Parts of a chatbot

So anyway, what does a good chatbot consist of?

Well, multiple things. For one, it needs to be able to send and receive messages, or it wouldn’t be a chatbot, right?

It also needs to understand messages and know their meaning (partially at least).

It should also keep track of conversation and remember the conversation history. In other words, it shouldn’t lose the thread in the middle of a conversation.

Finally, it should be able to generate messages that answer the users’ input.

Let’s go over these things, really quickly.

Messaging endpoints

Botshot supports popular messaging platforms such as Facebook messenger and Telegram. For each of these platforms, there is an endpoint that converts the messages to a universal format, so you don’t need to worry about compatibility.

To enable support for messaging platforms, just add the associated API token to BOT_CONFIG. Read more at Messaging endpoints.

Note

Botshot is fully extensible. If you need to support your private messaging API, just implement the endpoint and it will work.

Natural language understanding (NLU)

Natural language understanding is the process of analyzing text and extracting machine-interpretable information.

NLU is actually a topic in AI, but you don’t have to be an expert to make a chatbot. Today, there are many available services that do the job for you.

Botshot provides easy integration with the most popular tools, such as Facebook’s Wit.ai, Microsoft’s Luis.AI, or the offline Rasa NLU.

We also have our own NLU module, designed specifically for use with Botshot. Contact us if you’re interested.

Although you don’t have to, you should really use NLU. The above tools are very easy to use.

NLU tools are useful for these purposes:

1. Intent detection aka “What does the user want?”

   Classify the message into a category.

   Input: {"text": "Hi there!"}

   Output: {"intent": "greeting", "confidence": 0.9854, "source": "botshot_nlu"}

2. Entity extraction aka “How does the user want it?”

   Extract entities such as dates, places and names from text.

   Input: {"text": "Are there any interesting events today?"}

   Output: {"query": "events", "date": "2018-01-01"}

To enable support for NLU platforms, just add the associated API token to BOT_CONFIG. Read more at `Natural language understanding`_.

Note

You can use your own machine learning models for NLU if you wish. See `Entity extractors`_ for more details.

Dialogue Management & Conversation Context

If you’re building a chatbot that does more than say “hello”, you will need a way to keep track of the conversation

and remember what the user has said before. | Botshot has a Dialogue Manager that does exactly this. You can picture the conversation as a state machine with states like greeting and transitions that fire when a message is received. | There is also a Context Manager that you can query about past conversations and NLU entities.

Note

We would be really happy if we could just train a neural network instead. However, there are still many problems that need to be solved before production use.

Alright, enough chit chat. Let’s get coding!