[[infer-service-jinaai]]
=== JinaAI {infer} integration

Creates an {infer} endpoint to perform an {infer} task with the `jinaai` service.


[discrete]
[[infer-service-jinaai-api-request]]
==== {api-request-title}

`PUT /_inference/<task_type>/<inference_id>`

[discrete]
[[infer-service-jinaai-api-path-params]]
==== {api-path-parms-title}

`<inference_id>`::
(Required, string)
include::inference-shared.asciidoc[tag=inference-id]

`<task_type>`::
(Required, string)
include::inference-shared.asciidoc[tag=task-type]
+
--
Available task types:

* `text_embedding`,
* `rerank`.
--

[discrete]
[[infer-service-jinaai-api-request-body]]
==== {api-request-body-title}

`chunking_settings`::
(Optional, object)
include::inference-shared.asciidoc[tag=chunking-settings]

`max_chunk_size`:::
(Optional, integer)
include::inference-shared.asciidoc[tag=chunking-settings-max-chunking-size]

`overlap`:::
(Optional, integer)
include::inference-shared.asciidoc[tag=chunking-settings-overlap]

`sentence_overlap`:::
(Optional, integer)
include::inference-shared.asciidoc[tag=chunking-settings-sentence-overlap]

`strategy`:::
(Optional, string)
include::inference-shared.asciidoc[tag=chunking-settings-strategy]

`service`::
(Required, string)
The type of service supported for the specified task type. In this case, 
`jinaai`.

`service_settings`::
(Required, object)
include::inference-shared.asciidoc[tag=service-settings]
+
--
These settings are specific to the `jinaai` service.
--

`api_key`:::
(Required, string)
A valid API key for your JinaAI account.
You can find it at https://jina.ai/embeddings/.
+
--
include::inference-shared.asciidoc[tag=api-key-admonition]
--

`rate_limit`:::
(Optional, object)
The default rate limit for the `jinaai` service is 2000 requests per minute for all task types. 
You can modify this using the `requests_per_minute` setting in your service settings:
+
--
include::inference-shared.asciidoc[tag=request-per-minute-example]

More information about JinaAI's rate limits can be found in https://jina.ai/contact-sales/#rate-limit.
--
+
.`service_settings` for the `rerank` task type
[%collapsible%closed]
=====
`model_id`::
(Required, string)
The name of the model to use for the {infer} task.
To review the available `rerank` compatible models, refer to https://jina.ai/reranker.
=====
+
.`service_settings` for the `text_embedding` task type
[%collapsible%closed]
=====
`model_id`:::
(Optional, string)
The name of the model to use for the {infer} task.
To review the available `text_embedding` models, refer to the
https://jina.ai/embeddings/.

`similarity`:::
(Optional, string)
Similarity measure. One of `cosine`, `dot_product`, `l2_norm`.
Defaults based on the `embedding_type` (`float` -> `dot_product`, `int8/byte` -> `cosine`).
=====



`task_settings`::
(Optional, object)
include::inference-shared.asciidoc[tag=task-settings]
+
.`task_settings` for the `rerank` task type
[%collapsible%closed]
=====
`return_documents`::
(Optional, boolean)
Specify whether to return doc text within the results.

`top_n`::
(Optional, integer)
The number of most relevant documents to return, defaults to the number of the documents.
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.
=====
+
.`task_settings` for the `text_embedding` task type
[%collapsible%closed]
=====
`task`:::
(Optional, string)
Specifies the task passed to the model.
Valid values are:
* `classification`: use it for embeddings passed through a text classifier.
* `clustering`: use it for the embeddings run through a clustering algorithm.
* `ingest`: use it for storing document embeddings in a vector database.
* `search`: use it for storing embeddings of search queries run against a vector database to find relevant documents.
=====


[discrete]
[[inference-example-jinaai]]
==== JinaAI service examples

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.

First, we create the `embeddings` service:

[source,console]
------------------------------------------------------------
PUT _inference/text_embedding/jinaai-embeddings
{
    "service": "jinaai",
    "service_settings": {
        "model_id": "jina-embeddings-v3",
        "api_key": "<api_key>"
    }
}
------------------------------------------------------------
// TEST[skip:uses ML]

Then, we create the `rerank` service:
[source,console]
------------------------------------------------------------
PUT _inference/rerank/jinaai-rerank
{
    "service": "jinaai",
    "service_settings": {
        "api_key": "<api_key>",
        "model_id": "jina-reranker-v2-base-multilingual"
    },
    "task_settings": {
        "top_n": 10,
        "return_documents": true
    }
}
------------------------------------------------------------
// TEST[skip:uses ML]

Now we can create an index that will use `jinaai-embeddings` service to index the documents.

[source,console]
------------------------------------------------------------
PUT jinaai-index
{
  "mappings": {
    "properties": {
      "content": {
        "type": "semantic_text",
        "inference_id": "jinaai-embeddings"
      }
    }
  }
}
------------------------------------------------------------
// TEST[skip:uses ML]

[source,console]
------------------------------------------------------------
PUT jinaai-index/_bulk
{ "index" : { "_index" : "jinaai-index", "_id" : "1" } }
{"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."}
{ "index" : { "_index" : "jinaai-index", "_id" : "2" } }
{"content": "She spends months at a time diving in remote locations, meticulously documenting the intricate relationships between various marine species. "}
{ "index" : { "_index" : "jinaai-index", "_id" : "3" } }
{"content": "Her dedication to preserving these delicate underwater environments has inspired a new generation of conservationists."}
------------------------------------------------------------
// TEST[skip:uses ML]

Now, with the index created, we can search with and without the reranker service.

[source,console]
------------------------------------------------------------
GET jinaai-index/_search 
{
  "query": {
    "semantic": {
      "field": "content",
      "query": "who inspired taking care of the sea?"
    }
  }
}
------------------------------------------------------------
// TEST[skip:uses ML]

[source,console]
------------------------------------------------------------
POST jinaai-index/_search
{
  "retriever": {
    "text_similarity_reranker": {
      "retriever": {
        "standard": {
          "query": {
            "semantic": {
              "field": "content",
              "query": "who inspired taking care of the sea?"
            }
          }
        }
      },
      "field": "content",
      "rank_window_size": 100,
      "inference_id": "jinaai-rerank",
      "inference_text": "who inspired taking care of the sea?"
    }
  }
}
------------------------------------------------------------
// TEST[skip:uses ML]