examples in these cases is '                 'better described as locating an already learned task rather than '                 'meta-learning. This analysis motivates rethinking the role of '                 'prompts in controlling and evaluating powerful language models. '                 'In this work, we discuss methods of prompt programming, '                 'emphasizing the usefulness of considering prompts through the '                 'lens of natural language. We explore techniques for exploiting '                 'the capacity of narratives and cultural anchors to encode '                 'nuanced intentions and techniques for encouraging deconstruction '                 'of a problem into components before producing a verdict. '                 'Informed by this more encompassing theory of prompt programming, '                 'we also introduce the idea of a metaprompt that seeds the model '                 'to generate its own natural language prompts for a range of '                 'tasks. Finally, we discuss how these more general methods of '                 'interacting with language models can be incorporated into '                 'existing and future benchmarks and practical applications.',      'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot '               'Paradigm',      'link': 'http://arxiv.org/abs/2102.07350v1',      '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
This notebook goes over how to use a self hosted SearxNG search API to search the web.
This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: examples in these cases is '                 'better described as locating an already learned task rather than '                 'meta-learning. This analysis motivates rethinking the role of '                 'prompts in controlling and evaluating powerful language models. '                 'In this work, we discuss methods of prompt programming, '                 'emphasizing the usefulness of considering prompts through the '                 'lens of natural language. We explore techniques for exploiting '                 'the capacity of narratives and cultural anchors to encode '                 'nuanced intentions and techniques for encouraging deconstruction '                 'of a problem into components before producing a verdict. '                 'Informed by this more encompassing theory of prompt programming, '                 'we also introduce the idea of a metaprompt that seeds the model '                 'to generate its own natural language prompts for a range of '                 'tasks. Finally, we discuss how these more general methods of '                 'interacting with language models can be incorporated into '                 'existing and future benchmarks and practical applications.',      'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot '               'Paradigm',      'link': 'http://arxiv.org/abs/2102.07350v1',      '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