Documentation
¶
Overview ¶
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.
Index ¶
- Variables
- type NewPutElser
- type PutElser
- func (r *PutElser) ChunkingSettings(chunkingsettings types.InferenceChunkingSettingsVariant) *PutElser
- func (r PutElser) Do(providedCtx context.Context) (*Response, error)
- func (r *PutElser) ErrorTrace(errortrace bool) *PutElser
- func (r *PutElser) FilterPath(filterpaths ...string) *PutElser
- func (r *PutElser) Header(key, value string) *PutElser
- func (r *PutElser) HttpRequest(ctx context.Context) (*http.Request, error)
- func (r *PutElser) Human(human bool) *PutElser
- func (r PutElser) Perform(providedCtx context.Context) (*http.Response, error)
- func (r *PutElser) Pretty(pretty bool) *PutElser
- func (r *PutElser) Raw(raw io.Reader) *PutElser
- func (r *PutElser) Request(req *Request) *PutElser
- func (r *PutElser) Service(service elserservicetype.ElserServiceType) *PutElser
- func (r *PutElser) ServiceSettings(servicesettings types.ElserServiceSettingsVariant) *PutElser
- 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 NewPutElser ¶
NewPutElser type alias for index.
func NewPutElserFunc ¶
func NewPutElserFunc(tp elastictransport.Interface) NewPutElser
NewPutElserFunc returns a new instance of PutElser with the provided transport. Used in the index of the library this allows to retrieve every apis in once place.
type PutElser ¶
type PutElser struct {
// contains filtered or unexported fields
}
func New ¶
func New(tp elastictransport.Interface) *PutElser
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.
https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-inference-put-elser
func (*PutElser) ChunkingSettings ¶
func (r *PutElser) ChunkingSettings(chunkingsettings types.InferenceChunkingSettingsVariant) *PutElser
The chunking configuration object. API name: chunking_settings
func (PutElser) Do ¶
Do runs the request through the transport, handle the response and returns a putelser.Response
func (*PutElser) ErrorTrace ¶
ErrorTrace When set to `true` Elasticsearch will include the full stack trace of errors when they occur. API name: error_trace
func (*PutElser) FilterPath ¶
FilterPath Comma-separated list of filters in dot notation which reduce the response returned by Elasticsearch. API name: filter_path
func (*PutElser) HttpRequest ¶
HttpRequest returns the http.Request object built from the given parameters.
func (*PutElser) Human ¶
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 (PutElser) Perform ¶
Perform runs the http.Request through the provided transport and returns an http.Response.
func (*PutElser) Pretty ¶
Pretty If set to `true` the returned JSON will be "pretty-formatted". Only use this option for debugging only. API name: pretty
func (*PutElser) Raw ¶
Raw takes a json payload as input which is then passed to the http.Request If specified Raw takes precedence on Request method.
func (*PutElser) Service ¶
func (r *PutElser) Service(service elserservicetype.ElserServiceType) *PutElser
The type of service supported for the specified task type. In this case, `elser`. API name: service
func (*PutElser) ServiceSettings ¶
func (r *PutElser) ServiceSettings(servicesettings types.ElserServiceSettingsVariant) *PutElser
Settings used to install the inference model. These settings are specific to the `elser` service. API name: service_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,
// `elser`.
Service elserservicetype.ElserServiceType `json:"service"`
// ServiceSettings Settings used to install the inference model. These settings are specific to
// the `elser` service.
ServiceSettings types.ElserServiceSettings `json:"service_settings"`
}
Request holds the request body struct for the package putelser
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 putelser
Source Files