Einstein.ai plugin
$ npm install -g eaidc
$ sfdx COMMAND
running command...
$ sfdx (-v|--version|version)
eaidc/1.0.2 darwin-x64 node-v13.12.0
$ sfdx --help [COMMAND]
USAGE
$ sfdx COMMAND
...sfdx eai:apiusage [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:auth:gettoken [-c] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:auth:login -n <string> -f <string> [-e <number>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:datasets [-i <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:datasets:create -t <string> [-d <string> | -p <string>] [-l <string>] [-n <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:datasets:delete -i <string> [-c] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:datasets:delete:status -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:datasets:retrain -i <string> [-e <integer>] [-r <number>] [-p <string>] [-c] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:datasets:train -i <string> -n <string> [-e <integer>] [-r <number>] [-p <string>] [-c] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:datasets:train:status -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:examples [-i <string> | -l <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:examples:create -i <string> [-d <string> | -p <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:feedback:create -d <string> -l <string> -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:intent -i <string> -d <string> [-n <integer>] [-s <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:models -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:models:delete -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:models:metrics [-i <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:language:sentiment -i <string> -d <string> [-n <integer>] [-s <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:datasets [-i <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:datasets:create -n <string> -t <string> [-b <string>] [-l <string>] [-p <string>] [-d <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:datasets:delete -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:datasets:delete:status -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:datasets:retrain -i <string> [-e <integer>] [-r <number>] [-p <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:datasets:train -i <string> -n <string> [-e <integer>] [-r <number>] [-p <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:datasets:train:status -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:detect -i <string> [-n <integer>] [-s <string>] [-c <string> | -l <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:models -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:models:delete -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:models:learningcurve -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:models:metrics [-i <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:ocr [-i <string>] [-c <string> | -l <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]sfdx eai:vision:predict -i <string> [-n <integer>] [-b <string> | -c <string> | -l <string>] [-s <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
sfdx eai:apiusage [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
Returns prediction usage on a monthly basis for the current calendar month and future months.
USAGE
$ sfdx eai:apiusage [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:apiusage
API Usage Summary
Period Start Period End Remaining Used Maximum
──────────── ────────── ───────── ──── ───────
March/2020 April/2020 1990 10 2000
See code: lib/commands/eai/apiusage.js
sfdx eai:auth:gettoken [-c] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
USAGE
$ sfdx eai:auth:gettoken [-c] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-c, --toclipboard add token to clipboard without
displaying in terminal
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:auth:gettoke
Successfully obtained auth token
See code: lib/commands/eai/auth/gettoken.js
sfdx eai:auth:login -n <string> -f <string> [-e <number>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
download an OAuth token for your account
USAGE
$ sfdx eai:auth:login -n <string> -f <string> [-e <number>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-e, --expiration=expiration
[default: 1] number of minutes until token expires
-f, --pemlocation=pemlocation
(required) Local path to your Einsten private key certificat (<something>.pem)
-n, --name=name
(required) Your Einstein Platform Services username. You can find your username in the welcome email you receive
after you get an account. If you signed up using Salesforce, your username is the email address associated with the
org you signed up with.
--json
format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL)
[default: warn] logging level for this command invocation
EXAMPLE
$ sfdx eai:auth:login -n name@company.com -f einstein_platform.pem -e 1
Successfully obtained auth token for name@company.com
See code: lib/commands/eai/auth/login.js
sfdx eai:language:datasets [-i <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get a list of all your datasets, or provide an Id to get the details of a specific dataset
USAGE
$ sfdx eai:language:datasets [-i <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --datasetid=datasetid id of dataset to retrieve, if
missing all datasets are returned
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:datasets
Successfully retrieved language datasets
Id Name Created Updated Type Examples Labels Status
─────── ─────────── ───────────────────── ───────────────────── ─────────── ──────── ────── ─────────
1xx3663 atis.csv 3/25/2020, 2:35:18 PM 3/25/2020, 2:35:20 PM text-intent 763 8 SUCCEEDED
1xx9106 sampleLDS 5/5/2020, 3:53:21 PM 5/5/2020, 3:53:21 PM text-intent 150 5 SUCCEEDED
1xx1357 weather.csv 5/11/2020, 3:47:28 PM 5/11/2020, 3:47:28 PM text-intent 73 3 SUCCEEDED
See code: lib/commands/eai/language/datasets.js
sfdx eai:language:datasets:create -t <string> [-d <string> | -p <string>] [-l <string>] [-n <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
create a new dataset
USAGE
$ sfdx eai:language:datasets:create -t <string> [-d <string> | -p <string>] [-l <string>] [-n <string>] [--json]
[--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-d, --data=data URL of the .zip file. The maximum
.zip file size you can upload from a
web location is 50 MB.
-l, --language=language [default: N/A] Dataset language.
Optional. Default is N/A. Reserved
for future use.
-n, --name=name Name of the dataset. Maximum length
is 180 characters.
-p, --path=path URL of the .zip file. The maximum
.zip file size you can upload from a
web location is 50 MB.
-t, --type=type (required) Type of dataset data.
Valid values are text-intent and
text-sentiment. Available in
Einstein Vision API version 2.0 and
later.
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:datasets:create --type text-intent --data /mylocaldatapath.csv
See code: lib/commands/eai/language/datasets/create.js
sfdx eai:language:datasets:delete -i <string> [-c] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request that a dataset be deleted
USAGE
$ sfdx eai:language:datasets:delete -i <string> [-c] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-c, --clipboard places the dataset delete status
command in your clipboard
-i, --datasetid=datasetid (required) dataset id to retrieve,
if not specified all datasets are
retrieved
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:datasets:delete --datasetid 12345
successfully queued dataset 12345 for deletion
You can check the status of the delete requestio by entering the command below
sfdx eai.language:datasets:delete:status --datsetid 123345
See code: lib/commands/eai/language/datasets/delete.js
sfdx eai:language:datasets:delete:status -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get the status of a dataset delete request
USAGE
$ sfdx eai:language:datasets:delete:status -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --deletrequestid=deletrequestid (required) dataset id to retrieve
deletion status for
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:datasets:delete:status --deleterequestid
Successfully retrieved language dataset delete status
id: XSBIYHY6LOJOBQVNNVRAODOYOU
type: DATASET
status: QUEUED
deletedObjectId: 1186961
See code: lib/commands/eai/language/datasets/delete/status.js
sfdx eai:language:datasets:retrain -i <string> [-e <integer>] [-r <number>] [-p <string>] [-c] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request to retrain a dataset, optionally with new params
USAGE
$ sfdx eai:language:datasets:retrain -i <string> [-e <integer>] [-r <number>] [-p <string>] [-c] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-c, --clipboard
places the dataset retrain status command in your clipboard
-e, --epochs=epochs
Number of training iterations for the neural network. Optional. Valid values are 1–1,000.
-i, --modelid=modelid
(required) Id of the model to be retrained
-p, --trainparams=trainparams
JSON that contains parameters that specify how the model is created.
-r, --learningrate=learningrate
Specifies how much the gradient affects the optimization of the model at each time step. Optional. Use this
parameter to tune your model. Valid values are between 0.0001 and 0.01. If not specified, the default is 0.0001. We
recommend keeping this value between 0.0001 and 0.001. This parameter isn't used when training a detection
dataset.
--json
format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL)
[default: warn] logging level for this command invocation
EXAMPLE
$ sfdx eai:language:datasets:retrain --modelid 57
Successfully requested to retrain the model with id: TDD3UH52XGFRUMC2D63R24H4KM
datasetId: 1187599
modelId: HQSIQO6FMPTONEJ6R3T6LE2TAI
name: new model
status: QUEUED
progress: 0
createdAt: 2020-04-05T22:21:17.000+0000
You can check the status of the training by entering the command below
sfdx eai:language:datasets:train:status -i HQSIQO6FMPTONEJ6R3T6LE2TAI
See code: lib/commands/eai/language/datasets/retrain.js
sfdx eai:language:datasets:train -i <string> -n <string> [-e <integer>] [-r <number>] [-p <string>] [-c] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request that a dataset begin a training run
USAGE
$ sfdx eai:language:datasets:train -i <string> -n <string> [-e <integer>] [-r <number>] [-p <string>] [-c] [--json]
[--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-c, --clipboard
places the dataset train status command in your clipboard
-e, --epochs=epochs
Number of training iterations for the neural network. Optional. Valid values are 1–1,000.
-i, --datasetid=datasetid
(required) Id of the dataset to be trained
-n, --name=name
(required) Name of the model. Maximum length is 180 characters
-p, --trainparams=trainparams
JSON that contains parameters that specify how the model is created.
-r, --learningrate=learningrate
Specifies how much the gradient affects the optimization of the model at each time step. Optional. Use this
parameter to tune your model. Valid values are between 0.0001 and 0.01. If not specified, the default is 0.0001. We
recommend keeping this value between 0.0001 and 0.001. This parameter isn"t used when training a detection
dataset.
--json
format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL)
[default: warn] logging level for this command invocation
EXAMPLE
$ sfdx eai:language:datasets:train --datasetid 57
Successfully requested dataset '1187599' be trained, status is 'QUEUED'
You can check the status of the training by entering the command below
sfdx eai:language:datasets:train:status -i HQSIQO6FMPTONEJ6R3T6LE2TAI
See code: lib/commands/eai/language/datasets/train.js
sfdx eai:language:datasets:train:status -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
retrieve the progress of a dataset train request
USAGE
$ sfdx eai:language:datasets:train:status -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --modelid=modelid (required) language model id to
retrieve training status for
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:dataset:train:status --modelid TDD3UH52XGFRUMC2D63R24H4KM
Successfully retrieved training status
name: Simple Model
status: RUNNING
modelId: TDD3UH52XGFRUMC2D63R24H4KM
modelType: text-intent
updatedAt: 2020-04-05T21:20:10.000+0000
See code: lib/commands/eai/language/datasets/train/status.js
sfdx eai:language:examples [-i <string> | -l <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
adds examples from a .csv, .tsv, or .json file to a dataset.
USAGE
$ sfdx eai:language:examples [-i <string> | -l <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --datasetid=datasetid language dataset id to retrieve
examples for, if not specified all
examples are retrieved
-l, --labelid=labelid label id to retrieve examples for,
if not specified all examples are
retrieved
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:datasets:examples --datasetid 3454453
See code: lib/commands/eai/language/examples.js
sfdx eai:language:examples:create -i <string> [-d <string> | -p <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
create a new dataset
USAGE
$ sfdx eai:language:examples:create -i <string> [-d <string> | -p <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-d, --data=data local path to the .zip file. The
maximum .zip file size you can
upload from a web location is 50 MB.
-i, --datasetid=datasetid (required) dataset id to add the
examples to
-p, --path=path URL of the .zip file. The maximum
.zip file size you can upload from a
web location is 50 MB.
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:examples:create --datasetid 1187600 --path =http://einstein.ai/text/weather_update.csv
See code: lib/commands/eai/language/examples/create.js
sfdx eai:language:feedback:create -d <string> -l <string> -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
create a feedback for label
USAGE
$ sfdx eai:language:feedback:create -d <string> -l <string> -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-d, --document=document (required) Intent or sentiment
string to add to the dataset.
-i, --modelid=modelid (required) model id to add the
feedback to
-l, --expectedlabel=expectedlabel (required) Correct label for the
example. Must be a label that exists
in the dataset.
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:feeback:create --modelid 4353445 --document "Is it snowing in Denver" --expectedlabel
"current-weather"
See code: lib/commands/eai/language/feedback/create.js
sfdx eai:language:intent -i <string> -d <string> [-n <integer>] [-s <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
returns an intent prediction for the given string.
USAGE
$ sfdx eai:language:intent -i <string> -d <string> [-n <integer>] [-s <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-d, --document=document (required) the text to evaluate
-i, --modelid=modelid (required) model id to make
prediction against
-n, --numresults=numresults [default: 2] Number of probabilities
to return. Optional. If passed, must
be a number greater than zero.
-s, --sampleid=sampleid String that you can pass in to tag
the prediction. Optional. Can be any
value, and is returned in the
response
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:intent --modelid I3JZ7A5UJRRKU7EZGXZQLKEUZI --document "what is the weather in los angeles"
Oauth token obtained!
See code: lib/commands/eai/language/intent.js
sfdx eai:language:models -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get a list of all your models for a dataset
USAGE
$ sfdx eai:language:models -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --datasetid=datasetid (required) language dataset id to
retrieve models for
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:models --datasetid 13414123
See code: lib/commands/eai/language/models.js
sfdx eai:language:models:delete -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request that a dataset be deleted
USAGE
$ sfdx eai:language:models:delete -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --modelid=modelid (required) modelset id to delete
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:models:delete --modelid 4ZZEIOI4FXFWSTEYVFLZXEMOFU
See code: lib/commands/eai/language/models/delete.js
sfdx eai:language:models:metrics [-i <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
returns the metrics for a model, such as the f1 score, accuracy, and confusion matrix.
USAGE
$ sfdx eai:language:models:metrics [-i <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --modelid=modelid model id to retrieve, if not
specified all datasets are retrieved
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:models:metrics --modelid 4ZZEIOI4FXFWSTEYVFLZXEMOFU
See code: lib/commands/eai/language/models/metrics.js
sfdx eai:language:sentiment -i <string> -d <string> [-n <integer>] [-s <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
returns a sentiment prediction for the given string.
USAGE
$ sfdx eai:language:sentiment -i <string> -d <string> [-n <integer>] [-s <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-d, --document=document (required) The image contained in a
base64 string
-i, --modelid=modelid (required) model id to make
prediction against
-n, --numresults=numresults [default: 2] Number of probabilities
to return. Optional. If passed, must
be a number greater than zero.
-s, --sampleid=sampleid String that you can pass in to tag
the prediction. Optional. Can be any
value, and is returned in the
response
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:language:sentiment --modelid I3JZ7A5UJRRKU7EZGXZQLKEUZI --document "I can't tell you how much fun it was"
See code: lib/commands/eai/language/sentiment.js
sfdx eai:vision:datasets [-i <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get a list of all your datasets, or provide an Id to get the details of a specific dataset
USAGE
$ sfdx eai:vision:datasets [-i <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --datasetid=datasetid dataset id to retrieve, if not
specified all datasets are retrieved
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:datasets:vision:get --username myOrg@example.com --pemlocation secrets/einstein.pem
Oauth token obtained!
See code: lib/commands/eai/vision/datasets.js
sfdx eai:vision:datasets:create -n <string> -t <string> [-b <string>] [-l <string>] [-p <string>] [-d <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
create a new dataset
USAGE
$ sfdx eai:vision:datasets:create -n <string> -t <string> [-b <string>] [-l <string>] [-p <string>] [-d <string>]
[--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-b, --labels=labels Comma-separated list of labels.
Maximum number of labels per dataset
is 250
-d, --data=data local path to the .zip file. The
maximum .zip file size you can
upload is 50 MB.
-l, --language=language [default: N/A] Dataset language.
Optional. Default is N/A. Reserved
for future use.
-n, --name=name (required) Name of the dataset.
Maximum length is 180 characters.
-p, --path=path URL of the .zip file. The maximum
.zip file size you can upload from a
web location is 50 MB.
-t, --type=type (required) Type of dataset data.
Valid values are image and
image-multi-label. Available in
Einstein Vision API version 2.0 and
later.
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:datasets:vision:create --name MyDataset --type image --path http://einstein.ai/images/mountainvsbeach.zip
See code: lib/commands/eai/vision/datasets/create.js
sfdx eai:vision:datasets:delete -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request that a dataset be deleted
USAGE
$ sfdx eai:vision:datasets:delete -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --datasetid=datasetid (required) dataset id to retrieve,
if not specified all datasets are
retrieved
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:datasets:vision:get --username myOrg@example.com --pemlocation secrets/einstein.pem
Oauth token obtained!
See code: lib/commands/eai/vision/datasets/delete.js
sfdx eai:vision:datasets:delete:status -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get the status of a dataset delete request
USAGE
$ sfdx eai:vision:datasets:delete:status -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --deleterequestid=deleterequestid (required) dataset id to retrieve
deletion status for
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:vision:datasets:delete:status --deleterequestid RUAV4YTHOASZZH3VHJB3IROX3E
See code: lib/commands/eai/vision/datasets/delete/status.js
sfdx eai:vision:datasets:retrain -i <string> [-e <integer>] [-r <number>] [-p <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request to retrain a dataset, optionally with new params
USAGE
$ sfdx eai:vision:datasets:retrain -i <string> [-e <integer>] [-r <number>] [-p <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-e, --epochs=epochs
Number of training iterations for the neural network. Optional. Valid values are 1–1,000.
-i, --modelid=modelid
(required) Id of the model to be retrained
-p, --trainparams=trainparams
JSON that contains parameters that specify how the model is created.
-r, --learningrate=learningrate
Specifies how much the gradient affects the optimization of the model at each time step. Optional. Use this
parameter to tune your model. Valid values are between 0.0001 and 0.01. If not specified, the default is 0.0001. We
recommend keeping this value between 0.0001 and 0.001. This parameter isn't used when training a detection
dataset.
--json
format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL)
[default: warn] logging level for this command invocation
EXAMPLE
$ sfdx eai:vision:datasets:retrain --modelid 57
Oauth token obtained!
See code: lib/commands/eai/vision/datasets/retrain.js
sfdx eai:vision:datasets:train -i <string> -n <string> [-e <integer>] [-r <number>] [-p <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request that a dataset begin a trainging run
USAGE
$ sfdx eai:vision:datasets:train -i <string> -n <string> [-e <integer>] [-r <number>] [-p <string>] [--json]
[--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-e, --epochs=epochs
Number of training iterations for the neural network. Optional. Valid values are 1–1,000.
-i, --datasetid=datasetid
(required) Id of the dataset to be trained
-n, --name=name
(required) Name of the model. Maximum length is 180 characters
-p, --trainparams=trainparams
JSON that contains parameters that specify how the model is created.
-r, --learningrate=learningrate
Specifies how much the gradient affects the optimization of the model at each time step. Optional. Use this
parameter to tune your model. Valid values are between 0.0001 and 0.01. If not specified, the default is 0.0001. We
recommend keeping this value between 0.0001 and 0.001. This parameter isn't used when training a detection
dataset.
--json
format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL)
[default: warn] logging level for this command invocation
EXAMPLE
$ sfdx eai:vision:datasets:train --datasetid 57
Oauth token obtained!
See code: lib/commands/eai/vision/datasets/train.js
sfdx eai:vision:datasets:train:status -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
retrieve the progress of a dataset train request
USAGE
$ sfdx eai:vision:datasets:train:status -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --modelid=modelid (required) dataset id to retrieve
training status for
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:vision:datasets:train --modelid FTW2B7YSTZTALH7QTNM3A7DJEY
See code: lib/commands/eai/vision/datasets/train/status.js
sfdx eai:vision:detect -i <string> [-n <integer>] [-s <string>] [-c <string> | -l <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get a list of all your datasets, or provide an Id to get the details of a specific dataset
USAGE
$ sfdx eai:vision:detect -i <string> [-n <integer>] [-s <string>] [-c <string> | -l <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-c, --samplecontent=samplecontent Binary content of image file
-i, --modelid=modelid (required) model id to make
prediction against
-l, --samplelocation=samplelocation URL of the image file
-n, --numresults=numresults [default: 2] Number of probabilities
to return. Optional. If passed, must
be a number greater than zero.
-s, --sampleid=sampleid String that you can pass in to tag
the prediction. Optional. Can be any
value, and is returned in the
response
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:datasets:vision:get --username myOrg@example.com --pemlocation secrets/einstein.pem
Oauth token obtained!
See code: lib/commands/eai/vision/detect.js
sfdx eai:vision:models -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get a list of all your datasets, or provide an Id to get the details of a specific dataset
USAGE
$ sfdx eai:vision:models -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --datasetid=datasetid (required) dataset id to retrieve
the models for
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:datasets:vision:get --username myOrg@example.com --pemlocation secrets/einstein.pem
Oauth token obtained!
See code: lib/commands/eai/vision/models.js
sfdx eai:vision:models:delete -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request that a dataset be deleted
USAGE
$ sfdx eai:vision:models:delete -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --modelid=modelid (required) modelset id to delete
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:vision:models:delete --username myOrg@example.com --pemlocation secrets/einstein.pem
Oauth token obtained!
See code: lib/commands/eai/vision/models/delete.js
sfdx eai:vision:models:learningcurve -i <string> [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
request that a dataset be deleted
USAGE
$ sfdx eai:vision:models:learningcurve -i <string> [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --modelid=modelid (required) model id to retrieve, if
not specified all datasets are
retrieved
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:datasets:vision:get --username myOrg@example.com --pemlocation secrets/einstein.pem
Oauth token obtained!
See code: lib/commands/eai/vision/models/learningcurve.js
sfdx eai:vision:models:metrics [-i <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get a list of all your datasets, or provide an Id to get the details of a specific dataset
USAGE
$ sfdx eai:vision:models:metrics [-i <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-i, --modelid=modelid model id to retrieve, if not
specified all datasets are retrieved
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:datasets:vision:get --username myOrg@example.com --pemlocation secrets/einstein.pem
Oauth token obtained!
See code: lib/commands/eai/vision/models/metrics.js
sfdx eai:vision:ocr [-i <string>] [-c <string> | -l <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get a list of all your datasets, or provide an Id to get the details of a specific dataset
USAGE
$ sfdx eai:vision:ocr [-i <string>] [-c <string> | -l <string>] [--json] [--loglevel
trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-c, --samplecontent=samplecontent Path to the image file
-i, --modelid=modelid [default: OCRModel] model id to make
prediction against
-l, --samplelocation=samplelocation URL of the image file
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:vision:ocr --modelid 'OCRModel' --samplelocation
https://www.publicdomainpictures.net/pictures/240000/velka/emergency-evacuation-route-signpost.jpg
Probability Label BB MinX BB MinY BB MaxX BB MaxY
─────────── ────────── ─────── ─────── ─────── ───────
0.99937266 ROUTE 582 685 1151 815
0.99471515 EMERGENCY 361 208 1383 346
0.99469215 EVACUATION 331 438 1401 570
See code: lib/commands/eai/vision/ocr.js
sfdx eai:vision:predict -i <string> [-n <integer>] [-b <string> | -c <string> | -l <string>] [-s <string>] [--json] [--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
get a list of all your datasets, or provide an Id to get the details of a specific dataset
USAGE
$ sfdx eai:vision:predict -i <string> [-n <integer>] [-b <string> | -c <string> | -l <string>] [-s <string>] [--json]
[--loglevel trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL]
OPTIONS
-b, --samplebase64content=samplebase64content The image contained in a base64
string
-c, --samplecontent=samplecontent Binary content of image file
-i, --modelid=modelid (required) model id to make
prediction against
-l, --samplelocation=samplelocation URL of the image file
-n, --numresults=numresults [default: 2] Number of probabilities
to return. Optional. If passed, must
be a number greater than zero.
-s, --sampleid=sampleid String that you can pass in to tag
the prediction. Optional. Can be any
value, and is returned in the
response
--json format output as json
--loglevel=(trace|debug|info|warn|error|fatal|TRACE|DEBUG|INFO|WARN|ERROR|FATAL) [default: warn] logging level for
this command invocation
EXAMPLE
$ sfdx eai:datasets:vision:get --username myOrg@example.com --pemlocation secrets/einstein.pem
Oauth token obtained!
See code: lib/commands/eai/vision/predict.js
We recommend using the Visual Studio Code (VS Code) IDE for your plugin development. Included in the .vscode directory of this plugin is a launch.json config file, which allows you to attach a debugger to the node process when running your commands.
To debug the hello:org command:
- Start the inspector
If you linked your plugin to the sfdx cli, call your command with the dev-suspend switch:
$ sfdx hello:org -u myOrg@example.com --dev-suspendAlternatively, to call your command using the bin/run script, set the NODE_OPTIONS environment variable to --inspect-brk when starting the debugger:
$ NODE_OPTIONS=--inspect-brk bin/run hello:org -u myOrg@example.com- Set some breakpoints in your command code
- Click on the Debug icon in the Activity Bar on the side of VS Code to open up the Debug view.
- In the upper left hand corner of VS Code, verify that the "Attach to Remote" launch configuration has been chosen.
- Hit the green play button to the left of the "Attach to Remote" launch configuration window. The debugger should now be suspended on the first line of the program.
- Hit the green play button at the top middle of VS Code (this play button will be to the right of the play button that you clicked in step #5).
Congrats, you are debugging!
