abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales m√∫ltiples e iterativas de datos expresados en forma matricial o tensorial. 1'Obtaining results with metadata‚ÄãIn this example we will be looking for scientific paper using the categories parameter and limiting the results to a time_range (not all engines support the time range option).We also would like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")results = search.results(    "Large Language Model prompt",    num_results=5,    categories="science",    time_range="year",)pprint.pp(results)    [{'snippet': '‚Ä¶ on natural language instructions, large language models (‚Ä¶ the '                 'prompt used to steer the model, and most effective prompts ‚Ä¶ to '                 'prompt engineering, we propose Automatic Prompt ‚Ä¶',      'title': 'Large language models are human-level prompt engineers',      'link': 'https://arxiv.org/abs/2211.01910',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': '‚Ä¶ Large language models (LLMs) have introduced new possibilities '                 'for prototyping with AI [18]. Pre-trained on a large amount of '                 'text data, models ‚Ä¶ language instructions called prompts. ‚Ä¶',      'title': 'Promptchainer: Chaining large language model prompts through '               'visual programming',      'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': '‚Ä¶ can introspect the large prompt model. We derive the view '                 'œï0(X) and the model h0 from T01. However, instead of fully '                 'fine-tuning T0 during co-training, we focus on soft prompt '                 'tuning, ‚Ä¶',      'title': 'Co-training improves prompt-based
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. ->: abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales m√∫ltiples e iterativas de datos expresados en forma matricial o tensorial. 1'Obtaining results with metadata‚ÄãIn this example we will be looking for scientific paper using the categories parameter and limiting the results to a time_range (not all engines support the time range option).We also would like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")results = search.results(    "Large Language Model prompt",    num_results=5,    categories="science",    time_range="year",)pprint.pp(results)    [{'snippet': '‚Ä¶ on natural language instructions, large language models (‚Ä¶ the '                 'prompt used to steer the model, and most effective prompts ‚Ä¶ to '                 'prompt engineering, we propose Automatic Prompt ‚Ä¶',      'title': 'Large language models are human-level prompt engineers',      'link': 'https://arxiv.org/abs/2211.01910',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': '‚Ä¶ Large language models (LLMs) have introduced new possibilities '                 'for prototyping with AI [18]. Pre-trained on a large amount of '                 'text data, models ‚Ä¶ language instructions called prompts. ‚Ä¶',      'title': 'Promptchainer: Chaining large language model prompts through '               'visual programming',      'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': '‚Ä¶ can introspect the large prompt model. We derive the view '                 'œï0(X) and the model h0 from T01. However, instead of fully '                 'fine-tuning T0 during co-training, we focus on soft prompt '                 'tuning, ‚Ä¶',      'title': 'Co-training improves prompt-based