**Abstract**
Recent advances in Large Language Models (LLMs) have highlighted the importance of Retrieval-Augmented Generation (RAG) in improving factual accuracy, context relevance, and reasoning capabilities. However, most RAG pipelines treat data retrieval and semantic reasoning as disjoint processes, leading to inefficiencies in query execution and knowledge alignment.
In this work, we propose a hybrid database approach that bridges analytics and semantics by combining the structured querying power of SurrealDB with the dynamic reasoning capabilities of LLMs through LangChain and tool execution. Our framework enables fine-grained data access, semantic enrichment, and hybrid retrieval strategies that balance symbolic query execution with contextual generation.
We demonstrate how this integration improves interpretability, reduces hallucination, and enhances query efficiency in knowledge-intensive tasks. This work provides a foundation for building domain-adaptive RAG systems that are both scalable and semantically aware, opening pathways for applied AI in research, enterprise knowledge management, and intelligent assistants.
**Keywords:** Retrieval-Augmented Generation, Large Language Models, Hybrid Databases, SurrealDB, LangChain, Tool Execution, Semantic Retrieval, Knowledge Management, Hallucination Reduction, Query Efficiency
**Key Contributions:**
- Novel hybrid approach combining vector search and SQL analytics in RAG pipelines
- Integration of SurrealDB for unified semantic and structured data retrieval
- LangChain and LangGraph orchestration for intelligent tool selection
- Demonstrated 18% improvement in relevance accuracy over vector-only baselines
- Open-source implementation with modular, extensible architecture