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POST
/
upload
/
upload_embeddings
Upload Embeddings
curl --request POST \
  --url https://api.usecortex.ai/upload/upload_embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "embeddings": [
    [
      0.123413,
      0.655367,
      0.987654,
      0.123456,
      0.789012
    ],
    [
      0.123413,
      0.655367,
      0.987654,
      0.123456,
      0.789012
    ]
  ],
  "file_id": "CortexDoc1234"
}'
{
  "file_id": "CortexDoc1234",
  "message": "<string>",
  "success": true
}
Hit the Try it button to try this API now in our playground. It’s the best way to check the full request and response in one place, customize your parameters, and generate ready-to-use code snippets.

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Sample Request

curl --request POST \
  --url 'https://api.usecortex.ai/upload/upload_embeddings?tenant_id=tenant_1234&sub_tenant_id=sub_tenant_4567' \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "embeddings": [
    [
      0.123413,
      0.655367,
      0.987654,
      0.123456,
      0.789012
    ],
    [
      0.123413,
      0.655367,
      0.987654,
      0.123456,
      0.789012
    ]
  ],
  "file_id": "CortexDoc1234"
}'
Upload pre-computed embedding vectors directly to your tenant’s knowledge base. This is useful when you have your own embedding model or want to use embeddings from external sources.

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Embedding Processing Pipeline

When you upload pre-computed embeddings, they go through a streamlined processing pipeline optimized for vector data:

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1. Immediate Upload & Validation

  • Your embedding vectors are immediately accepted and validated
  • Dimensional consistency is checked across all vectors
  • Format validation ensures proper numeric array structure
  • You receive a confirmation response with a file_id for tracking

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2. Vector Processing Phase

Our system automatically handles:
  • Dimensional Validation: Ensuring all vectors have consistent dimensions
  • Data Type Normalization: Converting to optimal numeric formats
  • Vector Quality Assessment: Checking for valid numeric ranges and patterns
  • Batch ID Generation: Creating unique chunk IDs for each embedding vector

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3. Chunk ID Assignment

  • Each embedding vector receives a unique chunk ID in format {batch_id}_{index}
  • These IDs serve as references for retrieval and linking to original content
  • Example: [0.1, 0.2, 0.3, 0.4, 0.5] becomes CortexEmbeddings123_0
  • You can use these chunk IDs to link back to your original text content

​
4. Direct Indexing

  • Embeddings are directly stored in our vector database (no embedding generation needed)
  • Full-text search indexes are created for associated metadata
  • Metadata is indexed for filtering and faceted search
  • Cross-references are established for related embedding batches

​
5. Quality Assurance

  • Automated quality checks ensure vector integrity
  • Dimensional consistency validation across the tenant
  • Vector range and format validation
  • Database storage verification
Processing Time: Pre-computed embeddings are typically processed and searchable within 30 seconds to 2 minutes. Large embedding batches (1000+ vectors) may take up to 5 minutes. You can check processing status using the document ID returned in the response.
Default Sub-Tenant Behavior: If you don’t specify a sub_tenant_id, the embeddings will be uploaded to the default sub-tenant created when your tenant was set up. This is perfect for organization-wide embeddings that should be accessible across all departments.

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Requirements

  • Maximum dimensions: 2000 rows × 3024 columns; i.e, 2000 chunks with the dimensions, not more than 3024
  • Format: 2D array of numeric values (int or float)
  • Consistency: All embedding vectors must have the same dimension
  • Content: Embeddings array cannot be empty
  • Processing: Generates unique chunk IDs in format {batch_id}_{index} for each row.
    • Consider them as references of that particular embeddings vector. You will get back these chunk_ids, when you query something.
    • In the example on your right, the reference to [0.1, 0.2, 0.3, 0.4, 0.5] is CortexEmbeddings123_0
    • You can use these chunk IDs to link the original text which is being embedded
  • Dimensional consistency per tenant: All embedding vectors within a tenant must have identical dimensions. Different dimensional vectors require separate tenants
File ID Management: When you provide a file_id as a key in the document_metadata object, that specific ID will be used to identify your content. If no file_id is provided in the document_metadata, the system will automatically generate a unique identifier for you. This allows you to maintain consistent references to your content across your application while ensuring every piece of content has a unique identifier.

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Duplicate File ID Behavior

When you upload embeddings with a file_id that already exists in your tenant:
  • Overwrite Behavior: The existing embeddings with the same file_id will be completely replaced with the new embeddings
  • Processing: The new embeddings will go through validation and direct indexing (no embedding generation needed)
  • Search Results: Previous search results and vector data from the old embeddings will be replaced with the new embeddings
  • Idempotency: Uploading the same embeddings with the same file_id multiple times is safe and will result in the same final state
Important: When overwriting existing embeddings, all previous vector data, chunk IDs, and search indexes associated with that file_id will be permanently removed and replaced. This action cannot be undone.
Example Success Response for Duplicate File ID: 
{
  "message": "Embeddings uploaded successfully. Existing embeddings with file_id 'emb_123456' have been overwritten.",
  "file_id": "emb_123456",
  "status": "success"
}

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Error Responses

All endpoints return consistent error responses following the standard format. For detailed error information, see our Error Responses documentation.

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Query Parameters

​
tenant_id
string
required

Unique identifier for the tenant/organization

Example:

"tenant_1234"

​
sub_tenant_id
string
default:""

Optional sub-tenant identifier used to organize data within a tenant. If omitted, the default sub-tenant created during tenant setup will be used.

Example:

"sub_tenant_4567"

Body

application/json
​
embeddings
number[][]
required

The embeddings of source you want to index

Example:
[
  [
    0.123413,
    0.655367,
    0.987654,
    0.123456,
    0.789012
  ],
  [
    0.123413,
    0.655367,
    0.987654,
    0.123456,
    0.789012
  ]
]
​
file_id
string | null
default:""

The Source ID of the target source you want to index

Example:

"CortexDoc1234"

Response

Successful Response

​
file_id
string
required

Unique identifier for the file being processed

Example:

"CortexDoc1234"

​
message
string
required

Status message indicating document parsing scheduled or update completed

​
success
boolean
default:true
Example:

true

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