Search, in the context of artificial intelligence, refers to the set of methods, techniques, and processes aimed at retrieving, extracting or organizing relevant information from large volumes of structured or unstructured data. It involves formulating queries, semantic analysis, understanding context, and sometimes inferring new knowledge from existing data. Search is distinct from recommendation or classification systems by its primary goal: enabling a user or machine to efficiently locate specific information in response to an explicit query.
Use cases and examples
Search is ubiquitous, from web search engines to document management systems, databases, and even voice assistants. For example, a search engine like Google uses sophisticated algorithms to index, rank, and present relevant results for billions of queries daily. In the medical field, search enables quick access to scientific articles or patient records. In enterprises, it streamlines document management and internal knowledge access.
Main software tools, libraries, frameworks
Major software solutions include Elasticsearch, Solr (built on Apache Lucene), and OpenSearch for large-scale text search. In AI, Haystack, Vespa, and Milvus enable semantic and vector search, crucial for querying complex or unstructured corpora. Libraries like Whoosh (Python) provide lightweight solutions for smaller applications.
Latest developments, evolutions, and trends
The current trend is toward semantic and vector search, leveraging advances in natural language processing and deep learning models. The integration of models like BERT or GPT improves result relevance by understanding query context and intent. The merging of search with chatbots and conversational agents is also opening new perspectives for information access.