Chunks are short content snippets (maximum 500 characters each) pulled directly from the source. Use chunks_per_source to define the maximum number of relevant chunks returned per source and to control the raw_content length. Chunks will appear in the raw_content field as: <chunk 1> [...] <chunk 2> [...] <chunk 3>. Available only when query is provided. Must be between 1 and 5.
Default: 3Example: 3
extract_depthstring · enumOptional
The depth of the extraction process. advanced extraction retrieves more data, including tables and embedded content, with higher success but may increase latency.basic extraction costs 1 credit per 5 successful URL extractions, while advanced extraction costs 2 credits per 5 successful URL extractions.
Default: basicExample: basicPossible values:
include_imagesbooleanOptional
Include a list of images extracted from the URLs in the response. Default is false.
Default: false
include_faviconbooleanOptional
Whether to include the favicon URL for each result.
Default: false
formatstring · enumOptional
The format of the extracted web page content. markdown returns content in markdown format. text returns plain text and may increase latency.
Maximum time in seconds to wait for the URL extraction before timing out. Must be between 1.0 and 60.0 seconds. If not specified, default timeouts are applied based on extract_depth: 10 seconds for basic extraction and 30 seconds for advanced extraction.
Default: 60Example: 60
include_usagebooleanOptional
Whether to include credit usage information in the response. NOTE:The value may be 0 if the total successful URL extractions has not yet reached 5 calls.
{
"results": [
{
"url": "https://en.wikipedia.org/wiki/Artificial_intelligence",
"title": "Artificial intelligence - Wikipedia",
"raw_content": "Philosophy\n\nMain article: Philosophy of artificial intelligence\n\nPhilosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines.( Another major focus has been whether machines can be conscious, and the associated ethical implications.( Many other topics in philosophy are relevant to AI, such as epistemology and free will.( Rapid advancements have intensified public discussions on the philosophy and ethics of AI.(\n\n### Defining artificial intelligence\n\nSee also: Synthetic intelligence, Intelligent agent, Artificial mind \"Artificial mind (disambiguation)\"), Virtual intelligence, and Dartmouth workshop [...] There are various conflicting definitions and mathematical models of fairness. These notions depend on ethical assumptions, and are influenced by beliefs about society. One broad category is distributive fairness, which focuses on the outcomes, often identifying groups and seeking to compensate for statistical disparities. Representational fairness tries to ensure that AI systems do not reinforce negative stereotypes or render certain groups invisible. Procedural fairness focuses on the decision process rather than the outcome. The most relevant notions of fairness may depend on the context, notably the type of AI application and the stakeholders. The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them. Having access to sensitive [...] Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of \"fair use\". Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include \"the purpose and character of the use of the copyrighted work\" and \"the effect upon the potential market for the copyrighted work\".( Website owners can indicate that they do not want their content scraped via a \"robots.txt\" file.( However, some companies will scrape content regardless( because the robots.txt file has no real authority. In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI.( Another",
"images": []
},
{
"url": "https://en.wikipedia.org/wiki/Machine_learning",
"title": "Machine learning",
"raw_content": "See also: Deep learning\n\nArtificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems \"learn\" to perform tasks by considering examples, generally without being programmed with any task-specific rules. [...] The ethics of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes. This includes algorithmic biases, fairness \"Fairness (machine learning)\"), accountability, transparency, privacy, and regulation, particularly where systems influence or automate human decision-making. It also covers various emerging or potential future challenges such as machine ethics (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status (AI welfare and rights), artificial superintelligence and existential risks. [...] [edit]\n\n### Artificial intelligence\n\n[edit]\n\nAs a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline \"Discipline (academia)\"), some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed \"neural networks\"; these were mostly perceptrons and other models that were later found to be reinventions of the generalised linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.: 488",
"images": []
},
{
"url": "https://en.wikipedia.org/wiki/Data_science",
"title": "Data science",
"raw_content": "Data science involves working with larger datasets that often require advanced computational and statistical methods to analyze. Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models. Data science often uses statistical analysis, data preprocessing, and supervised learning.\n\nRecent studies indicate that AI is moving towards data-centric approaches, focusing on the quality of datasets rather than just improving AI models. This trend focuses on improving system performance by cleaning, refining, and labeling data (Bhatt et al., 2024). As AI systems grow larger, the data-centric view has become increasingly important.\n\n## Cloud computing for data science\n\n[edit] [...] Retrieved from \"\"\n\nCategories:\n\n Information science\n Computational fields of study\n Data analysis\n Data science\n\nHidden categories:\n\n Articles with short description\n Short description is different from Wikidata\n Use dmy dates from August 2023\n\nData science\n\nAdd topic [...] 39. ^ Qiu, Junfei; Wu, Qihui; Ding, Guoru; Xu, Yuhua; Feng, Shuo (2016). \"A survey of machine learning for big data processing\". EURASIP Journal on Advances in Signal Processing. 2016 (1) 67. Bibcode \"Bibcode (identifier)\"):2016EJASP2016...67Q. doi \"Doi (identifier)\"):10.1186/s13634-016-0355-x. ISSN \"ISSN (identifier)\") 1687-6180.\n40. ^ Armbrust, Michael; Xin, Reynold S.; Lian, Cheng; Huai, Yin; Liu, Davies; Bradley, Joseph K.; Meng, Xiangrui; Kaftan, Tomer; Franklin, Michael J.; Ghodsi, Ali; Zaharia, Matei (27 May 2015). \"Spark SQL: Relational Data Processing in Spark\". Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM. pp. 1383–1394. doi \"Doi (identifier)\"):10.1145/2723372.2742797. ISBN \"ISBN (identifier)\") 978-1-4503-2758-9.",
"images": []
}
],
"failed_results": [],
"response_time": 0.52,
"request_id": "66f68717-c044-4110-9aaf-42456c532aa2"
}
