Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.moorcheh.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
Overview

Uploads one or more precomputed vectors to a vector namespace. Each vector length must match the namespace vector_dimension set at creation. The upload runs asynchronously. Poll upload_job_status with the returned job_id.
Any extra fields on each vector object (for example source, category) are stored as metadata and can be used in search filters.

​
Method

client.upload_namespace_vectors(namespace_name: str, payload: dict) -> dict

​
API

POST /namespaces/{namespace_name}/vectors — see Upload vectors

​
Parameters

​
namespace_name
string
required
Target vector namespace.
​
vectors
array
required
Non-empty array of vector objects.
​
vectors[].id
string
required
Item id, unique within this namespace.
​
vectors[].vector
array
required
Array of finite numbers. Length must equal the namespace vector_dimension.
​
vectors[].{metadata}
any
Optional additional keys on each vector are saved as metadata (for example "source": "demo").

​
Example

from moorcheh import MoorchehApiClient, MoorchehApiError

client = MoorchehApiClient("http://localhost:8080")

try:
    resp = client.upload_namespace_vectors("my-embeddings", {
        "vectors": [
            {
                "id": "vec-1",
                "vector": [0.1] * 768,
                "source": "demo",
            },
        ],
    })
    job_id = resp["job_id"]
except MoorchehApiError as e:
    if e.is_item_limit_exceeded:
        print(e.body)
    else:
        raise
Poll with upload_job_status.

​
Returns

​
status
string
"success" when the upload job was started.
​
message
string
Human-readable result description.
​
job_id
string
Id of the async upload job. Poll upload_job_status with this value.
​
namespace_name
string
Namespace the vectors are being uploaded to.
​
total
number
Number of vectors accepted into the upload job.
​
items
number
Current total item count on the instance. Present on 409 item limit errors in MoorchehApiError.body.
​
max_items
number
Global item cap for this instance. Present on 409 item limit errors.
​
requested_new
number
Number of new item ids in the request that would exceed the cap. Present on 409 item limit errors.
Example return value
{
  "status": "success",
  "message": "Vectors upload started. Poll job status for progress.",
  "job_id": "job-d044a3bf6c0e4380bf6ad7e9df7999d0",
  "namespace_name": "my-embeddings",
  "total": 1,
}

​
Errors

Non-2xx responses raise MoorchehApiError. Use e.is_item_limit_exceeded for 409 quota errors. 
from moorcheh import MoorchehApiError

try:
    client.upload_namespace_vectors("my-embeddings", {"vectors": [...]})
except MoorchehApiError as e:
    print(e.status_code, e.body)
StatusCause
400Empty vectors, missing id/vector, non-finite values, dimension mismatch, or wrong namespace type
404Namespace not found
409Global item limit would be exceeded (job not started)
  • At most 100,000 items total across all namespaces
  • 409 is returned before the job starts if new ids would exceed the cap
  • Re-uploading an existing id in the same namespace updates the item and does not consume extra quota
  • Vector values must be finite (no NaN or Infinity)