Conversational AI Chatbot with Transformers in Python

In the below image, I have shown the sample we have created. The term “ChatterBot” was originally coined by Michael Mauldin (creator of the first Verbot) in 1994 to describe these conversational programs. If you’re not sure which to choose, learn more about installing packages. After creating your cleaning module, you can now head back over to bot.py and integrate the code into your pipeline.

In this section, we’ll shed light on some of these challenges and offer potential solutions to help you navigate your chatbot development journey. Interact with your chatbot by requesting a response to a greeting. To do this, you’re using spaCy’s named entity recognition feature.

How to Interact with the Language Model

However, at the time of writing, there are some issues if you try to use these resources straight out of the box. After importing ChatBot in line 3, you create an instance of ChatBot in line 5. The only required argument is a name, and you call this one “Chatpot”. No, that’s not a typo—you’ll actually build a chatty flowerpot chatbot in this tutorial! You’ll soon notice that pots may not be the best conversation partners after all.

Next we get the chat history from the cache, which will now include the most recent data we added. To handle chat history, we need to fall back to our JSON database. We’ll use the token to get the last chat data, and then when we get the response, append the response to the JSON database. We will not be building or deploying any language models on Hugginface. Instead, we’ll focus on using Huggingface’s accelerated inference API to connect to pre-trained models. Next, we test the Redis connection in main.py by running the code below.

AI Chat Bot in Python with AIML

Now, when we send a GET request to the /refresh_token endpoint with any token, the endpoint will fetch the data from the Redis database. As long as the socket connection is still open, the client should be able to receive the response. Next, we trim off the cache data and extract only the last 4 items. Then we consolidate the input data by extracting the msg in a list and join it to an empty string. Note that we are using the same hard-coded token to add to the cache and get from the cache, temporarily just to test this out. The jsonarrappend method provided by rejson appends the new message to the message array.

Eventually, you’ll use cleaner as a module and import the functionality directly into bot.py. But while you’re developing the script, it’s helpful to inspect intermediate outputs, for example with a print() call, as shown in line 18. Alternatively, you could parse the corpus files yourself using pyYAML because they’re stored as YAML files. ChatterBot uses the default SQLStorageAdapter and creates a SQLite file database unless you specify a different storage adapter.

The cache is initialized with a rejson client, and the method get_chat_history takes in a token to get the chat history for that token, from Redis. But remember that as the number of tokens we send to the model increases, the processing gets more expensive, and the response time is also longer. We created a Producer class that is initialized with a Redis client. We use this client to add data to the stream with the add_to_stream method, which takes the data and the Redis channel name.

• In this case we will create a basic

  file that matches one pattern and takes one action.

• A named entity is a real-world noun that has a name, like a person, or in our case, a city.
• Well, Python, with its extensive array of libraries like NLTK (Natural Language Toolkit), SpaCy, and TextBlob, makes NLP tasks much more manageable.
• In the .env file, add the following code – and make sure you update the fields with the credentials provided in your Redis Cluster.

Read more about https://www.metadialog.com/ here.