Our teams create individual bots for themselves and now we want to integrate them into the Enterprise Assistant.
What I need help with is Multi Bot Orchestration.
Has anyone been able to do this efficiently?
Basically, the user asks a question in the Enterprise Assistant and then the bot gets the answer from the respective child/skill QnA bot.
I am able to add skills like calendar, people, SAP, etc. but dealing with QnA bots is proving to be an impossible challenge.
To create a multi bot orchestration, we need to include multiple levels of dispatchers, pipelines for exception handling and fallbacks. It needs an expert system type agent who can handle multiple NLP models. A chat bot is designed based on NLP modelling. The NLP implementation will be depending on number of operations that enterprise application will handle. The following are the different NLP models required to handle a perfect enterprise application.
General QnA
Billing information
Calendar
Leave Planning
Employee storage disk
On demand service to the clients
Fault tolerance
Saving the QnA pattern of chat between bot and human
Calculating the accuracy
Taking users own request and performing NLP
like this we have so many requirements to implement a Bot. All the models will be trained and connected to the dispatcher.
The following are the things followed in bot orchestration.
It consists of 5 different implementations:
Ingress Pipeline
Egress Pipeline
Dispatcher
Exception pipeline
Fallback pipeline
Ingress Pipeline:
The ingress pipeline will be detection few things like
Language detect and translate
Entity recognition
User input redaction
Egress Pipeline:
the following are requirement and operations in egress pipeline
3rd party tools for analytics
Managed responses (default responses)
Data service redaction
Dispatcher:
The dispatcher will be having two different implementations.
Policies and rules,
NLU (Natural Language Understanding) strategy.
Policies and Rules
The policies and rules of the organization must be trained to the model. Because, if the user is asking for the financial status of the orgzanition, the bot must understand the policies and rules it must follow in order to answer for the question.
Natual Language Understanding
Natural Language Understanding (NLU) — The computer’s ability to understand what we say. The context what user is typing in his/her own words is the procedure of NLU.
The dispatcher will be communicating with the egress pipeline and ingress pipeline as it is the parent handler of all the operations and model training results.
To address the exceptions and rollbacks, we need to have exception handlers who are also expert system agents. An agent is having some expertise in handling the exceptions.
Example:
Question: Give the details of pithon projects of this monthh.
Handled Exceptions: In the above question "python" spelt like "pithon" and "month" was spelt like "monthh". NLP and NLU have to understand these cluases and have to handle in exception of mis-spelt words.
The following link can explain in detailed architecture of Multi-bot orchestration.
https://servisbot.com/multi-bot-orchestration-architecture/
I am trying to implement a chatbot using linked data. In a paper, I read that I can use SVM for the intent classification which is basically the first stage in conversational systems. It is the process of mapping queries to a predefined class. I wanted to know whether there is any other way or applications / programs that I can use to do this. Could someone please advise me?
Thank you in advance.
Rasa is an open-source platform that can be used to easily set up a chatbot, and through it you can set up several kinds of intent classification (including with an SVM).
It also allows you to annotate example utterances and map them to intents within the tool itself.
These tasks are certainly doable on your own, but if speed and easy integration with a working chatbot are priorities, then I recommend using a platform like that.
I want to build a chatbot for a customer service application. I tried SaaS services like Wit.Ai, Motion.Ai, Api.Ai, LUIS.ai etc. These cognitive services find the "intent" and "entities" when trained with the typical interactions model.
I need to build chatbot for on-premise solution, without using any of these SaaS services.
e.g Typical conversation would be as following -
Can you book me a ticket?
Is my ticket booked?
What is the status of my booking BK02?
I want to cancel the booking BK02.
Book the tickets
StandFord NLP toolkit looks promising but there are licensing constraints. Hence I started experimenting with the OpenNLP. I assume, there are two OpenNLP tasks involved -
Use 'Document Categorizer' to find out the intent
Use 'Named Entity Recognition' to find out entities
Once the context is identified, I will call my application APIS to build the response.
Is it a right approach?
How good OpenNLP is in parsing the text?
Can I use Facebook FASTTEXT library for Intent identification?
Is there any other open source library which can be helpful in building the BOT?
Will "SyntaxNet" be useful for my adventure?
I prefer to do this in Java. BUT open to node or python solution too.
PS - I am new to NLP.
Have a look at this. It says it is an Open-source language understanding for bots and a drop-in replacement for popular NLP tools like wit.ai, api.ai or LUIS
https://rasa.ai/
Have a look at my other answer for a plan of attack when using Luis.ai:
Creating an API for LUIS.AI or using .JSON files in order to train the bot for non-technical users
In short use Luis.ai and setup some intents, start with one or two and train it based on your domain. I am using asp.net to call the Cognitive Service API as outlined above. Then customize the response via some JQuery...you could search a list of your rules in a javascript array when each intent or action is raised by the response from Luis.
If your Bot is english based, then I would use OpenNLP's sentence parser to dump the customer input into a database (I do this today). I then use the OpenNLP tokenizer and push the keywords (less the stop words) and Parts of Speech into a database table for keyword analysis. I have a custom Sentiment model built for OpenNLP that will tag each sentence with a Pos, Neg, Neutral sentiment...You can then use this to identify negative customer service feedback. To build your own Sentiment model have a look at SentiWord.net and download their domain agnostic data file to build and train an OpenNLP model or have a look at this Node version...
https://www.npmjs.com/package/sentiword
Hope that helps.
I'd definitely recommend Rasa, it's great for your use case, working on-premise easily, handling intents and entities for you and on top of that it has a friendly community too.
Check out my repo for an example of how to build a chatbot with Rasa that interacts with a simple database: https://github.com/nmstoker/lockebot
I tried RASA, But one glitch I found there was the inability of Rasa to answer unmatched/untrained user texts.
Now, I'm using ChatterBot and I'm totally in love with it.
Use "ChatterBot", and host it locally using - 'flask-chatterbot-master"
Links:
ChatterBot Installation: https://chatterbot.readthedocs.io/en/stable/setup.html
Host Locally using - flask-chatterbot-master: https://github.com/chamkank/flask-chatterbot
Cheers,
Ratnakar
With the help of the RASA and Botkit framework we can build the onpremise chatbot and the NLP engine for any channel. Please follow this link for End to End steps on building the same. An awsome blog that helped me to create a one for my office
https://creospiders.blogspot.com/2018/03/complete-on-premise-and-fully.html
First of all any chatbot is going to be the program that runs along with the NLP, Its the NLP that brings the knowledge to the chatbot. NLP lies on the hands of the Machine learning techniques.
There are few reasons why the on premise chatbots are less.
We need to build the infrastructure
We need to train the model often
But using the cloud based NLP may not provide the data privacy and security and also the flexibility of including my business logic is very less.
All together going to the on premise or on cloud is based on the needs and the use case of the requirements.
How ever please refer this link for end to end knowledge on building the chatbot on premise with very few steps and easily and fully customisable.
Complete On-Premise and Fully Customisable Chat Bot - Part 1 - Overview
Complete On-Premise and Fully Customisable Chat Bot - Part 2 - Agent Building Using Botkit
Complete On-Premise and Fully Customisable Chat Bot - Part 3 - Communicating to the Agent that has been built
Complete On-Premise and Fully Customisable Chat Bot - Part 4 - Integrating the Natural Language Processor NLP
Disclaimer: I am the author of this package.
Abodit NLP (https://nlp.abodit.com) can do what you want but it's .NET only at present.
In particular you can easily connect it to databases and can provide custom Tokens that are queries against a database. It's all strongly-typed and adding new rules is as easy as adding a method in C#.
It's also particularly adept at turning date time expressions into queries. For example "next month on a Thursday after 4pm" becomes ((((DatePart(year,[DATEFIELD])=2019) AND (DatePart(month,[DATEFIELD])=7)) AND (DatePart(dw,[DATEFIELD])=4)) AND DatePart(hour,[DATEFIELD])>=16)
I'm planning to design a distributed hardware monitoring and controlling like provisioning user Environments,decommission, push updates etc. i herd puppet supports these features. Please let me know if there is a better freeware out there ? . or a good puppet reference.
I'd like to know about specific problems you - the SO reader - have solved using Workflow Engines and what libraries/frameworks you used if you didn't roll your own. I'd also like to know when a Workflow Engine wasn't the best choice and if/how you chose something simpler, like a TaskList/WorkList/Task-Management type application using state machines.
Questions:
What problems have you used workflow engines to solve?
What libraries/frameworks did you use?
When did a simpler State Machine/Task Management like system suffice?
Bonus: How did/do you make the distinction between Task Management and Workflow Engine?
I'm looking for first-hand experiences.
Some of the resources I've checked out:
Ruote and State Machine != Workflow Engine
StonePath and Docs
Creating and Managing Worklist Task Plans with Oracle
Design and Implementation of a Workflow Engine - Thesis
What to use Windows Workflow Foundation For
JBoss jBPM Docs
I'm biased as well, as I am the main author of StonePath.
I have developed workflow applications for the U.S. State Department, the Geneva Centre for Humanitarian Demining, several fortune 500 clients, and most recently the Washington DC Public School System. Every time I have seen a 'workflow engine' that tried to be the one master reference for business processes, I have seen an organization fighting itself to work around the tool. This may be due to the fact that these solutions have always been vendor/product driven, and then end up with a tactical team of 'consultants' constantly feeding the app... but because of this, I tend to react negatively when I hear the benefits of process-based tools that promise to 'centralize the workflow definitions in one place and make them repeatable'.
I very much like Ruote - I have been following that project for some time and should I need that kind of solution, it will be the next tool I'll be willing to try. StonePath has a very different purpose than ruote
Ruote is useful to Ruby in general,
StonePath is aimed at Rails, the web framework written in Ruby.
Ruote is about long-lived business processes and their associated definitions (Note - active development on ruote ceased).
StonePath is about managing State-based workflow and tasking.
Frankly, I think the distinction from the outside looking in might be subtle - many times the same kinds of business processes can be represented either way - the state-and-task-based model tends to map to my mental model though.
Let me describe the highlights of a state-based workflow.
States
Imagine a workflow revolving around the processing of something like a mortgage loan or a passport renewal. As the document moves 'around the office', it travels from state to state.
If you are responsible for the document, and your boss asked you for a status update you'd say things like
"It is in data entry"...
"We are checking the applicant's credentials now"...
"we are awaiting quality review"...
"We are done"... and so on.
These are the states in a state-based workflow. We move from state to state via transitions - like "approve", "apply", kickback", "deny", and so on. These tend to be action verbs. Things like this are modeled all the time in software as a state machine.
Tasks
The next part of a state/task-based workflow is the creation of tasks.
A Task is a unit of work, typically with a due date and handling instructions, that connects a work item (the loan application or passport renewal, for instance), to a users "in box".
Tasks can happen in parallel with each other or sequentially
Tasks can be created automatically when we enter states,
Create tasks manually as people realize work needs to get done
Require tasks to be completed before we can move onto a new state.
This kind of behavior is optional, and part of the workflow definition.
The rabbit hole can go a lot deeper than this, and I wrote an article about it for Issue #4 of PragPub, the Pragmatic Programmer's Magazine. Check out the repo link above for an updated PDF of that article.
In working with StonePath the last few months, I have found that the state-based model maps really well to restful web architectures - in particular, the tasks and state transitions map nicely as nested resources. Expect to see future writing from me on this subject.
I'm biased, I'm one of the authors of ruote.
variant 1) state machine attached to a resource (document, order, invoice, book, piece of furniture).
variant 2) state machine attached to a virtual resource named a task
variant 3) workflow engine interpreting workflow definitions
Now your question is tagged "BPM" we can be expanded into "Business Process management". How does that kind of management occur in each of the variant ?
In variant 1, the business process (or workflow) is scattered in the application. The state machine attached to the resource enforces some of the aspects of the workflow, but only those related to the resource. There may be other resources with their own state machine following the same business process.
In variant 2, the workflow can be concentrated around the task resource and represented by the state machine around that resource.
In variant 3, the workflow is enacted by interpreting a resource called a workflow definition (or business process definition).
What happens when the business process changes ? Is it worth having a workflow engine where business processes are manageable resources ?
Most of the state machine libraries have 1 set states + transitions. Workflow engines are, most of them, workflow definition interpreters and they allow multiple different workflows to run together.
What will be the cost of changing the workflow ?
The variants are not mutually exclusive. I have seen many examples where a workflow engine changes the state of multiple resources some of them guarded by state machines.
I also use variant 3 + 2 a lot, for human tasks : the workflow engine, at some points when running a process instance, hands a task (workitem) to a human participant (resource task is created and placed in state 'ready').
You can go a long way with variant 2 alone (the task manager variant).
We could also mention variant 0), where there is no state machine, no workflow engine, and the business process(es) are scattered and/or hardcoded in the application.
You can ask many questions, but if you don't take the time to read the answers and don't take the time to try out and experiment, you won't go very far, and will never acquire any flair for when to use this or that tool.
On a previous project I was working on i added some Workflow type rules to a set of Government Forms in the Healhcare industry.
Forms needed to be filled out by the end user , and depending on some answers other Forms were scheduled to be filled out at a later date. There were also external events that would cancel scheduled Forms or schedule new ones.
Sample Flow :
Patient Admitted -> Schedule Initial Assessment FOrm -> Schedule Quarterly Review Form -> Patient Died -> Cancel Review -> Schedule Discharge Assessment Form
Many other rules were based on things such as Patient age, where they were being admitted etc.
This was an ASP.NET app, the rules were basically a table in the database. I added scripting, so a script would run on Form completion to determine what to do next. This was a horrid design, and would have been perfect for a proper Workflow engine.
I'm one of the authors of the open source Temporal Workflow Engine we initially developed at Uber as Cadence. The difference between Temporal and the majority of the existing workflow engines is that it is developer focused and is extremely flexible and scalable (to tens of thousands updates per second and up to billions of open workflows). The workflows are written as object oriented programs and the engine ensures that the state of the workflow objects including thread stacks and local variables is fully preserved in case of host failures.
What problems have you used workflow engines to solve?
Temporal is used for practically any backend application that lives beyond a single request reply. Examples of usage are:
Distributed CRON jobs
Managing ML/Data pipelines
Reacting to business events. For example trip events at Uber. The workflow can accumulate state based on events received and execute activities when necessary.
Services Deployment to Mesos/ Kubernetes
CI Pipeline implementation
Ensuring that multiple service calls complete when a request is received. Including SAGA pattern implementation
Managing human worker tasks (similar to Amazon MTurk)
Media processing
Customer Support Ticket Routing
Order processing
Testing service similar to ChaosMonkey
and many others
The other set of use cases is based on porting existing workflow engines to run on Temporal. Practically any existing engine workflow specification language can be ported to run on Temporal. This way a single backend service can power multiple domain specific workflow systems.
What libraries/frameworks did you use?
Temporal is a self contained service written in Go with Go, Java, PHP, and Typescript client side SDKs (.NET and Python are coming in 2022). The only external dependency is storage. Cassandra, MySQL and, PostgreSQL are supported. Elasticsearch can be used for advanced indexing.
Temporal also support asynchronous cross region (using AWS terminology) replication.
When did a simpler State Machine/Task Management like system suffice?
Open source Temporal service can be self hosted or temporal.io cloud offering can be used. So the overhead of building any custom state machine/task management is always higher than using Temporal. Outside the company the service and storage for it need to be set up. If you already have an SQL database the service deployment is trivial through a docker image. The docker is also used to run a local Temporal service for development on a personal computer or laptop.
I am one of the authors of Imixs-Workflow. Imixs-Workflow is an open source workflow engine based on BPMN 2.0 and fully integrated into the Java EE technology stack.
I develop workflow engines by myself since more than 10 years. I will try to answer your question in short:
> What problems have you used workflow engines to solve?
My personal goal when I started to think about workflow engines was to avoid hard codding the business logic within my application. Many things in a business application can be reused so it makes sense to keep them configurable. For example:
sending out a notification
view open tasks
assigned a task to a person
describing the current task
From this function list you can see I am talking about human-centric workflows. In short: A human-centric workflow engine answers the questions: Who is responsible for a task and who needs to be informed next? And these are the typical questions in business requirements.
>What libraries/frameworks did you use?
5 years ago we started reimplementing Imixs-Workflow engine focusing on BPMN 2.0. BPMN is the common standard for process modeling. And the surprising thing for me was that we were suddenly able to describe even highly complex business processes that could be visualized and executed. I recommend everyone to use BPMN for modeling business processes.
> When did a simpler State Machine/Task Management like system suffice?
A simple state machine is sufficient if you just want to track the status of a business object. This is the case when you begin to introduce the 'status' attribute into your object model. But in case you need business processes with responsibilities, logging and flow control, then a state machine is no longer sufficient.
> Bonus: How did/do you make the distinction between Task Management and Workflow Engine?
This is exactly the point where many workflow engines mentioned here differ. For a human-centric workflow you typically need a task management to distribute tasks between human actors. For a process automation, this point is not so relevant. It is sufficient if the engine performs certain tasks. Task management and workflow engines can not be compared because task management is always a function of a workflow engine.
Check rails_workflow gem - I think this is close to what you searching.
I have an experience with using Activiti BPMN 2.0 engine for handling high-performance and high-throughput data transfer processes in an infrastructure of network nodes. The basic task was to allow configuration and monitoring of such transfer processes and control each network node (ie. request node1 to send a data file to node2 via specific transport layer).
There could be thousands of processes running at a time and overall tens or low hundreds of thousands processes per day.
There were bunch of different process definitions but it was not necessarily required that an operator of the system could create custom workflows. So the primary use case for the BPM engine itself was to be robust, scalable and allow monitoring of each process flow.
In the end it basically worked but what we learned from that project was that a BPMN platform, or rather the Activiti engine specifically, was not the best bet for such a high-throughput system.
The main challenges were task execution prioritization, DB locking, execution retries to name the few concerning the BPM itself. So we had to develop custom handling of these, for example:
Handling of retries in the BPM for cases when a node had no free worker for given task, or when the node was not running at all.
Execution of parallel transfer tasks in a single process and synchronization of the results (success/failure).
I don't know if other BPMN engines would be more suitable for such scenario since BPMN is mostly intended for long-running business tasks involving user interaction where performance is probably not the same issue as was in our case.
I rolled my own workflow engine to support phased processing of documents - cataloging, sending for image processing (we work with redaction sw), if needed sending to validation, then release and finally shipping back to the client. In our case we have a truckload of documents to process so sometimes we need to run each service separately to control delivery and resources usage. Simple in concept but high performance and distributed processing needed, and we could't find any off the shelf product that fit the bill for us.