RASA命令-原文

Command	Effect
`rasa init`	Creates a new project with example training data, actions, and config files.
`rasa train`	Trains a model using your NLU data and stories, saves trained model in `./models`.
`rasa interactive`	Starts an interactive learning session to create new training data by chatting to your assistant.
`rasa shell`	Loads your trained model and lets you talk to your assistant on the command line.
`rasa run`	Starts a server with your trained model.
`rasa run actions`	Starts an action server using the Rasa SDK.
`rasa visualize`	Generates a visual representation of your stories.
`rasa test`	Tests a trained Rasa model on any files starting with `test_`.
`rasa data split nlu`	Performs a 80/20 split of your NLU training data.
`rasa data convert`	Converts training data between different formats.
`rasa data validate`	Checks the domain, NLU and conversation data for inconsistencies.
`rasa export`	Exports conversations from a tracker store to an event broker.
`rasa x`	Launches Rasa X in local mode.
`rasa -h`	Shows all available commands.

rasa init#

这个命令用一些示例训练数据为您设置了一个完整的助手:

rasa init

It creates the following files:

.
├── actions
│   ├── __init__.py
│   └── actions.py
├── config.yml
├── credentials.yml
├── data
│   ├── nlu.yml
│   └── stories.yml
├── domain.yml
├── endpoints.yml
├── models
│   └── <timestamp>.tar.gz
└── tests
   └── test_stories.yml

它会问你是否想用这些数据训练一个初始模型。如果您回答no, models目录将是空的。

任何默认的CLI命令都需要这样的项目设置，所以这是开始的最佳方式。您可以运行rasa train, rasa shell和rasa test，而无需任何额外的配置。

rasa train#

下面的命令训练一个Rasa开源模型:

rasa train

如果您的目录中有现有的模型(默认情况下在models/下)，那么只有模型中已更改的部分将被重新培训。例如，如果您只编辑您的NLU训练数据而不做其他，则只会训练NLU部分。

如果你想单独训练一个NLU或对话模型，你可以runrasa train NLU或rasa train core。如果您仅为其中一个命令提供训练数据，则rasa train默认将退回到这些命令中的一个。

rasa train将把训练过的模型存储在——out定义的目录中，默认情况下是models/。模型的名称默认为<timestamp>.tar.gz。</timestamp>如果想用不同的方式命名模型，可以使用——fixed-model-name标志指定名称。

可以使用以下参数来配置训练过程:

usage: rasa train [-h] [-v] [-vv] [--quiet] [--data DATA [DATA ...]]
                  [-c CONFIG] [-d DOMAIN] [--out OUT] [--dry-run]
                  [--augmentation AUGMENTATION] [--debug-plots]
                  [--num-threads NUM_THREADS]
                  [--fixed-model-name FIXED_MODEL_NAME] [--persist-nlu-data]
                  [--force] [--finetune [FINETUNE]]
                  [--epoch-fraction EPOCH_FRACTION]
                  {core,nlu} ...

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. This can be a single YAML file,
                        or a directory that contains several files with domain
                        specifications in it. The content of these files will
                        be read and merged together. (default: domain.yml)
  --out OUT             Directory where your models should be stored.
                        (default: models)
  --dry-run             If enabled, no actual training will be performed.
                        Instead, it will be determined whether a model should
                        be re-trained and this information will be printed as
                        the output. The return code is a 4-bit bitmask that
                        can also be used to determine what exactly needs to be
                        retrained: - 1 means Core needs to be retrained - 2
                        means NLU needs to be retrained - 4 means responses in
                        the domain should be updated - 8 means the training
                        was forced (--force argument is specified) (default:
                        False)
  --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)
  --num-threads NUM_THREADS
                        Maximum amount of threads to use when training.
                        (default: 1)
  --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.
                        (default: False)
  --force               Force a model training even if the data has not
                        changed. (default: False)
  --finetune [FINETUNE]
                        Fine-tune a previously trained model. If no model path
                        is provided, Rasa Open Source will try to finetune the
                        latest trained model from the model directory
                        specified via '--out'. (default: None)
  --epoch-fraction EPOCH_FRACTION
                        Fraction of epochs which are currently specified in
                        the model configuration which should be used when
                        finetuning a model. (default: 1.0)

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)

为了提高助手的性能，练习CDD并根据用户与助手的对话方式添加新的训练示例是很有帮助的。您可以使用rasa train—finetune用一个已经训练过的模型初始化管道，并在包含额外训练示例的新训练数据集上进一步微调它。这将有助于减少新模型的训练时间。

默认情况下，该命令会选取models/目录中的最新模型。如果你有一个想要改进的特定模型，你可以通过运行rasa train来指定它的路径——finetune 。</path>与从零开始训练相比，调整模型训练DIETClassifier、ResponseSelector和tedpolicy等机器学习组件所需的时间通常更少。要么使用模型配置进行微调，它定义的纪元比以前少，要么使用标志——epoch-fraction。epoch-fraction将使用模型配置文件中为每个机器学习组件指定的epoch的一部分。例如，如果DIETClassifier被配置为使用100个epoch，则指定——epoch-fraction 0.5将只使用50个epoch进行微调。

您还可以通过分别使用rasa train nlu -finetune和rasa train core - finetunet来微调nlu -only或对话管理模型。

为了能够对模型进行微调，必须满足以下条件:

提供的配置应该与用于训练正在调整的模型的配置完全相同。唯一可以改变的参数是单个机器学习组件和策略的时代。

基础模型所训练的标签集(意图、动作、实体和槽)应该与用于微调的训练数据中呈现的标签集完全相同。这意味着在增量训练期间，您不能向训练数据添加新的意图、动作、实体或插槽标签。您仍然可以为每个现有标签添加新的训练示例。如果在培训数据中添加/删除了标签，则需要从头对管道进行培训。

要调优的模型使用当前安装的rasa版本的MINIMUM_COMPATIBLE_VERSION进行训练。

查看countvectorsfeataturizer和regexfeataturizer的文档，了解如何为增量培训适当地配置它们。

rasa interactive#

你可以在本地模式下使用Rasa X在UI中进行交互学习，查看文档了解更多细节。

如果你更喜欢使用命令行，你可以运行以下命令来启动一个交互式学习会话:

rasa interactive

这将首先训练模型，然后启动交互式shell会话。然后，你可以一边跟它说话，一边纠正助手的预测。

如果您使用——model参数提供了一个训练过的模型，那么训练将被跳过，而该模型将被加载。

在互动学习中，Rasa将从训练数据中绘制当前对话和一些类似的对话，以帮助您跟踪您所处的位置。一旦会话开始，您就可以在http://localhost:5005/visualization.html上查看可视化。这个图表需要一些时间来生成。要跳过可视化，运行rasa interactive——skip-visualization。

可以使用以下参数配置交互式学习会话:

usage: rasa interactive [-h] [-v] [-vv] [--quiet] [--e2e] [-p PORT] [-m MODEL]
                        [--data DATA [DATA ...]] [--skip-visualization]
                        [--conversation-id CONVERSATION_ID]
                        [--endpoints ENDPOINTS] [-c CONFIG] [-d DOMAIN]
                        [--out OUT] [--augmentation AUGMENTATION]
                        [--debug-plots] [--finetune [FINETUNE]]
                        [--epoch-fraction EPOCH_FRACTION] [--force]
                        [--persist-nlu-data]
                        {core} ... [model-as-positional-argument]

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. `/<intent>` 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)
  -p PORT, --port PORT  Port to run the server at. (default: 5005)
  -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)
  --conversation-id CONVERSATION_ID
                        Specify the id of the conversation the messages are
                        in. Defaults to a UUID that will be randomly
                        generated. (default: f41b89fc9d1943e08c0f419b4bb2cb0f)
  --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. This can be a single YAML file,
                        or a directory that contains several files with domain
                        specifications in it. The content of these files will
                        be read and merged together. (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)
  --finetune [FINETUNE]
                        Fine-tune a previously trained model. If no model path
                        is provided, Rasa Open Source will try to finetune the
                        latest trained model from the model directory
                        specified via '--out'. (default: None)
  --epoch-fraction EPOCH_FRACTION
                        Fraction of epochs which are currently specified in
                        the model configuration which should be used when
                        finetuning a model. (default: 1.0)
  --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)

rasa shell#

您可以在本地模式下使用Rasa X在UI中与您的助手对话。查看Rasa X文档了解更多细节。

如果你更喜欢使用命令行，你可以运行以下命令来启动聊天会话:

rasa shell

默认情况下，这将加载最新的训练模型。您可以使用——model标志指定要加载的不同模型。

如果您使用仅nlu模型启动shell，那么rasa shell将输出您输入的任何消息的预期意图和实体。

如果您已经训练了一个组合的Rasa模型，但是只希望看到您的模型从文本中提取的意图和实体，那么您可以使用命令Rasa shell nlu。

要增加用于调试的日志级别，可以执行以下命令:

Copy

rasa shell --debug

可以使用以下参数来配置该命令:

usage: rasa shell [-h] [-v] [-vv] [--quiet]
                  [--conversation-id CONVERSATION_ID] [-m MODEL]
                  [--log-file LOG_FILE] [--endpoints ENDPOINTS] [-p PORT]
                  [-t AUTH_TOKEN] [--cors [CORS [CORS ...]]] [--enable-api]
                  [--response-timeout RESPONSE_TIMEOUT]
                  [--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]

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
  --conversation-id CONVERSATION_ID
                        Set the conversation ID. (default:
                        14f6a63f1aca421194dec8652b6308eb)
  -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)
  --response-timeout RESPONSE_TIMEOUT
                        Maximum time a response can take to process (sec).
                        (default: 3600)
  --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)

rasa run#

To start a server running your trained model, run:

rasa run

默认情况下，Rasa服务器使用HTTP进行通信。为了保证与SSL的通信安全，并在HTTPS上运行服务器，您需要提供有效的证书和相应的私钥文件。您可以将这些文件指定为rasa run命令的一部分。如果在创建过程中使用密码加密了密钥文件，那么还需要添加——ssl-password。

rasa run --ssl-certificate myssl.crt --ssl-keyfile myssl.key --ssl-password mypassword

下面的参数可以用来配置你的Rasa服务器:

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]
                [--response-timeout RESPONSE_TIMEOUT]
                [--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]

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)
  --response-timeout RESPONSE_TIMEOUT
                        Maximum time a response can take to process (sec).
                        (default: 3600)
  --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)

For more information on the additional parameters, see Model Storage. See the Rasa HTTP API page for detailed documentation of all the endpoints.

rasa run actions#

To start an action server with the Rasa SDK, run:

rasa run actions

The following arguments can be used to adapt the server settings:

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] [--auto-reload]

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)
  --auto-reload         Enable auto-reloading of modules containing Action
                        subclasses. (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)

rasa visualize#

To generate a graph of your stories in the browser, run:

rasa visualize

If your stories are located somewhere other than the default location data/, you can specify their location with the --stories flag.

The following arguments can be used to configure this command:

usage: rasa visualize [-h] [-v] [-vv] [--quiet] [-d DOMAIN] [-s STORIES]
                      [-c CONFIG] [--out OUT] [--max-history MAX_HISTORY]
                      [-u NLU]

optional arguments:
  -h, --help            show this help message and exit
  -d DOMAIN, --domain DOMAIN
                        Domain specification. This can be a single YAML file,
                        or a directory that contains several files with domain
                        specifications in it. The content of these files will
                        be read and merged together. (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)

rasa test#

To evaluate a model on your test data, run:

rasa test

This will test your latest trained model on any end-to-end stories you have defined in files with the test_ prefix. If you want to use a different model, you can specify it using the --model flag.

If you want to evaluate the dialogue and NLU models separately, you can use the commands below:

rasa test core

and

rasa test nlu

You can find more details in Evaluating an NLU Model and Evaluating a Core Model.

The following arguments are available for rasa test:

usage: rasa test [-h] [-v] [-vv] [--quiet] [-m MODEL] [-s STORIES]
                 [--max-stories MAX_STORIES] [--endpoints ENDPOINTS]
                 [--fail-on-prediction-errors] [--url URL]
                 [--evaluate-model-directory] [-u NLU]
                 [-c CONFIG [CONFIG ...]] [--cross-validation] [-f FOLDS]
                 [-r RUNS] [-p PERCENTAGES [PERCENTAGES ...]] [--no-plot]
                 [--successes] [--no-errors] [--out OUT]
                 {core,nlu} ...

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)
  --no-plot             Don't render evaluation plots. (default: False)
  --successes           If set successful predictions will be written to a
                        file. (default: False)
  --no-errors           If set incorrect predictions will NOT be written to a
                        file. (default: False)
  --out OUT             Output path for any files created during the
                        evaluation. (default: results)

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:
                        tests)
  --max-stories MAX_STORIES
                        Maximum number of stories to test on. (default: None)
  --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 <config-1> <config-2>'. 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)
  -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)

rasa data split#

To create a train-test split of your NLU training data, run:

rasa data split nlu

This will create a 80/20 split of train/test data by default. You can specify the training data, the fraction, and the output directory using the following arguments:

usage: rasa data split nlu [-h] [-v] [-vv] [--quiet] [-u NLU]
                           [--training-fraction TRAINING_FRACTION]
                           [--random-seed RANDOM_SEED] [--out OUT]

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)

If you have NLG data for retrieval actions, this will be saved to seperate files:

ls train_test_split

      nlg_test_data.yml     test_data.yml
      nlg_training_data.yml training_data.yml

rasa data convert nlu#

You can convert NLU data from

LUIS data format,
WIT data format,
Dialogflow data format,
JSON, or
Markdown

YAML or
JSON or
Markdown.

You can start the converter by running:

rasa data convert nlu

You can specify the input file or directory, output file or directory, and the output format with the following arguments:

usage: rasa data convert nlu [-h] [-v] [-vv] [--quiet] [-f {json,md,yaml}]
                             --data DATA [--out OUT] [-l LANGUAGE]

optional arguments:
  -h, --help            show this help message and exit
  -f {json,md,yaml}, --format {json,md,yaml}
                        Output format the training data should be converted
                        into. Note: currently training data can be converted
                        to 'yaml' format only from 'md' format (default: yaml)
  --data DATA           Path to the file or directory containing Rasa NLU
                        data. (default: data)
  --out OUT             File (for `json` and `md`) or existing path (for
                        `yaml`) where to save training data in Rasa format.
                        (default: converted_data)
  -l LANGUAGE, --language LANGUAGE
                        Language of data. (default: en)

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)

rasa data convert core#

You can convert Core data from Markdown to YAML.

You can specify the input file or directory, output directory with the following arguments:

rasa data convert core --help

rasa data convert nlg#

You can convert NLG data from Markdown to YAML.

You can specify the input file or directory, output directory with the following arguments:

rasa data convert nlg --help

rasa data validate#

You can check your domain, NLU data, or story data for mistakes and inconsistencies. To validate your data, run this command:

rasa data validate

验证器搜索数据中的错误，例如，两个意图有一些相同的训练示例。验证器还会检查您是否有任何故事，在这些故事中，不同的助手操作来自相同的对话历史记录。故事之间的冲突会阻碍一个模型学习对话的正确模式。

如果您将max_history值传递给配置中的一个或多个策略。在验证器命令中使用——max-history <max_history>标志提供这些值中的最小值。</max_history>

你也可以通过运行这个命令来验证故事结构:

Copy

rasa data validate stories

You can use rasa data validate with additional arguments, e.g. to specify the location of your data and domain files:

usage: rasa data validate [-h] [-v] [-vv] [--quiet]
                          [--max-history MAX_HISTORY] [-c CONFIG]
                          [--fail-on-warnings] [-d DOMAIN] [--data DATA]
                          {stories} ...

positional arguments:
  {stories}
    stories             Checks for inconsistencies in the story files.

optional arguments:
  -h, --help            show this help message and exit
  --max-history MAX_HISTORY
                        Number of turns taken into account for story structure
                        validation. (default: None)
  -c CONFIG, --config CONFIG
                        The policy and NLU pipeline configuration of your bot.
                        (default: config.yml)
  --fail-on-warnings    Fail validation on warnings and errors. If omitted
                        only errors will result in a non zero exit code.
                        (default: False)
  -d DOMAIN, --domain DOMAIN
                        Domain specification. This can be a single YAML file,
                        or a directory that contains several files with domain
                        specifications in it. The content of these files will
                        be read and merged together. (default: domain.yml)
  --data DATA           Path to the file or directory containing Rasa data.
                        (default: data)

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)

rasa x#

Rasa X is a tool for practicing Conversation-Driven Development. You can find more information about it here.You can start Rasa X in local mode by executing

rasa x

To be able to start Rasa X you need to have Rasa X local mode installedand you need to be in a Rasa project directory.

The following arguments are available for rasa x:

usage: rasa x [-h] [-v] [-vv] [--quiet] [-m MODEL] [--data DATA] [-c CONFIG]
              [-d DOMAIN] [--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]
              [--response-timeout RESPONSE_TIMEOUT]
              [--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]

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)
  -d DOMAIN, --domain DOMAIN
                        Domain specification. This can be a single YAML file,
                        or a directory that contains several files with domain
                        specifications in it. The content of these files will
                        be read and merged together. (default: domain.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)
  --response-timeout RESPONSE_TIMEOUT
                        Maximum time a response can take to process (sec).
                        (default: 3600)
  --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)