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• chunking_settings object

  The chunking configuration object.

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  • 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 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.

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  • 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.

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  • 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

    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.

    Hide rate_limit attribute Show rate_limit attribute object

    • requests_per_minute number

      The number of requests allowed per minute.

  • similarity string

    For a 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.

• task_settings object

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

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  • return_documents boolean

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

  • task string

    For a text_embedding task, 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.

    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.