Bridging Analytics and Semantics: A Hybrid Database Approach to Retrieval-Augmented Generation

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

I. INTRODUCTION

The rapid advancement of Large Language Models (LLMs) has unlocked powerful new possibilities in natural language understanding and generation. However, despite their strong generative capabilities, LLMs often struggle with factual accuracy, real-time data access, and domain-specific knowledge. Retrieval-Augmented Generation (RAG) addresses these limitations by integrating external knowledge sources with LLMs, grounding responses in retrieved evidence to reduce hallucination and improve reliability.

Traditional RAG pipelines rely heavily on vector databases for semantic search. These systems excel at retrieving passages by similarity but cannot handle structured, analytical queries such as trend analysis, aggregations, or relational filtering. Conversely, SQL databases are optimized for structured analytics but perform poorly for semantic similarity and fuzzy matching. This creates a gap where neither system alone can fully address complex queries that require both semantic understanding and numeric reasoning.

To bridge this gap, we propose a hybrid approach that integrates SurrealDB—combining vector search and SQL-style querying—into the RAG pipeline orchestrated via Python, LangChain, and tool execution. Our framework enables the model to retrieve semantically relevant knowledge and execute structured analytics on the same backend, producing answers that are both context-aware and quantitatively grounded. For example, while a conventional RAG system can retrieve descriptions of a product, it cannot answer “What has been the sales trend of product X over the past three years, and how does it compare to product Y?” Our hybrid approach handles both the semantic and analytical components of such queries.

Structure of the Paper.

II. BACKGROUND AND RELATED WORK

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm that combines large language models (LLMs) with external knowledge sources. Traditional RAG frameworks typically rely on vector databases for semantic search, enabling LLMs to retrieve relevant context passages before generating an answer. While effective for unstructured knowledge, this approach struggles with queries requiring structured analysis, such as statistical trends, aggregations, or numerical comparisons. For instance, questions like “What is the year-on-year sales growth of product X?” are beyond the scope of purely semantic retrieval, since vector databases are not optimized for complex analytical operations.

On the other hand, relational databases and SQL engines excel at structured queries, providing aggregation, filtering, and analytical capabilities. However, they lack the semantic flexibility of vector search, making them inefficient for tasks such as natural language question answering, fuzzy search, or contextual retrieval.

Recent works have attempted to enhance LLM capabilities with structured knowledge integration. Approaches such as Text-to-SQL pipelines allow models to translate natural language into SQL queries, while others integrate vector search engines like Pinecone, Weaviate, or FAISS for semantic retrieval. Some hybrid solutions attempt loose coupling between SQL and vector stores, but these often involve separate infrastructure and ad-hoc orchestration.

SurrealDB introduces a unified approach by combining relational, document-oriented, and vector search capabilities within a single database engine. This offers an opportunity to bridge the gap between structured analytics and semantic reasoning without complex multi-database architectures. Our work leverages this capability to propose a hybrid RAG framework that supports both semantic similarity search and structured analytical queries in a seamless workflow.

This integration enables more intelligent decision-making pipelines, where LLMs can dynamically choose whether to retrieve semantic knowledge, execute structured queries, or combine both to generate richer and more context-aware responses.

III. Proposed Methodology

The proposed system integrates semantic retrieval and analytical reasoning to enhance Retrieval-Augmented Generation (RAG) through a hybrid database approach. The workflow can be described as follows:

This hybrid pipeline effectively bridges the gap between unstructured semantic search and structured analytical queries, offering a more robust foundation for RAG systems.

IV. IMPLEMENTATION

The framework is implemented using a modular Python stack, designed to integrate structured queries with semantic retrieval for retrieval-augmented generation (RAG). The system combines SurrealDB for hybrid data storage, LangChain for model–tool orchestration, and LangGraph for controlled reasoning over multiple tools.

4.1 SurrealDB Schema and Embeddings

SurrealDB serves as the unified persistence layer, accommodating both structured attributes and vector embeddings. Documents and metadata are ingested and pre-processed into dense vector representations using a transformer-based encoder. These embeddings are stored natively in SurrealDB, enabling efficient vector similarity search for semantic retrieval. The schema is designed with dual indexing:

This dual storage strategy ensures that factual information retrieval and semantic similarity-based search are both supported within the same database.

4.2 Tool Execution Interfaces

Two tool interfaces abstract access to stored knowledge:

These tools are surfaced to the LLM as callable functions, ensuring controlled execution with minimal hallucination risk.

4.3 Python Orchestration Layer

At the orchestration level, the system employs LangChain to mediate interactions between the LLM and tool interfaces. LangChain provides standardized wrappers for embedding generation, tool invocation, and database querying, ensuring that each stage of execution is modular and easily extensible.

4.4 Graph-Based Control with LangGraph

While LangChain provides tool integration, LangGraph is adopted to define explicit control flow for complex reasoning tasks. Instead of relying on ad-hoc agent behaviour, LangGraph structures the workflow as a directed graph of nodes, where each node represents a reasoning step (e.g., intent recognition, retrieval, filtering, synthesis).

This graph-driven orchestration provides greater transparency and reproducibility compared to monolithic agent loops, while also making the framework easier to extend with new tools (e.g., external APIs or reasoning modules).

4.5 Modularity and Extensibility

The architecture is designed to be plug-and-play:

V. Experiments & Case Studies

To evaluate the effectiveness of our hybrid database approach, we conducted a series of experiments that compared semantic retrieval, analytical querying, and hybrid orchestration. The goal was to demonstrate how combining SurrealDB’s vector search with structured query capabilities improves retrieval-augmented generation (RAG) workflows beyond traditional vector-only methods.

5.1 Datasets

We designed experiments on two categories of datasets:

5.2 Example Queries

We grouped representative queries into three categories:

5.3 Evaluation Metrics

We measured performance along three dimensions:

5.4 Results and Observations

VI. DISCUSSION

The hybrid architecture of SurrealDB-driven RAG offers several notable strengths. First, the integration of vector search and structured queries allows for seamless movement between semantic understanding and precise analytical retrieval. This duality enables the system to answer both natural language questions (e.g., "What themes are common in customer feedback?") and structured analytical queries (e.g., "How many purchases occurred last quarter by region?") within a unified workflow. Unlike traditional RAG pipelines, which rely solely on embeddings, our approach ensures that factual, tabular, and time-sensitive information is not lost in translation.

Another strength lies in the tool orchestration layer implemented using LangChain and LangGraph. By explicitly separating operations into semantic_retrieve and analytic_query, the system maintains interpretability and extensibility. New tools, such as a temporal reasoning engine or graph traversal query, can be plugged into the pipeline with minimal refactoring. This modularity is crucial for scalability across enterprise domains.

From a performance standpoint, early experiments suggest that hybrid queries outperform vector-only baselines in accuracy, particularly for decision-support tasks requiring both context and numeric grounding. However, the trade-off lies in latency: orchestrating multiple tool calls can introduce additional computational overhead compared to simpler pipelines. Optimizing embedding dimensionality, caching intermediate results, and using adaptive query routing are promising directions to mitigate this issue.

Scalability considerations are also significant. SurrealDB’s schema-flexible model and support for embeddings make it a natural fit for hybrid workloads, but large-scale deployments will demand efficient sharding strategies, concurrency control, and distributed indexing. Furthermore, ensuring consistency between analytical tables and semantic indexes remains a non-trivial challenge. Periodic synchronization or real-time update propagation will be required to avoid stale results in production settings.

Finally, the architecture raises security and governance considerations. Since the system involves semantic embeddings of potentially sensitive data, careful management of encryption, role-based access control, and audit logging becomes essential. Moreover, hybrid querying blurs the boundary between data retrieval and reasoning, which may complicate explainability and compliance in regulated industries. Building transparent logs of query execution, including both semantic and SQL steps, could enhance trust and accountability.

In summary, the hybrid model demonstrates strong potential for enterprise decision-support but requires careful attention to scalability, latency, and governance.

VII. FUTURE WORK

While the proposed hybrid database approach demonstrates significant promise, several avenues remain open for future exploration:

VIII. CONCLUSION

This paper introduced a hybrid database-driven approach to Retrieval-Augmented Generation (RAG) that integrates semantic vector search with analytical SQL reasoning through SurrealDB and LLM orchestration. Unlike vector-only RAG pipelines, the proposed framework enables queries that combine semantic similarity with structured aggregation and filtering, thereby bridging the gap between unstructured knowledge retrieval and structured decision analytics.

Through experimental evaluation and case studies, the system demonstrated enhanced accuracy and contextual relevance, particularly in scenarios demanding both semantic depth and analytical precision. The discussion highlighted its strengths in flexibility and expressiveness while acknowledging open challenges in scalability, governance, and multi-modal applicability.

Ultimately, this work contributes toward building next-generation decision-support systems that move beyond pure text retrieval into hybrid reasoning environments. By unifying analytics and semantics under a single architecture, the framework lays the foundation for future enterprise-grade AI systems that are context-aware, analytically capable, and semantically rich.

IX. REFERENCES