rasa-manage:1. 添加docker镜像构建文件
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# 设置基础镜像
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# 在137服务器中使用的是 rasa_dev:1.0.0镜像
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FROM rasa_dev:1.0.0
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ENV APP_HOME=/data/vp
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# 设置工作目录
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WORKDIR $APP_HOME
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# 复制java jar 到容器中
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COPY target/virtual-patient-rasa-1.0-SNAPSHOT.jar $APP_HOME/virtual-patient-rasa-1.0-SNAPSHOT.jar
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# 复制rasa配置文件到 rasa目录下
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COPY docs/rasa /rasa
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# 暴漏服务端口
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EXPOSE 8890
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# 设置启动命令
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CMD nohup java -jar -Duser.timezone=Asia/Shanghai $APP_HOME/virtual-patient-rasa-1.0-SNAPSHOT.jar
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recipe: default.v1
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language: zh
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pipeline:
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- name: JiebaTokenizer
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- name: LanguageModelFeaturizer
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model_name: "bert"
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model_weights: "/rasa/bert-base-chinese"
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- name: RegexFeaturizer
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- name: DIETClassifier
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epochs: 100
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learning_rate: 0.001
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tensorboard_log_directory: ./log
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- name: ResponseSelector
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epochs: 100
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learning_rate: 0.001
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- name: FallbackClassifier
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threshold: 0.87
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ambiguity_threshold: 0.1
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- name: EntitySynonymMapper
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policies:
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- name: MemoizationPolicy
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- name: TEDPolicy
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- name: RulePolicy
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core_fallback_threshold: 0.87
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core_fallback_action_name: "action_default_fallback"
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enable_fallback_prediction: True
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recipe: default.v1
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language: zh
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pipeline:
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- name: JiebaTokenizer
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- name: LanguageModelFeaturizer
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model_name: bert
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model_weights: bert-base-chinese
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- name: RegexFeaturizer
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- name: DIETClassifier
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epochs: 100
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learning_rate: 0.001
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tensorboard_log_directory: ./log
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- name: ResponseSelector
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epochs: 100
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learning_rate: 0.001
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- name: FallbackClassifier
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threshold: 0.87
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ambiguity_threshold: 0.1
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- name: EntitySynonymMapper
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policies:
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- name: MemoizationPolicy
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- name: TEDPolicy
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- name: RulePolicy
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core_fallback_threshold: 0.87
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core_fallback_action_name: "action_default_fallback"
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enable_fallback_prediction: True
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# This file contains the credentials for the voice & chat platforms
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# which your bot is using.
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# https://rasa.com/docs/rasa/messaging-and-voice-channels
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rest:
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# # you don't need to provide anything here - this channel doesn't
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# # require any credentials
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#facebook:
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# verify: "<verify>"
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# secret: "<your secret>"
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# page-access-token: "<your page access token>"
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#slack:
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# slack_token: "<your slack token>"
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# slack_channel: "<the slack channel>"
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# slack_signing_secret: "<your slack signing secret>"
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#socketio:
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# user_message_evt: <event name for user message>
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# bot_message_evt: <event name for bot messages>
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# session_persistence: <true/false>
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#mattermost:
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# url: "https://<mattermost instance>/api/v4"
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# token: "<bot token>"
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# webhook_url: "<callback URL>"
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# This entry is needed if you are using Rasa Enterprise. The entry represents credentials
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# for the Rasa Enterprise "channel", i.e. Talk to your bot and Share with guest testers.
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rasa:
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url: "http://localhost:5002/api"
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# This file contains the different endpoints your bot can use.
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# Server where the models are pulled from.
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# https://rasa.com/docs/rasa/model-storage#fetching-models-from-a-server
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#models:
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# url: http://my-server.com/models/default_core@latest
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# wait_time_between_pulls: 10 # [optional](default: 100)
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# Server which runs your custom actions.
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# https://rasa.com/docs/rasa/custom-actions
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action_endpoint:
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url: "http://192.168.10.137:5055/webhook"
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# Tracker store which is used to store the conversations.
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# By default the conversations are stored in memory.
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# https://rasa.com/docs/rasa/tracker-stores
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#tracker_store:
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# type: redis
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# url: <host of the redis instance, e.g. localhost>
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# port: <port of your redis instance, usually 6379>
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# db: <number of your database within redis, e.g. 0>
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# password: <password used for authentication>
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# use_ssl: <whether or not the communication is encrypted, default false>
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#tracker_store:
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# type: mongod
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# url: <url to your mongo instance, e.g. mongodb://localhost:27017>
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# db: <name of the db within your mongo instance, e.g. rasa>
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# username: <username used for authentication>
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# password: <password used for authentication>
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#tracker_store:
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# type: mongod
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# url: mongodb://192.168.10.137:27017
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# db: dialog_test
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# username:
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# password:
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# Event broker which all conversation events should be streamed to.
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# https://rasa.com/docs/rasa/event-brokers
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#event_broker:
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# url: localhost
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# username: username
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# password: password
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# queue: queue
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