AuthThe Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats.AvailabilityGenerally available since 8.17This functionality is generally available and ready for production usage.Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments.This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.
Modify task_settings, secrets (within service_settings), or num_allocations for an inference endpoint, depending on the specific endpoint service and task_type.
Important
The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.
However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
timeoutstringThe Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats.AvailabilityGenerally available since 9.5This functionality is generally available and ready for production usage.Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments.This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.
Specifies the amount of time to wait for the inference endpoint to be updated.
The default depends on the task type: 120s for completion and chat_completion, and 30s for all other task types.
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_sizenumber · 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.
overlapnumber · 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_overlapnumber · 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_groupstring
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.
separatorsstring[]
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.
strategystring · 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.
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_sizenumber · 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.
overlapnumber · 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_overlapnumber · 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_groupstring
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.
separatorsstring[]
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.
strategystring · 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.