Rasa教程系列-1-命令行交互
文章目录
- 0. 背景
- 1. 命令行速查表
- 2. 训练模型
- 3. 交互式学习
- 4. Talk to Assistant
- 5. 启动Rasa Server
- 6. 启动 Action Server
- 7. Stories的可视化
- 8. 用test数据对模型进行评估
- 9. 划分Train-test数据集
- 10. Markdown和JSON数据格式之间的转换
- 11. 启动Rasa X
0. 背景
本文主要介绍Rasa中常用的命令行交互方式。
1. 命令行速查表
| 命令 | 作用 |
|---|---|
| rasa init | 创建一个新项目，且带有训练数据集、actions和配置文件 |
| rasa train | 基于NLU数据和 Stories数据训练模型，并将结果模型保存与./models中 |
| rasa interactive | 启动一个交互式学习session以通过聊天的方式创建新的训练数据集 |
| rasa shell | 加载已经训练的model并以命令行方式与assistant进行对话 |
| rasa run | 以已训练的模型启动Rasa server，更多详情可以参考 Running the Server |
| rasa run actions | 基于Rasa SDK启动一个action server |
| rasa visualize | 可视化stories |
| rasa test | 使用test NLU data和stories对已经训练的Rasa模型进行test |
| rasa data split nlu | 根据指定的百分比对NLU data进行数据切分 |
| rasa data convert nlu | 对NLU training data进行不同的格式的转换 |
| rasa x | 在本地启动 Rasa X |
| rasa -h | 展示所有可能的命令 |
上一篇博文已经展示了rasa init的使用，这里不再赘述。单纯使用rasa init且不训练一个初始化模型的话，创建的项目就没有models目录。
2. 训练模型
训练模型的命令：
rasa train
该命令联合Rasa NLU 和 Rasa Core 模型训练一个Rasa 模型。如果仅想要训练 NLU 或 Core 模型，可以使用如下命令：rasa train nlu或rasa train core。值得一提的是，Rasa 会自动跳过 NLU 模型或 Core 模型的训练，当其对应的训练数据和配置文件没有改变时。
rasa train训练的结果模型可以用--out来指定，默认是./models。模型的名字默认是--fixed-model-name来自命名模型名字。下述的参数可以用以配置训练过程：
usage: rasa train [-h] [-v] [-vv] [--quiet] [--data DATA [DATA ...]]
[-c CONFIG] [-d DOMAIN] [--out OUT]
[--augmentation AUGMENTATION] [--debug-plots]
[--dump-stories] [--fixed-model-name FIXED_MODEL_NAME]
[--persist-nlu-data] [--force]
{core,nlu} ...
各个参数的详细说明：
positional arguments:
{core,nlu}
core Trains a Rasa Core model using your stories.
nlu Trains a Rasa NLU model using your NLU data.
optional arguments:
-h, --help show this help message and exit
--data DATA [DATA ...]
Paths to the Core and NLU data files. (default:
['data'])
-c CONFIG, --config CONFIG
The policy and NLU pipeline configuration of your bot.
(default: config.yml)
-d DOMAIN, --domain DOMAIN
Domain specification (yml file). (default: domain.yml)
--out OUT Directory where your models should be stored.
(default: models)
--augmentation AUGMENTATION
How much data augmentation to use during training.
(default: 50)
--debug-plots If enabled, will create plots showing checkpoints and
their connections between story blocks in a file
called `story_blocks_connections.html`. (default:
False)
--dump-stories If enabled, save flattened stories to a file.
(default: False)
--fixed-model-name FIXED_MODEL_NAME
If set, the name of the model file/directory will be
set to the given name. (default: None)
--persist-nlu-data Persist the nlu training data in the saved model (将nlu训练数据保存到保存的模型中).
(default: False)
--force Force a model training even if the data has not
changed. (default: False)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
PS：
训练时要确保 NLU 和 Core的训练数据集存在。如果只存在一者，rasa train会根据提供的训练数据集自动回退到rasa train nlu或rasa train core
3. 交互式学习
启动交互式学习session：
rasa interactive
当用--model提供已经训练过的模型，交互式可以始于该模型。如果没有指定模型，rasa interactive将会用data/目录(没有重新给--data指定新目录的话)下的数据训练一个新的Rasa模型。在训练完该初始模型后，交互式学习session将会正式开始。如果训练数据和配置没有改变，训练将被跳过。rasa interactive的相关参数如下：
usage: rasa interactive [-h] [-v] [-vv] [--quiet] [--e2e] [-m MODEL]
[--data DATA [DATA ...]] [--skip-visualization]
[--endpoints ENDPOINTS] [-c CONFIG] [-d DOMAIN]
[--out OUT] [--augmentation AUGMENTATION]
[--debug-plots] [--dump-stories] [--force]
[--persist-nlu-data]
{core} ... [model-as-positional-argument]
各个参数详解：
positional arguments:
{core}
core Starts an interactive learning session model to create
new training data for a Rasa Core model by chatting.
Uses the 'RegexInterpreter', i.e. `/` input
format.
model-as-positional-argument
Path to a trained Rasa model. If a directory is
specified, it will use the latest model in this
directory. (default: None)
optional arguments:
-h, --help show this help message and exit
--e2e Save story files in e2e format. In this format user
messages will be included in the stories. (default:
False)
-m MODEL, --model MODEL
Path to a trained Rasa model. If a directory is
specified, it will use the latest model in this
directory. (default: None)
--data DATA [DATA ...]
Paths to the Core and NLU data files. (default:
['data'])
--skip-visualization Disable plotting the visualization during interactive
learning. (default: False)
--endpoints ENDPOINTS
Configuration file for the model server and the
connectors as a yml file. (default: None)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
Train Arguments:
-c CONFIG, --config CONFIG
The policy and NLU pipeline configuration of your bot.
(default: config.yml)
-d DOMAIN, --domain DOMAIN
Domain specification (yml file). (default: domain.yml)
--out OUT Directory where your models should be stored.
(default: models)
--augmentation AUGMENTATION
How much data augmentation to use during training.
(default: 50)
--debug-plots If enabled, will create plots showing checkpoints and
their connections between story blocks in a file
called `story_blocks_connections.html`. (default:
False)
--dump-stories If enabled, save flattened stories to a file.
(default: False)
--force Force a model training even if the data has not
changed. (default: False)
--persist-nlu-data Persist the nlu training data in the saved model.
(default: False)
交互式学习示例：
4. Talk to Assistant
通过命令行方式直接与Assistant交流：
rasa shell
可以通过--model指定特定的模型。当只想要启动NLU模型时，可以通过rasa shell nlu来对输入的句子进行NLU分析，获得意图和实体。具体示例如下：
当模型中包含有 Core 模型时就可以与其进行对话，并看到Assistant的预测(即下一个action)。rasa shell的相关参数如下：
usage: rasa shell [-h] [-v] [-vv] [--quiet] [-m MODEL] [--log-file LOG_FILE]
[--endpoints ENDPOINTS] [-p PORT] [-t AUTH_TOKEN]
[--cors [CORS [CORS ...]]] [--enable-api]
[--remote-storage REMOTE_STORAGE]
[--ssl-certificate SSL_CERTIFICATE]
[--ssl-keyfile SSL_KEYFILE] [--ssl-ca-file SSL_CA_FILE]
[--ssl-password SSL_PASSWORD] [--credentials CREDENTIALS]
[--connector CONNECTOR] [--jwt-secret JWT_SECRET]
[--jwt-method JWT_METHOD]
{nlu} ... [model-as-positional-argument]
各个参数详解：
positional arguments:
{nlu}
nlu Interprets messages on the command line using your NLU
model.
model-as-positional-argument
Path to a trained Rasa model. If a directory is
specified, it will use the latest model in this
directory. (default: None)
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Path to a trained Rasa model. If a directory is
specified, it will use the latest model in this
directory. (default: models)
--log-file LOG_FILE Store logs in specified file. (default: None)
--endpoints ENDPOINTS
Configuration file for the model server and the
connectors as a yml file. (default: None)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
Server Settings:
-p PORT, --port PORT Port to run the server at. (default: 5005)
-t AUTH_TOKEN, --auth-token AUTH_TOKEN
Enable token based authentication. Requests need to
provide the token to be accepted. (default: None)
--cors [CORS [CORS ...]]
Enable CORS for the passed origin. Use * to whitelist
all origins. (default: None)
--enable-api Start the web server API in addition to the input
channel. (default: False)
--remote-storage REMOTE_STORAGE
Set the remote location where your Rasa model is
stored, e.g. on AWS. (default: None)
--ssl-certificate SSL_CERTIFICATE
Set the SSL Certificate to create a TLS secured
server. (default: None)
--ssl-keyfile SSL_KEYFILE
Set the SSL Keyfile to create a TLS secured server.
(default: None)
--ssl-ca-file SSL_CA_FILE
If your SSL certificate needs to be verified, you can
specify the CA file using this parameter. (default:
None)
--ssl-password SSL_PASSWORD
If your ssl-keyfile is protected by a password, you
can specify it using this paramer. (default: None)
Channels:
--credentials CREDENTIALS
Authentication credentials for the connector as a yml
file. (default: None)
--connector CONNECTOR
Service to connect to. (default: None)
JWT Authentication:
--jwt-secret JWT_SECRET
Public key for asymmetric JWT methods or shared
secretfor symmetric methods. Please also make sure to
use --jwt-method to select the method of the
signature, otherwise this argument will be ignored.
(default: None)
--jwt-method JWT_METHOD
Method used for the signature of the JWT
authentication payload. (default: HS256)
5. 启动Rasa Server
启动Rasa Server命令如下：
rasa run
相关的参数如下：
usage: rasa run [-h] [-v] [-vv] [--quiet] [-m MODEL] [--log-file LOG_FILE]
[--endpoints ENDPOINTS] [-p PORT] [-t AUTH_TOKEN]
[--cors [CORS [CORS ...]]] [--enable-api]
[--remote-storage REMOTE_STORAGE]
[--ssl-certificate SSL_CERTIFICATE]
[--ssl-keyfile SSL_KEYFILE] [--ssl-ca-file SSL_CA_FILE]
[--ssl-password SSL_PASSWORD] [--credentials CREDENTIALS]
[--connector CONNECTOR] [--jwt-secret JWT_SECRET]
[--jwt-method JWT_METHOD]
{actions} ... [model-as-positional-argument]
各参数详解：
positional arguments:
{actions}
actions Runs the action server.
model-as-positional-argument
Path to a trained Rasa model. If a directory is
specified, it will use the latest model in this
directory. (default: None)
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Path to a trained Rasa model. If a directory is
specified, it will use the latest model in this
directory. (default: models)
--log-file LOG_FILE Store logs in specified file. (default: None)
--endpoints ENDPOINTS
Configuration file for the model server and the
connectors as a yml file. (default: None)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
Server Settings:
-p PORT, --port PORT Port to run the server at. (default: 5005)
-t AUTH_TOKEN, --auth-token AUTH_TOKEN
Enable token based authentication. Requests need to
provide the token to be accepted. (default: None)
--cors [CORS [CORS ...]]
Enable CORS for the passed origin. Use * to whitelist
all origins. (default: None)
--enable-api Start the web server API in addition to the input
channel. (default: False)
--remote-storage REMOTE_STORAGE
Set the remote location where your Rasa model is
stored, e.g. on AWS. (default: None)
--ssl-certificate SSL_CERTIFICATE
Set the SSL Certificate to create a TLS secured
server. (default: None)
--ssl-keyfile SSL_KEYFILE
Set the SSL Keyfile to create a TLS secured server.
(default: None)
--ssl-ca-file SSL_CA_FILE
If your SSL certificate needs to be verified, you can
specify the CA file using this parameter. (default:
None)
--ssl-password SSL_PASSWORD
If your ssl-keyfile is protected by a password, you
can specify it using this paramer. (default: None)
Channels:
--credentials CREDENTIALS
Authentication credentials for the connector as a yml
file. (default: None)
--connector CONNECTOR
Service to connect to. (default: None)
JWT Authentication:
--jwt-secret JWT_SECRET
Public key for asymmetric JWT methods or shared
secretfor symmetric methods. Please also make sure to
use --jwt-method to select the method of the
signature, otherwise this argument will be ignored.
(default: None)
--jwt-method JWT_METHOD
Method used for the signature of the JWT
authentication payload. (default: HS256)
有关附加参数的更多信息，可以参见Running the Server。各个endpoints的详情可以参见HTTP API。
6. 启动 Action Server
启动Action Server的命令：
rasa run actions
该命令相关的参数如下：
usage: rasa run actions [-h] [-v] [-vv] [--quiet] [-p PORT]
[--cors [CORS [CORS ...]]] [--actions ACTIONS]
[--ssl-keyfile SSL_KEYFILE]
[--ssl-certificate SSL_CERTIFICATE]
[--ssl-password SSL_PASSWORD]
各个参数的详情：
optional arguments:
-h, --help show this help message and exit
-p PORT, --port PORT port to run the server at (default: 5055)
--cors [CORS [CORS ...]]
enable CORS for the passed origin. Use * to whitelist
all origins (default: None)
--actions ACTIONS name of action package to be loaded (default: None)
--ssl-keyfile SSL_KEYFILE
Set the SSL certificate to create a TLS secured
server. (default: None)
--ssl-certificate SSL_CERTIFICATE
Set the SSL certificate to create a TLS secured
server. (default: None)
--ssl-password SSL_PASSWORD
If your ssl-keyfile is protected by a password, you
can specify it using this paramer. (default: None)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
7. Stories的可视化
命令如下：
rasa visualize
默认情况下是对data/下的训练数据进行可视化，当然可以通过--stories指定特定的stories。该命令的参数如下：
usage: rasa visualize [-h] [-v] [-vv] [--quiet] [-d DOMAIN] [-s STORIES]
[-c CONFIG] [--out OUT] [--max-history MAX_HISTORY]
[-u NLU]
各个参数的详情：
optional arguments:
-h, --help show this help message and exit
-d DOMAIN, --domain DOMAIN
Domain specification (yml file). (default: domain.yml)
-s STORIES, --stories STORIES
File or folder containing your training stories.
(default: data)
-c CONFIG, --config CONFIG
The policy and NLU pipeline configuration of your bot.
(default: config.yml)
--out OUT Filename of the output path, e.g. 'graph.html'.
(default: graph.html)
--max-history MAX_HISTORY
Max history to consider when merging paths in the
output graph. (default: 2)
-u NLU, --nlu NLU File or folder containing your NLU data, used to
insert example messages into the graph. (default:
None)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
示例结果：
graph.html的可视化结果如下：
8. 用test数据对模型进行评估
模型评估命令：
rasa test
可以通过--model指定要评估的模型。更多关于模型的细节可以参考Evaluating an NLU Model和Evaluating a Core Model。
rasa test命令的相关参数如下：
usage: rasa test [-h] [-v] [-vv] [--quiet] [-m MODEL] [-s STORIES]
[--max-stories MAX_STORIES] [--e2e] [--endpoints ENDPOINTS]
[--fail-on-prediction-errors] [--url URL]
[--evaluate-model-directory] [-u NLU] [--out OUT]
[--successes] [--no-errors] [--histogram HISTOGRAM]
[--confmat CONFMAT] [-c CONFIG [CONFIG ...]]
[--cross-validation] [-f FOLDS] [-r RUNS]
[-p PERCENTAGES [PERCENTAGES ...]] [--no-plot]
{core,nlu} ...
各个参数的详情：
positional arguments:
{core,nlu}
core Tests Rasa Core models using your test stories.
nlu Tests Rasa NLU models using your test NLU data.
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Path to a trained Rasa model. If a directory is
specified, it will use the latest model in this
directory. (default: models)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
Core Test Arguments:
-s STORIES, --stories STORIES
File or folder containing your test stories. (default:
data)
--max-stories MAX_STORIES
Maximum number of stories to test on. (default: None)
--e2e, --end-to-end Run an end-to-end evaluation for combined action and
intent prediction. Requires a story file in end-to-end
format. (default: False)
--endpoints ENDPOINTS
Configuration file for the connectors as a yml file.
(default: None)
--fail-on-prediction-errors
If a prediction error is encountered, an exception is
thrown. This can be used to validate stories during
tests, e.g. on travis. (default: False)
--url URL If supplied, downloads a story file from a URL and
trains on it. Fetches the data by sending a GET
request to the supplied URL. (default: None)
--evaluate-model-directory
Should be set to evaluate models trained via 'rasa
train core --config '. All models
in the provided directory are evaluated and compared
against each other. (default: False)
NLU Test Arguments:
-u NLU, --nlu NLU File or folder containing your NLU data. (default:
data)
--out OUT Output path for any files created during the
evaluation. (default: results)
--successes If set successful predictions (intent and entities)
will be written to a file. (default: False)
--no-errors If set incorrect predictions (intent and entities)
will NOT be written to a file. (default: False)
--histogram HISTOGRAM
Output path for the confidence histogram. (default:
hist.png)
--confmat CONFMAT Output path for the confusion matrix plot. (default:
confmat.png)
-c CONFIG [CONFIG ...], --config CONFIG [CONFIG ...]
Model configuration file. If a single file is passed
and cross validation mode is chosen, cross-validation
is performed, if multiple configs or a folder of
configs are passed, models will be trained and
compared directly. (default: None)
--no-plot Don't render evaluation plots (default: False)
示例：
上述示例并没有指定--stories，所以是使用默认的./data下的数据进行评估，用训练数据集来评测，结果自然好得没话说。这里仅仅为了展示命令行的使用，并没有再造新的test数据集。
9. 划分Train-test数据集
可以通过rasa data split nlu对NLU划分为train和test数据集。默认情况下，train和test数据集的intents比例是8：2，默认情况下划分后的数据存放于./train_test_split目录。
rasa data split nlu的相关参数如下：
usage: rasa data split nlu [-h] [-v] [-vv] [--quiet] [-u NLU]
[--training-fraction TRAINING_FRACTION]
[--random-seed RANDOM_SEED] [--out OUT]
各个参数的详情：
optional arguments:
-h, --help show this help message and exit
-u NLU, --nlu NLU File or folder containing your NLU data. (default:
data)
--training-fraction TRAINING_FRACTION
Percentage of the data which should be in the training
data. (default: 0.8)
--random-seed RANDOM_SEED
Seed to generate the same train/test split. (default:
None)
--out OUT Directory where the split files should be stored.
(default: train_test_split)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
10. Markdown和JSON数据格式之间的转换
对NLU数据集，从LUIS、WIT、Dialogflow、JSON、Markdown转为JSON或Markdown：
rasa data convert nlu
可以通过如下参数指定输入、输出文件和输出格式：
usage: rasa data convert nlu [-h] [-v] [-vv] [--quiet] --data DATA --out OUT
[-l LANGUAGE] -f {json,md}
各个参数详情：
optional arguments:
-h, --help show this help message and exit
--data DATA Path to the file or directory containing Rasa NLU
data. (default: None)
--out OUT File where to save training data in Rasa format.
(default: None)
-l LANGUAGE, --language LANGUAGE
Language of data. (default: en)
-f {json,md}, --format {json,md}
Output format the training data should be converted
into. (default: None)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
11. 启动Rasa X
Rasa X是一个辅助创建、改善、部署 AI Assistants的工具，更多关于Rasa X可以参考这里。
通过下述命令rasa x启动Rasa X之前需要安装Rasa X。上篇博文已经简单介绍。Rasa X默认端口号是5002，可以通过--rasa-x-port重新指定。rasa x命令的相关参数如下：
usage: rasa x [-h] [-v] [-vv] [--quiet] [-m MODEL] [--data DATA] [-c CONFIG]
[--no-prompt] [--production] [--rasa-x-port RASA_X_PORT]
[--config-endpoint CONFIG_ENDPOINT] [--log-file LOG_FILE]
[--endpoints ENDPOINTS] [-p PORT] [-t AUTH_TOKEN]
[--cors [CORS [CORS ...]]] [--enable-api]
[--remote-storage REMOTE_STORAGE]
[--ssl-certificate SSL_CERTIFICATE] [--ssl-keyfile SSL_KEYFILE]
[--ssl-ca-file SSL_CA_FILE] [--ssl-password SSL_PASSWORD]
[--credentials CREDENTIALS] [--connector CONNECTOR]
[--jwt-secret JWT_SECRET] [--jwt-method JWT_METHOD]
各个参数的详情：
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Path to a trained Rasa model. If a directory is
specified, it will use the latest model in this
directory. (default: models)
--data DATA Path to the file or directory containing stories and
Rasa NLU data. (default: data)
-c CONFIG, --config CONFIG
The policy and NLU pipeline configuration of your bot.
(default: config.yml)
--no-prompt Automatic yes or default options to prompts and
oppressed warnings. (default: False)
--production Run Rasa X in a production environment. (default:
False)
--rasa-x-port RASA_X_PORT
Port to run the Rasa X server at. (default: 5002)
--config-endpoint CONFIG_ENDPOINT
Rasa X endpoint URL from which to pull the runtime
config. This URL typically contains the Rasa X token
for authentication. Example:
https://example.com/api/config?token=my_rasa_x_token
(default: None)
--log-file LOG_FILE Store logs in specified file. (default: None)
--endpoints ENDPOINTS
Configuration file for the model server and the
connectors as a yml file. (default: None)
Python Logging Options:
-v, --verbose Be verbose. Sets logging level to INFO. (default:
None)
-vv, --debug Print lots of debugging statements. Sets logging level
to DEBUG. (default: None)
--quiet Be quiet! Sets logging level to WARNING. (default:
None)
Server Settings:
-p PORT, --port PORT Port to run the server at. (default: 5005)
-t AUTH_TOKEN, --auth-token AUTH_TOKEN
Enable token based authentication. Requests need to
provide the token to be accepted. (default: None)
--cors [CORS [CORS ...]]
Enable CORS for the passed origin. Use * to whitelist
all origins. (default: None)
--enable-api Start the web server API in addition to the input
channel. (default: False)
--remote-storage REMOTE_STORAGE
Set the remote location where your Rasa model is
stored, e.g. on AWS. (default: None)
--ssl-certificate SSL_CERTIFICATE
Set the SSL Certificate to create a TLS secured
server. (default: None)
--ssl-keyfile SSL_KEYFILE
Set the SSL Keyfile to create a TLS secured server.
(default: None)
--ssl-ca-file SSL_CA_FILE
If your SSL certificate needs to be verified, you can
specify the CA file using this parameter. (default:
None)
--ssl-password SSL_PASSWORD
If your ssl-keyfile is protected by a password, you
can specify it using this paramer. (default: None)
Channels:
--credentials CREDENTIALS
Authentication credentials for the connector as a yml
file. (default: None)
--connector CONNECTOR
Service to connect to. (default: None)
JWT Authentication:
--jwt-secret JWT_SECRET
Public key for asymmetric JWT methods or shared
secretfor symmetric methods. Please also make sure to
use --jwt-method to select the method of the
signature, otherwise this argument will be ignored.
(default: None)
--jwt-method JWT_METHOD
Method used for the signature of the JWT
authentication payload. (default: HS256)
示例如下：
在浏览器中打开链接，选择模型，再进行对话，对话效果如下：
