Modern enterprises manage vast knowledge distributed across heterogeneous systems such as Jira, Git repositories, Confluence, and wikis. Conventional retrieval methods based on keyword search or static embeddings often fail to answer complex queries that require contextual reasoning and multi-hop inference across artifacts. We present a modular hybrid retrieval framework for adaptive enterprise information access that integrates Knowledge Base Language-Augmented Models (KBLam), DeepGraph representations, and embedding-driven semantic search. The framework builds a unified knowledge graph from parsed repositories including code, pull requests, and commit histories, enabling semantic similarity search, structural inference, and multi-hop reasoning. Query analysis dynamically determines the optimal retrieval strategy, supporting both structured and unstructured data sources through independent or fused processing. An interactive interface provides graph visualizations, subgraph exploration, and context-aware query routing to generate concise and explainable answers. Experiments on large-scale Git repositories show that the unified reasoning layer improves answer relevance by up to 80 percent compared with standalone GPT-based retrieval pipelines. By combining graph construction, hybrid reasoning, and interactive visualization, the proposed framework offers a scalable, explainable, and user-centric foundation for intelligent knowledge assistants in enterprise environments.

翻译：现代企业管理着分布在Jira、Git仓库、Confluence和维基等异构系统中的海量知识。基于关键词搜索或静态嵌入的传统检索方法通常难以回答需要跨制品进行上下文推理和多跳推理的复杂查询。本文提出一种用于自适应企业信息访问的模块化混合检索框架，该框架集成了知识库语言增强模型（KBLam）、深度图表示和嵌入驱动的语义搜索。该框架从解析的仓库（包括代码、拉取请求和提交历史）构建统一的知识图谱，支持语义相似性搜索、结构推理和多跳推理。查询分析模块动态确定最优检索策略，通过独立或融合处理同时支持结构化和非结构化数据源。交互式界面提供图谱可视化、子图探索和上下文感知查询路由功能，以生成简洁且可解释的答案。在大型Git仓库上的实验表明，与独立的基于GPT的检索流程相比，统一推理层将答案相关性提升了最高80%。通过结合图谱构建、混合推理和交互式可视化，所提出的框架为企业环境中的智能知识助手提供了可扩展、可解释且以用户为中心的基础架构。