Getting Started


Installation with pip

kogito can be installed using pip.

pip install kogito

It requires a minimum python version of 3.8.



kogito uses spacy under the hood for various text processing purposes, so, a spacy language package has to be installed before running the inference module.

python -m spacy download en_core_web_sm

By default, CommonsenseInference module uses en_core_web_sm to initialize spacy pipeline, but a different language pipeline can be specified as well.


If you also would like evaluate knowledge models using METEOR score, then you need to download the following nltk libraries:

import nltk"punkt")"wordnet")"omw-1.4")


kogito provides an easy interface to interact with knowledge inference or commonsense reasoning models such as COMET to generate inferences from a text input. Here is a sample usage of the library where you can initialize an inference module, a custom commonsense reasoning model, and generate a knowledge graph from text on the fly.

from kogito.models.bart.comet import COMETBART
from kogito.inference import CommonsenseInference

# Load pre-trained model from HuggingFace
model = COMETBART.from_pretrained("mismayil/comet-bart-ai2")

# Initialize inference module with a spacy language pipeline
csi = CommonsenseInference(language="en_core_web_sm")

# Run inference
text = "PersonX becomes a great basketball player"
kgraph = csi.infer(text, model)

# Save output knowledge graph to JSON file

Here is an excerpt from the result of the above code sample:

{"head": "PersonX becomes a great basketball player", "relation": "Causes", "tails": [" PersonX practices every day.", " PersonX plays basketball every day", " PersonX practices every day"]}
{"head": "basketball", "relation": "ObjectUse", "tails": [" play with friends", " play basketball with", " play basketball"]}
{"head": "player", "relation": "CapableOf", "tails": [" play game", " win game", " play football"]}
{"head": "great basketball player", "relation": "HasProperty", "tails": [" good at basketball", " good at sports", " very good"]}
{"head": "become player", "relation": "isAfter", "tails": [" play game", " become coach", " play with"]}

This is just one way to generate commonsense inferences and kogito offers much more. For information on more use-cases and a complete API reference, you can check out the User Guide and API Reference pages.