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[DOCS] Changes the cohere example to use a different model #106037

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Mar 6, 2024
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[DOCS] Changes the cohere example to use a different model #106037

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855cac7
[DOCS] Changes the cohere example to use a different model.
szabosteve Mar 6, 2024
ea04c9b
[DOCS] Changes search query text for cohere example.
szabosteve Mar 6, 2024
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6 changes: 3 additions & 3 deletions docs/reference/search/search-your-data/semantic-search-inference.asciidoc
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Expand Up @@ -6,9 +6,9 @@

The instructions in this tutorial shows you how to use the {infer} API with
various services to perform semantic search on your data. The following examples
use Cohere's `embed-english-light-v3.0` model and OpenAI's
`text-embedding-ada-002` second generation embedding model. You can use any
Cohere and OpenAI models, they are all supported by the {infer} API.
use Cohere's `embed-english-v3.0` model and OpenAI's `text-embedding-ada-002`
second generation embedding model. You can use any Cohere and OpenAI models,
they are all supported by the {infer} API.

Click the name of the service you want to use on any of the widgets below to
review the corresponding instructions.
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2 changes: 1 addition & 1 deletion docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc
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Expand Up @@ -8,7 +8,7 @@ PUT cohere-embeddings
"properties": {
"content_embedding": { <1>
"type": "dense_vector", <2>
"dims": 384, <3>
"dims": 1024, <3>
"element_type": "float"
},
"content": { <4>
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2 changes: 1 addition & 1 deletion docs/reference/tab-widgets/inference-api/infer-api-search.asciidoc
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Expand Up @@ -9,7 +9,7 @@ GET cohere-embeddings/_search
"query_vector_builder": {
"text_embedding": {
"model_id": "cohere_embeddings",
"model_text": "Calculate fuel cost"
"model_text": "Muscles in human body"
}
},
"k": 10,
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2 changes: 1 addition & 1 deletion docs/reference/tab-widgets/inference-api/infer-api-task.asciidoc
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Expand Up @@ -7,7 +7,7 @@ PUT _inference/text_embedding/cohere_embeddings <1>
"service": "cohere",
"service_settings": {
"api_key": "<api_key>", <2>
"model_id": "embed-english-light-v3.0", <3>
"model_id": "embed-english-v3.0", <3>
"embedding_type": "int8"
},
"task_settings": {
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