Abstract: There is provided a code retrieval method and apparatus based on a large language model, an electronic device and a readable storage medium, which relates to the field of artificial intelligence technologies, such as large language model technologies, big data technologies, cloud service technologies, or the like.

Abstract: A code knowledge graph generation method and apparatus, a code generation method and apparatus, a device, and a medium, relate to the field of data processing, specifically the technical fields of intelligent search, human-computer interaction, artificial intelligence, and large language models. The specific implementation solution includes acquiring a code tree, where the code tree includes first code elements and a structural relationship among the first code elements, where the code tree is generated by performing content parsing on a source code; generating at least one first graph node according to the first code elements; generating a first graph edge between corresponding graph nodes according to the structural relationship; generating a code knowledge graph according to the at least one first graph node and the first graph edge.

Abstract: A method for training a text retrieval model, a method for retrieving a text and corresponding apparatuses are provided. An implementation of method comprises: compressing each pair of a query term sample and a candidate text sample into one character string sample; processing the character string sample using a feature extraction sub-model in a text retrieval model to obtain hidden-layer features of tokens in the character string sample; calculating feature weights of the hidden-layer features of the tokens using a weight determination sub-model in the text retrieval model; calculating, based on the feature weights and the hidden-layer features of the corresponding tokens, to obtain a correlation score between the query term sample and the candidate text sample using a similarity calculation sub-model in the text retrieval model; and training, based on the correlation score, the text retrieval model by means of contrastive learning.

Abstract: The present disclosure provides a method and apparatus for processing code, a method and apparatus for training a code model, and a device, which relates to the field of artificial intelligence, and in particular, to the technical fields of software development, data processing, large models and computer communication. The method for processing code includes: acquiring target pasted code in response to a code paste event being monitored; performing dependency analysis for the target pasted code to obtain context-dependent data corresponding to the target pasted code; performing, according to the context-dependent data, code optimization for the target pasted code to obtain optimized code corresponding to the target pasted code; and outputting the optimized code. The present disclosure can greatly improve development efficiency, seamlessly integrate optimized code into the current code development environment, and improve code quality.

Abstract: The disclosure provides a code completion method based on a big model. The method includes: determining a first code element where a position to be completed is located in a first code file to be completed; determining a second code file having a dependency relationship with the first code file from a development project to which the first code file belongs; determining, according to the first code element, a second code element whose correlation with the first code element meets a preset condition, in which the second code element belongs to at least one of the first code file or the second code file; and generating a target code corresponding to the position to be completed through a big model based on a signature of the second code element.

Abstract: Embodiments determine wear. One such embodiment obtains, in memory associated with a processor, a finite element mesh representing a first object. For a given node of the obtained mesh, a wear variable is associated and linked to contact constraints associated with the node. A simulation of contact is performed, over movement increments, between the first object and a second object to determine wear at the node. Wear distance is iteratively determined for a given increment using the mesh, the associated variable, and the constraints. A position of the node in the mesh is iteratively updated based on the determined wear distance for the given increment, until the wear distance for each of the increments is determined. The wear at the node is determined based on the determined wear distance for each of the increments. An indication of the determined wear is output.

Abstract: There is provided a code retrieval method and apparatus based on a large language model, an electronic device and a readable storage medium, which relates to the field of artificial intelligence technologies, such as large language model technologies, big data technologies, cloud service technologies, or the like.

Abstract: There is provided a code recommendation method and apparatus, an electronic device and a storage medium, which relates to the field of software development technologies, artificial intelligence technologies, or the like. A specific implementation includes: acquiring a relevant code segment required by a recommended code from a current code file; acquiring code dependence information required by the recommended code; and performing code recommendation based on the relevant code segment, the code dependence information and a pre-trained code recommendation model.