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[docs] Add documentation for JinaAI service (#118782)
# Conflicts: # docs/reference/inference/put-inference.asciidoc
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‎docs/reference/inference/inference-apis.asciidoc‎

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include::service-google-ai-studio.asciidoc[]
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include::service-google-vertex-ai.asciidoc[]
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include::service-hugging-face.asciidoc[]
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include::service-jinaai.asciidoc[]
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include::service-mistral.asciidoc[]
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include::service-openai.asciidoc[]
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include::service-watsonx-ai.asciidoc[]

‎docs/reference/inference/put-inference.asciidoc‎

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* <<infer-service-mistral,Mistral>> (`text_embedding`)
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* <<infer-service-openai,OpenAI>> (`completion`, `text_embedding`)
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* <<infer-service-watsonx-ai>> (`text_embedding`)
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* <<infer-service-jinaai,JinaAI>> (`text_embedding`, `rerank`)
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The {es} and ELSER services run on a {ml} node in your {es} cluster. The rest of
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the services connect to external providers.

‎docs/reference/inference/service-jinaai.asciidoc‎

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[[infer-service-jinaai]]
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=== JinaAI {infer} service
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Creates an {infer} endpoint to perform an {infer} task with the `jinaai` service.
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[discrete]
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[[infer-service-jinaai-api-request]]
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==== {api-request-title}
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`PUT /_inference/<task_type>/<inference_id>`
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[discrete]
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[[infer-service-jinaai-api-path-params]]
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==== {api-path-parms-title}
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`<inference_id>`::
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(Required, string)
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include::inference-shared.asciidoc[tag=inference-id]
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`<task_type>`::
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(Required, string)
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include::inference-shared.asciidoc[tag=task-type]
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--
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Available task types:
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* `text_embedding`,
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* `rerank`.
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--
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[discrete]
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[[infer-service-jinaai-api-request-body]]
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==== {api-request-body-title}
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`chunking_settings`::
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(Optional, object)
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include::inference-shared.asciidoc[tag=chunking-settings]
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`max_chunking_size`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-max-chunking-size]
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`overlap`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-overlap]
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`sentence_overlap`:::
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(Optional, integer)
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include::inference-shared.asciidoc[tag=chunking-settings-sentence-overlap]
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`strategy`:::
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(Optional, string)
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include::inference-shared.asciidoc[tag=chunking-settings-strategy]
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`service`::
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(Required, string)
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The type of service supported for the specified task type. In this case,
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`jinaai`.
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`service_settings`::
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(Required, object)
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include::inference-shared.asciidoc[tag=service-settings]
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--
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These settings are specific to the `jinaai` service.
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--
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`api_key`:::
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(Required, string)
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A valid API key for your JinaAI account.
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You can find it at https://jina.ai/embeddings/.
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--
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include::inference-shared.asciidoc[tag=api-key-admonition]
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--
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`rate_limit`:::
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(Optional, object)
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The default rate limit for the `jinaai` service is 2000 requests per minute for all task types.
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You can modify this using the `requests_per_minute` setting in your service settings:
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--
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include::inference-shared.asciidoc[tag=request-per-minute-example]
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More information about JinaAI's rate limits can be found in https://jina.ai/contact-sales/#rate-limit.
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--
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.`service_settings` for the `rerank` task type
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[%collapsible%closed]
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=====
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`model_id`::
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(Required, string)
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The name of the model to use for the {infer} task.
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To review the available `rerank` compatible models, refer to https://jina.ai/reranker.
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=====
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.`service_settings` for the `text_embedding` task type
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[%collapsible%closed]
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=====
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`model_id`:::
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(Optional, string)
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The name of the model to use for the {infer} task.
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To review the available `text_embedding` models, refer to the
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https://jina.ai/embeddings/.
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`similarity`:::
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(Optional, string)
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Similarity measure. One of `cosine`, `dot_product`, `l2_norm`.
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Defaults based on the `embedding_type` (`float` -> `dot_product`, `int8/byte` -> `cosine`).
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=====
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`task_settings`::
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(Optional, object)
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include::inference-shared.asciidoc[tag=task-settings]
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.`task_settings` for the `rerank` task type
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[%collapsible%closed]
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=====
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`return_documents`::
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(Optional, boolean)
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Specify whether to return doc text within the results.
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`top_n`::
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(Optional, integer)
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The number of most relevant documents to return, defaults to the number of the documents.
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If this {infer} endpoint is used in a `text_similarity_reranker` retriever query and `top_n` is set, it must be greater than or equal to `rank_window_size` in the query.
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=====
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.`task_settings` for the `text_embedding` task type
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[%collapsible%closed]
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=====
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`task`:::
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(Optional, string)
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Specifies the task passed to the model.
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Valid values are:
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* `classification`: use it for embeddings passed through a text classifier.
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* `clustering`: use it for the embeddings run through a clustering algorithm.
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* `ingest`: use it for storing document embeddings in a vector database.
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* `search`: use it for storing embeddings of search queries run against a vector database to find relevant documents.
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=====
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[discrete]
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[[inference-example-jinaai]]
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==== JinaAI service examples
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The following examples demonstrate how to create {infer} endpoints for `text_embeddings` and `rerank` tasks using the JinaAI service and use them in search requests.
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First, we create the `embeddings` service:
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[source,console]
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------------------------------------------------------------
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PUT _inference/text_embedding/jinaai-embeddings
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{
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"service": "jinaai",
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"service_settings": {
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"model_id": "jina-embeddings-v3",
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"api_key": "<api_key>"
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}
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}
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------------------------------------------------------------
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// TEST[skip:uses ML]
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Then, we create the `rerank` service:
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[source,console]
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------------------------------------------------------------
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PUT _inference/rerank/jinaai-rerank
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{
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"service": "jinaai",
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"service_settings": {
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"api_key": "<api_key>",
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"model_id": "jina-reranker-v2-base-multilingual"
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},
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"task_settings": {
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"top_n": 10,
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"return_documents": true
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}
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}
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------------------------------------------------------------
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// TEST[skip:uses ML]
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Now we can create an index that will use `jinaai-embeddings` service to index the documents.
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[source,console]
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------------------------------------------------------------
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PUT jinaai-index
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{
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"mappings": {
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"properties": {
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"content": {
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"type": "semantic_text",
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"inference_id": "jinaai-embeddings"
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}
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}
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}
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}
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------------------------------------------------------------
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// TEST[skip:uses ML]
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[source,console]
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------------------------------------------------------------
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PUT jinaai-index/_bulk
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{ "index" : { "_index" : "jinaai-index", "_id" : "1" } }
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{"content": "Sarah Johnson is a talented marine biologist working at the Oceanographic Institute. Her groundbreaking research on coral reef ecosystems has garnered international attention and numerous accolades."}
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{ "index" : { "_index" : "jinaai-index", "_id" : "2" } }
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{"content": "She spends months at a time diving in remote locations, meticulously documenting the intricate relationships between various marine species. "}
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{ "index" : { "_index" : "jinaai-index", "_id" : "3" } }
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{"content": "Her dedication to preserving these delicate underwater environments has inspired a new generation of conservationists."}
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------------------------------------------------------------
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// TEST[skip:uses ML]
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Now, with the index created, we can search with and without the reranker service.
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[source,console]
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------------------------------------------------------------
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GET jinaai-index/_search
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{
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"query": {
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"semantic": {
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"field": "content",
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"query": "who inspired taking care of the sea?"
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}
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}
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}
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------------------------------------------------------------
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// TEST[skip:uses ML]
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[source,console]
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------------------------------------------------------------
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POST jinaai-index/_search
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{
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"retriever": {
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"text_similarity_reranker": {
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"retriever": {
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"standard": {
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"query": {
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"semantic": {
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"field": "content",
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"query": "who inspired taking care of the sea?"
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}
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}
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}
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},
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"field": "content",
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"rank_window_size": 100,
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"inference_id": "jinaai-rerank",
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"inference_text": "who inspired taking care of the sea?"
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}
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}
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}
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------------------------------------------------------------
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// TEST[skip:uses ML]

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