Body Required

• chunking_settings object

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

  External documentation
  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 lower than 20 (for sentence strategy) or 10 (for word strategy). This value should not exceed the window size for the associated model.

    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.

  • 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 array[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

    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.

    Default value is sentence.

    External documentation
• service string Required

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

  Value is jinaai.

• service_settings object Required

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

  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.

    External documentation
  • 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 documentation
    Hide rate_limit attribute Show rate_limit attribute object

    • 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
      • 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 string

    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 are cosine, dot_product, or 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 documentation
  • element_type string

    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 are binary, bit, or float.

  • multimodal_model boolean

    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.

    Default value is true.

• task_settings object

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

  Hide task_settings attributes Show task_settings attributes object

  • return_documents boolean

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

  • input_type string

    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 are classification, clustering, ingest, or 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.