DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation

• URL: http://arxiv.org/abs/2509.25716v1
• Date: Tue, 30 Sep 2025 03:23:27 GMT
• Title: DeepCodeSeek: Real-Time API Retrieval for Context-Aware Code Generation
• Authors: Esakkivel Esakkiraja, Denis Akhiyarov, Aditya Shanmugham, Chitra Ganapathy,
• Abstract summary: Current search techniques are limited to standard RAG query-document applications.<n>We propose a novel technique to expand the code and index for predicting the required APIs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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