text: 'engines': ['arxiv'],      'category': 'science'}]In this example we query for large language models under the it category. We then filter the results that come from github.results = search.results("large language model", num_results=20, categories="it")pprint.pp(list(filter(lambda r: r["engines"][0] == "github", results)))    [{'snippet': 'Guide to using pre-trained large language models of source code',      'title': 'Code-LMs',      'link': 'https://github.com/VHellendoorn/Code-LMs',      'engines': ['github'],      'category': 'it'},     {'snippet': 'Dramatron uses large language models to generate coherent '                 'scripts and screenplays.',      'title': 'dramatron',      'link': 'https://github.com/deepmind/dramatron',      'engines': ['github'],      'category': 'it'}]We could also directly query for results from github and other source forges.results = search.results(    "large language model", num_results=20, engines=["github", "gitlab"])pprint.pp(results)    [{'snippet': "Implementation of 'A Watermark for Large Language Models' paper "                 'by Kirchenbauer & Geiping et. al.',      'title': 'Peutlefaire / LMWatermark',      'link': 'https://gitlab.com/BrianPulfer/LMWatermark',