Create a Cohere inference endpoint
- PUT /_inference/{task_type}/{cohere_inference_id}
Create an inference endpoint to perform an inference task with the cohere service.
Required authorization
- Cluster privileges manage_inference
Request body
-
chunking_settingsobject -
The chunking configuration object. Applies only to the
text_embeddingtask type. Not applicable to thererankorcompletiontask type. - Reference
-
max_chunk_sizenumber · default: 250 -
The maximum size of a chunk in words. This value cannot be lower than
20(forsentencestrategy) or10(forwordstrategy). This value should not exceed the window size for the associated model. -
overlapnumber · default: 100 -
The number of overlapping words for chunks. It is applicable only to a
wordchunking strategy. This value cannot be higher than half themax_chunk_sizevalue. -
sentence_overlapnumber · default: 1 -
The number of overlapping sentences for chunks. It is applicable only for a
sentencechunking strategy. It can be either1or0. -
separator_groupstring -
Only applicable to the
recursivestrategy and required when using it.Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be
markdownorplaintext.Using this parameter is an alternative to manually specifying a custom
separatorslist. -
separatorsstring -
Only applicable to the
recursivestrategy and required when using it.A list of strings used as possible split points when chunking text.
Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the
max_chunk_sizelimit, to reduce the total number of chunks generated. -
strategystring · default: sentence -
The chunking strategy:
sentence,word,noneorrecursive.- If
strategyis set torecursive, you must also specify:
max_chunk_size- either
separatorsorseparator_group
Learn more about different chunking strategies in the linked documentation.
- If
- Reference
-
servicerequired -
The type of service supported for the specified task type. In this case,
cohere. -
Values:
cohere -
service_settingsobjectrequired -
Settings used to install the inference model. These settings are specific to the
cohereservice. -
api_keystringrequired -
A valid API key for your Cohere account. You can find or create your Cohere API keys on the Cohere API key settings page.
ImportantYou need to provide the API key only once, during the inference model creation. The get inference endpoint API does not retrieve your API key.
- External docs
-
embedding_type· default: float -
For a
text_embeddingtask, the types of embeddings you want to get back. Usebinaryfor binary embeddings, which are encoded as bytes with signed int8 precision. Usebitfor binary embeddings, which are encoded as bytes with signed int8 precision (this is a synonym ofbinary). Usebytefor signed int8 embeddings (this is a synonym ofint8). Usefloatfor the default float embeddings. Useint8for signed int8 embeddings. -
Values:
binarybitbytefloatint8 -
model_idstringrequired -
For a
completion,rerank, ortext_embeddingtask, the name of the model to use for the inference task.- For the available
completionmodels, refer to the Cohere command docs. - For the available
rerankmodels, refer to the Cohere rerank docs. - For the available
text_embeddingmodels, refer to Cohere embed docs.
- For the available
-
rate_limitobject -
This setting helps to minimize the number of rate limit errors returned from Cohere. By default, the
cohereservice sets the number of requests allowed per minute to 10000. -
requests_per_minutenumber -
The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:
alibabacloud-ai-searchservice:1000amazonbedrockservice:240anthropicservice:50azureaistudioservice:240azureopenaiservice and task typetext_embedding:1440azureopenaiservice and task typescompletionorchat_completion:120cohereservice:10000contextualaiservice:1000elasticservice and task typechat_completion:240fireworksaiservice:6000googleaistudioservice:360googlevertexaiservice:30000hugging_faceservice:3000jinaaiservice:2000llamaservice:3000mistralservice:240openaiservice and task typetext_embedding:3000openaiservice and task typecompletion:500openshift_aiservice:3000voyageaiservice:2000watsonxaiservice:120
-
task_settingsobject -
Settings to configure the inference task. These settings are specific to the task type you specified.
-
input_typerequired -
For a
text_embeddingtask, the type of input passed to the model. Valid values are:classification: Use it for embeddings passed through a text classifier.clustering: Use it for the embeddings run through a clustering algorithm.ingest: Use it for storing document embeddings in a vector database.search: Use it for storing embeddings of search queries run against a vector database to find relevant documents.
ImportantThe
input_typefield is required when using embedding modelsv3and higher. -
Values:
classificationclusteringingestsearch -
top_nnumber -
For a
reranktask, the number of most relevant documents to return. It defaults to the number of the documents. If this inference endpoint is used in atext_similarity_rerankerretriever query andtop_nis set, it must be greater than or equal torank_window_sizein the query. -
truncate -
For a
text_embeddingtask, the method to handle inputs longer than the maximum token length. Valid values are:END: When the input exceeds the maximum input token length, the end of the input is discarded.NONE: When the input exceeds the maximum input token length, an error is returned.START: When the input exceeds the maximum input token length, the start of the input is discarded.
-
Values:
ENDNONESTART
Response
-
chunking_settingsobject -
The chunking configuration object. Applies only to the
embedding,sparse_embeddingandtext_embeddingtask types. Not applicable to thererank,completion, orchat_completiontask types. -
max_chunk_sizenumber · default: 250 -
The maximum size of a chunk in words. This value cannot be lower than
20(forsentencestrategy) or10(forwordstrategy). This value should not exceed the window size for the associated model. -
overlapnumber · default: 100 -
The number of overlapping words for chunks. It is applicable only to a
wordchunking strategy. This value cannot be higher than half themax_chunk_sizevalue. -
sentence_overlapnumber · default: 1 -
The number of overlapping sentences for chunks. It is applicable only for a
sentencechunking strategy. It can be either1or0. -
separator_groupstring -
Only applicable to the
recursivestrategy and required when using it.Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be
markdownorplaintext.Using this parameter is an alternative to manually specifying a custom
separatorslist. -
separatorsstring -
Only applicable to the
recursivestrategy and required when using it.A list of strings used as possible split points when chunking text.
Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the
max_chunk_sizelimit, to reduce the total number of chunks generated. -
strategystring · default: sentence -
The chunking strategy:
sentence,word,noneorrecursive.- If
strategyis set torecursive, you must also specify:
max_chunk_size- either
separatorsorseparator_group
Learn more about different chunking strategies in the linked documentation.
- If
- Reference