Dispatch is a tool to create and evaluate LUIS models used to dispatch intent across multiple bot modules such as LUIS models, QnA knowledge bases and others (added to dispatch as a file type).
Use the Dispatch model in cases when:
- Your bot consists of multiple modules and you need assistance in routing user's utterances to these modules and evaluate the bot integration.
- Evaluate quality of intents classification of a single LUIS model.
- Create a text classification model from text files.
- Node.js version 8.5 or higher
- For installation on Linux, please pre-install .NET Core runtime by following instructions here
- If install fails, try the workaround described here
To install:
npm install -g botdispatchThis will install dispatch into your global path.
To initialize dispatch:
dispatch init [options]It will ask for the name of the dispatch, LUIS authoring key and region needed to create a LUIS application. This commands then creates {dispatchName}.dispatch file. To bypass the prompts, values could be passed in via arguments below.
Arguments:
| Option | Description |
|---|---|
| -n, --name | (optional) Name of the dispatch |
| --luisAuthoringKey | (optional) LUIS authoring key |
| --luisAuthoringRegion | (optional) LUIS authoring region |
| -b, --bot | (optional) .bot file path |
| -s, --secret | (optional) .bot file secret |
| -c, --culture | (optional) Used to set LUIS app culture for dispatch. Required if none of dispatch source(s) is LUIS app. |
| --hierarchical | (optional) Default to true. If false, existing intents from source LUIS model(s) will be available as the dispatch intents. |
| --dataFolder | (optional) Dispatch working directory |
| -h, --help | Output usage information |
Example:
dispatch init -n TestDispatch --luisAuthoringKey "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" --luisAuthoringRegion westus
dispatch init --bot c:\src\bot\testbot.botThis step is not needed if you have a .bot file already connected with services (i.e., LUIS/QnA). Dispatch will take the services in .bot file and add each of the services it can dispatch to .dispatch file. Currently, a maximum of 500 dispatch sources could be added to a Dispatch model.
dispatch add -t luis -i xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx -n TestLuisApp -v 0.1 -k xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
dispatch add -t luis -i xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx -n TestLuisApp --intentName foo -v 0.1 -k xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
dispatch add -t qna -i xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx -n Faq -k xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
dispatch add -t qna -i xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx -n Faq -k xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx --includeAnswersForTraining true
dispatch add -t file -n TestModule -f c:\src\testmodule.tsv
dispatch add -t file -n TestModule2 -f c:\src\testmodule2.txt
dispatch add -t file -n TestModule3 -f c:\src\testmodule3.jsonArguments
| Option | Description |
|---|---|
| -t, --type | luis, qna, file |
| -i, --id | (required only if type is luis/qna) LUIS app id or QnA kb id from application settings page |
| -n, --name | LUIS app name or QnA name (from application settings page) or module/file name for file type |
| -k, --key | (required only if type is luis/qna) LUIS authoring key (from https://www.luis.ai/user/settings, see https://aka.ms/luiskeys for more information on LUIS keys) or QnA maker subscription key (from https://ms.portal.azure.com, see https://aka.ms/qnamakerkeys for more information about QnA Maker keys) |
| -v, --version | (Required only if type is luis) LUIS app version |
| -f, --filePath | (Required only if type is file) Path to tsv file containing tab delimited intent and utterance fields or .txt file with an utterance on each line |
| --intentName | (optional) Dispatch intent name for this source, name param value will be used otherwise |
| --includedIntents | (optional) Comma separated list of intents to be included in the Dispatch model, all intents are included otherwise |
| --ignoreWordAlterations | (optional) Disable expansions of QnA kb questions with QnA word alterations |
| --includeAnswersForTraining | (optional for QnA only) If set to true, QnA KB answers will be included in the training set |
| --dispatch | (optional) Path to .dispatch file |
| --dataFolder | (optional) Dispatch working directory |
| -h, --help | Output usage information |
Supported file types:
| File extension | Description |
|---|---|
| .tsv | Lines of tab delimited fields of intent and utterance (in that order) |
| .txt | Lines of utterances with intent as file name |
| .json | Exported LUIS or QnA Maker json file |
To remove one of the services from .dispatch file, run
dispatch remove -t luis -i xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
dispatch remove -t qna -i xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
dispatch remove -t file -f c:\src\testmodule.jsonArguments
| Option | Description |
|---|---|
| -t, --type | luis, qna, file |
| -i, --id | (required only if type is luis/qna) LUIS app id or QnA kb id - from application settings page) |
| -n, --name | LUIS app name or QnA name (from application settings page) or module/file name for file type |
| -f, --filePath | (Required only if type is file) Path to tsv file containing tab delimited intent and utterance fields or .txt file with an utterance on each line |
| --dispatch | (optional) Path to .dispatch file |
| --dataFolder | (optional) Dispatch working directory |
| -h, --help | Output usage information |
To create, train and publish your new dispatch model:
dispatch create [options]
dispatch create --bot c:\src\bot\testbot.bot --secret <your_bot_file_secret>Options:
| Option | Description |
|---|---|
| -b, --bot | (optional) Path to .bot file or bot services json file |
| -s, --secret | (optional) Secret used to encrypt/decrypt .bot file |
| -c, --culture | (optional) Used to set LUIS app culture for dispatch. Required if none of dispatch source(s) is LUIS app. |
| --dispatch | (optional) Path to .dispatch file |
| --dataFolder | (optional) Dispatch working directory |
| --hierarchical | (optional) Default to true, set to false when evaluating a single LUIS model |
| --useAllTrainingData | (optional) Default to false. LUIS UseAllTrainingData flag (see https://westus.dev.cognitive.microsoft.com/docs/services/5890b47c39e2bb17b84a55ff/operations/versions-update-application-version-settings) |
| --dontReviseUtterance | (optional) Default to false. Dispatch sometimes minorly revises an utterance for generalization. If false, utterances won't be revised |
| --publishToStaging | (optional) Default to false. Publish to LUIS staging instead of production platform |
| --dedupeTrainingSet | (optional) Default to false. If false, Dispatch won't dedupe duplicated training instances |
| --doAutoActiveLearning | (optional) Default to false. LUIS limit on training-set size is 15000. When a LUIS app has much more utterances for training, Dispatch's auto active learning process can intelligently down sample the utterances |
| --aalNumberOfInstancesPerIteration | (optional) Default to 2500. Max #instances processed during each auto-active-learning down-sampling iteration |
| --aalMaxNumberOfActiveLearningIterations | (optional) Default to -1. Max number of auto active learning iterations, each processes a fixed batch of instances. Negative setting enables scanning through all available instances. |
| --aalFixedNumberOfActiveLearningIterations | (optional) Default to -1. Fixed number of iterations to process all available instances. Number of instance batch in each instance is calaulated based on this parameter. |
| --aalInitialNumberOfSamplesPerIntent | (optional) Default to 20. Initial #instances randomly sampled for each intent before kicking off the auto-active-learning down-sampling process. |
| --aalInitialNumberOfInstancesPerIteration | (optional) Default to 200. #instance per iteration can be configured to grow until reaching aalNumberOfInstancesPerIteration. |
| --aalNumberOfSamplesPerIntentGrowthRatio | (optional) Default to 1.5. The growth rate of #instances per auto-active-learning iteration. |
| -h, --help | Output usage information |
This command creates a brand new LUIS application.
To train and publish your existing dispatch model after modification:
dispatch refresh [options]
dispatch refresh --bot c:\src\bot\testbot.bot --secret <your_bot_file_secret>With the following options
| Option | Description |
|---|---|
| -v, --version | (optional) Dispatch LUIS app version. A new version will be created if param value is different than previously created version. |
| -b, --bot | (optional) .bot file path |
| -s, --secret | (optional) .bot file secret |
| --dispatch | (optional) .dispatch file path |
| --dataFolder | (optional) Dispatch working directory |
| -h, --help | Output usage information |
This command updates existing LUIS application in .dispatch file.
This command will run cross validation evaluation on the dispatch model and generate a summary of the evaluation:
dispatch eval [options]Options:
| Option | Description |
|---|---|
| --luisSubscriptionKey | (optional, will be prompted) Cognitive Service LUIS key from portal.azure.com |
| --luisSubscriptionRegion | (optional, will be prompted) Cognitive Service LUIS region from portal.azure.com |
| --dispatch | (optional) .dispatch file path |
| --dataFolder | (optional) Dispatch working directory |
| --doAutoActiveLearning | (optional) Default to false. LUIS limit on training-set size is 15000. When a LUIS app has much more utterances for training, Dispatch's auto active learning process can intelligently down sample the utterances |
| --aalNumberOfInstancesPerIteration | (optional) Default to 2500. Max #instances processed during each auto-active-learning down-sampling iteration |
| --aalMaxNumberOfActiveLearningIterations | (optional) Default to -1. Max number of auto active learning iterations, each processes a fixed batch of instances. Negative setting enables scanning through all available instances. |
| --aalFixedNumberOfActiveLearningIterations | (optional) Default to -1. Fixed number of iterations to process all available instances. Number of instance batch in each instance is calaulated based on this parameter. |
| --aalInitialNumberOfSamplesPerIntent | (optional) Default to 20. Initial #instances randomly sampled for each intent before kicking off the auto-active-learning down-sampling process. |
| --aalInitialNumberOfInstancesPerIteration | (optional) Default to 200. #instance per iteration can be configured to grow until reaching aalNumberOfInstancesPerIteration. |
| --aalNumberOfSamplesPerIntentGrowthRatio | (optional) Default to 1.5. The growth rate of #instances per auto-active-learning iteration. |
| --aalTestingInstancesDownsamplingRatio | (optional) Default to 1. Ratio of test instances that will be used for evaluating the model. |
| -h, --help | Output usage information |
If no options are supplied, the tool will prompt for the required information it needs to run model evaluation.
To test your dispatch model against test set:
dispatch test [options]Options:
| Option | Description |
|---|---|
| --testFilePath | Path to a tsv file with three (or two) fields: expected intent, weight and utterance in that order; the first line (header) will be skipped; the weight column is optional |
| --luisSubscriptionKey | (optional) Cognitive Service LUIS key from portal.azure.com |
| --luisSubscriptionRegion | (optional) Cognitive Service LUIS region from portal.azure.com |
| --dispatch | (optional) .dispatch file path |
| --dataFolder | (optional) Dispatch working directory |
| --doAutoActiveLearning | (optional) Default to false. If true, will also run 'test' against the local model created during the auto active learning process |
| -h, --help | Output usage information |
To run prediction against your new dispatch model, run
dispatch predict [options]With the following options
| Option | Description |
|---|---|
| --luisSubscriptionKey | (optional) Cognitive Service LUIS key from portal.azure.com |
| --luisSubscriptionRegion | (optional) Cognitive Service LUIS region from portal.azure.com |
| --dispatch | (optional) .dispatch file path |
| --dataFolder | (optional) Dispatch working directory |
| --doAutoActiveLearning | (optional) Default to false. If true, will also run 'predict' using the local model created during the auto active learning process |
| -h, --help | Output usage information |
You'll then be prompted to enter the utterance you'd like to run prediction on.
To print your current dispatch configuration, run
dispatch list [options]With the following options
| Option | Description |
|---|---|
| --dispatch | (optional) .dispatch file path |
| --dataFolder | (optional) Dispatch working directory |
| -h, --help | Output usage information |
If you have a .bot file containing one or more LUIS model(s) and/or one or more QnA Maker knowledge base(s), you could create Dispatch model without having to initialize Dispatch and add all of the sources separately. Running the eval command is optional but it provides insight into how well the newly created or updated Dispatch model will perform. In addition, it provides suggestions for improving the bot components.
dispatch create --bot c:\src\bot\testbot.bot --secret <your_bot_file_secret>
dispatch eval --luisSubscriptionKey <azure_luis_key> --luisSubscriptionRegion <azure_luis_region>If any of your LUIS/QnA Maker models have changed or if you have added more LUIS/QnA maker component(s) to your bot, update your Dispatch model with refresh command.
dispatch refresh --bot c:\src\bot\testbot.bot --secret <your_bot_file_secret>
dispatch eval --luisSubscriptionKey <azure_luis_key> --luisSubscriptionRegion <azure_luis_region>End-to-end example of a bot consisting of a LUIS module and a QnA Maker knowledge base module:
dispatch init -n mybot_dispatch --luisAuthoringKey <luis_authoring_key> --luisAuthoringRegion <region>
dispatch add -t luis -i <luis_app_id> -n <luis_app_name> -v <luis_app_version> -k <luis_app_authoring_key>
dispatch add -t qna -i <qna_kb_id> -n <kb_name> -k <qna_maker_key>
dispatch create
dispatch eval --luisSubscriptionKey <azure_luis_key> --luisSubscriptionRegion <azure_luis_region>The output is Summary.html file located in local file system directory where the commands were issued. It includes all the evaluation results and suggestions for improving the bot components.
Evaluate a LUIS model performing cross validation:
dispatch init -n mybot_dispatch --luisAuthoringKey <luis_authoring_key> --luisAuthoringRegion <region>
dispatch add -t luis -i <luis_app_id> -n <luis_app_name> -v <luis_app_version> -k <luis_app_authoring_key>
dispatch create --hierarchical false
dispatch eval --luisSubscriptionKey <azure_luis_key> --luisSubscriptionRegion <azure_luis_region>The output, Summary.html, contains all the evaluation results. The file is located in local file system directory where the commands were issued.
Suppose the dispatcher model was already created following the steps of one of the above tasks. To test this model with a tab-delimited text file run these commands:
dispatch test --testFilePath <text_file>The output, Summary.html, contains all the evaluation results. The file is located in the location of the test file.
Tutorial: https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-dispatch
If you are using the Dispatch command line tool in Azure Pipelines with a Microsoft-hosted agent, you may encounter the following error:
Unable to install/use dotnet framework
To fix this, make sure you are using the correct agent pool. In order to successfully run the .NET commands that Dispatch relies on, you will need to use Visual Studio 2017 on Windows Server 2016 (vs2017-win2016). In the web UI, you would select "Hosted VS2017":
Nightly builds are based on the latest development code which means they may or may not be stable and probably won't be documented. These builds are better suited for more experienced users and developers although everyone is welcome to give them a shot and provide feedback.
You can get the latest nightly build of Dispatch from the BotBuilder MyGet feed. To install the nightly -
npm config set registry https://botbuilder.myget.org/F/botbuilder-tools-daily/npm/Install using npm:
npm i -g botdispatchTo reset registry:
npm config set registry https://registry.npmjs.org/