Searching and reading literature is a basic task in scientific research. According to statistics, researchers spend about 51% of their research time searching and digesting scientific and technological data. Is there a possibility to turn literature into a knowledge base or database, using artificial intelligence methods to reduce the "burden" of researchers searching and reading literature? On May 30th, at the 2023 Zhongguancun Forum "Artificial Intelligence Driven Scientific Research Forum", the literature knowledge base based on large language models and vector databases - Science Navigator (hereinafter referred to as the literature knowledge base) was officially released. This is a research achievement that allows researchers to conduct literature retrieval, reading, analysis, and management through dialogue and questioning. This achievement was jointly developed by Beijing Institute of Scientific Intelligence, Computer Network Information Center and Mochi Technology. From the earliest 'search based' retrieval using eye search and hand flipping, to the later 'search based' retrieval based on search engines and the Internet, and now to the leapfrog development of artificial intelligence technology, we have seen for the first time that big language models approach the level of human intelligence in understanding problems and answering questions. "said Meng Zhuofei, Vice President of Mocky Technology, with the release of the literature knowledge base, Coincidentally caught up with the development trend of the retrieval mode entering the era of dialogue. The performance advantages of literature knowledge bases can be described by the four words' multiple, fast, good, and economical '. Meng Zhuofei introduced that' multiple 'is reflected in' multimodal, multi model, and multi data '; Fast "refers to" fast query, fast import, and fast iteration "; Good "is reflected in" more real-time data, more reliable citation, and more professional understanding "; The "province" approach significantly reduces the computational cost of data through extreme system optimization and self-developed vector algorithms. The development direction of literature knowledge base is to incorporate more experimental data into vector databases. At that time, the design principles, experimental methods, experimental conclusions, and corresponding thinking behind the conclusions involved in scientific experiments can all be used as query targets. Meng Zhuofei stated that with the help of large models and vector databases, researchers can propose directional problems. The machine will complete a set of processes such as disassembling problems, questioning, designing experiments, and simulating experiments. It can even reflect, deduce, and iterate problems based on the results, further freeing up the time and energy of researchers to devote themselves to solving key problems and innovative thinking. (New News Agency)