Create an JinaAI inference endpoint | Elasticsearch API documentation (v8)

Create an JinaAI inference endpoint Generally available; Added in 8.18.0

PUT /_inference/{task_type}/{jinaai_inference_id}

Create an inference endpoint to perform an inference task with the jinaai service.

To review the available rerank models, refer to https://jina.ai/reranker. To review the available text_embedding models, refer to the https://jina.ai/embeddings/.

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform.

Values are rerank or text_embedding.
jinaai_inference_id string Required

The unique identifier of the inference endpoint.

application/json

Body

chunking_settings object

Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
- overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
- sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
- strategy string
  
  The chunking strategy: sentence or word.
  
  Default value is sentence.
service string Required

Value is jinaai.
service_settings object Required
Hide service_settings attributes Show service_settings attributes object
- api_key string Required
  
  A valid API key of your JinaAI account.
  
  IMPORTANT: You need to provide the API key only once, during the inference model creation. The get inference endpoint API does not retrieve your API key. After creating the inference model, you cannot change the associated API key. If you want to use a different API key, delete the inference model and recreate it with the same name and the updated API key.
  
  External documentation
- model_id string
  
  The name of the model to use for the inference task. For a rerank task, it is required. For a text_embedding task, it is optional.
- rate_limit object
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute.
- similarity string
  
  Values are cosine, dot_product, or l2_norm.
task_settings object
Hide task_settings attributes Show task_settings attributes object
- return_documents boolean
  
  For a rerank task, return the doc text within the results.
- task string
  
  Values are classification, clustering, ingest, or search.
- top_n number
  
  For a rerank task, the number of most relevant documents to return. It defaults to the number of the documents. If this inference 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.

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  strategy string
  
  The chunking strategy: sentence or word.
  
  Default value is sentence.
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Values are text_embedding or rerank.

PUT /_inference/{task_type}/{jinaai_inference_id}

PUT _inference/text_embedding/jinaai-embeddings { "service": "jinaai", "service_settings": { "model_id": "jina-embeddings-v3", "api_key": "JinaAi-Api-key" } }

resp = client.inference.put( task_type="text_embedding", inference_id="jinaai-embeddings", inference_config={ "service": "jinaai", "service_settings": { "model_id": "jina-embeddings-v3", "api_key": "JinaAi-Api-key" } }, )

const response = await client.inference.put({ task_type: "text_embedding", inference_id: "jinaai-embeddings", inference_config: { service: "jinaai", service_settings: { model_id: "jina-embeddings-v3", api_key: "JinaAi-Api-key", }, }, });

response = client.inference.put( task_type: "text_embedding", inference_id: "jinaai-embeddings", body: { "service": "jinaai", "service_settings": { "model_id": "jina-embeddings-v3", "api_key": "JinaAi-Api-key" } } )

$resp = $client->inference()->put([ "task_type" => "text_embedding", "inference_id" => "jinaai-embeddings", "body" => [ "service" => "jinaai", "service_settings" => [ "model_id" => "jina-embeddings-v3", "api_key" => "JinaAi-Api-key", ], ], ]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"jinaai","service_settings":{"model_id":"jina-embeddings-v3","api_key":"JinaAi-Api-key"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/jinaai-embeddings"

client.inference().put(p -> p .inferenceId("jinaai-embeddings") .taskType(TaskType.TextEmbedding) .inferenceConfig(i -> i .service("jinaai") .serviceSettings(JsonData.fromJson("{\"model_id\":\"jina-embeddings-v3\",\"api_key\":\"JinaAi-Api-key\"}")) ) );

Request examples

Run `PUT _inference/text_embedding/jinaai-embeddings` to create an inference endpoint for text embedding tasks using the JinaAI service.

{ "service": "jinaai", "service_settings": { "model_id": "jina-embeddings-v3", "api_key": "JinaAi-Api-key" } }

Run `PUT _inference/rerank/jinaai-rerank` to create an inference endpoint for rerank tasks using the JinaAI service.

{ "service": "jinaai", "service_settings": { "api_key": "JinaAI-Api-key", "model_id": "jina-reranker-v2-base-multilingual" }, "task_settings": { "top_n": 10, "return_documents": true } }