Create an JinaAI inference endpoint

Auth

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 embedding and text_embedding models, refer to https://jina.ai/embeddings/.

Required authorization

Cluster privileges manage_inference

Path parameters

task_type enum required

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

Values: embedding rerank text_embedding
jinaai_inference_id string required

The unique identifier of the inference endpoint.

Query parameters

timeout string · default: 30s

Specifies the amount of time to wait for the inference endpoint to be created.

Request body

chunking_settings object

The chunking configuration object. Applies only to the embedding and text_embeddingtask types. Not applicable to thererank` task type.

Reference
max_chunk_size number · default: 250

The maximum size of a chunk in words. This value cannot be lower than 20 (for sentence strategy) or 10 (for word strategy). This value should not exceed the window size for the associated model.

overlap number · default: 100

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.

sentence_overlap number · default: 1

The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

separator_group string

Only applicable to the recursive strategy and required when using it.

Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.

Using this parameter is an alternative to manually specifying a custom separators list.

separators string[]

Only applicable to the recursive strategy and required when using it.

A list of strings used as possible split points when chunking text.

Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.

After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.

strategy string · default: sentence

The chunking strategy: sentence, word, none or recursive.

  • If strategy is set to recursive, you must also specify:
  • max_chunk_size
  • either separators orseparator_group

Learn more about different chunking strategies in the linked documentation.

Reference
service enum required

The type of service supported for the specified task type. In this case, jinaai.

Values: jinaai
service_settings object required

Settings used to install the inference model. These settings are specific to the jinaai service.

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.

External docs
model_id string required

The name of the model to use for the inference task.

rate_limit object

This setting helps to minimize the number of rate limit errors returned from JinaAI. By default, the jinaai service sets the number of requests allowed per minute to 2000 for all task types.

External docs
requests_per_minute number

The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:

  • alibabacloud-ai-search service: 1000
  • amazonbedrock service: 240
  • anthropic service: 50
  • azureaistudio service: 240
  • azureopenai service and task type text_embedding: 1440
  • azureopenai service and task types completion or chat_completion: 120
  • cohere service: 10000
  • contextualai service: 1000
  • elastic service and task type chat_completion: 240
  • fireworksai service: 6000
  • googleaistudio service: 360
  • googlevertexai service: 30000
  • hugging_face service: 3000
  • jinaai service: 2000
  • llama service: 3000
  • mistral service: 240
  • openai service and task type text_embedding: 3000
  • openai service and task type completion: 500
  • openshift_ai service: 3000
  • voyageai service: 2000
  • watsonxai service: 120
similarity enum

For an embedding or text_embedding task, the similarity measure. One of cosine, dot_product, l2_norm. The default values varies with the embedding type. For example, a float embedding type uses a dot_product similarity measure by default.

Values: cosine dot_product l2_norm
dimensions number

For an embedding or text_embedding task, the number of dimensions the resulting output embeddings should have. By default, the model's standard output dimension is used. Refer to the Jina documentation for more information.

External docs
embedding_type enum · default: float

For an embedding or text_embedding task, the data type returned by the model. Use bit for binary embeddings, which are encoded as bytes with signed int8 precision. Use binary for binary embeddings, which are encoded as bytes with signed int8 precision (this is a synonym of bit). Use float for the default float embeddings.

Values: binary bit float
multimodal_model boolean · default: true

For the embedding task, whether the model supports multimodal inputs. If true, requests sent to the Jina model will use the multimodal request format (a list of objects). If false, requests sent to the model will use the same format as the text_embedding task (a list of strings). Setting this to false allows the embedding task to be used with models that do not support multimodal requests.

task_settings object

Settings to configure the inference task. These settings are specific to the task type you specified.

return_documents boolean

For a rerank task, return the doc text within the results.

input_type enum

For an embedding or text_embedding task, the task passed to the model. Valid values are:

  • classification: Use it for embeddings passed through a 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.
Values: classification clustering ingest search
late_chunking boolean

For an embedding or text_embedding task, controls when text is split into chunks. When set to true, a request from Elasticsearch contains only chunks related to a single document. Instead of batching chunks across documents, Elasticsearch sends them in separate requests. This ensures that chunk embeddings retain context from the entire document, improving semantic quality.

If a document exceeds the model's context limits, or if the document contains non-text inputs (relevant when using the multimodal embedding task), late chunking is automatically disabled for that document only and standard chunking is used instead.

If not specified, defaults to false.

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.

Response

application/json
chunking_settings object

The chunking configuration object. Applies only to the embedding, sparse_embedding and text_embedding task types. Not applicable to the rerank, completion, or chat_completion task types.

max_chunk_size number · default: 250

The maximum size of a chunk in words. This value cannot be lower than 20 (for sentence strategy) or 10 (for word strategy). This value should not exceed the window size for the associated model.

overlap number · default: 100

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.

sentence_overlap number · default: 1

The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

separator_group string

Only applicable to the recursive strategy and required when using it.

Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.

Using this parameter is an alternative to manually specifying a custom separators list.

separators string[]

Only applicable to the recursive strategy and required when using it.

A list of strings used as possible split points when chunking text.

Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.

After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.

strategy string · default: sentence

The chunking strategy: sentence, word, none or recursive.

  • If strategy is set to recursive, you must also specify:
  • max_chunk_size
  • either separators orseparator_group

Learn more about different chunking strategies in the linked documentation.

Reference
service string required

The service type

service_settings object required

Settings specific to the service

task_settings object

Task settings specific to the service and task type

inference_id string required

The inference Id

task_type enum required

The task type

Values: embedding text_embedding rerank