Failed to parse trained model vocabulary hit getId – How to solve this Elasticsearch error

Opster Team

Aug-23, Version: 8.3-8.9

Before you dig into reading this guide, have you tried asking OpsGPT what this log means? You’ll receive a customized analysis of your log.

Try OpsGPT now for step-by-step guidance and tailored insights into your Elasticsearch operation.

Briefly, this error occurs when Elasticsearch is unable to parse the vocabulary of a trained machine learning model. This could be due to a corrupted or incompatible model, or issues with the model’s vocabulary. To resolve this, you can try retraining your model with the correct parameters, ensure the model’s vocabulary is compatible with your Elasticsearch version, or check for any corruption in the model’s files. If the error persists, consider using a different model or updating your Elasticsearch version.

For a complete solution to your to your search operation, try for free AutoOps for Elasticsearch & OpenSearch . With AutoOps and Opster’s proactive support, you don’t have to worry about your search operation – we take charge of it. Get improved performance & stability with less hardware.

This guide will help you check for common problems that cause the log ” failed to parse trained model vocabulary [” + hit.getId() + “] ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: plugin.

Log Context

Log “failed to parse trained model vocabulary [” + hit.getId() + “]” classname is
We extracted the following from Elasticsearch source code for those seeking an in-depth context :

        ) {
            return Vocabulary.PARSER.apply(parser; null);
        } catch (IOException e) {
            logger.error(() -> "failed to parse trained model vocabulary [" + hit.getId() + "]"; e);
            throw e;

    public void stopDeployment(TrainedModelDeploymentTask task) {


How helpful was this guide?

We are sorry that this post was not useful for you!

Let us improve this post!

Tell us how we can improve this post?