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POST
/
v1
/
vectors
/
upsert
Upsert Vectors
curl --request POST \
  --url https://api.example.com/v1/vectors/upsert
Add new vectors or update existing ones in the encrypted index.

​
Authentication

Required - API key via X-API-Key header: 
X-API-Key: cyborg_your_api_key_here
You can get an API key from the CyborgDB Admin Dashboard. For more info, follow this guide.

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Request Body

{
  "index_name": "my_index",
  "index_key": "64_character_hex_string_representing_32_bytes",
  "items": [
    {
      "id": "item_1",
      "vector": [0.1, 0.2, 0.3, 0.4],
      "contents": "Hello world!",
      "metadata": {"category": "greeting", "language": "en"}
    },
    {
      "id": "item_2",
      "vector": [0.5, 0.6, 0.7, 0.8],
      "contents": "Bonjour monde!",
      "metadata": {"category": "greeting", "language": "fr"}
    }
  ]
}

​
Response

Standard Success Response: 
{
  "status": "success",
  "message": "Upserted 2 vectors"
}
With Auto-Training Triggered: 
{
  "status": "success",
  "message": "Upserted 2 vectors",
  "training_triggered": true,
  "training_message": "Index training has been triggered (vectors: 5000, threshold: 10000)"
}
With Training Check Warning: 
{
  "status": "success",
  "message": "Upserted 2 vectors",
  "warning": "Training check failed but upsert succeeded"
}
When the number of vectors in an index exceeds the automatic training threshold, the upsert response will include training_triggered and training_message fields. Training happens asynchronously in the background.In rare cases where the training check encounters an error but the upsert operation itself succeeds, a warning field will be included instead. The vectors are still successfully upserted in this scenario.

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Exceptions

  • 401: Authentication failed (invalid API key) or wrong index_key on SDK-supplied indexes — see error model
  • 404: Index not found
  • 422: Invalid request parameters or vector dimensions
  • 500: Internal server error

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Example Usage

Basic Upsert: 
curl -X POST "http://localhost:8000/v1/vectors/upsert" \
     -H "X-API-Key: cyborg_your_api_key_here" \
     -H "Content-Type: application/json" \
     -d '{
       "index_name": "my_index",
       "index_key": "your_64_character_hex_key_here",
       "items": [
         {
           "id": "item_1",
           "vector": [0.1, 0.2, 0.3, 0.4],
           "contents": "Hello world!"
         }
       ]
     }'
With Metadata: 
curl -X POST "http://localhost:8000/v1/vectors/upsert" \
     -H "X-API-Key: cyborg_your_api_key_here" \
     -H "Content-Type: application/json" \
     -d '{
       "index_name": "my_index",
       "index_key": "your_64_character_hex_key_here",
       "items": [
         {
           "id": "doc_1",
           "vector": [0.1, 0.2, 0.3, 0.4],
           "contents": "Important document content",
           "metadata": {
             "category": "documentation",
             "author": "admin",
             "created": "2024-01-15",
             "priority": "high"
           }
         }
       ]
     }'
Auto-Generated Embeddings: 
# For indexes created with embedding_model
curl -X POST "http://localhost:8000/v1/vectors/upsert" \
     -H "X-API-Key: cyborg_your_api_key_here" \
     -H "Content-Type: application/json" \
     -d '{
       "index_name": "semantic_index",
       "index_key": "your_64_character_hex_key_here",
       "items": [
         {
           "id": "text_1",
           "contents": "This text will be automatically embedded",
           "metadata": {"type": "auto_embedded"}
         }
       ]
     }'
Batch Upsert: 
curl -X POST "http://localhost:8000/v1/vectors/upsert" \
     -H "X-API-Key: cyborg_your_api_key_here" \
     -H "Content-Type: application/json" \
     -d '{
       "index_name": "my_index",
       "index_key": "your_64_character_hex_key_here",
       "items": [
         {"id": "batch_1", "vector": [0.1, 0.2, 0.3]},
         {"id": "batch_2", "vector": [0.4, 0.5, 0.6]},
         {"id": "batch_3", "vector": [0.7, 0.8, 0.9]},
         {"id": "batch_4", "vector": [0.2, 0.3, 0.4]},
         {"id": "batch_5", "vector": [0.5, 0.6, 0.7]}
       ]
     }'
When embedding_model is configured for the index, the vector parameter becomes optional. If provided, it will be used directly; if omitted, a vector will be auto-generated from the contents field.
For large datasets, use batch upserts with multiple items in a single request to improve performance and reduce network overhead.