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The terms that the text is expanded into by the model _are not_ synonyms for the search terms; they are learned associations. These expanded terms are weighted as some of them are more significant than others. Then the {es} -{ref}/rank-features.html[sparse vector (or rank features) field type] is used to -store the terms and weights at index time, and to search against later. +{ref}/sparse-vector.html[sparse vector] +(or {ref}/rank-features.html[rank features]) field type is used to store the +terms and weights at index time, and to search against later. [discrete] @@ -82,7 +83,7 @@ how to reindex your data through the pipeline. == Download and deploy ELSER You can download and deploy ELSER either from **{ml-app}** > **Trained Models**, -from **{ents}** > **Indices**, or by using the Dev Console. +from **Search** > **Indices**, or by using the Dev Console. [discrete] [[trained-model]] @@ -113,22 +114,65 @@ image::images/ml-nlp-deployment-id-elser-v2.png[alt="Deploying ELSER",align="cen [discrete] -[[enterprise-search]] -=== Using the Indices page in {ents} +[[elasticsearch]] +=== Using the search indices UI -You can also download and deploy ELSER to an {infer} pipeline directly from the -{ents} app. +Alternatively, you can download and deploy ELSER to an {infer} pipeline using +the search indices UI. -1. In {kib}, navigate to **{ents}** > **Indices**. +1. In {kib}, navigate to **Search** > **Indices**. 2. Select the index from the list that has an {infer} pipeline in which you want to use ELSER. 3. Navigate to the **Pipelines** tab. 4. Under **{ml-app} {infer-cap} Pipelines**, click the **Deploy** button to begin downloading the ELSER model. This may take a few minutes depending on your -network. Once it's downloaded, click the **Start single-threaded** button to +network. ++ +-- +[role="screenshot"] +image::images/ml-nlp-deploy-elser-v2-es.png[alt="Deploying ELSER in Elasticsearch",align="center"] +-- +5. Once the model is downloaded, click the **Start single-threaded** button to start the model with basic configuration or select the **Fine-tune performance** option to navigate to the **Trained Models** page where you can configure the model deployment. ++ +-- +[role="screenshot"] +image::images/ml-nlp-start-elser-v2-es.png[alt="Start ELSER in Elasticsearch",align="center"] +-- + +When your ELSER model is deployed and started, it is ready to be used in a +pipeline. + + +[discrete] +[[elasticsearch-ingest-pipeline]] +==== Adding ELSER to an ingest pipeline + +To add ELSER to an ingest pipeline, you need to copy the default ingest +pipeline and then customize it according to your needs. + +1. Click **Copy and customize** under the **Unlock your custom pipelines** block +at the top of the page. This enables the **Add inference pipeline** button. ++ +-- +[role="screenshot"] +image::images/ml-nlp-pipeline-copy-customize.png[alt="Start ELSER in Elasticsearch",align="center"] +-- +2. Under **{ml-app} {infer-cap} Pipelines**, click **Add inference pipeline**. +3. Give a name to the pipeline, select ELSER from the list of trained ML models, +and click **Continue**. +4. Select the source text field, define the target field, and click **Add** then +**Continue**. +5. Review the index mappings updates. Click **Back** if you want to change the +mappings. Click **Continue** if you are satisfied with the updated index +mappings. +6. You can optionally test your pipeline. Click **Continue**. +7. **Create pipeline**. + +Once your pipeline is created, you are ready to ingest documents and utilize +ELSER for text expansions in your search queries. [discrete] @@ -295,6 +339,23 @@ clicking **Reload examples**. image::images/ml-nlp-elser-v2-test.png[alt="Testing ELSER",align="center"] +[discrete] +[[performance]] +== Performance considerations + +* ELSER works best on small-to-medium sized fields that contain natural +language. For connector or web crawler use cases, this aligns best with fields +like _title_, _description_, _summary_, or _abstract_. As ELSER encodes the +first 512 tokens of a field, it may not be as good a match for `body_content` on +web crawler documents, or body fields resulting from extracting text from office +documents with connectors. +* Larger documents take longer at ingestion time, and {infer} time per +document also increases the more fields in a document that need to be processed. +* The more fields your pipeline has to perform inference on, the longer it takes +per document to ingest. + +To learn more about ELSER performance, refer to the <>. + [discrete] [[further-readings]] == Further reading diff --git a/docs/en/stack/ml/nlp/ml-nlp-limitations.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-limitations.asciidoc index 1ae0856e9..db9f55680 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-limitations.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-limitations.asciidoc @@ -11,10 +11,10 @@ the Elastic {nlp} trained models feature. [discrete] [[ml-nlp-elser-v1-limit-512]] -== ELSER v1 semantic search is limited to 512 tokens per field that inference is applied to +== ELSER semantic search is limited to 512 tokens per field that inference is applied to -When you use ELSER v1 for semantic search, only the first 512 extracted tokens -from each field of the ingested documents that ELSER is applied to are taken -into account for the search process. If your data set contains long documents, -divide them into smaller segments before ingestion if you need the full text to -be searchable. +When you use ELSER for semantic search, only the first 512 extracted tokens from +each field of the ingested documents that ELSER is applied to are taken into +account for the search process. If your data set contains long documents, divide +them into smaller segments before ingestion if you need the full text to be +searchable.