DeepCodeSeek API Engine

• DeepCodeSeek is a system for real-time API recommendation using LLM-driven code expansion and a learnable reranker.
• It employs domain-informed semantic filtering and structured JSDoc indexing to enhance API matching in enterprise code auto-completion.
• The approach boosts retrieval accuracy and reduces latency, effectively handling ambiguous or incomplete API queries in large codebases.

DeepCodeSeek is a retrieval-augmented system for real-time API recommendation and context-aware code synthesis in enterprise software development. It integrates structured indexing, semantic filtering, LLM–driven query augmentation, and a tightly optimized reranking architecture. The primary application focus is code auto-completion and agent-driven code generation within environments characterized by ambiguous or incomplete API usage intent. DeepCodeSeek is designed to minimize computational and memory overhead while maximizing retrieval accuracy under the complex codebase constraints typical of large, enterprise platforms.

1. Problem Motivation and System Design

DeepCodeSeek specifically targets the limitations of conventional retrieval-augmented generation (RAG) and code search approaches in real-world scenarios where code snippets require non-obvious API recommendations. Standard RAG and keyword/vector–based searches frequently fail when essential context is implicit or scattered across partial code segments. DeepCodeSeek introduces a system that predicts required APIs for successful auto-completion or automation by expanding both the code and the indexing layer, ensuring effective retrieval even when the developer’s intent is only weakly expressed in the query fragment (Esakkiraja et al., 30 Sep 2025).

Key elements of the system include:

• A hierarchical, domain-informed knowledge graph to filter the candidate API space.
• Structured enrichment of the API index, aggregating methods under namespaces and pairing raw code signatures with curated JSDoc documentation.
• LLM–powered (LLM) code expansion, which extrapolates from incomplete code to generated completions or synthetic queries, enabling better intent capture for API matching.
• A learnable reranker, trained on synthetic and real-world scenarios, to maximize retrieval precision in the top-k candidates for downstream code generation.

2. Semantic Filtering and Domain-Adaptive Indexing

DeepCodeSeek's first processing step leverages semantic filtering through a knowledge graph derived from platform metadata. This graph encodes the hierarchical relationships among packages, script scopes, and specialized extensions (e.g., ServiceNow Script Includes), enabling the system to restrict the retrieval space to only those APIs relevant to the active project or domain context.

API indexing is enriched beyond flat code signatures: all methods are grouped with their logical parents (e.g., modules/namespaces), and each is paired with its associated JSDoc entries. This dual representation reduces ambiguity in retrieval, as it allows the system to reason over both structural and semantic descriptions of available APIs.

A tabular summary of these enhancement layers follows:

Component	Source/Organization	Purpose
Knowledge Graph Pruning	Platform metadata	Candidate API space reduction
Structured JSDoc Indexing	JSDoc + code signatures	Enhanced semantic resolution
Method Grouping	Namespace affiliation	Disambiguation, relevance

Through this multi-pronged approach, DeepCodeSeek is less susceptible to “API leak” artifacts common in open code benchmarks and is robust to obfuscated or incomplete API usage in the query fragment.

3. LLM-Assisted Code Expansion and Query Synthesis

Recognizing that developer queries are often incomplete or ambiguous in the API usage context, DeepCodeSeek employs a LLM to expand the input code. This expansion generates hypothetical code completions or paraphrased queries, making explicit the latent intent behind the developer’s fragment.

This process improves the effectiveness of the retrieval stage in two ways:

• By simulating plausible completions, it bridges the gap between partial code and the typical positive examples seen in training, increasing retrieval of the correct API reference even in non-canonical cases.
• By broadening query representation, it increases the likelihood that the index lookup phase intersects with the correct semantic slice of the API corpus.

Evaluation results confirm that this LLM-powered expansion step can increase top-40 recall from a baseline (BM25) of 53.02% to 87.86% (Esakkiraja et al., 30 Sep 2025).

4. Post-Training Pipeline and Reranker Optimization

DeepCodeSeek’s real-time API prediction is enabled by a latency-optimized post-training pipeline for its reranker model:

• Synthetic dataset generation is performed by prompting an LLM to produce diverse, JSDoc-augmented examples. This data is mixed with open-source corpora (e.g., CodeR-Pile) and real-world proprietary samples.
• Supervised fine-tuning (SFT) is conducted via a negative log-likelihood objective:

$\mathcal{L} = -\log P(y|x)$

where $y$ is the true API label and $x$ is the input (either pre-expanded code or a JSDoc-featured fragment).

• Reinforcement learning (RL) further polishes the reranker. The reward function assigns +1 to “yes” completions (correct API in context), –1 to “no” responses, and optimization proceeds via Generalized Reweighted Policy Optimization (GRPO), where $n$ completions are sampled and batch-updated per rollout.

A critical engineering choice is the selection of a 0.6B parameter reranker: this model, after SFT and RL, matches or exceeds the ranking quality of an 8B model, while achieving an inference latency reduced by 2.5×. In deployment, this enables low-latency, high-concurrency operation suitable for integration within real-time IDE or agentic AI applications.

5. Evaluation Benchmarks and Empirical Results

To contend with API leak issues found in standard code-to-code benchmarks, the DeepCodeSeek team constructed a new dataset from ServiceNow’s production Script Includes. Each sample comprises a “code_before” context, a withheld invocation (“code_middle”), and a “code_after” tail, with the ground-truth Script Include reference as the API label. Of ~850 scenarios, 83% had intent clarity validated by an LLM judge.

On this benchmark, the system demonstrates:

Metric	Baseline BM25	DeepCodeSeek Reranker (0.6B)
Top-40 Retrieval Accuracy	53.02%	87.86%
Top-5 (postreranking) Acc.	–	68.58%
Latency (relative)	–	2.5× faster than 8B

This demonstrates that the reranking architecture achieves both superior ranking accuracy and operational efficiency relative to both simpler IR baselines and larger LLMs.

6. Applications and Real-World Impact

DeepCodeSeek is designed for integration into:

• Real-time code auto-completion in IDEs, where retrieval latency and domain specificity are critical.
• AI agent frameworks requiring context-aware, enterprise API infusion for reliable task automation.
• Enterprise platforms (e.g., ServiceNow) where custom API corpora and organizational constraints preclude reliance on generic code search or RAG paradigms.

The approach reduces developer time spent in manual API lookup, mitigates risk of API leaks and ambiguous code completions, and enables a scalable, cost-effective alternative to large-model–dependent solutions.

7. Future Directions

Potential extensions include:

• Generalization of the domain-adaptive retrieval and reranking framework to additional enterprise platforms and multi-language environments.
• Incorporation of more sophisticated user feedback for reinforcement learning and active hard negative construction.
• Evaluation of the pipeline’s adaptability to new code conventions, API versioning, and evolving enterprise knowledge bases.
• Further reduction in latency and model size through quantization and architectural distillation, without loss of retrieval accuracy.

This transparent, multi-stage retrieval-augmentation approach defines a new paradigm for enterprise-scale, context-aware API recommendation, distinctly advancing beyond established RAG and code search baselines (Esakkiraja et al., 30 Sep 2025).