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Updates NLP overview to list the semantic_text workflow #2788

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Sep 10, 2024
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Updates NLP overview to list the semantic_text workflow #2788

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Updates NLP overview to list the semantic_text workflow.
szabosteve Sep 10, 2024
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Update docs/en/stack/ml/nlp/ml-nlp-overview.asciidoc
szabosteve Sep 10, 2024
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23 changes: 9 additions & 14 deletions docs/en/stack/ml/nlp/ml-nlp-overview.asciidoc
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Expand Up @@ -11,22 +11,17 @@ natural language in spoken word or written text.
Elastic offers a wide range of possibilities to leverage natural language
processing.

You can **integrate NLP models from different providers** such as Cohere,
HuggingFace, or OpenAI and use them as a service through the
{ref}/inference-apis.html[{infer} API]. You can also use <<ml-nlp-elser,ELSER>>
(the retrieval model trained by Elastic) and <<ml-nlp-e5,E5>> in the same way.
This {ref}/semantic-search-inference.html[tutorial] walks you through the
process of using the various services with the {infer} API.
You can **integrate NLP models from different providers** such as Cohere, HuggingFace, or OpenAI and use them as a service through the {ref}/semantic-search-semantic-text.html[semantic_text] workflow.
You can also use <<ml-nlp-elser,ELSER>> (the retrieval model trained by Elastic) and <<ml-nlp-e5,E5>> in the same way.

You can **upload and manage NLP models** using the Eland client and the
<<ml-nlp-deploy-models,{stack}>>. Find the
<<ml-nlp-model-ref,list of recommended and compatible models here>>. Refer to
<<ml-nlp-examples>> to learn more about how to use {ml} models deployed in your
cluster.
The {ref}/inference-apis.html[{infer} API] enables you to use the same services with a more complex workflow which - in turn - offers greater control over the configurations settings.
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This {ref}/semantic-search-inference.html[tutorial] walks you through the process of using the various services with the {infer} API.

You can **store embeddings in your {es} vector database** if you generate
{ref}/dense-vector.html[dense vector] or {ref}/sparse-vector.html[sparse vector]
model embeddings outside of {es}.
You can **upload and manage NLP models** using the Eland client and the <<ml-nlp-deploy-models,{stack}>>.
Find the <<ml-nlp-model-ref,list of recommended and compatible models here>>.
Refer to <<ml-nlp-examples>> to learn more about how to use {ml} models deployed in your cluster.

You can **store embeddings in your {es} vector database** if you generate {ref}/dense-vector.html[dense vector] or {ref}/sparse-vector.html[sparse vector] model embeddings outside of {es}.


[discrete]
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