Building a RAG Pipeline for Semantic Code Search

A developer at JetBrains describes the process of building a retrieval-augmented generation (RAG) pipeline for semantic code search. The pipeline involves using a combination of a large language model and a graph database to retrieve relevant code snippets from a large dataset. The developer used the LLaMA large language model and the GraphDB graph database to build the pipeline. The pipeline was initially tested on a dataset of 1.4 million lines of code from the Apache Commons project. The results showed that the pipeline was able to retrieve relevant code snippets with an accuracy of 90%. The developer notes that the pipeline can be used to improve code completion, code review, and other development tasks. The article includes code snippets and diagrams to illustrate the process.