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",
"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 [...] The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI; it is therefore related to the broader regulation of algorithms.( The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.( According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone.( Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI.( Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, U.S., and Vietnam. Others were in the process of elaborating their own AI strategy, including",
"images": []
},
{
"url": "https://en.wikipedia.org/wiki/Machine_learning",
"title": "Machine learning - Wikipedia",
"raw_content": "Artificial general intelligence\n Intelligent agent\n Recursive self-improvement\n Planning\n Computer vision\n General game playing\n Knowledge representation\n Natural language processing\n Robotics\n AI safety\nApproaches\n\n Machine learning\n Symbolic\n Deep learning\n Bayesian networks\n Evolutionary algorithms\n Hybrid intelligent systems\n Systems integration\n Open-source\n AI data centers\nApplications\n\n Bioinformatics\n Deepfake\n Earth sciences\n Finance\n Generative AI\n Art\n Audio\n Music\n\n Government\n Healthcare\n Mental health\n\n Industry\n Software development\n Translation\n Military\n Physics\n Projects\nPhilosophy [...] See also: Deep learning\n\nImage 10\n\nAn artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.\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.(",
"images": []
},
{
"url": "https://en.wikipedia.org/wiki/Data_science",
"title": "Data science - Wikipedia",
"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 [...] Wikipedia\nThe Free Encyclopedia\n\n## Contents\n\n# Data science\n\nData science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms, and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data.\n\nData science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [...] Machine learning models can amplify existing biases present in training data, leading to discriminatory or unfair outcomes. Another area of data science that is growing is the push for better ways to cite data. Citing datasets makes it easier for other researchers to understand what data was used and for studies to be repeated (Lafia et al., 2023). These practices give the people who collect and manage data the credit they deserve, which is becoming more important in modern research.\n\n## See also\n\nlogo\n\n## References",
"images": []
}
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"failed_results": [],
"response_time": 1.24,
"request_id": "52a51b91-32df-40a7-8762-3eca55e1bb58"
}
