Repository-level augmentation of prompts for code completion

A computer-implemented method, comprising:

• obtaining a partially-formed source code snippet from a source code program to complete, wherein the source code program is associated with a private repository comprising custom data and publicly-accessible source code, wherein the custom data is defined in the private repository and comprises custom classes;
• extracting a local context of the partially-formed source code snippet from the source code program, wherein the local context includes a context of the partially-formed source code snippet;
• searching for a few-shot example, of a plurality of few-shot examples from the private repository, wherein the few-shot example comprises a code fragment from the private repository, outside of the source code program, the few-shot example, of the plurality of few few-shot examples from the private repository, having a closest similarity to the partially-formed source code snippet;
• extracting a focal context for the partially-formed source code snippet from the private repository, wherein the focal context comprises method signatures of methods of the custom classes defined in the private repository;
• generating a repository-level context from the private repository, wherein the repository-level context includes the few-shot example and the focal context;
• and
• sending a prompt to a large language model trained on publicly-accessible source code, the prompt including instructions that cause the large language model to complete the partially-formed source code snippet, the prompt including the repository-level context and the local context.

A computer-implemented method, comprising:

• identifying a partially-formed source code snippet associated with a private repository having private data and publicly-accessible source code;
• accessing a database of few-shot examples, wherein each few-shot example of the few-shot examples includes a code fragment from the private repository;
• selecting a few-shot example from the database of few-shot examples based on a similarity of the few-shot example to the partially-formed source code snippet;
• extracting a focal context for the partially-formed source code snippet containing method signatures and namespace information of methods of custom classes defined in the private repository;
• extracting a local context of the partially-formed source code snippet;
• constructing a prompt including the focal context, the selected few-shot example, and the local context of the partially-formed source code snippet;
• transmitting the prompt to a large language model trained on publicly-accessible source code, the prompt including instructions for the large language model to provide a code completion candidate to complete the partially-formed source code snippet;
• and
• receiving from the large language model the code completion candidate.