# Error on connecting external connector and embedding model

**URL:** <https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487>\
**Category:** Machine Learning\
**Created:** [November 14, 2025, 2:59pm UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487 "2025-11-14T14:59:24Z")\
**Posts on this page:** 8\
**Page:** 1

<div class="post-metadata">

**Author:** ![joaoolavo](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.opensearch.org/joaoolavo/32/6832_2.png) [@joaoolavo](https://forum.opensearch.org/u/joaoolavo)\
**Post date:** [November 14, 2025, 2:59pm UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487/1 "2025-11-14T14:59:24Z")

</div>

**Versions** : OpenSearch 2.19 (AWS managed servers)

**Describe the issue** :

I’ve configured a connector (that goes on company’s internal API embedding model) and a model to use it. Running a predict on model works just fine (got back the “inference\_results”, the embedding vector, etc).

Now I’m trying to create a pipeline in order to ingest a document and store its embedded data, as it’s supposed to be integrated with the model and connector.

The issue is that putting a new doc on a index, using this pipeline, give me back the error below:

> `PUT /index-01/_doc/1`  
> `{`  
> `“text”: “Hello world”,`  
> `“id”: “s1”`  
> `}`  
> `{`  
> `“error”: {`  
> `“root_cause”: [`  
> `{`  
> `“type”: “illegal_state_exception”,`  
> `“reason”: “failed while calling model, check error log for details”`  
> `}`  
> `],`  
> `“type”: “illegal_state_exception”,`  
> `“reason”: “failed while calling model, check error log for details”`  
> `},`  
> `“status”: 500`  
> `}`

Please, help me understanding what is the issue and how to fix it.

I don’t see how the “input” parameter on connector is linked to the index “text” field. I can’t find on official docs what should I do.

I also don’t know which log is it talking about. I’ve log groups configurated on CloudWatch, but couldn’t find any regarding this issue. Where can I find it?

Am I missing something? What should I change?

**Configuration:**

#################

CONNECTOR

#################

> `GET /_plugins/_ml/connectors/zQTjfpoBTaRJSyt75N7r`  
> `{`  
> `“name”: “LiteLLM embeddings - text-embedding-3-small”,`  
> `“version”: “1”,`  
> `“description”: “LiteLLM embeddings - text-embedding-3-small”,`  
> `“protocol”: “http”,`  
> `“parameters”: {`  
> `“endpoint”: “``api.example.com``”,`  
> `“model”: “text-embedding-3-small”`  
> `},`  
> `“actions”: [`  
> `{`  
> `“action_type”: “PREDICT”,`  
> `“method”: “POST”,`  
> `“url”: “``https://$``{parameters.endpoint}/ia/texto/v1/litellm/litellm/engines/${parameters.model}/embeddings”,`  
> `“headers”: {`  
> `“api-key”: “${credential.secretArn.api-key}”,`  
> `“Host”: “``api.example.com``”,`  
> `“Content-Type”: “application/json”`  
> `},`  
> `“request_body”: “”“{ “input”: ${parameters.input} }”“”`  
> `}`  
> `],`  
> `“owner”: {`  
> `“name”: “xxx”,`  
> `“backend_roles”: [`  
> `“xxx”`  
> `],`  
> `“roles”: [`  
> `“manage_snapshots”,`  
> `“ml_full_access”,`  
> `“snapshot_management_read_access”`  
> `],`  
> `“custom_attribute_names”: ``,`  
> `“user_requested_tenant”: “null”,`  
> `“user_requested_tenant_access”: “NONE”`  
> `},`  
> `“access”: “public”,`  
> `“created_time”: 1763065455850,`  
> `“last_updated_time”: 1763065455850,`  
> `“client_config”: {`  
> `“max_connection”: 30,`  
> `“connection_timeout”: 30000,`  
> `“read_timeout”: 30000,`  
> `“retry_backoff_millis”: 200,`  
> `“retry_timeout_seconds”: 30,`  
> `“max_retry_times”: 0,`  
> `“retry_backoff_policy”: “constant”,`  
> `“verify_ssl”: false`  
> `}`  
> `}`

#################

MODEL GROUP

#################

> `GET /_plugins/_ml/model_groups/URXlfpoBMnrhtYk0Va3l`  
> `{`  
> `“name”: “LiteLLM embeddings - text-embedding-3-small”,`  
> `“latest_version”: 2,`  
> `“description”: “LiteLLM embeddings - text-embedding-3-small”,`  
> `“backend_roles”: [`  
> `“xxx”`  
> `],`  
> `“owner”: {`  
> `“name”: “xxx”,`  
> `“backend_roles”: ``,`  
> `“roles”: [`  
> `“security_manager”,`  
> `“all_access”`  
> `],`  
> `“custom_attribute_names”: ``,`  
> `“user_requested_tenant”: null,`  
> `“user_requested_tenant_access”: “WRITE”`  
> `},`  
> `“access”: “restricted”,`  
> `“created_time”: 1763065550309,`  
> `“last_updated_time”: 1763066083432`  
> `}`

#################

MODEL

#################

> `GET /_plugins/_ml/models/0hXrfpoBMnrhtYk0lcXw`  
> `{`  
> `“name”: “LiteLLM embeddings - text-embedding-3-small”,`  
> `“model_group_id”: “URXlfpoBMnrhtYk0Va3l”,`  
> `“algorithm”: “REMOTE”,`  
> `“model_version”: “1”,`  
> `“description”: “LiteLLM embeddings - text-embedding-3-small”,`  
> `“model_state”: “DEPLOYED”,`  
> `“created_time”: 1763065959920,`  
> `“last_updated_time”: 1763066030159,`  
> `“last_deployed_time”: 1763066030159,`  
> `“auto_redeploy_retry_times”: 0,`  
> `“planning_worker_node_count”: 3,`  
> `“current_worker_node_count”: 3,`  
> `“planning_worker_nodes”: [`  
> `“WYAJWdCOTpqTP2zF4LjzhA”,`  
> `“Lgkv7fYeTou7zX0mtAoJMQ”,`  
> `“NzhBKp-VTLW99scG79875A”`  
> `],`  
> `“deploy_to_all_nodes”: true,`  
> `“is_hidden”: false,`  
> `“connector_id”: “zQTjfpoBTaRJSyt75N7r”`  
> `}`

Validating connector:

> `POST /_plugins/_ml/models/0hXrfpoBMnrhtYk0lcXw/_predict`  
> `{`  
> `“parameters”: {`  
> `“input”: [“Hello world”]`  
> `}`  
> `}`  
> `{`  
> `“inference_results”: [`  
> `{`  
> `“output”: [`  
> `{`  
> `“name”: “response”,`  
> `“dataAsMap”: {`  
> `“model”: “text-embedding-3-small”,`  
> `“data”: [`  
> `{`  
> `“embedding”: [`  
> `-0.002078542485833168,`  
> `-0.04908587411046028,`  
> `[…]`  
> `-0.006101156119257212`  
> `],`  
> `“index”: 0,`  
> `“object”: “embedding”`  
> `}`  
> `],`  
> `“object”: “list”,`  
> `“usage”: {`  
> `“completion_tokens”: 0,`  
> `“prompt_tokens”: 2,`  
> `“total_tokens”: 2`  
> `}`  
> `}`  
> `}`  
> `],`  
> `“status_code”: 200`  
> `}`  
> `]`  
> `}`

#################

PIPELINE

#################

> `GET _ingest/pipeline/api_text-embedding-3-small_field_text`  
> `{`  
> `“api_text-embedding-3-small_field_text”: {`  
> `“description”: “LiteLLM embeddings - text-embedding-3-small”,`  
> `“processors”: [`  
> `{`  
> `“text_embedding”: {`  
> `“model_id”: “0hXrfpoBMnrhtYk0lcXw”,`  
> `“field_map”: {`  
> `“text”: “passage_embedding”`  
> `}`  
> `}`  
> `}`  
> `]`  
> `}`  
> `}`

#################

INDEX

#################

> `GET index-01`  
> `{`  
> `“index-01”: {`  
> `“aliases”: {},`  
> `“mappings”: {`  
> `“properties”: {`  
> `“id”: {`  
> `“type”: “text”`  
> `},`  
> `“passage_embedding”: {`  
> `“type”: “knn_vector”,`  
> `“dimension”: 3072,`  
> `“method”: {`  
> `“engine”: “lucene”,`  
> `“space_type”: “l2”,`  
> `“name”: “hnsw”,`  
> `“parameters”: {}`  
> `}`  
> `},`  
> `“text”: {`  
> `“type”: “text”`  
> `}`  
> `}`  
> `},`  
> `“settings”: {`  
> `“index”: {`  
> `“replication”: {`  
> `“type”: “DOCUMENT”`  
> `},`  
> `“number_of_shards”: “1”,`  
> `“provided_name”: “index-01”,`  
> `“default_pipeline”: “api_text-embedding-3-small_field_text”,`  
> `“knn”: “true”,`  
> `“creation_date”: “1763037729696”,`  
> `“number_of_replicas”: “1”,`  
> `“uuid”: “XU2HJSpsQZa4lGpnIjivNg”,`  
> `“version”: {`  
> `“created”: “136407827”`  
> `}`  
> `}`  
> `}`  
> `}`  
> `}`

Getting error when inserting data:

> `PUT /index-01/_doc/1`  
> `{`  
> `“text”: “Hello world”,`  
> `“id”: “s1”`  
> `}`  
> `{`  
> `“error”: {`  
> `“root_cause”: [`  
> `{`  
> `“type”: “illegal_state_exception”,`  
> `“reason”: “failed while calling model, check error log for details”`  
> `}`  
> `],`  
> `“type”: “illegal_state_exception”,`  
> `“reason”: “failed while calling model, check error log for details”`  
> `},`  
> `“status”: 500`  
> `}`

---

<div class="post-metadata">

**Author:** ![pablo](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.opensearch.org/pablo/32/10363_2.png) [@pablo](https://forum.opensearch.org/u/pablo)\
**Post date:** [November 15, 2025, 12:03am UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487/2 "2025-11-15T00:03:21Z")

</div>

@joaoolavo This is my working example.  
I used this document.

> <https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/openai_connector_embedding_blueprint.md>

```json
POST /_plugins/_ml/connectors/_create
{
  "name": "myopenai",
  "description": "myopenai",
  "version": "1.0",
  "protocol": "http",
  "parameters": {
    "model": "text-embedding-3-small"
  },
  "credential": {
    "openAI_key": "sk-..."
  },
  "actions": [
    {
      "action_type": "predict",
      "method": "POST",
      "url": "https://api.openai.com/v1/embeddings",
      "headers": {
        "Authorization": "Bearer ${credential.openAI_key}"
      },
      "request_body": "{ \"input\": ${parameters.input}, \"model\": \"${parameters.model}\" }",
      "pre_process_function": "connector.pre_process.openai.embedding",
      "post_process_function": "connector.post_process.openai.embedding"
    }
  ]
}

POST /_plugins/_ml/model_groups/_register
{
    "name": "remote_model_group",
    "description": "This is an example description"
}

POST /_plugins/_ml/models/_register
{
  "name": "OpenAI embedding model",
  "function_name": "remote",
  "model_group_id": "ec3BhJoBLdsWuLU2km91",
  "description": "test model",
  "connector_id": "SM20hJoBLdsWuLU2am8n"
}

POST /_plugins/_ml/models/fs3ChJoBLdsWuLU2KG8l/_deploy

POST /_plugins/_ml/models/fs3ChJoBLdsWuLU2KG8l/_predict
{
  "parameters": {
    "input": ["What is the meaning of life?"]
  }
}

PUT /_ingest/pipeline/nlp-ingest-pipeline
{
  "description": "A text embedding pipeline",
  "processors": [
    {
      "text_embedding": {
        "model_id": "fs3ChJoBLdsWuLU2KG8l",
        "field_map": {
          "passage_text": "passage_embedding"
        }
      }
    }
  ]
}

PUT /index-01
{
  "settings": {
    "index": {
      "number_of_shards": 1,
      "number_of_replicas": 1,
      "knn": true,
      "replication": {
        "type": "DOCUMENT"
      },
      "default_pipeline": "nlp-ingest-pipeline"
    }
  },
  "mappings": {
    "properties": {
      "id": {
        "type": "text"
      },
      "passage_embedding": {
        "type": "knn_vector",
        "dimension": 1536,
        "method": {
          "engine": "lucene",
          "space_type": "l2",
          "name": "hnsw",
          "parameters": {}
        }
      },
      "text": {
        "type": "text"
      }
    }
  }
}

PUT /index-01/_doc/1
{
  "passage_text": "Hello world",
  "id": "s1"
}

```

```json
GET index-01/_search

{
  "took": 16,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 1,
    "hits": [
      {
        "_index": "index-01",
        "_id": "1",
        "_score": 1,
        "_source": {
          "passage_text": "Hello world",
          "passage_embedding": [
            -0.0020785425,
            -0.049085874,
            0.02094679,
            0.031351026,
            -0.045305308,
            -0.026402483,
            -0.028999701,
            0.060304623,
            -0.025710916,

```

---

<div class="post-metadata">

**Author:** ![pablo](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.opensearch.org/pablo/32/10363_2.png) [@pablo](https://forum.opensearch.org/u/pablo)\
**Post date:** [November 15, 2025, 12:05am UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487/3 "2025-11-15T00:05:03Z")

</div>

@joaoolavo One more thing. 3072 dimension is for large model.  
Since you’re using small model, you must use 1536 in the index definition.

---

<div class="post-metadata">

**Author:** ![joaoolavo](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.opensearch.org/joaoolavo/32/6832_2.png) [@joaoolavo](https://forum.opensearch.org/u/joaoolavo)\
**Post date:** [November 17, 2025, 1:36pm UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487/4 "2025-11-17T13:36:27Z")

</div>

@pablo thank you for your reply.

I can’t find any relevant difference between your steps and mine.

The thing that I don’t understand is the “magic trick” that makes the content of the field “passage\_text” be passed to the ${parameters.input} parameter on the connector.

You specify the ‘“input”: ${parameters.input}’, you even use it on the predict model test, but I don’t see what’s the connection between this parameter and the “passage\_text” parameter when putting a new doc.

If you have any other suggesting, please, let me know!

PS: You are right, the correct model dimension is also 1536, I already fixed it, but the same issue persistis.

---

<div class="post-metadata">

**Author:** ![pablo](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.opensearch.org/pablo/32/10363_2.png) [@pablo](https://forum.opensearch.org/u/pablo)\
**Post date:** [November 17, 2025, 2:26pm UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487/5 "2025-11-17T14:26:52Z")

</div>

@joaoolavo Do you still get error 500? Do you get anything in OpenSearch logs?

---

<div class="post-metadata">

**Author:** ![pablo](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.opensearch.org/pablo/32/10363_2.png) [@pablo](https://forum.opensearch.org/u/pablo)\
**Post date:** [November 17, 2025, 8:12pm UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487/6 "2025-11-17T20:12:02Z")

</div>

@joaoolavo Regarding the connection. The Input and passage text are connected through pipeline. When you create an index you use the pipeline in the settings which already have mapping to the passage\_text and the model.

---

<div class="post-metadata">

**Author:** ![joaoolavo](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.opensearch.org/joaoolavo/32/6832_2.png) [@joaoolavo](https://forum.opensearch.org/u/joaoolavo)\
**Post date:** [November 18, 2025, 6:45pm UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487/7 "2025-11-18T18:45:06Z")

</div>

Ok, I did it! 😃

@pablo Sorry, I missed it, I see now that you said that at first.

The solution is to add the following line when creating the connector:

```auto
            "post_process_function": "connector.post_process.openai.embedding"

```

As below:

```auto
[...]
    "actions": [
        {
            "headers": {
                "api-key": "${credential.secretArn.api-key}",
                "Host": api.example.com,
                "Content-Type": "application/json"
            },
            "method": "POST",
            "request_body": "{ \"input\": ${parameters.input} }",
            "action_type": "PREDICT",
            "url": "https://${parameters.endpoint}/ia/texto/v1/litellm/litellm/engines/${parameters.model}/embeddings",
            "post_process_function": "connector.post_process.openai.embedding" // ADD THIS TO MAKE IT WORK
        }
    ]
[...]

```

It seems that, when using a remote model connector with OpenAI-compatible API (LiteLLM in my case) for text embeddings, the `text_embedding` processor in an ingest pipeline was failing with:

“org.opensearch.neuralsearch.ml.MLCommonsClientAccessor.buildVectorFromResponse(MLCommonsClientAccessor.java:251)“

```auto
[2025-11-18T17:08:15,694][WARN][r.suppressed] [f0793a6f6c3034c26a916a882cc90276] path: __PATH__ params: {pretty=true, index=index01, id=1}
IngestProcessorException[java.lang.IllegalStateException: failed while calling model, check error log for details]; nested: IllegalStateException[failed while calling model, check error log for details];
	at org.opensearch.ingest.CompoundProcessor.newCompoundProcessorException(CompoundProcessor.java:356)
	at org.opensearch.ingest.CompoundProcessor.lambda$innerExecute$4(CompoundProcessor.java:278)
	at org.opensearch.neuralsearch.processor.TextEmbeddingProcessor.lambda$doExecute$1(TextEmbeddingProcessor.java:53)
	at org.opensearch.core.action.ActionListener$1.onFailure(ActionListener.java:90)
	at org.opensearch.neuralsearch.util.RetryUtil.handleRetryOrFailure(RetryUtil.java:48)
	at org.opensearch.neuralsearch.ml.MLCommonsClientAccessor.lambda$retryableInferenceSentencesWithVectorResult$6(MLCommonsClientAccessor.java:192)
	at org.opensearch.core.action.ActionListener$1.onFailure(ActionListener.java:90)
	at org.opensearch.core.action.ActionListener$1.onResponse(ActionListener.java:84)
	at org.opensearch.ml.client.MachineLearningNodeClient.lambda$getMlPredictionTaskResponseActionListener$5(MachineLearningNodeClient.java:378)
	at org.opensearch.core.action.ActionListener$1.onResponse(ActionListener.java:82)
	at org.opensearch.ml.client.MachineLearningNodeClient.lambda$wrapActionListener$6(MachineLearningNodeClient.java:394)
	at org.opensearch.core.action.ActionListener$1.onResponse(ActionListener.java:82)
	at org.opensearch.action.support.TransportAction$1.onResponse(TransportAction.java:115)
	at org.opensearch.action.support.TransportAction$1.onResponse(TransportAction.java:109)
	at org.opensearch.core.action.ActionListener$6.onResponse(ActionListener.java:301)
	at org.opensearch.core.action.ActionListener$5.onResponse(ActionListener.java:268)
	at org.opensearch.action.ActionListenerResponseHandler.handleResponse(ActionListenerResponseHandler.java:70)
	at org.opensearch.security.transport.SecurityInterceptor$RestoringTransportResponseHandler.handleResponse(SecurityInterceptor.java:441)
	at org.opensearch.transport.TransportService$ContextRestoreResponseHandler.handleResponse(TransportService.java:1517)
	at org.opensearch.transport.NativeMessageHandler.doHandleResponse(NativeMessageHandler.java:427)
	at org.opensearch.transport.NativeMessageHandler.handleResponse(NativeMessageHandler.java:419)
	at org.opensearch.transport.NativeMessageHandler.handleMessage(NativeMessageHandler.java:174)
	at org.opensearch.transport.NativeMessageHandler.messageReceived(NativeMessageHandler.java:126)
	at org.opensearch.transport.InboundHandler.messageReceivedFromPipeline(InboundHandler.java:120)
	at org.opensearch.transport.InboundHandler.inboundMessage(InboundHandler.java:112)
	at org.opensearch.transport.TcpTransport.inboundMessage(TcpTransport.java:768)
	at org.opensearch.transport.InboundBytesHandler.forwardFragments(InboundBytesHandler.java:137)
	at org.opensearch.transport.InboundBytesHandler.doHandleBytes(InboundBytesHandler.java:77)
	at org.opensearch.transport.InboundPipeline.doHandleBytes(InboundPipeline.java:124)
	at org.opensearch.transport.InboundPipeline.handleBytes(InboundPipeline.java:113)
	at org.opensearch.transport.netty4.Netty4MessageChannelHandler.channelRead(Netty4MessageChannelHandler.java:95)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:442)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:420)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:412)
	at io.netty.handler.logging.LoggingHandler.channelRead(LoggingHandler.java:280)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:442)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:420)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:412)
	at io.netty.handler.ssl.SslHandler.unwrap(SslHandler.java:1515)
	at io.netty.handler.ssl.SslHandler.decodeJdkCompatible(SslHandler.java:1378)
	at io.netty.handler.ssl.SslHandler.decode(SslHandler.java:1427)
	at io.netty.handler.codec.ByteToMessageDecoder.decodeRemovalReentryProtection(ByteToMessageDecoder.java:530)
	at io.netty.handler.codec.ByteToMessageDecoder.callDecode(ByteToMessageDecoder.java:469)
	at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:290)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:444)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:420)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:412)
	at io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1357)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:440)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:420)
	at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:868)
	at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:166)
	at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:796)
	at io.netty.channel.nio.NioEventLoop.processSelectedKeysPlain(NioEventLoop.java:697)
	at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:660)
	at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:562)
	at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:998)
	at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
	at __PATH__ (Thread.java:1583)
Caused by: java.lang.IllegalStateException: failed while calling model, check error log for details
	at org.opensearch.neuralsearch.ml.MLCommonsClientAccessor.buildVectorFromResponse(MLCommonsClientAccessor.java:251)
	at org.opensearch.neuralsearch.ml.MLCommonsClientAccessor.lambda$retryableInferenceSentencesWithVectorResult$4(MLCommonsClientAccessor.java:189)
	at org.opensearch.core.action.ActionListener$1.onResponse(ActionListener.java:82)
	... 51 more

```

(I was able to see this error on CloudWatch \> OpenSearch error log group)

As explained by an AI chat, the OpenAI API returns embeddings in this format:

```json
{
  "data": [
    {
      "embedding": [0.1, 0.2, 0.3, ...],
      "index": 0,
      "object": "embedding"
    }
  ],
  "object": "list"
}

```

The `text_embedding` processor expects the vector to be directly accessible in the response structure, but without post-processing, it was wrapped in `dataAsMap.data[0].embedding`, making it impossible for the processor to extract the vector array.

And it explained that adding the `post_process_function` parameter to the connector configuration would fix it.

“This built-in function transforms the OpenAI response format into the structure expected by the `text_embedding` processor, extracting the embedding array from `data[0].embedding` and making it directly accessible.”

So, now I can insert a doc and save its embedded vector:

> PUT /index-01/\_doc/1  
> {  
> “text”: “Hello world”,  
> “id”: “s2”  
> }  
> GET /index-01/\_doc/1
> 
> {  
> “\_index”: “index-01”,  
> “\_id”: “1”,  
> “\_version”: 1,  
> “\_seq\_no”: 0,  
> “\_primary\_term”: 1,  
> “found”: true,  
> “\_source”: {  
> “passage\_embedding”: [  
> -0.0020785425,  
> -0.049085874,  
> […]  
> -0.013985017,  
> -0.006101156  
> ],  
> “text”: “Hello world”,  
> “id”: “s2”  
> }  
> }

---

<div class="post-metadata">

**Author:** ![system](https://us1.discourse-cdn.com/flex019/uploads/mauve_hedgehog/original/2X/3/33547ea01a5b12dcca2958411d3edd97ae2ea8c1.png) [@system](https://forum.opensearch.org/u/system)\
**Post date:** [January 17, 2026, 6:45pm UTC](https://forum.opensearch.org/t/error-on-connecting-external-connector-and-embedding-model/27487/8 "2026-01-17T18:45:08Z")

</div>

Closed on January 17, 2026, 6:45pm UTC

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