DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation
Authors: name: Esakkivel Esakkiraja, name: Denis Akhiyarov, name: Aditya Shanmugham, name: Chitra Ganapathy
Organization: ServiceNow-AI

Current search techniques are limited to standard RAG query-document
applications. In this paper, we propose a novel technique to expand the code
and index for predicting the required APIs, directly enabling high-quality,
end-to-end code generation for auto-completion and agentic AI applications. We
address the problem of API leaks in current code-to-code benchmark datasets by
introducing a new dataset built from real-world ServiceNow Script Includes that
capture the challenge of unclear API usage intent in the code. Our evaluation
metrics show that this method achieves 87.86% top-40 retrieval accuracy,
allowing the critical context with APIs needed for successful downstream code
generation. To enable real-time predictions, we develop a comprehensive
post-training pipeline that optimizes a compact 0.6B reranker through synthetic
dataset generation, supervised fine-tuning, and reinforcement learning. This
approach enables our compact reranker to outperform a much larger 8B model
while maintaining 2.5x reduced latency, effectively addressing the nuances of
enterprise-specific code without the computational overhead of larger models.