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.
- Reference
-
max_chunk_sizenumber · default: 250 -
The maximum size of a chunk in words. This value cannot be higher than
300or lower than20(forsentencestrategy) or10(forwordstrategy). -
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. -
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_idstring -
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.
The default value for a text embedding task is
embed-english-v2.0. - For the available
-
task_settingsobject -
Settings to configure the inference task. These settings are specific to the task type you specified.
-
input_type -
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 -
Chunking configuration object
-
max_chunk_sizenumber · default: 250 -
The maximum size of a chunk in words. This value cannot be higher than
300or lower than20(forsentencestrategy) or10(forwordstrategy). -
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.