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¶
Overview ¶
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.
Index ¶
- Variables
- type NewPutJinaai
- type PutJinaai
- func (r *PutJinaai) ChunkingSettings(chunkingsettings types.InferenceChunkingSettingsVariant) *PutJinaai
- func (r PutJinaai) Do(providedCtx context.Context) (*Response, error)
- func (r *PutJinaai) ErrorTrace(errortrace bool) *PutJinaai
- func (r *PutJinaai) FilterPath(filterpaths ...string) *PutJinaai
- func (r *PutJinaai) Header(key, value string) *PutJinaai
- func (r *PutJinaai) HttpRequest(ctx context.Context) (*http.Request, error)
- func (r *PutJinaai) Human(human bool) *PutJinaai
- func (r PutJinaai) Perform(providedCtx context.Context) (*http.Response, error)
- func (r *PutJinaai) Pretty(pretty bool) *PutJinaai
- func (r *PutJinaai) Raw(raw io.Reader) *PutJinaai
- func (r *PutJinaai) Request(req *Request) *PutJinaai
- func (r *PutJinaai) Service(service jinaaiservicetype.JinaAIServiceType) *PutJinaai
- func (r *PutJinaai) ServiceSettings(servicesettings types.JinaAIServiceSettingsVariant) *PutJinaai
- func (r *PutJinaai) TaskSettings(tasksettings types.JinaAITaskSettingsVariant) *PutJinaai
- 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 NewPutJinaai ¶
NewPutJinaai type alias for index.
func NewPutJinaaiFunc ¶
func NewPutJinaaiFunc(tp elastictransport.Interface) NewPutJinaai
NewPutJinaaiFunc returns a new instance of PutJinaai with the provided transport. Used in the index of the library this allows to retrieve every apis in once place.
type PutJinaai ¶
type PutJinaai struct {
// contains filtered or unexported fields
}
func New ¶
func New(tp elastictransport.Interface) *PutJinaai
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.
https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-service-jinaai.html
func (*PutJinaai) ChunkingSettings ¶
func (r *PutJinaai) ChunkingSettings(chunkingsettings types.InferenceChunkingSettingsVariant) *PutJinaai
The chunking configuration object. API name: chunking_settings
func (PutJinaai) Do ¶
Do runs the request through the transport, handle the response and returns a putjinaai.Response
func (*PutJinaai) ErrorTrace ¶
ErrorTrace When set to `true` Elasticsearch will include the full stack trace of errors when they occur. API name: error_trace
func (*PutJinaai) FilterPath ¶
FilterPath Comma-separated list of filters in dot notation which reduce the response returned by Elasticsearch. API name: filter_path
func (*PutJinaai) HttpRequest ¶
HttpRequest returns the http.Request object built from the given parameters.
func (*PutJinaai) 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 (PutJinaai) Perform ¶
Perform runs the http.Request through the provided transport and returns an http.Response.
func (*PutJinaai) Pretty ¶
Pretty If set to `true` the returned JSON will be "pretty-formatted". Only use this option for debugging only. API name: pretty
func (*PutJinaai) 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 (*PutJinaai) Request ¶
Request allows to set the request property with the appropriate payload.
func (*PutJinaai) Service ¶
func (r *PutJinaai) Service(service jinaaiservicetype.JinaAIServiceType) *PutJinaai
The type of service supported for the specified task type. In this case, `jinaai`. API name: service
func (*PutJinaai) ServiceSettings ¶
func (r *PutJinaai) ServiceSettings(servicesettings types.JinaAIServiceSettingsVariant) *PutJinaai
Settings used to install the inference model. These settings are specific to the `jinaai` service. API name: service_settings
func (*PutJinaai) TaskSettings ¶
func (r *PutJinaai) TaskSettings(tasksettings types.JinaAITaskSettingsVariant) *PutJinaai
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,
// `jinaai`.
Service jinaaiservicetype.JinaAIServiceType `json:"service"`
// ServiceSettings Settings used to install the inference model. These settings are specific to
// the `jinaai` service.
ServiceSettings types.JinaAIServiceSettings `json:"service_settings"`
// TaskSettings Settings to configure the inference task.
// These settings are specific to the task type you specified.
TaskSettings *types.JinaAITaskSettings `json:"task_settings,omitempty"`
}
Request holds the request body struct for the package putjinaai
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 putjinaai
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