Nota: También he probado creando una clase con un código de python simple y da el mismo error. ¿Puede ser que esta imagen de docker tenga problemas con Python?

Class St.Vectorsearch.Vector Extends %RegisteredObject
{
ClassMethod Embedding(Test) [ Language = python ]
{
   return (Test)
}
}

Compilation started on 04/16/2026 06:12:25 with qualifiers 'cuk'
Compiling class St.Vectorsearch.Vector
ERROR #7802: Worker job/s '1199:40' unexpectedly shut down in group '#Default:(2988722416963):0'.
ERROR #7812: Work queue unexpectedly removed, shutting down.
ERROR #5002: ObjectScript error: <THROW>WaitForComplete+215^%SYS.WorkQueueMgr *%Exception.StatusException ERROR #7802: Worker job/s '1199:40' unexpectedly shut down in group '#Default:(2988722416963):0'.
ERROR #7812: Work queue unexpectedly removed, shutting down.
Detected 3 errors during compilation in 1.014s.

Hola @Luis Angel Pérez Ramos 

He reiniciado mi docker para que vuelva a compilar todo desde cero para ver si así volvía a la vida, pero ahora me aparece el siguiente error cuando compila la clase St.Vectorsearch.Vector

USER>do $system.OBJ.Compile("St.Vectorsearch.Vector","ck")
Compilation started on 04/16/2026 06:05:04 with qualifiers 'ck'
Compiling class St.Vectorsearch.Vector
ERROR #7802: Worker job/s '878:23' unexpectedly shut down in group '#Default:(4743230725894):0'.
ERROR #7812: Work queue unexpectedly removed, shutting down.
ERROR #5002: ObjectScript error: <THROW>WaitForComplete+215^%SYS.WorkQueueMgr *%Exception.StatusException ERROR #7802: Worker job/s '878:23' unexpectedly shut down in group '#Default:(4743230725894):0'.
ERROR #7812: Work queue unexpectedly removed, shutting down.
Detected 3 errors during compilation in 1.003s.

He probado a usar tanto la versión de sentence-transforme que me habías pasado como usando la última versión, y da el mismo error al compilarlo.

¿Alguna idea?

Esta es mi clase St.Vectorsearch.Vector

Class St.Vectorsearch.Vector Extends %RegisteredObject
{
ClassMethod Embedding(Test) [ Language = python ]
{
   from transformers import AutoTokenizer, AutoModel
   import torch
   import torch.nn.functional as F
   #Mean Pooling - Take attention mask into account for correct averaging
   def mean_pooling(model_output, attention_mask):
       token_embeddings = model_output[0] #First element of model_output contains all token embeddings
       input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
       return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

   # Sentences we want sentence embeddings for
   # sentences = ['This is an example sentence', 'Each sentence is converted']
   sentences = [Test]
   # Load model from HuggingFace Hub
   tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
   model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
   # Tokenize sentences
   encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
   # Compute token embeddings
   with torch.no_grad():
       model_output = model(**encoded_input)
   # Perform pooling
   sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
   # Normalize embeddings
   sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
   #print(sentence_embeddings)
   return str(sentence_embeddings[0].tolist())
}
}

Estoy usando la imagen de docker de community

No, en ningún momento. Simplemente se queda esperando una respuesta del método Embedding y ahí se queda

Este mensaje de error me lo dá cuando reinicio el componente, porque se queda totalmente bloqueado. Esa linea es la que apunta a la llamada al método 

set vector = ##class(St.Vectorsearch.Vector).Embedding(request.StringValue)

Voy a probar.

Es curioso porque si lo ejecuto desde el termina, tarda un poco pero responde. En cambio si es invocado desde un BP, se queda pillado y da un error en el BP.

El código de invocación es el que he puesto en el mensaje.

Este es mi código

ClassMethod Embedding(Text) [ Language = python ]
{
   from transformers import AutoTokenizer, AutoModel
   import torch
   import torch.nn.functional as F
   #Mean Pooling - Take attention mask into account for correct averaging
   def mean_pooling(model_output, attention_mask):
       token_embeddings = model_output[0] #First element of model_output contains all token embeddings
       input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
       return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

   # Sentences we want sentence embeddings for
   # sentences = ['This is an example sentence', 'Each sentence is converted']
   sentences = [Text]
   # Load model from HuggingFace Hub
   #; tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
   #; model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
   
   # Load model from multilingual model
   tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
   model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
   # Tokenize sentences
   encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
   # Compute token embeddings
   with torch.no_grad():
       model_output = model(**encoded_input)
   # Perform pooling
   sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
   # Normalize embeddings
   sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
   #print(sentence_embeddings)
   return str(sentence_embeddings[0].tolist())
}

Thanks Eduard.

Indeed, adding the class name and the element name makes it work.

Thanks for the information. I'll have a look the instructions how to implement a Semaphore.

Thanks, Evgeny, as always.

It's not a bad idea to use an abbreviation map and Python for this. I can create a table with the code name and replace it with the UTC value.

https://www.timeanddate.com/time/zones/

This is a complete list of time zone codes and their UTC values.

What's the problem? Some are duplicated.

According to this list, Thursday, July 3rd at 08:20:00 AST, 2025 could be:

AST: Arabia Standard Time - UTC+03

AST: Atlantic Standard Time - UTC-04

Perhaps there's a mistake in the list.

Thanks @Brett Saviano 

I've already taken a look at the api/atelier APIs, and they've been very helpful in retrieving namespace names.

In our check method, in addition to server information, we return more information about production, such as the version number, last installation date, connection status with external databases, etc.

We have two mirrored servers, so if one of the servers goes down, the backup server is activated.

If the server name is different than expected, it's because the backup server has been activated and is providing service without issue, but we have an alert that the primary server has gone down.

This is an example of what our check service returns:

{
  "result": "OK",
  "methods": [
    {
      "test": "SQL_USERS",
      "result": "OK"
    },
    {
      "test": "SQL_APPOINTMENT",
      "result": "OK"
    },
    {
      "test": "VERSION",
      "result": "1.14.0.0327"
    },
    {
      "test": "INSTALLED",
      "result": "2024-12-04T08:04:52+01:00"
    }
  ],
  "serverName": "SRVPROD01",
  "serverApi": "10.168.1.1",
  "serverInstance": "HEALTHCONNECT",
  "execution_time": "00:00:00.0597418"
}