Auto-generated API code (#2691)f
This commit is contained in:
Elastic Machine
@ -503,7 +503,7 @@ client.deleteByQuery({ index })
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** *`default_operator` (Optional, Enum("and" | "or"))*: The default operator for query string query: `AND` or `OR`. This parameter can be used only when the `q` query string parameter is specified.
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** *`df` (Optional, string)*: The field to use as default where no field prefix is given in the query string. This parameter can be used only when the `q` query string parameter is specified.
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** *`expand_wildcards` (Optional, Enum("all" | "open" | "closed" | "hidden" | "none") | Enum("all" | "open" | "closed" | "hidden" | "none")[])*: The type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. It supports a list of values, such as `open,hidden`.
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** *`from` (Optional, number)*: Starting offset (default: 0)
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** *`from` (Optional, number)*: Skips the specified number of documents.
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** *`ignore_unavailable` (Optional, boolean)*: If `false`, the request returns an error if it targets a missing or closed index.
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** *`lenient` (Optional, boolean)*: If `true`, format-based query failures (such as providing text to a numeric field) in the query string will be ignored. This parameter can be used only when the `q` query string parameter is specified.
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** *`preference` (Optional, string)*: The node or shard the operation should be performed on. It is random by default.
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@ -1318,6 +1318,7 @@ client.openPointInTime({ index, keep_alive })
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** *`routing` (Optional, string)*: A custom value that is used to route operations to a specific shard.
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** *`expand_wildcards` (Optional, Enum("all" | "open" | "closed" | "hidden" | "none") | Enum("all" | "open" | "closed" | "hidden" | "none")[])*: The type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. It supports a list of values, such as `open,hidden`. Valid values are: `all`, `open`, `closed`, `hidden`, `none`.
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** *`allow_partial_search_results` (Optional, boolean)*: Indicates whether the point in time tolerates unavailable shards or shard failures when initially creating the PIT. If `false`, creating a point in time request when a shard is missing or unavailable will throw an exception. If `true`, the point in time will contain all the shards that are available at the time of the request.
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** *`max_concurrent_shard_requests` (Optional, number)*: Maximum number of concurrent shard requests that each sub-search request executes per node.
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[discrete]
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=== ping
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@ -2268,7 +2269,7 @@ client.updateByQuery({ index })
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** *`default_operator` (Optional, Enum("and" | "or"))*: The default operator for query string query: `AND` or `OR`. This parameter can be used only when the `q` query string parameter is specified.
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** *`df` (Optional, string)*: The field to use as default where no field prefix is given in the query string. This parameter can be used only when the `q` query string parameter is specified.
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** *`expand_wildcards` (Optional, Enum("all" | "open" | "closed" | "hidden" | "none") | Enum("all" | "open" | "closed" | "hidden" | "none")[])*: The type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. It supports a list of values, such as `open,hidden`. Valid values are: `all`, `open`, `closed`, `hidden`, `none`.
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** *`from` (Optional, number)*: Starting offset (default: 0)
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** *`from` (Optional, number)*: Skips the specified number of documents.
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** *`ignore_unavailable` (Optional, boolean)*: If `false`, the request returns an error if it targets a missing or closed index.
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** *`lenient` (Optional, boolean)*: If `true`, format-based query failures (such as providing text to a numeric field) in the query string will be ignored. This parameter can be used only when the `q` query string parameter is specified.
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** *`pipeline` (Optional, string)*: The ID of the pipeline to use to preprocess incoming documents. If the index has a default ingest pipeline specified, then setting the value to `_none` disables the default ingest pipeline for this request. If a final pipeline is configured it will always run, regardless of the value of this parameter.
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@ -7157,7 +7158,7 @@ a new date field is added instead of string.
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not used at all by Elasticsearch, but can be used to store
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application-specific metadata.
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** *`numeric_detection` (Optional, boolean)*: Automatically map strings into numeric data types for all fields.
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** *`properties` (Optional, Record<string, { type } | { boost, fielddata, index, null_value, type } | { type, enabled, null_value, boost, coerce, script, on_script_error, ignore_malformed, time_series_metric, analyzer, eager_global_ordinals, index, index_options, index_phrases, index_prefixes, norms, position_increment_gap, search_analyzer, search_quote_analyzer, term_vector, format, precision_step, locale } | { relations, eager_global_ordinals, type } | { boost, eager_global_ordinals, index, index_options, script, on_script_error, normalizer, norms, null_value, similarity, split_queries_on_whitespace, time_series_dimension, type } | { type, fields, meta, copy_to } | { type } | { positive_score_impact, type } | { positive_score_impact, type } | { analyzer, index, index_options, max_shingle_size, norms, search_analyzer, search_quote_analyzer, similarity, term_vector, type } | { analyzer, boost, eager_global_ordinals, fielddata, fielddata_frequency_filter, index, index_options, index_phrases, index_prefixes, norms, position_increment_gap, search_analyzer, search_quote_analyzer, similarity, term_vector, type } | { type } | { type, null_value } | { boost, format, ignore_malformed, index, script, on_script_error, null_value, precision_step, type } | { boost, fielddata, format, ignore_malformed, index, script, on_script_error, null_value, precision_step, locale, type } | { type, default_metric, metrics, time_series_metric } | { type, dims, element_type, index, index_options, similarity } | { boost, depth_limit, doc_values, eager_global_ordinals, index, index_options, null_value, similarity, split_queries_on_whitespace, type } | { enabled, include_in_parent, include_in_root, type } | { enabled, subobjects, type } | { type, enabled, priority, time_series_dimension } | { type, meta, inference_id, search_inference_id } | { type } | { analyzer, contexts, max_input_length, preserve_position_increments, preserve_separators, search_analyzer, type } | { value, type } | { type, index } | { path, type } | { ignore_malformed, type } | { boost, index, ignore_malformed, null_value, on_script_error, script, time_series_dimension, type } | { type } | { analyzer, boost, index, null_value, enable_position_increments, type } | { ignore_malformed, ignore_z_value, null_value, index, on_script_error, script, type } | { coerce, ignore_malformed, ignore_z_value, index, orientation, strategy, type } | { ignore_malformed, ignore_z_value, null_value, type } | { coerce, ignore_malformed, ignore_z_value, orientation, type } | { type, null_value } | { type, null_value } | { type, null_value } | { type, null_value } | { type, null_value } | { type, null_value } | { type, null_value, scaling_factor } | { type, null_value } | { type, null_value } | { format, type } | { type } | { type } | { type } | { type } | { type } | { type, norms, index_options, index, null_value, rules, language, country, variant, strength, decomposition, alternate, case_level, case_first, numeric, variable_top, hiragana_quaternary_mode }>)*: Mapping for a field. For new fields, this mapping can include:
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** *`properties` (Optional, Record<string, { type } | { boost, fielddata, index, null_value, ignore_malformed, script, on_script_error, time_series_dimension, type } | { type, enabled, null_value, boost, coerce, script, on_script_error, ignore_malformed, time_series_metric, analyzer, eager_global_ordinals, index, index_options, index_phrases, index_prefixes, norms, position_increment_gap, search_analyzer, search_quote_analyzer, term_vector, format, precision_step, locale } | { relations, eager_global_ordinals, type } | { boost, eager_global_ordinals, index, index_options, script, on_script_error, normalizer, norms, null_value, similarity, split_queries_on_whitespace, time_series_dimension, type } | { type, fields, meta, copy_to } | { type } | { positive_score_impact, type } | { positive_score_impact, type } | { analyzer, index, index_options, max_shingle_size, norms, search_analyzer, search_quote_analyzer, similarity, term_vector, type } | { analyzer, boost, eager_global_ordinals, fielddata, fielddata_frequency_filter, index, index_options, index_phrases, index_prefixes, norms, position_increment_gap, search_analyzer, search_quote_analyzer, similarity, term_vector, type } | { type } | { type, null_value } | { boost, format, ignore_malformed, index, script, on_script_error, null_value, precision_step, type } | { boost, fielddata, format, ignore_malformed, index, script, on_script_error, null_value, precision_step, locale, type } | { type, default_metric, metrics, time_series_metric } | { type, dims, element_type, index, index_options, similarity } | { boost, depth_limit, doc_values, eager_global_ordinals, index, index_options, null_value, similarity, split_queries_on_whitespace, type } | { enabled, include_in_parent, include_in_root, type } | { enabled, subobjects, type } | { type, enabled, priority, time_series_dimension } | { type, meta, inference_id, search_inference_id } | { type } | { analyzer, contexts, max_input_length, preserve_position_increments, preserve_separators, search_analyzer, type } | { value, type } | { type, index } | { path, type } | { ignore_malformed, type } | { boost, index, ignore_malformed, null_value, on_script_error, script, time_series_dimension, type } | { type } | { analyzer, boost, index, null_value, enable_position_increments, type } | { ignore_malformed, ignore_z_value, null_value, index, on_script_error, script, type } | { coerce, ignore_malformed, ignore_z_value, index, orientation, strategy, type } | { ignore_malformed, ignore_z_value, null_value, type } | { coerce, ignore_malformed, ignore_z_value, orientation, type } | { type, null_value } | { type, null_value } | { type, null_value } | { type, null_value } | { type, null_value } | { type, null_value } | { type, null_value, scaling_factor } | { type, null_value } | { type, null_value } | { format, type } | { type } | { type } | { type } | { type } | { type } | { type, norms, index_options, index, null_value, rules, language, country, variant, strength, decomposition, alternate, case_level, case_first, numeric, variable_top, hiragana_quaternary_mode }>)*: Mapping for a field. For new fields, this mapping can include:
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- Field name
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- Field data type
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@ -7970,7 +7971,7 @@ Perform chat completion inference
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{ref}/chat-completion-inference-api.html[Endpoint documentation]
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[source,ts]
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----
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client.inference.chatCompletionUnified({ inference_id, messages })
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client.inference.chatCompletionUnified({ inference_id })
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----
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[discrete]
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@ -7978,14 +7979,7 @@ client.inference.chatCompletionUnified({ inference_id, messages })
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* *Request (object):*
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** *`inference_id` (string)*: The inference Id
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** *`messages` ({ content, role, tool_call_id, tool_calls }[])*: A list of objects representing the conversation.
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** *`model` (Optional, string)*: The ID of the model to use.
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** *`max_completion_tokens` (Optional, number)*: The upper bound limit for the number of tokens that can be generated for a completion request.
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** *`stop` (Optional, string[])*: A sequence of strings to control when the model should stop generating additional tokens.
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** *`temperature` (Optional, float)*: The sampling temperature to use.
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** *`tool_choice` (Optional, string | { type, function })*: Controls which tool is called by the model.
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** *`tools` (Optional, { type, function }[])*: A list of tools that the model can call.
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** *`top_p` (Optional, float)*: Nucleus sampling, an alternative to sampling with temperature.
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** *`chat_completion_request` (Optional, { messages, model, max_completion_tokens, stop, temperature, tool_choice, tools, top_p })*
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** *`timeout` (Optional, string | -1 | 0)*: Specifies the amount of time to wait for the inference request to complete.
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[discrete]
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@ -8044,6 +8038,25 @@ client.inference.get({ ... })
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** *`task_type` (Optional, Enum("sparse_embedding" | "text_embedding" | "rerank" | "completion" | "chat_completion"))*: The task type
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** *`inference_id` (Optional, string)*: The inference Id
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[discrete]
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==== post_eis_chat_completion
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Perform a chat completion task through the Elastic Inference Service (EIS).
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Perform a chat completion inference task with the `elastic` service.
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{ref}/post-inference-api.html[Endpoint documentation]
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[source,ts]
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----
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client.inference.postEisChatCompletion({ eis_inference_id })
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----
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[discrete]
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==== Arguments
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* *Request (object):*
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** *`eis_inference_id` (string)*: The unique identifier of the inference endpoint.
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** *`chat_completion_request` (Optional, { messages, model, max_completion_tokens, stop, temperature, tool_choice, tools, top_p })*
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[discrete]
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==== put
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Create an inference endpoint.
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@ -8071,6 +8084,199 @@ client.inference.put({ inference_id })
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** *`task_type` (Optional, Enum("sparse_embedding" | "text_embedding" | "rerank" | "completion" | "chat_completion"))*: The task type
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** *`inference_config` (Optional, { chunking_settings, service, service_settings, task_settings })*
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[discrete]
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==== put_alibabacloud
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Create an AlibabaCloud AI Search inference endpoint.
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Create an inference endpoint to perform an inference task with the `alibabacloud-ai-search` service.
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When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
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After creating the endpoint, wait for the model deployment to complete before using it.
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To verify the deployment status, use the get trained model statistics API.
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Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
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Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
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{ref}/infer-service-alibabacloud-ai-search.html[Endpoint documentation]
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[source,ts]
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----
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client.inference.putAlibabacloud({ task_type, alibabacloud_inference_id, service, service_settings })
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----
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[discrete]
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==== Arguments
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* *Request (object):*
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** *`task_type` (Enum("completion" | "rerank" | "space_embedding" | "text_embedding"))*: The type of the inference task that the model will perform.
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** *`alibabacloud_inference_id` (string)*: The unique identifier of the inference endpoint.
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** *`service` (Enum("alibabacloud-ai-search"))*: The type of service supported for the specified task type. In this case, `alibabacloud-ai-search`.
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** *`service_settings` ({ api_key, host, rate_limit, service_id, workspace })*: Settings used to install the inference model. These settings are specific to the `alibabacloud-ai-search` service.
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** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
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** *`task_settings` (Optional, { input_type, return_token })*: Settings to configure the inference task.
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These settings are specific to the task type you specified.
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[discrete]
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==== put_amazonbedrock
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Create an Amazon Bedrock inference endpoint.
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Creates an inference endpoint to perform an inference task with the `amazonbedrock` service.
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>info
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> You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys.
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When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
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After creating the endpoint, wait for the model deployment to complete before using it.
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To verify the deployment status, use the get trained model statistics API.
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Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
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Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
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{ref}/infer-service-amazon-bedrock.html[Endpoint documentation]
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[source,ts]
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----
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client.inference.putAmazonbedrock({ task_type, amazonbedrock_inference_id, service, service_settings })
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----
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[discrete]
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==== Arguments
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* *Request (object):*
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** *`task_type` (Enum("completion" | "text_embedding"))*: The type of the inference task that the model will perform.
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** *`amazonbedrock_inference_id` (string)*: The unique identifier of the inference endpoint.
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** *`service` (Enum("amazonbedrock"))*: The type of service supported for the specified task type. In this case, `amazonbedrock`.
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** *`service_settings` ({ access_key, model, provider, region, rate_limit, secret_key })*: Settings used to install the inference model. These settings are specific to the `amazonbedrock` service.
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** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
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** *`task_settings` (Optional, { max_new_tokens, temperature, top_k, top_p })*: Settings to configure the inference task.
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These settings are specific to the task type you specified.
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[discrete]
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==== put_anthropic
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Create an Anthropic inference endpoint.
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Create an inference endpoint to perform an inference task with the `anthropic` service.
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When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
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After creating the endpoint, wait for the model deployment to complete before using it.
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To verify the deployment status, use the get trained model statistics API.
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Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
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Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
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{ref}/infer-service-anthropic.html[Endpoint documentation]
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[source,ts]
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----
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client.inference.putAnthropic({ task_type, anthropic_inference_id, service, service_settings })
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----
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[discrete]
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==== Arguments
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* *Request (object):*
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** *`task_type` (Enum("completion"))*: The task type.
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The only valid task type for the model to perform is `completion`.
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** *`anthropic_inference_id` (string)*: The unique identifier of the inference endpoint.
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** *`service` (Enum("anthropic"))*: The type of service supported for the specified task type. In this case, `anthropic`.
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** *`service_settings` ({ api_key, model_id, rate_limit })*: Settings used to install the inference model. These settings are specific to the `watsonxai` service.
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** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
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** *`task_settings` (Optional, { max_tokens, temperature, top_k, top_p })*: Settings to configure the inference task.
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These settings are specific to the task type you specified.
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[discrete]
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==== put_azureaistudio
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Create an Azure AI studio inference endpoint.
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Create an inference endpoint to perform an inference task with the `azureaistudio` service.
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When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
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{ref}/infer-service-azure-ai-studio.html[Endpoint documentation]
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[source,ts]
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----
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client.inference.putAzureaistudio({ task_type, azureaistudio_inference_id, service, service_settings })
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----
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[discrete]
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==== Arguments
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* *Request (object):*
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** *`task_type` (Enum("completion" | "text_embedding"))*: The type of the inference task that the model will perform.
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** *`azureaistudio_inference_id` (string)*: The unique identifier of the inference endpoint.
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** *`service` (Enum("azureaistudio"))*: The type of service supported for the specified task type. In this case, `azureaistudio`.
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** *`service_settings` ({ api_key, endpoint_type, target, provider, rate_limit })*: Settings used to install the inference model. These settings are specific to the `openai` service.
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** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
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** *`task_settings` (Optional, { do_sample, max_new_tokens, temperature, top_p, user })*: Settings to configure the inference task.
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These settings are specific to the task type you specified.
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[discrete]
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==== put_azureopenai
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Create an Azure OpenAI inference endpoint.
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Create an inference endpoint to perform an inference task with the `azureopenai` service.
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The list of chat completion models that you can choose from in your Azure OpenAI deployment include:
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* [GPT-4 and GPT-4 Turbo models](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#gpt-4-and-gpt-4-turbo-models)
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* [GPT-3.5](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#gpt-35)
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The list of embeddings models that you can choose from in your deployment can be found in the [Azure models documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#embeddings).
|
||||
|
||||
When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
|
||||
{ref}/infer-service-azure-openai.html[Endpoint documentation]
|
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[source,ts]
|
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----
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client.inference.putAzureopenai({ task_type, azureopenai_inference_id, service, service_settings })
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----
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[discrete]
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||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("completion" | "text_embedding"))*: The type of the inference task that the model will perform.
|
||||
NOTE: The `chat_completion` task type only supports streaming and only through the _stream API.
|
||||
** *`azureopenai_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("azureopenai"))*: The type of service supported for the specified task type. In this case, `azureopenai`.
|
||||
** *`service_settings` ({ api_key, api_version, deployment_id, entra_id, rate_limit, resource_name })*: Settings used to install the inference model. These settings are specific to the `azureopenai` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
** *`task_settings` (Optional, { user })*: Settings to configure the inference task.
|
||||
These settings are specific to the task type you specified.
|
||||
|
||||
[discrete]
|
||||
==== put_cohere
|
||||
Create a Cohere inference endpoint.
|
||||
|
||||
Create an inference endpoint to perform an inference task with the `cohere` service.
|
||||
|
||||
When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
|
||||
{ref}/infer-service-cohere.html[Endpoint documentation]
|
||||
[source,ts]
|
||||
----
|
||||
client.inference.putCohere({ task_type, cohere_inference_id, service, service_settings })
|
||||
----
|
||||
|
||||
[discrete]
|
||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("completion" | "rerank" | "text_embedding"))*: The type of the inference task that the model will perform.
|
||||
** *`cohere_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("cohere"))*: The type of service supported for the specified task type. In this case, `cohere`.
|
||||
** *`service_settings` ({ api_key, embedding_type, model_id, rate_limit, similarity })*: Settings used to install the inference model.
|
||||
These settings are specific to the `cohere` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
** *`task_settings` (Optional, { input_type, return_documents, top_n, truncate })*: Settings to configure the inference task.
|
||||
These settings are specific to the task type you specified.
|
||||
|
||||
[discrete]
|
||||
==== put_eis
|
||||
Create an Elastic Inference Service (EIS) inference endpoint.
|
||||
@ -8094,21 +8300,244 @@ NOTE: The `chat_completion` task type only supports streaming and only through t
|
||||
** *`service_settings` ({ model_id, rate_limit })*: Settings used to install the inference model. These settings are specific to the `elastic` service.
|
||||
|
||||
[discrete]
|
||||
==== put_mistral
|
||||
Configure a Mistral inference endpoint
|
||||
==== put_elasticsearch
|
||||
Create an Elasticsearch inference endpoint.
|
||||
|
||||
{ref}/infer-service-mistral.html[Endpoint documentation]
|
||||
Create an inference endpoint to perform an inference task with the `elasticsearch` service.
|
||||
|
||||
> info
|
||||
> Your Elasticsearch deployment contains preconfigured ELSER and E5 inference endpoints, you only need to create the enpoints using the API if you want to customize the settings.
|
||||
|
||||
If you use the ELSER or the E5 model through the `elasticsearch` service, the API request will automatically download and deploy the model if it isn't downloaded yet.
|
||||
|
||||
> info
|
||||
> You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.
|
||||
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
|
||||
{ref}/infer-service-elasticsearch.html[Endpoint documentation]
|
||||
[source,ts]
|
||||
----
|
||||
client.inference.putMistral()
|
||||
client.inference.putElasticsearch({ task_type, elasticsearch_inference_id, service, service_settings })
|
||||
----
|
||||
|
||||
[discrete]
|
||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("rerank" | "sparse_embedding" | "text_embedding"))*: The type of the inference task that the model will perform.
|
||||
** *`elasticsearch_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
The must not match the `model_id`.
|
||||
** *`service` (Enum("elasticsearch"))*: The type of service supported for the specified task type. In this case, `elasticsearch`.
|
||||
** *`service_settings` ({ adaptive_allocations, deployment_id, model_id, num_allocations, num_threads })*: Settings used to install the inference model. These settings are specific to the `elasticsearch` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
** *`task_settings` (Optional, { return_documents })*: Settings to configure the inference task.
|
||||
These settings are specific to the task type you specified.
|
||||
|
||||
[discrete]
|
||||
==== put_elser
|
||||
Create an ELSER inference endpoint.
|
||||
|
||||
Create an inference endpoint to perform an inference task with the `elser` service.
|
||||
You can also deploy ELSER by using the Elasticsearch inference integration.
|
||||
|
||||
> info
|
||||
> Your Elasticsearch deployment contains a preconfigured ELSER inference endpoint, you only need to create the enpoint using the API if you want to customize the settings.
|
||||
|
||||
The API request will automatically download and deploy the ELSER model if it isn't already downloaded.
|
||||
|
||||
> info
|
||||
> You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.
|
||||
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
|
||||
{ref}/infer-service-elser.html[Endpoint documentation]
|
||||
[source,ts]
|
||||
----
|
||||
client.inference.putElser({ task_type, elser_inference_id, service, service_settings })
|
||||
----
|
||||
|
||||
[discrete]
|
||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("sparse_embedding"))*: The type of the inference task that the model will perform.
|
||||
** *`elser_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("elser"))*: The type of service supported for the specified task type. In this case, `elser`.
|
||||
** *`service_settings` ({ adaptive_allocations, num_allocations, num_threads })*: Settings used to install the inference model. These settings are specific to the `elser` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
|
||||
[discrete]
|
||||
==== put_googleaistudio
|
||||
Create an Google AI Studio inference endpoint.
|
||||
|
||||
Create an inference endpoint to perform an inference task with the `googleaistudio` service.
|
||||
|
||||
When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
|
||||
{ref}/infer-service-google-ai-studio.html[Endpoint documentation]
|
||||
[source,ts]
|
||||
----
|
||||
client.inference.putGoogleaistudio({ task_type, googleaistudio_inference_id, service, service_settings })
|
||||
----
|
||||
|
||||
[discrete]
|
||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("completion" | "text_embedding"))*: The type of the inference task that the model will perform.
|
||||
** *`googleaistudio_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("googleaistudio"))*: The type of service supported for the specified task type. In this case, `googleaistudio`.
|
||||
** *`service_settings` ({ api_key, model_id, rate_limit })*: Settings used to install the inference model. These settings are specific to the `googleaistudio` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
|
||||
[discrete]
|
||||
==== put_googlevertexai
|
||||
Create a Google Vertex AI inference endpoint.
|
||||
|
||||
Create an inference endpoint to perform an inference task with the `googlevertexai` service.
|
||||
|
||||
When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
|
||||
{ref}/infer-service-google-vertex-ai.html[Endpoint documentation]
|
||||
[source,ts]
|
||||
----
|
||||
client.inference.putGooglevertexai({ task_type, googlevertexai_inference_id, service, service_settings })
|
||||
----
|
||||
|
||||
[discrete]
|
||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("rerank" | "text_embedding"))*: The type of the inference task that the model will perform.
|
||||
** *`googlevertexai_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("googlevertexai"))*: The type of service supported for the specified task type. In this case, `googlevertexai`.
|
||||
** *`service_settings` ({ location, model_id, project_id, rate_limit, service_account_json })*: Settings used to install the inference model. These settings are specific to the `googlevertexai` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
** *`task_settings` (Optional, { auto_truncate, top_n })*: Settings to configure the inference task.
|
||||
These settings are specific to the task type you specified.
|
||||
|
||||
[discrete]
|
||||
==== put_hugging_face
|
||||
Create a Hugging Face inference endpoint.
|
||||
|
||||
Create an inference endpoint to perform an inference task with the `hugging_face` service.
|
||||
|
||||
You must first create an inference endpoint on the Hugging Face endpoint page to get an endpoint URL.
|
||||
Select the model you want to use on the new endpoint creation page (for example `intfloat/e5-small-v2`), then select the sentence embeddings task under the advanced configuration section.
|
||||
Create the endpoint and copy the URL after the endpoint initialization has been finished.
|
||||
|
||||
The following models are recommended for the Hugging Face service:
|
||||
|
||||
* `all-MiniLM-L6-v2`
|
||||
* `all-MiniLM-L12-v2`
|
||||
* `all-mpnet-base-v2`
|
||||
* `e5-base-v2`
|
||||
* `e5-small-v2`
|
||||
* `multilingual-e5-base`
|
||||
* `multilingual-e5-small`
|
||||
|
||||
When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
|
||||
{ref}/infer-service-hugging-face.html[Endpoint documentation]
|
||||
[source,ts]
|
||||
----
|
||||
client.inference.putHuggingFace({ task_type, huggingface_inference_id, service, service_settings })
|
||||
----
|
||||
|
||||
[discrete]
|
||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("text_embedding"))*: The type of the inference task that the model will perform.
|
||||
** *`huggingface_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("hugging_face"))*: The type of service supported for the specified task type. In this case, `hugging_face`.
|
||||
** *`service_settings` ({ api_key, rate_limit, url })*: Settings used to install the inference model. These settings are specific to the `hugging_face` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
|
||||
[discrete]
|
||||
==== put_jinaai
|
||||
Create an JinaAI inference endpoint.
|
||||
|
||||
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/>.
|
||||
|
||||
When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
|
||||
{ref}/infer-service-jinaai.html[Endpoint documentation]
|
||||
[source,ts]
|
||||
----
|
||||
client.inference.putJinaai({ task_type, jinaai_inference_id, service, service_settings })
|
||||
----
|
||||
|
||||
[discrete]
|
||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("rerank" | "text_embedding"))*: The type of the inference task that the model will perform.
|
||||
** *`jinaai_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("jinaai"))*: The type of service supported for the specified task type. In this case, `jinaai`.
|
||||
** *`service_settings` ({ api_key, model_id, rate_limit, similarity })*: Settings used to install the inference model. These settings are specific to the `jinaai` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
** *`task_settings` (Optional, { return_documents, task, top_n })*: Settings to configure the inference task.
|
||||
These settings are specific to the task type you specified.
|
||||
|
||||
[discrete]
|
||||
==== put_mistral
|
||||
Create a Mistral inference endpoint.
|
||||
|
||||
Creates an inference endpoint to perform an inference task with the `mistral` service.
|
||||
|
||||
When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
To verify the deployment status, use the get trained model statistics API.
|
||||
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
|
||||
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
[source,ts]
|
||||
----
|
||||
client.inference.putMistral({ task_type, mistral_inference_id, service, service_settings })
|
||||
----
|
||||
|
||||
[discrete]
|
||||
==== Arguments
|
||||
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("text_embedding"))*: The task type.
|
||||
The only valid task type for the model to perform is `text_embedding`.
|
||||
** *`mistral_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("mistral"))*: The type of service supported for the specified task type. In this case, `mistral`.
|
||||
** *`service_settings` ({ api_key, max_input_tokens, model, rate_limit })*: Settings used to install the inference model. These settings are specific to the `mistral` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
|
||||
[discrete]
|
||||
==== put_openai
|
||||
Create an OpenAI inference endpoint.
|
||||
|
||||
Create an inference endpoint to perform an inference task with the `openai` service.
|
||||
Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.
|
||||
|
||||
When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
|
||||
After creating the endpoint, wait for the model deployment to complete before using it.
|
||||
@ -8129,7 +8558,7 @@ client.inference.putOpenai({ task_type, openai_inference_id, service, service_se
|
||||
** *`task_type` (Enum("chat_completion" | "completion" | "text_embedding"))*: The type of the inference task that the model will perform.
|
||||
NOTE: The `chat_completion` task type only supports streaming and only through the _stream API.
|
||||
** *`openai_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("elastic"))*: The type of service supported for the specified task type. In this case, `openai`.
|
||||
** *`service` (Enum("openai"))*: The type of service supported for the specified task type. In this case, `openai`.
|
||||
** *`service_settings` ({ api_key, dimensions, model_id, organization_id, rate_limit, url })*: Settings used to install the inference model. These settings are specific to the `openai` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
** *`task_settings` (Optional, { user })*: Settings to configure the inference task.
|
||||
@ -8155,7 +8584,7 @@ client.inference.putVoyageai({ task_type, voyageai_inference_id, service, servic
|
||||
* *Request (object):*
|
||||
** *`task_type` (Enum("text_embedding" | "rerank"))*: The type of the inference task that the model will perform.
|
||||
** *`voyageai_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("elastic"))*: The type of service supported for the specified task type. In this case, `voyageai`.
|
||||
** *`service` (Enum("voyageai"))*: The type of service supported for the specified task type. In this case, `voyageai`.
|
||||
** *`service_settings` ({ dimensions, model_id, rate_limit, embedding_type })*: Settings used to install the inference model. These settings are specific to the `voyageai` service.
|
||||
** *`chunking_settings` (Optional, { max_chunk_size, overlap, sentence_overlap, strategy })*: The chunking configuration object.
|
||||
** *`task_settings` (Optional, { input_type, return_documents, top_k, truncation })*: Settings to configure the inference task.
|
||||
@ -8188,7 +8617,7 @@ client.inference.putWatsonx({ task_type, watsonx_inference_id, service, service_
|
||||
** *`task_type` (Enum("text_embedding"))*: The task type.
|
||||
The only valid task type for the model to perform is `text_embedding`.
|
||||
** *`watsonx_inference_id` (string)*: The unique identifier of the inference endpoint.
|
||||
** *`service` (Enum("elastic"))*: The type of service supported for the specified task type. In this case, `watsonxai`.
|
||||
** *`service` (Enum("watsonxai"))*: The type of service supported for the specified task type. In this case, `watsonxai`.
|
||||
** *`service_settings` ({ api_key, api_version, model_id, project_id, rate_limit, url })*: Settings used to install the inference model. These settings are specific to the `watsonxai` service.
|
||||
|
||||
[discrete]
|
||||
@ -10305,7 +10734,7 @@ specified.
|
||||
** *`definition` (Optional, { preprocessors, trained_model })*: The inference definition for the model. If definition is specified, then
|
||||
compressed_definition cannot be specified.
|
||||
** *`description` (Optional, string)*: A human-readable description of the inference trained model.
|
||||
** *`inference_config` (Optional, { regression, classification, text_classification, zero_shot_classification, fill_mask, ner, pass_through, text_embedding, text_expansion, question_answering })*: The default configuration for inference. This can be either a regression
|
||||
** *`inference_config` (Optional, { regression, classification, text_classification, zero_shot_classification, fill_mask, learning_to_rank, ner, pass_through, text_embedding, text_expansion, question_answering })*: The default configuration for inference. This can be either a regression
|
||||
or classification configuration. It must match the underlying
|
||||
definition.trained_model's target_type. For pre-packaged models such as
|
||||
ELSER the config is not required.
|
||||
@ -15986,7 +16415,10 @@ To indicate that the request should never timeout, set it to `-1`.
|
||||
Update Watcher index settings.
|
||||
Update settings for the Watcher internal index (`.watches`).
|
||||
Only a subset of settings can be modified.
|
||||
This includes `index.auto_expand_replicas` and `index.number_of_replicas`.
|
||||
This includes `index.auto_expand_replicas`, `index.number_of_replicas`, `index.routing.allocation.exclude.*`,
|
||||
`index.routing.allocation.include.*` and `index.routing.allocation.require.*`.
|
||||
Modification of `index.routing.allocation.include._tier_preference` is an exception and is not allowed as the
|
||||
Watcher shards must always be in the `data_content` tier.
|
||||
|
||||
{ref}/watcher-api-update-settings.html[Endpoint documentation]
|
||||
[source,ts]
|
||||
|
||||
@ -53,22 +53,15 @@ export default class Inference {
|
||||
async chatCompletionUnified (this: That, params: T.InferenceChatCompletionUnifiedRequest | TB.InferenceChatCompletionUnifiedRequest, options?: TransportRequestOptions): Promise<T.InferenceChatCompletionUnifiedResponse>
|
||||
async chatCompletionUnified (this: That, params: T.InferenceChatCompletionUnifiedRequest | TB.InferenceChatCompletionUnifiedRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['inference_id']
|
||||
const acceptedBody: string[] = ['messages', 'model', 'max_completion_tokens', 'stop', 'temperature', 'tool_choice', 'tools', 'top_p']
|
||||
const acceptedBody: string[] = ['chat_completion_request']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
let body: any = params.body ?? undefined
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
body = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
@ -216,6 +209,43 @@ export default class Inference {
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Perform a chat completion task through the Elastic Inference Service (EIS). Perform a chat completion inference task with the `elastic` service.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/post-inference-api.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async postEisChatCompletion (this: That, params: T.InferencePostEisChatCompletionRequest | TB.InferencePostEisChatCompletionRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePostEisChatCompletionResponse>
|
||||
async postEisChatCompletion (this: That, params: T.InferencePostEisChatCompletionRequest | TB.InferencePostEisChatCompletionRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePostEisChatCompletionResponse, unknown>>
|
||||
async postEisChatCompletion (this: That, params: T.InferencePostEisChatCompletionRequest | TB.InferencePostEisChatCompletionRequest, options?: TransportRequestOptions): Promise<T.InferencePostEisChatCompletionResponse>
|
||||
async postEisChatCompletion (this: That, params: T.InferencePostEisChatCompletionRequest | TB.InferencePostEisChatCompletionRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['eis_inference_id']
|
||||
const acceptedBody: string[] = ['chat_completion_request']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
let body: any = params.body ?? undefined
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
// @ts-expect-error
|
||||
body = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'POST'
|
||||
const path = `/_inference/chat_completion/${encodeURIComponent(params.eis_inference_id.toString())}/_stream`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.post_eis_chat_completion',
|
||||
pathParts: {
|
||||
eis_inference_id: params.eis_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an inference endpoint. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources. 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, Mistral, Azure OpenAI, 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.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/put-inference-api.html | Elasticsearch API documentation}
|
||||
@ -261,6 +291,276 @@ export default class Inference {
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an AlibabaCloud AI Search inference endpoint. Create an inference endpoint to perform an inference task with the `alibabacloud-ai-search` service. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-alibabacloud-ai-search.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putAlibabacloud (this: That, params: T.InferencePutAlibabacloudRequest | TB.InferencePutAlibabacloudRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutAlibabacloudResponse>
|
||||
async putAlibabacloud (this: That, params: T.InferencePutAlibabacloudRequest | TB.InferencePutAlibabacloudRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutAlibabacloudResponse, unknown>>
|
||||
async putAlibabacloud (this: That, params: T.InferencePutAlibabacloudRequest | TB.InferencePutAlibabacloudRequest, options?: TransportRequestOptions): Promise<T.InferencePutAlibabacloudResponse>
|
||||
async putAlibabacloud (this: That, params: T.InferencePutAlibabacloudRequest | TB.InferencePutAlibabacloudRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'alibabacloud_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.alibabacloud_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_alibabacloud',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
alibabacloud_inference_id: params.alibabacloud_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an Amazon Bedrock inference endpoint. Creates an inference endpoint to perform an inference task with the `amazonbedrock` service. >info > You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-amazon-bedrock.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putAmazonbedrock (this: That, params: T.InferencePutAmazonbedrockRequest | TB.InferencePutAmazonbedrockRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutAmazonbedrockResponse>
|
||||
async putAmazonbedrock (this: That, params: T.InferencePutAmazonbedrockRequest | TB.InferencePutAmazonbedrockRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutAmazonbedrockResponse, unknown>>
|
||||
async putAmazonbedrock (this: That, params: T.InferencePutAmazonbedrockRequest | TB.InferencePutAmazonbedrockRequest, options?: TransportRequestOptions): Promise<T.InferencePutAmazonbedrockResponse>
|
||||
async putAmazonbedrock (this: That, params: T.InferencePutAmazonbedrockRequest | TB.InferencePutAmazonbedrockRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'amazonbedrock_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.amazonbedrock_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_amazonbedrock',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
amazonbedrock_inference_id: params.amazonbedrock_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an Anthropic inference endpoint. Create an inference endpoint to perform an inference task with the `anthropic` service. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-anthropic.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putAnthropic (this: That, params: T.InferencePutAnthropicRequest | TB.InferencePutAnthropicRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutAnthropicResponse>
|
||||
async putAnthropic (this: That, params: T.InferencePutAnthropicRequest | TB.InferencePutAnthropicRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutAnthropicResponse, unknown>>
|
||||
async putAnthropic (this: That, params: T.InferencePutAnthropicRequest | TB.InferencePutAnthropicRequest, options?: TransportRequestOptions): Promise<T.InferencePutAnthropicResponse>
|
||||
async putAnthropic (this: That, params: T.InferencePutAnthropicRequest | TB.InferencePutAnthropicRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'anthropic_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.anthropic_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_anthropic',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
anthropic_inference_id: params.anthropic_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an Azure AI studio inference endpoint. Create an inference endpoint to perform an inference task with the `azureaistudio` service. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-azure-ai-studio.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putAzureaistudio (this: That, params: T.InferencePutAzureaistudioRequest | TB.InferencePutAzureaistudioRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutAzureaistudioResponse>
|
||||
async putAzureaistudio (this: That, params: T.InferencePutAzureaistudioRequest | TB.InferencePutAzureaistudioRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutAzureaistudioResponse, unknown>>
|
||||
async putAzureaistudio (this: That, params: T.InferencePutAzureaistudioRequest | TB.InferencePutAzureaistudioRequest, options?: TransportRequestOptions): Promise<T.InferencePutAzureaistudioResponse>
|
||||
async putAzureaistudio (this: That, params: T.InferencePutAzureaistudioRequest | TB.InferencePutAzureaistudioRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'azureaistudio_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.azureaistudio_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_azureaistudio',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
azureaistudio_inference_id: params.azureaistudio_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an Azure OpenAI inference endpoint. Create an inference endpoint to perform an inference task with the `azureopenai` service. The list of chat completion models that you can choose from in your Azure OpenAI deployment include: * [GPT-4 and GPT-4 Turbo models](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#gpt-4-and-gpt-4-turbo-models) * [GPT-3.5](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#gpt-35) The list of embeddings models that you can choose from in your deployment can be found in the [Azure models documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#embeddings). When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-azure-openai.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putAzureopenai (this: That, params: T.InferencePutAzureopenaiRequest | TB.InferencePutAzureopenaiRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutAzureopenaiResponse>
|
||||
async putAzureopenai (this: That, params: T.InferencePutAzureopenaiRequest | TB.InferencePutAzureopenaiRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutAzureopenaiResponse, unknown>>
|
||||
async putAzureopenai (this: That, params: T.InferencePutAzureopenaiRequest | TB.InferencePutAzureopenaiRequest, options?: TransportRequestOptions): Promise<T.InferencePutAzureopenaiResponse>
|
||||
async putAzureopenai (this: That, params: T.InferencePutAzureopenaiRequest | TB.InferencePutAzureopenaiRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'azureopenai_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.azureopenai_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_azureopenai',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
azureopenai_inference_id: params.azureopenai_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a Cohere inference endpoint. Create an inference endpoint to perform an inference task with the `cohere` service. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-cohere.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putCohere (this: That, params: T.InferencePutCohereRequest | TB.InferencePutCohereRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutCohereResponse>
|
||||
async putCohere (this: That, params: T.InferencePutCohereRequest | TB.InferencePutCohereRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutCohereResponse, unknown>>
|
||||
async putCohere (this: That, params: T.InferencePutCohereRequest | TB.InferencePutCohereRequest, options?: TransportRequestOptions): Promise<T.InferencePutCohereResponse>
|
||||
async putCohere (this: That, params: T.InferencePutCohereRequest | TB.InferencePutCohereRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'cohere_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.cohere_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_cohere',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
cohere_inference_id: params.cohere_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an Elastic Inference Service (EIS) inference endpoint. Create an inference endpoint to perform an inference task through the Elastic Inference Service (EIS).
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-elastic.html | Elasticsearch API documentation}
|
||||
@ -307,22 +607,304 @@ export default class Inference {
|
||||
}
|
||||
|
||||
/**
|
||||
* Configure a Mistral inference endpoint
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-mistral.html | Elasticsearch API documentation}
|
||||
* Create an Elasticsearch inference endpoint. Create an inference endpoint to perform an inference task with the `elasticsearch` service. > info > Your Elasticsearch deployment contains preconfigured ELSER and E5 inference endpoints, you only need to create the enpoints using the API if you want to customize the settings. If you use the ELSER or the E5 model through the `elasticsearch` service, the API request will automatically download and deploy the model if it isn't downloaded yet. > info > You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-elasticsearch.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putMistral (this: That, params?: T.TODO | TB.TODO, options?: TransportRequestOptionsWithOutMeta): Promise<T.TODO>
|
||||
async putMistral (this: That, params?: T.TODO | TB.TODO, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.TODO, unknown>>
|
||||
async putMistral (this: That, params?: T.TODO | TB.TODO, options?: TransportRequestOptions): Promise<T.TODO>
|
||||
async putMistral (this: That, params?: T.TODO | TB.TODO, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'mistral_inference_id']
|
||||
async putElasticsearch (this: That, params: T.InferencePutElasticsearchRequest | TB.InferencePutElasticsearchRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutElasticsearchResponse>
|
||||
async putElasticsearch (this: That, params: T.InferencePutElasticsearchRequest | TB.InferencePutElasticsearchRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutElasticsearchResponse, unknown>>
|
||||
async putElasticsearch (this: That, params: T.InferencePutElasticsearchRequest | TB.InferencePutElasticsearchRequest, options?: TransportRequestOptions): Promise<T.InferencePutElasticsearchResponse>
|
||||
async putElasticsearch (this: That, params: T.InferencePutElasticsearchRequest | TB.InferencePutElasticsearchRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'elasticsearch_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
const body = undefined
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
params = params ?? {}
|
||||
for (const key in params) {
|
||||
if (acceptedPath.includes(key)) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.elasticsearch_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_elasticsearch',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
elasticsearch_inference_id: params.elasticsearch_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an ELSER inference endpoint. Create an inference endpoint to perform an inference task with the `elser` service. You can also deploy ELSER by using the Elasticsearch inference integration. > info > Your Elasticsearch deployment contains a preconfigured ELSER inference endpoint, you only need to create the enpoint using the API if you want to customize the settings. The API request will automatically download and deploy the ELSER model if it isn't already downloaded. > info > You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-elser.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putElser (this: That, params: T.InferencePutElserRequest | TB.InferencePutElserRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutElserResponse>
|
||||
async putElser (this: That, params: T.InferencePutElserRequest | TB.InferencePutElserRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutElserResponse, unknown>>
|
||||
async putElser (this: That, params: T.InferencePutElserRequest | TB.InferencePutElserRequest, options?: TransportRequestOptions): Promise<T.InferencePutElserResponse>
|
||||
async putElser (this: That, params: T.InferencePutElserRequest | TB.InferencePutElserRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'elser_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.elser_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_elser',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
elser_inference_id: params.elser_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an Google AI Studio inference endpoint. Create an inference endpoint to perform an inference task with the `googleaistudio` service. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-google-ai-studio.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putGoogleaistudio (this: That, params: T.InferencePutGoogleaistudioRequest | TB.InferencePutGoogleaistudioRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutGoogleaistudioResponse>
|
||||
async putGoogleaistudio (this: That, params: T.InferencePutGoogleaistudioRequest | TB.InferencePutGoogleaistudioRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutGoogleaistudioResponse, unknown>>
|
||||
async putGoogleaistudio (this: That, params: T.InferencePutGoogleaistudioRequest | TB.InferencePutGoogleaistudioRequest, options?: TransportRequestOptions): Promise<T.InferencePutGoogleaistudioResponse>
|
||||
async putGoogleaistudio (this: That, params: T.InferencePutGoogleaistudioRequest | TB.InferencePutGoogleaistudioRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'googleaistudio_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.googleaistudio_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_googleaistudio',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
googleaistudio_inference_id: params.googleaistudio_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a Google Vertex AI inference endpoint. Create an inference endpoint to perform an inference task with the `googlevertexai` service. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-google-vertex-ai.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putGooglevertexai (this: That, params: T.InferencePutGooglevertexaiRequest | TB.InferencePutGooglevertexaiRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutGooglevertexaiResponse>
|
||||
async putGooglevertexai (this: That, params: T.InferencePutGooglevertexaiRequest | TB.InferencePutGooglevertexaiRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutGooglevertexaiResponse, unknown>>
|
||||
async putGooglevertexai (this: That, params: T.InferencePutGooglevertexaiRequest | TB.InferencePutGooglevertexaiRequest, options?: TransportRequestOptions): Promise<T.InferencePutGooglevertexaiResponse>
|
||||
async putGooglevertexai (this: That, params: T.InferencePutGooglevertexaiRequest | TB.InferencePutGooglevertexaiRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'googlevertexai_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.googlevertexai_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_googlevertexai',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
googlevertexai_inference_id: params.googlevertexai_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a Hugging Face inference endpoint. Create an inference endpoint to perform an inference task with the `hugging_face` service. You must first create an inference endpoint on the Hugging Face endpoint page to get an endpoint URL. Select the model you want to use on the new endpoint creation page (for example `intfloat/e5-small-v2`), then select the sentence embeddings task under the advanced configuration section. Create the endpoint and copy the URL after the endpoint initialization has been finished. The following models are recommended for the Hugging Face service: * `all-MiniLM-L6-v2` * `all-MiniLM-L12-v2` * `all-mpnet-base-v2` * `e5-base-v2` * `e5-small-v2` * `multilingual-e5-base` * `multilingual-e5-small` When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-hugging-face.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putHuggingFace (this: That, params: T.InferencePutHuggingFaceRequest | TB.InferencePutHuggingFaceRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutHuggingFaceResponse>
|
||||
async putHuggingFace (this: That, params: T.InferencePutHuggingFaceRequest | TB.InferencePutHuggingFaceRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutHuggingFaceResponse, unknown>>
|
||||
async putHuggingFace (this: That, params: T.InferencePutHuggingFaceRequest | TB.InferencePutHuggingFaceRequest, options?: TransportRequestOptions): Promise<T.InferencePutHuggingFaceResponse>
|
||||
async putHuggingFace (this: That, params: T.InferencePutHuggingFaceRequest | TB.InferencePutHuggingFaceRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'huggingface_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.huggingface_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_hugging_face',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
huggingface_inference_id: params.huggingface_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an JinaAI inference endpoint. 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/>. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-jinaai.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putJinaai (this: That, params: T.InferencePutJinaaiRequest | TB.InferencePutJinaaiRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutJinaaiResponse>
|
||||
async putJinaai (this: That, params: T.InferencePutJinaaiRequest | TB.InferencePutJinaaiRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutJinaaiResponse, unknown>>
|
||||
async putJinaai (this: That, params: T.InferencePutJinaaiRequest | TB.InferencePutJinaaiRequest, options?: TransportRequestOptions): Promise<T.InferencePutJinaaiResponse>
|
||||
async putJinaai (this: That, params: T.InferencePutJinaaiRequest | TB.InferencePutJinaaiRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'jinaai_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings', 'task_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
|
||||
const method = 'PUT'
|
||||
const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.jinaai_inference_id.toString())}`
|
||||
const meta: TransportRequestMetadata = {
|
||||
name: 'inference.put_jinaai',
|
||||
pathParts: {
|
||||
task_type: params.task_type,
|
||||
jinaai_inference_id: params.jinaai_inference_id
|
||||
}
|
||||
}
|
||||
return await this.transport.request({ path, method, querystring, body, meta }, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a Mistral inference endpoint. Creates an inference endpoint to perform an inference task with the `mistral` service. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/{brnach}/infer-service-mistral.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putMistral (this: That, params: T.InferencePutMistralRequest | TB.InferencePutMistralRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutMistralResponse>
|
||||
async putMistral (this: That, params: T.InferencePutMistralRequest | TB.InferencePutMistralRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutMistralResponse, unknown>>
|
||||
async putMistral (this: That, params: T.InferencePutMistralRequest | TB.InferencePutMistralRequest, options?: TransportRequestOptions): Promise<T.InferencePutMistralResponse>
|
||||
async putMistral (this: That, params: T.InferencePutMistralRequest | TB.InferencePutMistralRequest, options?: TransportRequestOptions): Promise<any> {
|
||||
const acceptedPath: string[] = ['task_type', 'mistral_inference_id']
|
||||
const acceptedBody: string[] = ['chunking_settings', 'service', 'service_settings']
|
||||
const querystring: Record<string, any> = {}
|
||||
// @ts-expect-error
|
||||
const userBody: any = params?.body
|
||||
let body: Record<string, any> | string
|
||||
if (typeof userBody === 'string') {
|
||||
body = userBody
|
||||
} else {
|
||||
body = userBody != null ? { ...userBody } : undefined
|
||||
}
|
||||
|
||||
for (const key in params) {
|
||||
if (acceptedBody.includes(key)) {
|
||||
body = body ?? {}
|
||||
// @ts-expect-error
|
||||
body[key] = params[key]
|
||||
} else if (acceptedPath.includes(key)) {
|
||||
continue
|
||||
} else if (key !== 'body') {
|
||||
// @ts-expect-error
|
||||
querystring[key] = params[key]
|
||||
}
|
||||
}
|
||||
@ -340,7 +922,7 @@ export default class Inference {
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an OpenAI inference endpoint. Create an inference endpoint to perform an inference task with the `openai` service. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* Create an OpenAI inference endpoint. Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs. When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-openai.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async putOpenai (this: That, params: T.InferencePutOpenaiRequest | TB.InferencePutOpenaiRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutOpenaiResponse>
|
||||
|
||||
@ -481,7 +481,7 @@ export default class Watcher {
|
||||
}
|
||||
|
||||
/**
|
||||
* Update Watcher index settings. Update settings for the Watcher internal index (`.watches`). Only a subset of settings can be modified. This includes `index.auto_expand_replicas` and `index.number_of_replicas`.
|
||||
* Update Watcher index settings. Update settings for the Watcher internal index (`.watches`). Only a subset of settings can be modified. This includes `index.auto_expand_replicas`, `index.number_of_replicas`, `index.routing.allocation.exclude.*`, `index.routing.allocation.include.*` and `index.routing.allocation.require.*`. Modification of `index.routing.allocation.include._tier_preference` is an exception and is not allowed as the Watcher shards must always be in the `data_content` tier.
|
||||
* @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/watcher-api-update-settings.html | Elasticsearch API documentation}
|
||||
*/
|
||||
async updateSettings (this: That, params?: T.WatcherUpdateSettingsRequest | TB.WatcherUpdateSettingsRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.WatcherUpdateSettingsResponse>
|
||||
|
||||
645
src/api/types.ts
645
src/api/types.ts
@ -760,7 +760,7 @@ export interface MsearchMultisearchBody {
|
||||
knn?: KnnSearch | KnnSearch[]
|
||||
from?: integer
|
||||
highlight?: SearchHighlight
|
||||
indices_boost?: Record<IndexName, double>[]
|
||||
indices_boost?: Partial<Record<IndexName, double>>[]
|
||||
min_score?: double
|
||||
post_filter?: QueryDslQueryContainer
|
||||
profile?: boolean
|
||||
@ -898,6 +898,7 @@ export interface OpenPointInTimeRequest extends RequestBase {
|
||||
routing?: Routing
|
||||
expand_wildcards?: ExpandWildcards
|
||||
allow_partial_search_results?: boolean
|
||||
max_concurrent_shard_requests?: integer
|
||||
index_filter?: QueryDslQueryContainer
|
||||
}
|
||||
|
||||
@ -1198,7 +1199,7 @@ export interface SearchRequest extends RequestBase {
|
||||
from?: integer
|
||||
highlight?: SearchHighlight
|
||||
track_total_hits?: SearchTrackHits
|
||||
indices_boost?: Record<IndexName, double>[]
|
||||
indices_boost?: Partial<Record<IndexName, double>>[]
|
||||
docvalue_fields?: (QueryDslFieldAndFormat | Field)[]
|
||||
knn?: KnnSearch | KnnSearch[]
|
||||
rank?: RankContainer
|
||||
@ -5305,6 +5306,10 @@ export interface MappingBooleanProperty extends MappingDocValuesPropertyBase {
|
||||
fielddata?: IndicesNumericFielddata
|
||||
index?: boolean
|
||||
null_value?: boolean
|
||||
ignore_malformed?: boolean
|
||||
script?: Script | string
|
||||
on_script_error?: MappingOnScriptError
|
||||
time_series_dimension?: boolean
|
||||
type: 'boolean'
|
||||
}
|
||||
|
||||
@ -5386,7 +5391,7 @@ export interface MappingDenseVectorIndexOptions {
|
||||
type: MappingDenseVectorIndexOptionsType
|
||||
}
|
||||
|
||||
export type MappingDenseVectorIndexOptionsType = 'flat' | 'hnsw' | 'int4_flat' | 'int4_hnsw' | 'int8_flat' | 'int8_hnsw'
|
||||
export type MappingDenseVectorIndexOptionsType = 'bbq_flat' | 'bbq_hnsw' | 'flat' | 'hnsw' | 'int4_flat' | 'int4_hnsw' | 'int8_flat' | 'int8_hnsw'
|
||||
|
||||
export interface MappingDenseVectorProperty extends MappingPropertyBase {
|
||||
type: 'dense_vector'
|
||||
@ -6761,7 +6766,7 @@ export interface AsyncSearchSubmitRequest extends RequestBase {
|
||||
from?: integer
|
||||
highlight?: SearchHighlight
|
||||
track_total_hits?: SearchTrackHits
|
||||
indices_boost?: Record<IndexName, double>[]
|
||||
indices_boost?: Partial<Record<IndexName, double>>[]
|
||||
docvalue_fields?: (QueryDslFieldAndFormat | Field)[]
|
||||
knn?: KnnSearch | KnnSearch[]
|
||||
min_score?: double
|
||||
@ -10252,10 +10257,11 @@ export interface EnrichDeletePolicyRequest extends RequestBase {
|
||||
|
||||
export type EnrichDeletePolicyResponse = AcknowledgedResponseBase
|
||||
|
||||
export type EnrichExecutePolicyEnrichPolicyPhase = 'SCHEDULED' | 'RUNNING' | 'COMPLETE' | 'FAILED'
|
||||
export type EnrichExecutePolicyEnrichPolicyPhase = 'SCHEDULED' | 'RUNNING' | 'COMPLETE' | 'FAILED' | 'CANCELLED'
|
||||
|
||||
export interface EnrichExecutePolicyExecuteEnrichPolicyStatus {
|
||||
phase: EnrichExecutePolicyEnrichPolicyPhase
|
||||
step?: string
|
||||
}
|
||||
|
||||
export interface EnrichExecutePolicyRequest extends RequestBase {
|
||||
@ -10266,7 +10272,7 @@ export interface EnrichExecutePolicyRequest extends RequestBase {
|
||||
|
||||
export interface EnrichExecutePolicyResponse {
|
||||
status?: EnrichExecutePolicyExecuteEnrichPolicyStatus
|
||||
task_id?: TaskId
|
||||
task?: TaskId
|
||||
}
|
||||
|
||||
export interface EnrichGetPolicyRequest extends RequestBase {
|
||||
@ -10579,7 +10585,7 @@ export interface FleetSearchRequest extends RequestBase {
|
||||
from?: integer
|
||||
highlight?: SearchHighlight
|
||||
track_total_hits?: SearchTrackHits
|
||||
indices_boost?: Record<IndexName, double>[]
|
||||
indices_boost?: Partial<Record<IndexName, double>>[]
|
||||
docvalue_fields?: (QueryDslFieldAndFormat | Field)[]
|
||||
min_score?: double
|
||||
post_filter?: QueryDslQueryContainer
|
||||
@ -12840,6 +12846,130 @@ export interface IndicesValidateQueryResponse {
|
||||
error?: string
|
||||
}
|
||||
|
||||
export interface InferenceAdaptiveAllocations {
|
||||
enabled?: boolean
|
||||
max_number_of_allocations?: integer
|
||||
min_number_of_allocations?: integer
|
||||
}
|
||||
|
||||
export interface InferenceAlibabaCloudServiceSettings {
|
||||
api_key: string
|
||||
host: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
service_id: string
|
||||
workspace: string
|
||||
}
|
||||
|
||||
export type InferenceAlibabaCloudServiceType = 'alibabacloud-ai-search'
|
||||
|
||||
export interface InferenceAlibabaCloudTaskSettings {
|
||||
input_type?: string
|
||||
return_token?: boolean
|
||||
}
|
||||
|
||||
export type InferenceAlibabaCloudTaskType = 'completion' | 'rerank' | 'space_embedding' | 'text_embedding'
|
||||
|
||||
export interface InferenceAmazonBedrockServiceSettings {
|
||||
access_key: string
|
||||
model: string
|
||||
provider?: string
|
||||
region: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
secret_key: string
|
||||
}
|
||||
|
||||
export type InferenceAmazonBedrockServiceType = 'amazonbedrock'
|
||||
|
||||
export interface InferenceAmazonBedrockTaskSettings {
|
||||
max_new_tokens?: integer
|
||||
temperature?: float
|
||||
top_k?: float
|
||||
top_p?: float
|
||||
}
|
||||
|
||||
export type InferenceAmazonBedrockTaskType = 'completion' | 'text_embedding'
|
||||
|
||||
export interface InferenceAnthropicServiceSettings {
|
||||
api_key: string
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceAnthropicServiceType = 'anthropic'
|
||||
|
||||
export interface InferenceAnthropicTaskSettings {
|
||||
max_tokens: integer
|
||||
temperature?: float
|
||||
top_k?: integer
|
||||
top_p?: float
|
||||
}
|
||||
|
||||
export type InferenceAnthropicTaskType = 'completion'
|
||||
|
||||
export interface InferenceAzureAiStudioServiceSettings {
|
||||
api_key: string
|
||||
endpoint_type: string
|
||||
target: string
|
||||
provider: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceAzureAiStudioServiceType = 'azureaistudio'
|
||||
|
||||
export interface InferenceAzureAiStudioTaskSettings {
|
||||
do_sample?: float
|
||||
max_new_tokens?: integer
|
||||
temperature?: float
|
||||
top_p?: float
|
||||
user?: string
|
||||
}
|
||||
|
||||
export type InferenceAzureAiStudioTaskType = 'completion' | 'text_embedding'
|
||||
|
||||
export interface InferenceAzureOpenAIServiceSettings {
|
||||
api_key?: string
|
||||
api_version: string
|
||||
deployment_id: string
|
||||
entra_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
resource_name: string
|
||||
}
|
||||
|
||||
export type InferenceAzureOpenAIServiceType = 'azureopenai'
|
||||
|
||||
export interface InferenceAzureOpenAITaskSettings {
|
||||
user?: string
|
||||
}
|
||||
|
||||
export type InferenceAzureOpenAITaskType = 'completion' | 'text_embedding'
|
||||
|
||||
export type InferenceCohereEmbeddingType = 'byte' | 'float' | 'int8'
|
||||
|
||||
export type InferenceCohereInputType = 'classification' | 'clustering' | 'ingest' | 'search'
|
||||
|
||||
export interface InferenceCohereServiceSettings {
|
||||
api_key: string
|
||||
embedding_type?: InferenceCohereEmbeddingType
|
||||
model_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
similarity?: InferenceCohereSimilarityType
|
||||
}
|
||||
|
||||
export type InferenceCohereServiceType = 'cohere'
|
||||
|
||||
export type InferenceCohereSimilarityType = 'cosine' | 'dot_product' | 'l2_norm'
|
||||
|
||||
export interface InferenceCohereTaskSettings {
|
||||
input_type?: InferenceCohereInputType
|
||||
return_documents?: boolean
|
||||
top_n?: integer
|
||||
truncate?: InferenceCohereTruncateType
|
||||
}
|
||||
|
||||
export type InferenceCohereTaskType = 'completion' | 'rerank' | 'text_embedding'
|
||||
|
||||
export type InferenceCohereTruncateType = 'END' | 'NONE' | 'START'
|
||||
|
||||
export interface InferenceCompletionInferenceResult {
|
||||
completion: InferenceCompletionResult[]
|
||||
}
|
||||
@ -12848,6 +12978,34 @@ export interface InferenceCompletionResult {
|
||||
result: string
|
||||
}
|
||||
|
||||
export interface InferenceCompletionTool {
|
||||
type: string
|
||||
function: InferenceCompletionToolFunction
|
||||
}
|
||||
|
||||
export interface InferenceCompletionToolChoice {
|
||||
type: string
|
||||
function: InferenceCompletionToolChoiceFunction
|
||||
}
|
||||
|
||||
export interface InferenceCompletionToolChoiceFunction {
|
||||
name: string
|
||||
}
|
||||
|
||||
export interface InferenceCompletionToolFunction {
|
||||
description?: string
|
||||
name: string
|
||||
parameters?: any
|
||||
strict?: boolean
|
||||
}
|
||||
|
||||
export type InferenceCompletionToolType = string | InferenceCompletionToolChoice
|
||||
|
||||
export interface InferenceContentObject {
|
||||
text: string
|
||||
type: string
|
||||
}
|
||||
|
||||
export interface InferenceDeleteInferenceEndpointResult extends AcknowledgedResponseBase {
|
||||
pipelines: string[]
|
||||
}
|
||||
@ -12856,7 +13014,79 @@ export type InferenceDenseByteVector = byte[]
|
||||
|
||||
export type InferenceDenseVector = float[]
|
||||
|
||||
export interface InferenceInferenceChunkingSettings extends InferenceInferenceEndpoint {
|
||||
export interface InferenceEisServiceSettings {
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceEisServiceType = 'elastic'
|
||||
|
||||
export type InferenceEisTaskType = 'chat_completion'
|
||||
|
||||
export interface InferenceElasticsearchServiceSettings {
|
||||
adaptive_allocations?: InferenceAdaptiveAllocations
|
||||
deployment_id?: string
|
||||
model_id: string
|
||||
num_allocations?: integer
|
||||
num_threads: integer
|
||||
}
|
||||
|
||||
export type InferenceElasticsearchServiceType = 'elasticsearch'
|
||||
|
||||
export interface InferenceElasticsearchTaskSettings {
|
||||
return_documents?: boolean
|
||||
}
|
||||
|
||||
export type InferenceElasticsearchTaskType = 'rerank' | 'sparse_embedding' | 'text_embedding'
|
||||
|
||||
export interface InferenceElserServiceSettings {
|
||||
adaptive_allocations?: InferenceAdaptiveAllocations
|
||||
num_allocations: integer
|
||||
num_threads: integer
|
||||
}
|
||||
|
||||
export type InferenceElserServiceType = 'elser'
|
||||
|
||||
export type InferenceElserTaskType = 'sparse_embedding'
|
||||
|
||||
export type InferenceGoogleAiServiceType = 'googleaistudio'
|
||||
|
||||
export interface InferenceGoogleAiStudioServiceSettings {
|
||||
api_key: string
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceGoogleAiStudioTaskType = 'completion' | 'text_embedding'
|
||||
|
||||
export interface InferenceGoogleVertexAIServiceSettings {
|
||||
location: string
|
||||
model_id: string
|
||||
project_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
service_account_json: string
|
||||
}
|
||||
|
||||
export type InferenceGoogleVertexAIServiceType = 'googlevertexai'
|
||||
|
||||
export interface InferenceGoogleVertexAITaskSettings {
|
||||
auto_truncate?: boolean
|
||||
top_n?: integer
|
||||
}
|
||||
|
||||
export type InferenceGoogleVertexAITaskType = 'rerank' | 'text_embedding'
|
||||
|
||||
export interface InferenceHuggingFaceServiceSettings {
|
||||
api_key: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url: string
|
||||
}
|
||||
|
||||
export type InferenceHuggingFaceServiceType = 'hugging_face'
|
||||
|
||||
export type InferenceHuggingFaceTaskType = 'text_embedding'
|
||||
|
||||
export interface InferenceInferenceChunkingSettings {
|
||||
max_chunk_size?: integer
|
||||
overlap?: integer
|
||||
sentence_overlap?: integer
|
||||
@ -12875,6 +13105,64 @@ export interface InferenceInferenceEndpointInfo extends InferenceInferenceEndpoi
|
||||
task_type: InferenceTaskType
|
||||
}
|
||||
|
||||
export interface InferenceJinaAIServiceSettings {
|
||||
api_key: string
|
||||
model_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
similarity?: InferenceJinaAISimilarityType
|
||||
}
|
||||
|
||||
export type InferenceJinaAIServiceType = 'jinaai'
|
||||
|
||||
export type InferenceJinaAISimilarityType = 'cosine' | 'dot_product' | 'l2_norm'
|
||||
|
||||
export interface InferenceJinaAITaskSettings {
|
||||
return_documents?: boolean
|
||||
task?: InferenceJinaAITextEmbeddingTask
|
||||
top_n?: integer
|
||||
}
|
||||
|
||||
export type InferenceJinaAITaskType = 'rerank' | 'text_embedding'
|
||||
|
||||
export type InferenceJinaAITextEmbeddingTask = 'classification' | 'clustering' | 'ingest' | 'search'
|
||||
|
||||
export interface InferenceMessage {
|
||||
content?: InferenceMessageContent
|
||||
role: string
|
||||
tool_call_id?: Id
|
||||
tool_calls?: InferenceToolCall[]
|
||||
}
|
||||
|
||||
export type InferenceMessageContent = string | InferenceContentObject[]
|
||||
|
||||
export interface InferenceMistralServiceSettings {
|
||||
api_key: string
|
||||
max_input_tokens?: integer
|
||||
model: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceMistralServiceType = 'mistral'
|
||||
|
||||
export type InferenceMistralTaskType = 'text_embedding'
|
||||
|
||||
export interface InferenceOpenAIServiceSettings {
|
||||
api_key: string
|
||||
dimensions?: integer
|
||||
model_id: string
|
||||
organization_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url?: string
|
||||
}
|
||||
|
||||
export type InferenceOpenAIServiceType = 'openai'
|
||||
|
||||
export interface InferenceOpenAITaskSettings {
|
||||
user?: string
|
||||
}
|
||||
|
||||
export type InferenceOpenAITaskType = 'chat_completion' | 'completion' | 'text_embedding'
|
||||
|
||||
export interface InferenceRankedDocument {
|
||||
index: integer
|
||||
relevance_score: float
|
||||
@ -12885,6 +13173,17 @@ export interface InferenceRateLimitSetting {
|
||||
requests_per_minute?: integer
|
||||
}
|
||||
|
||||
export interface InferenceRequestChatCompletion {
|
||||
messages: InferenceMessage[]
|
||||
model?: string
|
||||
max_completion_tokens?: long
|
||||
stop?: string[]
|
||||
temperature?: float
|
||||
tool_choice?: InferenceCompletionToolType
|
||||
tools?: InferenceCompletionTool[]
|
||||
top_p?: float
|
||||
}
|
||||
|
||||
export interface InferenceRerankedInferenceResult {
|
||||
rerank: InferenceRankedDocument[]
|
||||
}
|
||||
@ -12918,69 +13217,56 @@ export interface InferenceTextEmbeddingResult {
|
||||
embedding: InferenceDenseVector
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedCompletionTool {
|
||||
export interface InferenceToolCall {
|
||||
id: Id
|
||||
function: InferenceToolCallFunction
|
||||
type: string
|
||||
function: InferenceChatCompletionUnifiedCompletionToolFunction
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedCompletionToolChoice {
|
||||
type: string
|
||||
function: InferenceChatCompletionUnifiedCompletionToolChoiceFunction
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedCompletionToolChoiceFunction {
|
||||
export interface InferenceToolCallFunction {
|
||||
arguments: string
|
||||
name: string
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedCompletionToolFunction {
|
||||
description?: string
|
||||
name: string
|
||||
parameters?: any
|
||||
strict?: boolean
|
||||
export interface InferenceVoyageAIServiceSettings {
|
||||
dimensions?: integer
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
embedding_type?: float
|
||||
}
|
||||
|
||||
export type InferenceChatCompletionUnifiedCompletionToolType = string | InferenceChatCompletionUnifiedCompletionToolChoice
|
||||
export type InferenceVoyageAIServiceType = 'voyageai'
|
||||
|
||||
export interface InferenceChatCompletionUnifiedContentObject {
|
||||
text: string
|
||||
type: string
|
||||
export interface InferenceVoyageAITaskSettings {
|
||||
input_type?: string
|
||||
return_documents?: boolean
|
||||
top_k?: integer
|
||||
truncation?: boolean
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedMessage {
|
||||
content?: InferenceChatCompletionUnifiedMessageContent
|
||||
role: string
|
||||
tool_call_id?: Id
|
||||
tool_calls?: InferenceChatCompletionUnifiedToolCall[]
|
||||
export type InferenceVoyageAITaskType = 'text_embedding' | 'rerank'
|
||||
|
||||
export interface InferenceWatsonxServiceSettings {
|
||||
api_key: string
|
||||
api_version: string
|
||||
model_id: string
|
||||
project_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url: string
|
||||
}
|
||||
|
||||
export type InferenceChatCompletionUnifiedMessageContent = string | InferenceChatCompletionUnifiedContentObject[]
|
||||
export type InferenceWatsonxServiceType = 'watsonxai'
|
||||
|
||||
export type InferenceWatsonxTaskType = 'text_embedding'
|
||||
|
||||
export interface InferenceChatCompletionUnifiedRequest extends RequestBase {
|
||||
inference_id: Id
|
||||
timeout?: Duration
|
||||
messages: InferenceChatCompletionUnifiedMessage[]
|
||||
model?: string
|
||||
max_completion_tokens?: long
|
||||
stop?: string[]
|
||||
temperature?: float
|
||||
tool_choice?: InferenceChatCompletionUnifiedCompletionToolType
|
||||
tools?: InferenceChatCompletionUnifiedCompletionTool[]
|
||||
top_p?: float
|
||||
chat_completion_request?: InferenceRequestChatCompletion
|
||||
}
|
||||
|
||||
export type InferenceChatCompletionUnifiedResponse = StreamResult
|
||||
|
||||
export interface InferenceChatCompletionUnifiedToolCall {
|
||||
id: Id
|
||||
function: InferenceChatCompletionUnifiedToolCallFunction
|
||||
type: string
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedToolCallFunction {
|
||||
arguments: string
|
||||
name: string
|
||||
}
|
||||
|
||||
export interface InferenceCompletionRequest extends RequestBase {
|
||||
inference_id: Id
|
||||
timeout?: Duration
|
||||
@ -13008,6 +13294,13 @@ export interface InferenceGetResponse {
|
||||
endpoints: InferenceInferenceEndpointInfo[]
|
||||
}
|
||||
|
||||
export interface InferencePostEisChatCompletionRequest extends RequestBase {
|
||||
eis_inference_id: Id
|
||||
chat_completion_request?: InferenceRequestChatCompletion
|
||||
}
|
||||
|
||||
export type InferencePostEisChatCompletionResponse = StreamResult
|
||||
|
||||
export interface InferencePutRequest extends RequestBase {
|
||||
task_type?: InferenceTaskType
|
||||
inference_id: Id
|
||||
@ -13016,103 +13309,185 @@ export interface InferencePutRequest extends RequestBase {
|
||||
|
||||
export type InferencePutResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutEisEisServiceSettings {
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
export interface InferencePutAlibabacloudRequest extends RequestBase {
|
||||
task_type: InferenceAlibabaCloudTaskType
|
||||
alibabacloud_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAlibabaCloudServiceType
|
||||
service_settings: InferenceAlibabaCloudServiceSettings
|
||||
task_settings?: InferenceAlibabaCloudTaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutEisEisTaskType = 'chat_completion'
|
||||
export type InferencePutAlibabacloudResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutAmazonbedrockRequest extends RequestBase {
|
||||
task_type: InferenceAmazonBedrockTaskType
|
||||
amazonbedrock_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAmazonBedrockServiceType
|
||||
service_settings: InferenceAmazonBedrockServiceSettings
|
||||
task_settings?: InferenceAmazonBedrockTaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutAmazonbedrockResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutAnthropicRequest extends RequestBase {
|
||||
task_type: InferenceAnthropicTaskType
|
||||
anthropic_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAnthropicServiceType
|
||||
service_settings: InferenceAnthropicServiceSettings
|
||||
task_settings?: InferenceAnthropicTaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutAnthropicResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutAzureaistudioRequest extends RequestBase {
|
||||
task_type: InferenceAzureAiStudioTaskType
|
||||
azureaistudio_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAzureAiStudioServiceType
|
||||
service_settings: InferenceAzureAiStudioServiceSettings
|
||||
task_settings?: InferenceAzureAiStudioTaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutAzureaistudioResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutAzureopenaiRequest extends RequestBase {
|
||||
task_type: InferenceAzureOpenAITaskType
|
||||
azureopenai_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAzureOpenAIServiceType
|
||||
service_settings: InferenceAzureOpenAIServiceSettings
|
||||
task_settings?: InferenceAzureOpenAITaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutAzureopenaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutCohereRequest extends RequestBase {
|
||||
task_type: InferenceCohereTaskType
|
||||
cohere_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceCohereServiceType
|
||||
service_settings: InferenceCohereServiceSettings
|
||||
task_settings?: InferenceCohereTaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutCohereResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutEisRequest extends RequestBase {
|
||||
task_type: InferencePutEisEisTaskType
|
||||
task_type: InferenceEisTaskType
|
||||
eis_inference_id: Id
|
||||
service: InferencePutEisServiceType
|
||||
service_settings: InferencePutEisEisServiceSettings
|
||||
service: InferenceEisServiceType
|
||||
service_settings: InferenceEisServiceSettings
|
||||
}
|
||||
|
||||
export type InferencePutEisResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export type InferencePutEisServiceType = 'elastic'
|
||||
|
||||
export interface InferencePutOpenaiOpenAIServiceSettings {
|
||||
api_key: string
|
||||
dimensions?: integer
|
||||
model_id: string
|
||||
organization_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url?: string
|
||||
export interface InferencePutElasticsearchRequest extends RequestBase {
|
||||
task_type: InferenceElasticsearchTaskType
|
||||
elasticsearch_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceElasticsearchServiceType
|
||||
service_settings: InferenceElasticsearchServiceSettings
|
||||
task_settings?: InferenceElasticsearchTaskSettings
|
||||
}
|
||||
|
||||
export interface InferencePutOpenaiOpenAITaskSettings {
|
||||
user?: string
|
||||
export type InferencePutElasticsearchResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutElserRequest extends RequestBase {
|
||||
task_type: InferenceElserTaskType
|
||||
elser_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceElserServiceType
|
||||
service_settings: InferenceElserServiceSettings
|
||||
}
|
||||
|
||||
export type InferencePutOpenaiOpenAITaskType = 'chat_completion' | 'completion' | 'text_embedding'
|
||||
export type InferencePutElserResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutGoogleaistudioRequest extends RequestBase {
|
||||
task_type: InferenceGoogleAiStudioTaskType
|
||||
googleaistudio_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceGoogleAiServiceType
|
||||
service_settings: InferenceGoogleAiStudioServiceSettings
|
||||
}
|
||||
|
||||
export type InferencePutGoogleaistudioResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutGooglevertexaiRequest extends RequestBase {
|
||||
task_type: InferenceGoogleVertexAITaskType
|
||||
googlevertexai_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceGoogleVertexAIServiceType
|
||||
service_settings: InferenceGoogleVertexAIServiceSettings
|
||||
task_settings?: InferenceGoogleVertexAITaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutGooglevertexaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutHuggingFaceRequest extends RequestBase {
|
||||
task_type: InferenceHuggingFaceTaskType
|
||||
huggingface_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceHuggingFaceServiceType
|
||||
service_settings: InferenceHuggingFaceServiceSettings
|
||||
}
|
||||
|
||||
export type InferencePutHuggingFaceResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutJinaaiRequest extends RequestBase {
|
||||
task_type: InferenceJinaAITaskType
|
||||
jinaai_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceJinaAIServiceType
|
||||
service_settings: InferenceJinaAIServiceSettings
|
||||
task_settings?: InferenceJinaAITaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutJinaaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutMistralRequest extends RequestBase {
|
||||
task_type: InferenceMistralTaskType
|
||||
mistral_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceMistralServiceType
|
||||
service_settings: InferenceMistralServiceSettings
|
||||
}
|
||||
|
||||
export type InferencePutMistralResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutOpenaiRequest extends RequestBase {
|
||||
task_type: InferencePutOpenaiOpenAITaskType
|
||||
task_type: InferenceOpenAITaskType
|
||||
openai_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferencePutOpenaiServiceType
|
||||
service_settings: InferencePutOpenaiOpenAIServiceSettings
|
||||
task_settings?: InferencePutOpenaiOpenAITaskSettings
|
||||
service: InferenceOpenAIServiceType
|
||||
service_settings: InferenceOpenAIServiceSettings
|
||||
task_settings?: InferenceOpenAITaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutOpenaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export type InferencePutOpenaiServiceType = 'openai'
|
||||
|
||||
export interface InferencePutVoyageaiRequest extends RequestBase {
|
||||
task_type: InferencePutVoyageaiVoyageAITaskType
|
||||
task_type: InferenceVoyageAITaskType
|
||||
voyageai_inference_id: Id
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferencePutVoyageaiServiceType
|
||||
service_settings: InferencePutVoyageaiVoyageAIServiceSettings
|
||||
task_settings?: InferencePutVoyageaiVoyageAITaskSettings
|
||||
service: InferenceVoyageAIServiceType
|
||||
service_settings: InferenceVoyageAIServiceSettings
|
||||
task_settings?: InferenceVoyageAITaskSettings
|
||||
}
|
||||
|
||||
export type InferencePutVoyageaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export type InferencePutVoyageaiServiceType = 'voyageai'
|
||||
|
||||
export interface InferencePutVoyageaiVoyageAIServiceSettings {
|
||||
dimensions?: integer
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
embedding_type?: float
|
||||
}
|
||||
|
||||
export interface InferencePutVoyageaiVoyageAITaskSettings {
|
||||
input_type?: string
|
||||
return_documents?: boolean
|
||||
top_k?: integer
|
||||
truncation?: boolean
|
||||
}
|
||||
|
||||
export type InferencePutVoyageaiVoyageAITaskType = 'text_embedding' | 'rerank'
|
||||
|
||||
export interface InferencePutWatsonxRequest extends RequestBase {
|
||||
task_type: InferencePutWatsonxWatsonxTaskType
|
||||
task_type: InferenceWatsonxTaskType
|
||||
watsonx_inference_id: Id
|
||||
service: InferencePutWatsonxServiceType
|
||||
service_settings: InferencePutWatsonxWatsonxServiceSettings
|
||||
service: InferenceWatsonxServiceType
|
||||
service_settings: InferenceWatsonxServiceSettings
|
||||
}
|
||||
|
||||
export type InferencePutWatsonxResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export type InferencePutWatsonxServiceType = 'watsonxai'
|
||||
|
||||
export interface InferencePutWatsonxWatsonxServiceSettings {
|
||||
api_key: string
|
||||
api_version: string
|
||||
model_id: string
|
||||
project_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url: string
|
||||
}
|
||||
|
||||
export type InferencePutWatsonxWatsonxTaskType = 'text_embedding'
|
||||
|
||||
export interface InferenceRerankRequest extends RequestBase {
|
||||
inference_id: Id
|
||||
timeout?: Duration
|
||||
@ -14654,6 +15029,8 @@ export interface MlExponentialAverageCalculationContext {
|
||||
previous_exponential_average_ms?: DurationValue<UnitFloatMillis>
|
||||
}
|
||||
|
||||
export type MlFeatureExtractor = MlQueryFeatureExtractor
|
||||
|
||||
export interface MlFillMaskInferenceOptions {
|
||||
mask_token?: string
|
||||
num_top_classes?: integer
|
||||
@ -14719,6 +15096,7 @@ export interface MlInferenceConfigCreateContainer {
|
||||
text_classification?: MlTextClassificationInferenceOptions
|
||||
zero_shot_classification?: MlZeroShotClassificationInferenceOptions
|
||||
fill_mask?: MlFillMaskInferenceOptions
|
||||
learning_to_rank?: MlLearningToRankConfig
|
||||
ner?: MlNerInferenceOptions
|
||||
pass_through?: MlPassThroughInferenceOptions
|
||||
text_embedding?: MlTextEmbeddingInferenceOptions
|
||||
@ -14865,6 +15243,12 @@ export interface MlJobTimingStats {
|
||||
minimum_bucket_processing_time_ms?: DurationValue<UnitFloatMillis>
|
||||
}
|
||||
|
||||
export interface MlLearningToRankConfig {
|
||||
default_params?: Record<string, any>
|
||||
feature_extractors?: Record<string, MlFeatureExtractor>[]
|
||||
num_top_feature_importance_values: integer
|
||||
}
|
||||
|
||||
export type MlMemoryStatus = 'ok' | 'soft_limit' | 'hard_limit'
|
||||
|
||||
export interface MlModelPackageConfig {
|
||||
@ -15007,6 +15391,12 @@ export interface MlPerPartitionCategorization {
|
||||
|
||||
export type MlPredictedValue = ScalarValue | ScalarValue[]
|
||||
|
||||
export interface MlQueryFeatureExtractor {
|
||||
default_score?: float
|
||||
feature_name: string
|
||||
query: QueryDslQueryContainer
|
||||
}
|
||||
|
||||
export interface MlQuestionAnsweringInferenceOptions {
|
||||
num_top_classes?: integer
|
||||
tokenization?: MlTokenizationConfigContainer
|
||||
@ -15051,6 +15441,7 @@ export interface MlTextClassificationInferenceOptions {
|
||||
tokenization?: MlTokenizationConfigContainer
|
||||
results_field?: string
|
||||
classification_labels?: string[]
|
||||
vocabulary?: MlVocabulary
|
||||
}
|
||||
|
||||
export interface MlTextClassificationInferenceUpdateOptions {
|
||||
@ -15093,6 +15484,7 @@ export interface MlTokenizationConfigContainer {
|
||||
bert_ja?: MlNlpBertTokenizationConfig
|
||||
mpnet?: MlNlpBertTokenizationConfig
|
||||
roberta?: MlNlpRobertaTokenizationConfig
|
||||
xlm_roberta?: MlXlmRobertaTokenizationConfig
|
||||
}
|
||||
|
||||
export type MlTokenizationTruncate = 'first' | 'second' | 'none'
|
||||
@ -15130,6 +15522,11 @@ export interface MlTrainedModelAssignment {
|
||||
task_parameters: MlTrainedModelAssignmentTaskParameters
|
||||
}
|
||||
|
||||
export interface MlTrainedModelAssignmentRoutingStateAndReason {
|
||||
reason?: string
|
||||
routing_state: MlRoutingState
|
||||
}
|
||||
|
||||
export interface MlTrainedModelAssignmentRoutingTable {
|
||||
reason?: string
|
||||
routing_state: MlRoutingState
|
||||
@ -15170,6 +15567,7 @@ export interface MlTrainedModelConfig {
|
||||
model_size_bytes?: ByteSize
|
||||
model_package?: MlModelPackageConfig
|
||||
location?: MlTrainedModelLocation
|
||||
platform_architecture?: string
|
||||
prefix_strings?: MlTrainedModelPrefixStrings
|
||||
}
|
||||
|
||||
@ -15203,8 +15601,8 @@ export interface MlTrainedModelDeploymentNodesStats {
|
||||
number_of_allocations?: integer
|
||||
number_of_pending_requests?: integer
|
||||
peak_throughput_per_minute: long
|
||||
rejection_execution_count?: integer
|
||||
routing_state: MlTrainedModelAssignmentRoutingTable
|
||||
rejected_execution_count?: integer
|
||||
routing_state: MlTrainedModelAssignmentRoutingStateAndReason
|
||||
start_time?: EpochTime<UnitMillis>
|
||||
threads_per_allocation?: integer
|
||||
throughput_last_minute: integer
|
||||
@ -15305,6 +15703,9 @@ export interface MlVocabulary {
|
||||
index: IndexName
|
||||
}
|
||||
|
||||
export interface MlXlmRobertaTokenizationConfig extends MlCommonTokenizationConfig {
|
||||
}
|
||||
|
||||
export interface MlZeroShotClassificationInferenceOptions {
|
||||
tokenization?: MlTokenizationConfigContainer
|
||||
hypothesis_template?: string
|
||||
@ -20426,6 +20827,7 @@ export interface TransformGetTransformResponse {
|
||||
export interface TransformGetTransformTransformSummary {
|
||||
authorization?: MlTransformAuthorization
|
||||
create_time?: EpochTime<UnitMillis>
|
||||
create_time_string?: DateTime
|
||||
description?: string
|
||||
dest: ReindexDestination
|
||||
frequency?: Duration
|
||||
@ -20451,11 +20853,12 @@ export interface TransformGetTransformStatsCheckpointStats {
|
||||
|
||||
export interface TransformGetTransformStatsCheckpointing {
|
||||
changes_last_detected_at?: long
|
||||
changes_last_detected_at_date_time?: DateTime
|
||||
changes_last_detected_at_string?: DateTime
|
||||
last: TransformGetTransformStatsCheckpointStats
|
||||
next?: TransformGetTransformStatsCheckpointStats
|
||||
operations_behind?: long
|
||||
last_search_time?: long
|
||||
last_search_time_string?: DateTime
|
||||
}
|
||||
|
||||
export interface TransformGetTransformStatsRequest extends RequestBase {
|
||||
@ -20471,6 +20874,15 @@ export interface TransformGetTransformStatsResponse {
|
||||
transforms: TransformGetTransformStatsTransformStats[]
|
||||
}
|
||||
|
||||
export interface TransformGetTransformStatsTransformHealthIssue {
|
||||
type: string
|
||||
issue: string
|
||||
details?: string
|
||||
count: integer
|
||||
first_occurrence?: EpochTime<UnitMillis>
|
||||
first_occurence_string?: DateTime
|
||||
}
|
||||
|
||||
export interface TransformGetTransformStatsTransformIndexerStats {
|
||||
delete_time_in_ms?: EpochTime<UnitMillis>
|
||||
documents_indexed: long
|
||||
@ -20511,6 +20923,7 @@ export interface TransformGetTransformStatsTransformStats {
|
||||
|
||||
export interface TransformGetTransformStatsTransformStatsHealth {
|
||||
status: HealthStatus
|
||||
issues?: TransformGetTransformStatsTransformHealthIssue[]
|
||||
}
|
||||
|
||||
export interface TransformPreviewTransformRequest extends RequestBase {
|
||||
|
||||
@ -788,7 +788,7 @@ export interface MsearchMultisearchBody {
|
||||
knn?: KnnSearch | KnnSearch[]
|
||||
from?: integer
|
||||
highlight?: SearchHighlight
|
||||
indices_boost?: Record<IndexName, double>[]
|
||||
indices_boost?: Partial<Record<IndexName, double>>[]
|
||||
min_score?: double
|
||||
post_filter?: QueryDslQueryContainer
|
||||
profile?: boolean
|
||||
@ -931,6 +931,7 @@ export interface OpenPointInTimeRequest extends RequestBase {
|
||||
routing?: Routing
|
||||
expand_wildcards?: ExpandWildcards
|
||||
allow_partial_search_results?: boolean
|
||||
max_concurrent_shard_requests?: integer
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
index_filter?: QueryDslQueryContainer
|
||||
@ -1256,7 +1257,7 @@ export interface SearchRequest extends RequestBase {
|
||||
from?: integer
|
||||
highlight?: SearchHighlight
|
||||
track_total_hits?: SearchTrackHits
|
||||
indices_boost?: Record<IndexName, double>[]
|
||||
indices_boost?: Partial<Record<IndexName, double>>[]
|
||||
docvalue_fields?: (QueryDslFieldAndFormat | Field)[]
|
||||
knn?: KnnSearch | KnnSearch[]
|
||||
rank?: RankContainer
|
||||
@ -5382,6 +5383,10 @@ export interface MappingBooleanProperty extends MappingDocValuesPropertyBase {
|
||||
fielddata?: IndicesNumericFielddata
|
||||
index?: boolean
|
||||
null_value?: boolean
|
||||
ignore_malformed?: boolean
|
||||
script?: Script | string
|
||||
on_script_error?: MappingOnScriptError
|
||||
time_series_dimension?: boolean
|
||||
type: 'boolean'
|
||||
}
|
||||
|
||||
@ -5463,7 +5468,7 @@ export interface MappingDenseVectorIndexOptions {
|
||||
type: MappingDenseVectorIndexOptionsType
|
||||
}
|
||||
|
||||
export type MappingDenseVectorIndexOptionsType = 'flat' | 'hnsw' | 'int4_flat' | 'int4_hnsw' | 'int8_flat' | 'int8_hnsw'
|
||||
export type MappingDenseVectorIndexOptionsType = 'bbq_flat' | 'bbq_hnsw' | 'flat' | 'hnsw' | 'int4_flat' | 'int4_hnsw' | 'int8_flat' | 'int8_hnsw'
|
||||
|
||||
export interface MappingDenseVectorProperty extends MappingPropertyBase {
|
||||
type: 'dense_vector'
|
||||
@ -6840,7 +6845,7 @@ export interface AsyncSearchSubmitRequest extends RequestBase {
|
||||
from?: integer
|
||||
highlight?: SearchHighlight
|
||||
track_total_hits?: SearchTrackHits
|
||||
indices_boost?: Record<IndexName, double>[]
|
||||
indices_boost?: Partial<Record<IndexName, double>>[]
|
||||
docvalue_fields?: (QueryDslFieldAndFormat | Field)[]
|
||||
knn?: KnnSearch | KnnSearch[]
|
||||
min_score?: double
|
||||
@ -10417,10 +10422,11 @@ export interface EnrichDeletePolicyRequest extends RequestBase {
|
||||
|
||||
export type EnrichDeletePolicyResponse = AcknowledgedResponseBase
|
||||
|
||||
export type EnrichExecutePolicyEnrichPolicyPhase = 'SCHEDULED' | 'RUNNING' | 'COMPLETE' | 'FAILED'
|
||||
export type EnrichExecutePolicyEnrichPolicyPhase = 'SCHEDULED' | 'RUNNING' | 'COMPLETE' | 'FAILED' | 'CANCELLED'
|
||||
|
||||
export interface EnrichExecutePolicyExecuteEnrichPolicyStatus {
|
||||
phase: EnrichExecutePolicyEnrichPolicyPhase
|
||||
step?: string
|
||||
}
|
||||
|
||||
export interface EnrichExecutePolicyRequest extends RequestBase {
|
||||
@ -10431,7 +10437,7 @@ export interface EnrichExecutePolicyRequest extends RequestBase {
|
||||
|
||||
export interface EnrichExecutePolicyResponse {
|
||||
status?: EnrichExecutePolicyExecuteEnrichPolicyStatus
|
||||
task_id?: TaskId
|
||||
task?: TaskId
|
||||
}
|
||||
|
||||
export interface EnrichGetPolicyRequest extends RequestBase {
|
||||
@ -10759,7 +10765,7 @@ export interface FleetSearchRequest extends RequestBase {
|
||||
from?: integer
|
||||
highlight?: SearchHighlight
|
||||
track_total_hits?: SearchTrackHits
|
||||
indices_boost?: Record<IndexName, double>[]
|
||||
indices_boost?: Partial<Record<IndexName, double>>[]
|
||||
docvalue_fields?: (QueryDslFieldAndFormat | Field)[]
|
||||
min_score?: double
|
||||
post_filter?: QueryDslQueryContainer
|
||||
@ -13082,6 +13088,130 @@ export interface IndicesValidateQueryResponse {
|
||||
error?: string
|
||||
}
|
||||
|
||||
export interface InferenceAdaptiveAllocations {
|
||||
enabled?: boolean
|
||||
max_number_of_allocations?: integer
|
||||
min_number_of_allocations?: integer
|
||||
}
|
||||
|
||||
export interface InferenceAlibabaCloudServiceSettings {
|
||||
api_key: string
|
||||
host: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
service_id: string
|
||||
workspace: string
|
||||
}
|
||||
|
||||
export type InferenceAlibabaCloudServiceType = 'alibabacloud-ai-search'
|
||||
|
||||
export interface InferenceAlibabaCloudTaskSettings {
|
||||
input_type?: string
|
||||
return_token?: boolean
|
||||
}
|
||||
|
||||
export type InferenceAlibabaCloudTaskType = 'completion' | 'rerank' | 'space_embedding' | 'text_embedding'
|
||||
|
||||
export interface InferenceAmazonBedrockServiceSettings {
|
||||
access_key: string
|
||||
model: string
|
||||
provider?: string
|
||||
region: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
secret_key: string
|
||||
}
|
||||
|
||||
export type InferenceAmazonBedrockServiceType = 'amazonbedrock'
|
||||
|
||||
export interface InferenceAmazonBedrockTaskSettings {
|
||||
max_new_tokens?: integer
|
||||
temperature?: float
|
||||
top_k?: float
|
||||
top_p?: float
|
||||
}
|
||||
|
||||
export type InferenceAmazonBedrockTaskType = 'completion' | 'text_embedding'
|
||||
|
||||
export interface InferenceAnthropicServiceSettings {
|
||||
api_key: string
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceAnthropicServiceType = 'anthropic'
|
||||
|
||||
export interface InferenceAnthropicTaskSettings {
|
||||
max_tokens: integer
|
||||
temperature?: float
|
||||
top_k?: integer
|
||||
top_p?: float
|
||||
}
|
||||
|
||||
export type InferenceAnthropicTaskType = 'completion'
|
||||
|
||||
export interface InferenceAzureAiStudioServiceSettings {
|
||||
api_key: string
|
||||
endpoint_type: string
|
||||
target: string
|
||||
provider: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceAzureAiStudioServiceType = 'azureaistudio'
|
||||
|
||||
export interface InferenceAzureAiStudioTaskSettings {
|
||||
do_sample?: float
|
||||
max_new_tokens?: integer
|
||||
temperature?: float
|
||||
top_p?: float
|
||||
user?: string
|
||||
}
|
||||
|
||||
export type InferenceAzureAiStudioTaskType = 'completion' | 'text_embedding'
|
||||
|
||||
export interface InferenceAzureOpenAIServiceSettings {
|
||||
api_key?: string
|
||||
api_version: string
|
||||
deployment_id: string
|
||||
entra_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
resource_name: string
|
||||
}
|
||||
|
||||
export type InferenceAzureOpenAIServiceType = 'azureopenai'
|
||||
|
||||
export interface InferenceAzureOpenAITaskSettings {
|
||||
user?: string
|
||||
}
|
||||
|
||||
export type InferenceAzureOpenAITaskType = 'completion' | 'text_embedding'
|
||||
|
||||
export type InferenceCohereEmbeddingType = 'byte' | 'float' | 'int8'
|
||||
|
||||
export type InferenceCohereInputType = 'classification' | 'clustering' | 'ingest' | 'search'
|
||||
|
||||
export interface InferenceCohereServiceSettings {
|
||||
api_key: string
|
||||
embedding_type?: InferenceCohereEmbeddingType
|
||||
model_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
similarity?: InferenceCohereSimilarityType
|
||||
}
|
||||
|
||||
export type InferenceCohereServiceType = 'cohere'
|
||||
|
||||
export type InferenceCohereSimilarityType = 'cosine' | 'dot_product' | 'l2_norm'
|
||||
|
||||
export interface InferenceCohereTaskSettings {
|
||||
input_type?: InferenceCohereInputType
|
||||
return_documents?: boolean
|
||||
top_n?: integer
|
||||
truncate?: InferenceCohereTruncateType
|
||||
}
|
||||
|
||||
export type InferenceCohereTaskType = 'completion' | 'rerank' | 'text_embedding'
|
||||
|
||||
export type InferenceCohereTruncateType = 'END' | 'NONE' | 'START'
|
||||
|
||||
export interface InferenceCompletionInferenceResult {
|
||||
completion: InferenceCompletionResult[]
|
||||
}
|
||||
@ -13090,6 +13220,34 @@ export interface InferenceCompletionResult {
|
||||
result: string
|
||||
}
|
||||
|
||||
export interface InferenceCompletionTool {
|
||||
type: string
|
||||
function: InferenceCompletionToolFunction
|
||||
}
|
||||
|
||||
export interface InferenceCompletionToolChoice {
|
||||
type: string
|
||||
function: InferenceCompletionToolChoiceFunction
|
||||
}
|
||||
|
||||
export interface InferenceCompletionToolChoiceFunction {
|
||||
name: string
|
||||
}
|
||||
|
||||
export interface InferenceCompletionToolFunction {
|
||||
description?: string
|
||||
name: string
|
||||
parameters?: any
|
||||
strict?: boolean
|
||||
}
|
||||
|
||||
export type InferenceCompletionToolType = string | InferenceCompletionToolChoice
|
||||
|
||||
export interface InferenceContentObject {
|
||||
text: string
|
||||
type: string
|
||||
}
|
||||
|
||||
export interface InferenceDeleteInferenceEndpointResult extends AcknowledgedResponseBase {
|
||||
pipelines: string[]
|
||||
}
|
||||
@ -13098,7 +13256,79 @@ export type InferenceDenseByteVector = byte[]
|
||||
|
||||
export type InferenceDenseVector = float[]
|
||||
|
||||
export interface InferenceInferenceChunkingSettings extends InferenceInferenceEndpoint {
|
||||
export interface InferenceEisServiceSettings {
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceEisServiceType = 'elastic'
|
||||
|
||||
export type InferenceEisTaskType = 'chat_completion'
|
||||
|
||||
export interface InferenceElasticsearchServiceSettings {
|
||||
adaptive_allocations?: InferenceAdaptiveAllocations
|
||||
deployment_id?: string
|
||||
model_id: string
|
||||
num_allocations?: integer
|
||||
num_threads: integer
|
||||
}
|
||||
|
||||
export type InferenceElasticsearchServiceType = 'elasticsearch'
|
||||
|
||||
export interface InferenceElasticsearchTaskSettings {
|
||||
return_documents?: boolean
|
||||
}
|
||||
|
||||
export type InferenceElasticsearchTaskType = 'rerank' | 'sparse_embedding' | 'text_embedding'
|
||||
|
||||
export interface InferenceElserServiceSettings {
|
||||
adaptive_allocations?: InferenceAdaptiveAllocations
|
||||
num_allocations: integer
|
||||
num_threads: integer
|
||||
}
|
||||
|
||||
export type InferenceElserServiceType = 'elser'
|
||||
|
||||
export type InferenceElserTaskType = 'sparse_embedding'
|
||||
|
||||
export type InferenceGoogleAiServiceType = 'googleaistudio'
|
||||
|
||||
export interface InferenceGoogleAiStudioServiceSettings {
|
||||
api_key: string
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceGoogleAiStudioTaskType = 'completion' | 'text_embedding'
|
||||
|
||||
export interface InferenceGoogleVertexAIServiceSettings {
|
||||
location: string
|
||||
model_id: string
|
||||
project_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
service_account_json: string
|
||||
}
|
||||
|
||||
export type InferenceGoogleVertexAIServiceType = 'googlevertexai'
|
||||
|
||||
export interface InferenceGoogleVertexAITaskSettings {
|
||||
auto_truncate?: boolean
|
||||
top_n?: integer
|
||||
}
|
||||
|
||||
export type InferenceGoogleVertexAITaskType = 'rerank' | 'text_embedding'
|
||||
|
||||
export interface InferenceHuggingFaceServiceSettings {
|
||||
api_key: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url: string
|
||||
}
|
||||
|
||||
export type InferenceHuggingFaceServiceType = 'hugging_face'
|
||||
|
||||
export type InferenceHuggingFaceTaskType = 'text_embedding'
|
||||
|
||||
export interface InferenceInferenceChunkingSettings {
|
||||
max_chunk_size?: integer
|
||||
overlap?: integer
|
||||
sentence_overlap?: integer
|
||||
@ -13117,6 +13347,64 @@ export interface InferenceInferenceEndpointInfo extends InferenceInferenceEndpoi
|
||||
task_type: InferenceTaskType
|
||||
}
|
||||
|
||||
export interface InferenceJinaAIServiceSettings {
|
||||
api_key: string
|
||||
model_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
similarity?: InferenceJinaAISimilarityType
|
||||
}
|
||||
|
||||
export type InferenceJinaAIServiceType = 'jinaai'
|
||||
|
||||
export type InferenceJinaAISimilarityType = 'cosine' | 'dot_product' | 'l2_norm'
|
||||
|
||||
export interface InferenceJinaAITaskSettings {
|
||||
return_documents?: boolean
|
||||
task?: InferenceJinaAITextEmbeddingTask
|
||||
top_n?: integer
|
||||
}
|
||||
|
||||
export type InferenceJinaAITaskType = 'rerank' | 'text_embedding'
|
||||
|
||||
export type InferenceJinaAITextEmbeddingTask = 'classification' | 'clustering' | 'ingest' | 'search'
|
||||
|
||||
export interface InferenceMessage {
|
||||
content?: InferenceMessageContent
|
||||
role: string
|
||||
tool_call_id?: Id
|
||||
tool_calls?: InferenceToolCall[]
|
||||
}
|
||||
|
||||
export type InferenceMessageContent = string | InferenceContentObject[]
|
||||
|
||||
export interface InferenceMistralServiceSettings {
|
||||
api_key: string
|
||||
max_input_tokens?: integer
|
||||
model: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
}
|
||||
|
||||
export type InferenceMistralServiceType = 'mistral'
|
||||
|
||||
export type InferenceMistralTaskType = 'text_embedding'
|
||||
|
||||
export interface InferenceOpenAIServiceSettings {
|
||||
api_key: string
|
||||
dimensions?: integer
|
||||
model_id: string
|
||||
organization_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url?: string
|
||||
}
|
||||
|
||||
export type InferenceOpenAIServiceType = 'openai'
|
||||
|
||||
export interface InferenceOpenAITaskSettings {
|
||||
user?: string
|
||||
}
|
||||
|
||||
export type InferenceOpenAITaskType = 'chat_completion' | 'completion' | 'text_embedding'
|
||||
|
||||
export interface InferenceRankedDocument {
|
||||
index: integer
|
||||
relevance_score: float
|
||||
@ -13127,6 +13415,17 @@ export interface InferenceRateLimitSetting {
|
||||
requests_per_minute?: integer
|
||||
}
|
||||
|
||||
export interface InferenceRequestChatCompletion {
|
||||
messages: InferenceMessage[]
|
||||
model?: string
|
||||
max_completion_tokens?: long
|
||||
stop?: string[]
|
||||
temperature?: float
|
||||
tool_choice?: InferenceCompletionToolType
|
||||
tools?: InferenceCompletionTool[]
|
||||
top_p?: float
|
||||
}
|
||||
|
||||
export interface InferenceRerankedInferenceResult {
|
||||
rerank: InferenceRankedDocument[]
|
||||
}
|
||||
@ -13160,72 +13459,57 @@ export interface InferenceTextEmbeddingResult {
|
||||
embedding: InferenceDenseVector
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedCompletionTool {
|
||||
export interface InferenceToolCall {
|
||||
id: Id
|
||||
function: InferenceToolCallFunction
|
||||
type: string
|
||||
function: InferenceChatCompletionUnifiedCompletionToolFunction
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedCompletionToolChoice {
|
||||
type: string
|
||||
function: InferenceChatCompletionUnifiedCompletionToolChoiceFunction
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedCompletionToolChoiceFunction {
|
||||
export interface InferenceToolCallFunction {
|
||||
arguments: string
|
||||
name: string
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedCompletionToolFunction {
|
||||
description?: string
|
||||
name: string
|
||||
parameters?: any
|
||||
strict?: boolean
|
||||
export interface InferenceVoyageAIServiceSettings {
|
||||
dimensions?: integer
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
embedding_type?: float
|
||||
}
|
||||
|
||||
export type InferenceChatCompletionUnifiedCompletionToolType = string | InferenceChatCompletionUnifiedCompletionToolChoice
|
||||
export type InferenceVoyageAIServiceType = 'voyageai'
|
||||
|
||||
export interface InferenceChatCompletionUnifiedContentObject {
|
||||
text: string
|
||||
type: string
|
||||
export interface InferenceVoyageAITaskSettings {
|
||||
input_type?: string
|
||||
return_documents?: boolean
|
||||
top_k?: integer
|
||||
truncation?: boolean
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedMessage {
|
||||
content?: InferenceChatCompletionUnifiedMessageContent
|
||||
role: string
|
||||
tool_call_id?: Id
|
||||
tool_calls?: InferenceChatCompletionUnifiedToolCall[]
|
||||
export type InferenceVoyageAITaskType = 'text_embedding' | 'rerank'
|
||||
|
||||
export interface InferenceWatsonxServiceSettings {
|
||||
api_key: string
|
||||
api_version: string
|
||||
model_id: string
|
||||
project_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url: string
|
||||
}
|
||||
|
||||
export type InferenceChatCompletionUnifiedMessageContent = string | InferenceChatCompletionUnifiedContentObject[]
|
||||
export type InferenceWatsonxServiceType = 'watsonxai'
|
||||
|
||||
export type InferenceWatsonxTaskType = 'text_embedding'
|
||||
|
||||
export interface InferenceChatCompletionUnifiedRequest extends RequestBase {
|
||||
inference_id: Id
|
||||
timeout?: Duration
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
messages: InferenceChatCompletionUnifiedMessage[]
|
||||
model?: string
|
||||
max_completion_tokens?: long
|
||||
stop?: string[]
|
||||
temperature?: float
|
||||
tool_choice?: InferenceChatCompletionUnifiedCompletionToolType
|
||||
tools?: InferenceChatCompletionUnifiedCompletionTool[]
|
||||
top_p?: float
|
||||
}
|
||||
/** @deprecated The use of the 'body' key has been deprecated, use 'chat_completion_request' instead. */
|
||||
body?: InferenceRequestChatCompletion
|
||||
}
|
||||
|
||||
export type InferenceChatCompletionUnifiedResponse = StreamResult
|
||||
|
||||
export interface InferenceChatCompletionUnifiedToolCall {
|
||||
id: Id
|
||||
function: InferenceChatCompletionUnifiedToolCallFunction
|
||||
type: string
|
||||
}
|
||||
|
||||
export interface InferenceChatCompletionUnifiedToolCallFunction {
|
||||
arguments: string
|
||||
name: string
|
||||
}
|
||||
|
||||
export interface InferenceCompletionRequest extends RequestBase {
|
||||
inference_id: Id
|
||||
timeout?: Duration
|
||||
@ -13256,6 +13540,14 @@ export interface InferenceGetResponse {
|
||||
endpoints: InferenceInferenceEndpointInfo[]
|
||||
}
|
||||
|
||||
export interface InferencePostEisChatCompletionRequest extends RequestBase {
|
||||
eis_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, use 'chat_completion_request' instead. */
|
||||
body?: InferenceRequestChatCompletion
|
||||
}
|
||||
|
||||
export type InferencePostEisChatCompletionResponse = StreamResult
|
||||
|
||||
export interface InferencePutRequest extends RequestBase {
|
||||
task_type?: InferenceTaskType
|
||||
inference_id: Id
|
||||
@ -13265,115 +13557,236 @@ export interface InferencePutRequest extends RequestBase {
|
||||
|
||||
export type InferencePutResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutEisEisServiceSettings {
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
export interface InferencePutAlibabacloudRequest extends RequestBase {
|
||||
task_type: InferenceAlibabaCloudTaskType
|
||||
alibabacloud_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAlibabaCloudServiceType
|
||||
service_settings: InferenceAlibabaCloudServiceSettings
|
||||
task_settings?: InferenceAlibabaCloudTaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutEisEisTaskType = 'chat_completion'
|
||||
export type InferencePutAlibabacloudResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutAmazonbedrockRequest extends RequestBase {
|
||||
task_type: InferenceAmazonBedrockTaskType
|
||||
amazonbedrock_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAmazonBedrockServiceType
|
||||
service_settings: InferenceAmazonBedrockServiceSettings
|
||||
task_settings?: InferenceAmazonBedrockTaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutAmazonbedrockResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutAnthropicRequest extends RequestBase {
|
||||
task_type: InferenceAnthropicTaskType
|
||||
anthropic_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAnthropicServiceType
|
||||
service_settings: InferenceAnthropicServiceSettings
|
||||
task_settings?: InferenceAnthropicTaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutAnthropicResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutAzureaistudioRequest extends RequestBase {
|
||||
task_type: InferenceAzureAiStudioTaskType
|
||||
azureaistudio_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAzureAiStudioServiceType
|
||||
service_settings: InferenceAzureAiStudioServiceSettings
|
||||
task_settings?: InferenceAzureAiStudioTaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutAzureaistudioResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutAzureopenaiRequest extends RequestBase {
|
||||
task_type: InferenceAzureOpenAITaskType
|
||||
azureopenai_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceAzureOpenAIServiceType
|
||||
service_settings: InferenceAzureOpenAIServiceSettings
|
||||
task_settings?: InferenceAzureOpenAITaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutAzureopenaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutCohereRequest extends RequestBase {
|
||||
task_type: InferenceCohereTaskType
|
||||
cohere_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceCohereServiceType
|
||||
service_settings: InferenceCohereServiceSettings
|
||||
task_settings?: InferenceCohereTaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutCohereResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutEisRequest extends RequestBase {
|
||||
task_type: InferencePutEisEisTaskType
|
||||
task_type: InferenceEisTaskType
|
||||
eis_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
service: InferencePutEisServiceType
|
||||
service_settings: InferencePutEisEisServiceSettings
|
||||
service: InferenceEisServiceType
|
||||
service_settings: InferenceEisServiceSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutEisResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export type InferencePutEisServiceType = 'elastic'
|
||||
|
||||
export interface InferencePutOpenaiOpenAIServiceSettings {
|
||||
api_key: string
|
||||
dimensions?: integer
|
||||
model_id: string
|
||||
organization_id?: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url?: string
|
||||
export interface InferencePutElasticsearchRequest extends RequestBase {
|
||||
task_type: InferenceElasticsearchTaskType
|
||||
elasticsearch_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceElasticsearchServiceType
|
||||
service_settings: InferenceElasticsearchServiceSettings
|
||||
task_settings?: InferenceElasticsearchTaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export interface InferencePutOpenaiOpenAITaskSettings {
|
||||
user?: string
|
||||
export type InferencePutElasticsearchResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutElserRequest extends RequestBase {
|
||||
task_type: InferenceElserTaskType
|
||||
elser_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceElserServiceType
|
||||
service_settings: InferenceElserServiceSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutOpenaiOpenAITaskType = 'chat_completion' | 'completion' | 'text_embedding'
|
||||
export type InferencePutElserResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutGoogleaistudioRequest extends RequestBase {
|
||||
task_type: InferenceGoogleAiStudioTaskType
|
||||
googleaistudio_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceGoogleAiServiceType
|
||||
service_settings: InferenceGoogleAiStudioServiceSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutGoogleaistudioResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutGooglevertexaiRequest extends RequestBase {
|
||||
task_type: InferenceGoogleVertexAITaskType
|
||||
googlevertexai_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceGoogleVertexAIServiceType
|
||||
service_settings: InferenceGoogleVertexAIServiceSettings
|
||||
task_settings?: InferenceGoogleVertexAITaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutGooglevertexaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutHuggingFaceRequest extends RequestBase {
|
||||
task_type: InferenceHuggingFaceTaskType
|
||||
huggingface_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceHuggingFaceServiceType
|
||||
service_settings: InferenceHuggingFaceServiceSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutHuggingFaceResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutJinaaiRequest extends RequestBase {
|
||||
task_type: InferenceJinaAITaskType
|
||||
jinaai_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceJinaAIServiceType
|
||||
service_settings: InferenceJinaAIServiceSettings
|
||||
task_settings?: InferenceJinaAITaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutJinaaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutMistralRequest extends RequestBase {
|
||||
task_type: InferenceMistralTaskType
|
||||
mistral_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferenceMistralServiceType
|
||||
service_settings: InferenceMistralServiceSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutMistralResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export interface InferencePutOpenaiRequest extends RequestBase {
|
||||
task_type: InferencePutOpenaiOpenAITaskType
|
||||
task_type: InferenceOpenAITaskType
|
||||
openai_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferencePutOpenaiServiceType
|
||||
service_settings: InferencePutOpenaiOpenAIServiceSettings
|
||||
task_settings?: InferencePutOpenaiOpenAITaskSettings
|
||||
service: InferenceOpenAIServiceType
|
||||
service_settings: InferenceOpenAIServiceSettings
|
||||
task_settings?: InferenceOpenAITaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutOpenaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export type InferencePutOpenaiServiceType = 'openai'
|
||||
|
||||
export interface InferencePutVoyageaiRequest extends RequestBase {
|
||||
task_type: InferencePutVoyageaiVoyageAITaskType
|
||||
task_type: InferenceVoyageAITaskType
|
||||
voyageai_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
chunking_settings?: InferenceInferenceChunkingSettings
|
||||
service: InferencePutVoyageaiServiceType
|
||||
service_settings: InferencePutVoyageaiVoyageAIServiceSettings
|
||||
task_settings?: InferencePutVoyageaiVoyageAITaskSettings
|
||||
service: InferenceVoyageAIServiceType
|
||||
service_settings: InferenceVoyageAIServiceSettings
|
||||
task_settings?: InferenceVoyageAITaskSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutVoyageaiResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export type InferencePutVoyageaiServiceType = 'voyageai'
|
||||
|
||||
export interface InferencePutVoyageaiVoyageAIServiceSettings {
|
||||
dimensions?: integer
|
||||
model_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
embedding_type?: float
|
||||
}
|
||||
|
||||
export interface InferencePutVoyageaiVoyageAITaskSettings {
|
||||
input_type?: string
|
||||
return_documents?: boolean
|
||||
top_k?: integer
|
||||
truncation?: boolean
|
||||
}
|
||||
|
||||
export type InferencePutVoyageaiVoyageAITaskType = 'text_embedding' | 'rerank'
|
||||
|
||||
export interface InferencePutWatsonxRequest extends RequestBase {
|
||||
task_type: InferencePutWatsonxWatsonxTaskType
|
||||
task_type: InferenceWatsonxTaskType
|
||||
watsonx_inference_id: Id
|
||||
/** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
|
||||
body?: {
|
||||
service: InferencePutWatsonxServiceType
|
||||
service_settings: InferencePutWatsonxWatsonxServiceSettings
|
||||
service: InferenceWatsonxServiceType
|
||||
service_settings: InferenceWatsonxServiceSettings
|
||||
}
|
||||
}
|
||||
|
||||
export type InferencePutWatsonxResponse = InferenceInferenceEndpointInfo
|
||||
|
||||
export type InferencePutWatsonxServiceType = 'watsonxai'
|
||||
|
||||
export interface InferencePutWatsonxWatsonxServiceSettings {
|
||||
api_key: string
|
||||
api_version: string
|
||||
model_id: string
|
||||
project_id: string
|
||||
rate_limit?: InferenceRateLimitSetting
|
||||
url: string
|
||||
}
|
||||
|
||||
export type InferencePutWatsonxWatsonxTaskType = 'text_embedding'
|
||||
|
||||
export interface InferenceRerankRequest extends RequestBase {
|
||||
inference_id: Id
|
||||
timeout?: Duration
|
||||
@ -14942,6 +15355,8 @@ export interface MlExponentialAverageCalculationContext {
|
||||
previous_exponential_average_ms?: DurationValue<UnitFloatMillis>
|
||||
}
|
||||
|
||||
export type MlFeatureExtractor = MlQueryFeatureExtractor
|
||||
|
||||
export interface MlFillMaskInferenceOptions {
|
||||
mask_token?: string
|
||||
num_top_classes?: integer
|
||||
@ -15007,6 +15422,7 @@ export interface MlInferenceConfigCreateContainer {
|
||||
text_classification?: MlTextClassificationInferenceOptions
|
||||
zero_shot_classification?: MlZeroShotClassificationInferenceOptions
|
||||
fill_mask?: MlFillMaskInferenceOptions
|
||||
learning_to_rank?: MlLearningToRankConfig
|
||||
ner?: MlNerInferenceOptions
|
||||
pass_through?: MlPassThroughInferenceOptions
|
||||
text_embedding?: MlTextEmbeddingInferenceOptions
|
||||
@ -15153,6 +15569,12 @@ export interface MlJobTimingStats {
|
||||
minimum_bucket_processing_time_ms?: DurationValue<UnitFloatMillis>
|
||||
}
|
||||
|
||||
export interface MlLearningToRankConfig {
|
||||
default_params?: Record<string, any>
|
||||
feature_extractors?: Record<string, MlFeatureExtractor>[]
|
||||
num_top_feature_importance_values: integer
|
||||
}
|
||||
|
||||
export type MlMemoryStatus = 'ok' | 'soft_limit' | 'hard_limit'
|
||||
|
||||
export interface MlModelPackageConfig {
|
||||
@ -15295,6 +15717,12 @@ export interface MlPerPartitionCategorization {
|
||||
|
||||
export type MlPredictedValue = ScalarValue | ScalarValue[]
|
||||
|
||||
export interface MlQueryFeatureExtractor {
|
||||
default_score?: float
|
||||
feature_name: string
|
||||
query: QueryDslQueryContainer
|
||||
}
|
||||
|
||||
export interface MlQuestionAnsweringInferenceOptions {
|
||||
num_top_classes?: integer
|
||||
tokenization?: MlTokenizationConfigContainer
|
||||
@ -15339,6 +15767,7 @@ export interface MlTextClassificationInferenceOptions {
|
||||
tokenization?: MlTokenizationConfigContainer
|
||||
results_field?: string
|
||||
classification_labels?: string[]
|
||||
vocabulary?: MlVocabulary
|
||||
}
|
||||
|
||||
export interface MlTextClassificationInferenceUpdateOptions {
|
||||
@ -15381,6 +15810,7 @@ export interface MlTokenizationConfigContainer {
|
||||
bert_ja?: MlNlpBertTokenizationConfig
|
||||
mpnet?: MlNlpBertTokenizationConfig
|
||||
roberta?: MlNlpRobertaTokenizationConfig
|
||||
xlm_roberta?: MlXlmRobertaTokenizationConfig
|
||||
}
|
||||
|
||||
export type MlTokenizationTruncate = 'first' | 'second' | 'none'
|
||||
@ -15418,6 +15848,11 @@ export interface MlTrainedModelAssignment {
|
||||
task_parameters: MlTrainedModelAssignmentTaskParameters
|
||||
}
|
||||
|
||||
export interface MlTrainedModelAssignmentRoutingStateAndReason {
|
||||
reason?: string
|
||||
routing_state: MlRoutingState
|
||||
}
|
||||
|
||||
export interface MlTrainedModelAssignmentRoutingTable {
|
||||
reason?: string
|
||||
routing_state: MlRoutingState
|
||||
@ -15458,6 +15893,7 @@ export interface MlTrainedModelConfig {
|
||||
model_size_bytes?: ByteSize
|
||||
model_package?: MlModelPackageConfig
|
||||
location?: MlTrainedModelLocation
|
||||
platform_architecture?: string
|
||||
prefix_strings?: MlTrainedModelPrefixStrings
|
||||
}
|
||||
|
||||
@ -15491,8 +15927,8 @@ export interface MlTrainedModelDeploymentNodesStats {
|
||||
number_of_allocations?: integer
|
||||
number_of_pending_requests?: integer
|
||||
peak_throughput_per_minute: long
|
||||
rejection_execution_count?: integer
|
||||
routing_state: MlTrainedModelAssignmentRoutingTable
|
||||
rejected_execution_count?: integer
|
||||
routing_state: MlTrainedModelAssignmentRoutingStateAndReason
|
||||
start_time?: EpochTime<UnitMillis>
|
||||
threads_per_allocation?: integer
|
||||
throughput_last_minute: integer
|
||||
@ -15593,6 +16029,9 @@ export interface MlVocabulary {
|
||||
index: IndexName
|
||||
}
|
||||
|
||||
export interface MlXlmRobertaTokenizationConfig extends MlCommonTokenizationConfig {
|
||||
}
|
||||
|
||||
export interface MlZeroShotClassificationInferenceOptions {
|
||||
tokenization?: MlTokenizationConfigContainer
|
||||
hypothesis_template?: string
|
||||
@ -21003,6 +21442,7 @@ export interface TransformGetTransformResponse {
|
||||
export interface TransformGetTransformTransformSummary {
|
||||
authorization?: MlTransformAuthorization
|
||||
create_time?: EpochTime<UnitMillis>
|
||||
create_time_string?: DateTime
|
||||
description?: string
|
||||
dest: ReindexDestination
|
||||
frequency?: Duration
|
||||
@ -21028,11 +21468,12 @@ export interface TransformGetTransformStatsCheckpointStats {
|
||||
|
||||
export interface TransformGetTransformStatsCheckpointing {
|
||||
changes_last_detected_at?: long
|
||||
changes_last_detected_at_date_time?: DateTime
|
||||
changes_last_detected_at_string?: DateTime
|
||||
last: TransformGetTransformStatsCheckpointStats
|
||||
next?: TransformGetTransformStatsCheckpointStats
|
||||
operations_behind?: long
|
||||
last_search_time?: long
|
||||
last_search_time_string?: DateTime
|
||||
}
|
||||
|
||||
export interface TransformGetTransformStatsRequest extends RequestBase {
|
||||
@ -21048,6 +21489,15 @@ export interface TransformGetTransformStatsResponse {
|
||||
transforms: TransformGetTransformStatsTransformStats[]
|
||||
}
|
||||
|
||||
export interface TransformGetTransformStatsTransformHealthIssue {
|
||||
type: string
|
||||
issue: string
|
||||
details?: string
|
||||
count: integer
|
||||
first_occurrence?: EpochTime<UnitMillis>
|
||||
first_occurence_string?: DateTime
|
||||
}
|
||||
|
||||
export interface TransformGetTransformStatsTransformIndexerStats {
|
||||
delete_time_in_ms?: EpochTime<UnitMillis>
|
||||
documents_indexed: long
|
||||
@ -21088,6 +21538,7 @@ export interface TransformGetTransformStatsTransformStats {
|
||||
|
||||
export interface TransformGetTransformStatsTransformStatsHealth {
|
||||
status: HealthStatus
|
||||
issues?: TransformGetTransformStatsTransformHealthIssue[]
|
||||
}
|
||||
|
||||
export interface TransformPreviewTransformRequest extends RequestBase {
|
||||
|
||||
Reference in New Issue
Block a user