id": 1
      },
      {
        "name": "Bobby",
        "_id": "b_023"
      }
    ]
  }
}'

Insert documents and specify insertion behavior

curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/COLLECTION_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "insertMany": {
    "documents": [
      {
        "name": "Jane Doe",
        "age": 42
      },
      {
        "nickname": "Bobby",
        "color": "blue",
        "foods": ["carrots", "chocolate"]
      }
    ],
    "options": {
      "ordered": false
    }
  }
}'

Insert documents 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 '{
  "insertMany": {
    "documents": [
      {
        "exampleBinary": {"$binary": "PfvnbT7peNU/Sfvn"}
      }
    ]
  }
}'

Insert documents 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 '{
  "insertMany": {
    "documents": [
      {
        "title": "Hidden Shadows of the Past",
        "genres": ["Biography", "Graphic Novel", "Dystopian", "Drama"],
        "metadata": {
          "isbn": "978-1-905585-40-3",
          "language": "French",
          "edition": "Anniversary Edition"
        }
      },
      {
      "title": "Bake a Dozen",
      "genres": ["Biography", "Fiction"],
      "metadata": {
        "isbn": "342-2-875587-50-2",
        "language": "English",
        "edition": "Illustrated Edition"
      }
    }
    ]
  }
}'

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