Documentation
¶
Overview ¶
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.
Index ¶
- Variables
- type NewPutElasticsearch
- type PutElasticsearch
- func (r *PutElasticsearch) ChunkingSettings(chunkingsettings types.InferenceChunkingSettingsVariant) *PutElasticsearch
- func (r PutElasticsearch) Do(providedCtx context.Context) (*Response, error)
- func (r *PutElasticsearch) ErrorTrace(errortrace bool) *PutElasticsearch
- func (r *PutElasticsearch) FilterPath(filterpaths ...string) *PutElasticsearch
- func (r *PutElasticsearch) Header(key, value string) *PutElasticsearch
- func (r *PutElasticsearch) HttpRequest(ctx context.Context) (*http.Request, error)
- func (r *PutElasticsearch) Human(human bool) *PutElasticsearch
- func (r PutElasticsearch) Perform(providedCtx context.Context) (*http.Response, error)
- func (r *PutElasticsearch) Pretty(pretty bool) *PutElasticsearch
- func (r *PutElasticsearch) Raw(raw io.Reader) *PutElasticsearch
- func (r *PutElasticsearch) Request(req *Request) *PutElasticsearch
- func (r *PutElasticsearch) Service(service elasticsearchservicetype.ElasticsearchServiceType) *PutElasticsearch
- func (r *PutElasticsearch) ServiceSettings(servicesettings types.ElasticsearchServiceSettingsVariant) *PutElasticsearch
- func (r *PutElasticsearch) TaskSettings(tasksettings types.ElasticsearchTaskSettingsVariant) *PutElasticsearch
- type Request
- type Response
Constants ¶
This section is empty.
Variables ¶
var ErrBuildPath = errors.New("cannot build path, check for missing path parameters")
ErrBuildPath is returned in case of missing parameters within the build of the request.
Functions ¶
This section is empty.
Types ¶
type NewPutElasticsearch ¶
type NewPutElasticsearch func(tasktype, elasticsearchinferenceid string) *PutElasticsearch
NewPutElasticsearch type alias for index.
func NewPutElasticsearchFunc ¶
func NewPutElasticsearchFunc(tp elastictransport.Interface) NewPutElasticsearch
NewPutElasticsearchFunc returns a new instance of PutElasticsearch with the provided transport. Used in the index of the library this allows to retrieve every apis in once place.
type PutElasticsearch ¶
type PutElasticsearch struct {
// contains filtered or unexported fields
}
func New ¶
func New(tp elastictransport.Interface) *PutElasticsearch
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.
https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-service-elasticsearch.html
func (*PutElasticsearch) ChunkingSettings ¶
func (r *PutElasticsearch) ChunkingSettings(chunkingsettings types.InferenceChunkingSettingsVariant) *PutElasticsearch
The chunking configuration object. API name: chunking_settings
func (PutElasticsearch) Do ¶
func (r PutElasticsearch) Do(providedCtx context.Context) (*Response, error)
Do runs the request through the transport, handle the response and returns a putelasticsearch.Response
func (*PutElasticsearch) ErrorTrace ¶
func (r *PutElasticsearch) ErrorTrace(errortrace bool) *PutElasticsearch
ErrorTrace When set to `true` Elasticsearch will include the full stack trace of errors when they occur. API name: error_trace
func (*PutElasticsearch) FilterPath ¶
func (r *PutElasticsearch) FilterPath(filterpaths ...string) *PutElasticsearch
FilterPath Comma-separated list of filters in dot notation which reduce the response returned by Elasticsearch. API name: filter_path
func (*PutElasticsearch) Header ¶
func (r *PutElasticsearch) Header(key, value string) *PutElasticsearch
Header set a key, value pair in the PutElasticsearch headers map.
func (*PutElasticsearch) HttpRequest ¶
HttpRequest returns the http.Request object built from the given parameters.
func (*PutElasticsearch) Human ¶
func (r *PutElasticsearch) Human(human bool) *PutElasticsearch
Human When set to `true` will return statistics in a format suitable for humans. For example `"exists_time": "1h"` for humans and `"eixsts_time_in_millis": 3600000` for computers. When disabled the human readable values will be omitted. This makes sense for responses being consumed only by machines. API name: human
func (PutElasticsearch) Perform ¶
Perform runs the http.Request through the provided transport and returns an http.Response.
func (*PutElasticsearch) Pretty ¶
func (r *PutElasticsearch) Pretty(pretty bool) *PutElasticsearch
Pretty If set to `true` the returned JSON will be "pretty-formatted". Only use this option for debugging only. API name: pretty
func (*PutElasticsearch) Raw ¶
func (r *PutElasticsearch) Raw(raw io.Reader) *PutElasticsearch
Raw takes a json payload as input which is then passed to the http.Request If specified Raw takes precedence on Request method.
func (*PutElasticsearch) Request ¶
func (r *PutElasticsearch) Request(req *Request) *PutElasticsearch
Request allows to set the request property with the appropriate payload.
func (*PutElasticsearch) Service ¶
func (r *PutElasticsearch) Service(service elasticsearchservicetype.ElasticsearchServiceType) *PutElasticsearch
The type of service supported for the specified task type. In this case, `elasticsearch`. API name: service
func (*PutElasticsearch) ServiceSettings ¶
func (r *PutElasticsearch) ServiceSettings(servicesettings types.ElasticsearchServiceSettingsVariant) *PutElasticsearch
Settings used to install the inference model. These settings are specific to the `elasticsearch` service. API name: service_settings
func (*PutElasticsearch) TaskSettings ¶
func (r *PutElasticsearch) TaskSettings(tasksettings types.ElasticsearchTaskSettingsVariant) *PutElasticsearch
Settings to configure the inference task. These settings are specific to the task type you specified. API name: task_settings
type Request ¶
type Request struct { // ChunkingSettings The chunking configuration object. ChunkingSettings *types.InferenceChunkingSettings `json:"chunking_settings,omitempty"` // Service The type of service supported for the specified task type. In this case, // `elasticsearch`. Service elasticsearchservicetype.ElasticsearchServiceType `json:"service"` // ServiceSettings Settings used to install the inference model. These settings are specific to // the `elasticsearch` service. ServiceSettings types.ElasticsearchServiceSettings `json:"service_settings"` // TaskSettings Settings to configure the inference task. // These settings are specific to the task type you specified. TaskSettings *types.ElasticsearchTaskSettings `json:"task_settings,omitempty"` }
Request holds the request body struct for the package putelasticsearch
type Response ¶
type Response struct { // ChunkingSettings Chunking configuration object ChunkingSettings *types.InferenceChunkingSettings `json:"chunking_settings,omitempty"` // InferenceId The inference Id InferenceId string `json:"inference_id"` // Service The service type Service string `json:"service"` // ServiceSettings Settings specific to the service ServiceSettings json.RawMessage `json:"service_settings"` // TaskSettings Task settings specific to the service and task type TaskSettings json.RawMessage `json:"task_settings,omitempty"` // TaskType The task type TaskType tasktype.TaskType `json:"task_type"` }
Response holds the response body struct for the package putelasticsearch