This is the Starter Challenge for ImagineCode 2018, hosted in EINA Unizar from September 28th to 30th.
News! Telegram integration instructions available at Rasa Docker
In May 2018, Google demostrated how a Chatbot could make an appointment on a real hairdressing salon speaking with a human in a phone call.
https://www.youtube.com/watch?v=D5VN56jQMWM
Chatbot software is designed to provide conversational solutions to identified intents or goals. This conversation can be performed by using raw text (chatting services) or natural voice.
Every major software manufacturer has its own Cloud Service to provide these features:
When developing on these platforms, your source code is executed in the Cloud and your data is also stored out of your control.
But not every Conversational AI Engine has to be used in the Cloud, there are some Open Source alternatives that can be executed on premises.
Rasa is providing Open Source conversational AI tools based in NLU for developers and the product can be easily deployed by using Docker.
Additionally, when dealing with Content, there are also some Open Source ECM products available. We recommend you to use Alfresco, which is an standard CMIS Repository that can be also deployed by using Docker in your development environment.
Let's say we have a Document Library including a set of information for every Final Degree Project in the University.
We can define different intents for our Chatbot service:
- Send an email to someone including references for an specific topic, year, area/department or author
- Download a package with references for an specific topic, year, area/department or author
Sample conversation
User: Hello
Bot: Hey, how can I help you?
User: I am looking for Docker related projects... Any
suggestions?
Bot: We have 432 projects including Docker topics inside, would you like
to filter?
User: Sure! How many projects were presented in 2017?
Bot: We have 23 projects in 2017 including Docker, do you want me to send an email with the list or do you want to download the contents?
User: Can you send an email with this list to Angel?
Bot: Great, just sent the email for you.
User: Thanks a lot!
Bot: Glad I could help!
Sample architecture
[Chatbot] > [RASA] > [CMIS] > [Alfresco]
^^
[Training] > [CMIS] > [Alfresco]
Sample content model
Aspect THESIS
- Property TOPIC (string)
- Property YEAR (number)
- Property AREA (string)
- Property AUTHOR (string)
Content model implementation is provided by default in docker-alfresco project.
Participants can solve an additional mini-puzzle. The winner will be the first pull-request to this repository with the right implementation for es.keensoft.alfresco.mug.MugContest.getPdfDocuments() method according to requirements set in JUnit Test available at es.keensoft.alfresco.mug.test.TestMugContest.getPdfDocuments().
More details at CMIS Client project.
We are providing default configurations for:
- RASA UI, the software required for the Chatbot
- Alfresco, the software required for the Content
- CMIS Client, the software required to upload and retrieve information from Alfresco
- Docker 18
- Docker Compose 1.2
- JDK 1.8 or later
- Maven 3.2+
Building Docker image and starting default RASA UI.
$ cd rasa-docker
$ docker build --tag keensoft/rasa_nlu .
$ docker run -p 5000:5000 keensoft/rasa_nluTesting the chatbot by using the command line in Docker Container.
$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
f0240da37c1e keensoft/rasa_nlu "./entrypoint.sh sta…" 4 seconds ago Up 3 seconds 0.0.0.0:5000->5000/tcp epic_engelbart
$ docker exec -it f0240da37c1e /bin/bash
$ tree models
models
└── current
├── dialogue
│ ├── domain.json
│ ├── domain.yml
│ ├── policy_0_MemoizationPolicy
│ │ ├── featurizer.json
│ │ └── memorized_turns.json
│ ├── policy_1_KerasPolicy
│ │ ├── featurizer.json
│ │ ├── keras_arch.json
│ │ ├── keras_policy.json
│ │ └── keras_weights.h5
│ ├── policy_metadata.json
│ └── stories.md
└── nlu_model
└── default
└── model_20180910-142902
├── checkpoint
├── intent_classifier_tensorflow_embedding.ckpt.data-00000-of-00001
├── intent_classifier_tensorflow_embedding.ckpt.index
├── intent_classifier_tensorflow_embedding.ckpt.meta
├── intent_classifier_tensorflow_embedding_encoded_all_intents.pkl
├── intent_classifier_tensorflow_embedding_inv_intent_dict.pkl
├── intent_featurizer_count_vectors.pkl
├── metadata.json
└── training_data.json
$ python -m rasa_core.run -d models/current/dialogue -u models/current/nlu_model/default/model_20180910-142902
Bot loaded. Type a message and press enter:
Hello
Hey, how can I help you?
I would like to join a meetup
Rasa Bots Berlin meetup is definitely worth checking out! They are having an event today at Behrenstraße 42. Would you like to join?
No, thanks.
Bye!Building Alfresco image and starting default Alfresco Repository.
$ cd docker-alfresco
$ docker-compose up --buildAlfresco includes a collection of services running as containers inside Docker.
$ docker ps --format '{{.Names}}\t{{.Image}}\t{{.Ports}}'
proxy nginx:stable-alpine 0.0.0.0:80->80/tcp
share docker-alfresco_share 8080/tcp
content-app alfresco/alfresco-content-app:master-latest 80/tcp
solr6 alfresco/alfresco-search-services:1.2.0 8983/tcp
alfresco docker-alfresco_alfresco 8080/tcp
postgres postgres:10 5432/tcpA Web Proxy has been included to make easier accessing to these services:
- http://localhost/alfresco: Repository backend and CMIS Server
- http://localhost/share: UI for the Repository
You should be able to access both URLs once Docker is up & running.
Content Model for THESIS is just deployed inside this Alfresco Server. You can find definition for reference at
docker-alfresco/alfresco/shared/library-model.xmlfolder.
A sample Spring Boot Client to gather information from CMIS Server has been created at cmis-client folder.
Building and running the program can be done by using following commands.
$ cd cmis-client
$ mvn clean package
$ java -jar target/cmis-client-1.1.0.jar --action=dump