Short guide to using the design_kgex package that is published in PyPI.
Copyright - L. Siddharth, DDI, SUTD.
Based on the following research:
Siddharth, L., Luo, J., 2024. Retrieval-Augmented Generation using Engineering Design Knowledge. (cs.CL) https://arxiv.org/abs/2307.06985
pip install design_kgex
Note: Please ensure spaCy 3.6 is installed. The package does not support versions 3.7 and above. The above command will take into account all the necessary dependencies. Please make sure the environment is running Python > 3.8.
Usage: Please run the following code to get raw patent text as a list of formatted and cleaned sentences.
from design_kgex import patent_text
sentences = patent_text.getPatentText(<Patent Number in String Format>)
Upon importing the module, necessary models will be downloaded.
The patent number can be identified as follows.
Image Source----Modular wall climbing robot with transition capability, Google Patents
In the above image, the number "7520356" is a unique patent identifier that is also referenced as "patent_id" in various sources. Upon providing the input as a string, the following code will scrape sentences from the patent document, format these, and return a list of cleaned sentences.
from design_kgex import patent_text
sentences = patent_text.getPatentText("7520356")
#displaying the first 5 sentences
print(sentences[:5])
['The present invention is a mobile robot generally including a first suction module, a second suction module and a hinge assembly pivotably connecting the suction modules together.', 'Each of the suction modules includes a support frame defining a vacuum chamber and a vacuum unit supported on the support frame and communicating with the vacuum chamber.', 'The vacuum unit includes a rotating impeller and an exhaust cowling surrounding the impeller.', 'The impeller has an axis of rotation and is adapted to draw air from the vacuum chamber into the impeller in a direction generally parallel to the impeller axis of rotation.', 'The impeller is further adapted to discharge the drawn air from the impeller in a direction substantially perpendicular to the impeller axis of rotation.']
from design_kgex import design_knowledge
knowledge = design_knowledge.extractDesignKnowledge(<Sentences in list format>)
The above code will return knowledge as a list, wherein, each item is a dictionary pertaining to the following format.
{ "sentence": "...", "entities": ["entity #1", "entity #1"...], "facts": [["head entity", "relationship", "tail entity"], ["head entity", "relationship", "tail entity"]...] }
Let us utilise the list of sentences obtained from the patent to extract design knowledge.
from design_kgex import patent_text, design_knowledge
sentences = patent_text.getPatentText("7520356")[:5]
knowledge = design_knowledge.extractDesignKnowledge(sentences)
#printing the first item in the list
print(knowledge[0])
Sorry, no GPU is available! Processing will be performed in normal time.
100%|████████████████████████████████████████████████████████████| 5/5 [00:11<00:00, 2.38s/it]
{'sentence': 'The present invention is a mobile robot generally including a first suction module, a second suction module and a hinge assembly pivotably connecting the suction modules together.', 'entities': ['the suction modules', 'a hinge assembly', 'a first suction module', 'the present invention', 'a second suction module', 'a mobile robot'], 'facts': [['the present invention', 'is', 'a mobile robot'], ['a mobile robot', 'generally including', 'a first suction module'], ['a mobile robot', 'generally including', 'a second suction module'], ['a mobile robot', 'generally including', 'a hinge assembly'], ['a hinge assembly', 'pivotably connecting', 'the suction modules']]}
A note is given in the above output that GPU is not available. As the models used to extract design knowledge demand hardware support, it is recommended to use these in a GPU environment. If GPU is available, the package will automatically utilise the hardware support.
Besides, the output includes the sentence in a cleaned format, the entities and facts.
- Entities are subsets of noun-phrases in the sentence. The appropriate ones that communicate design knowledge are identified by the models that we have trained included in the package.
- Facts associate a pair of the above list entities using a relationship that is communicated in the sentence. The fact is given in the form of a triple "head entity, relationship, tail entity".
The above exracted facts constitute a graph that represents design knowledge extracted from a list of sentences. To visualise the graph, various libraries like networkx or vis.js.
For any queries, please write to siddharthl.iitrpr.sutd@gmail.com.