A hybrid RAG and rule-based reasoning framework for technical document analysis.

The ability to access specific operational guidelines in unstructured technical documentation is a crucial and unsolved problem in safety sensitive areas like aerospace, clinical care, and regulatory compliance, where approximate retrieval and hallucination are unacceptable. Current systems of Retrieval Augmented Generation (RAG) relying on dense vector embeddings are unable to offer the deterministic accuracy such environments require. In this paper, a neurosymbolic framework is provided to overcome this limitation through the translation of natural language documents into three complementary representations of symbolic knowledge: a SQLite (Lightweight SQL Database Engine) database of SAT (Boolean Satisfiability) - based propositional reasoning Conjunctive Normal Form (CNF) clauses, a directed property graph relational traversal and a geometric spatial index which represents the quantitative conditional rules as axis aligned hyper rectangles in an eight dimensional parameter space. Each of the three representations is built on the same LLM (Large Language Model) - based rule extraction pipeline at the same time during offline stage, where there is no embedding of vectors of any type. Online three path reasoning architecture combines SAT at query time entailment checking, knowledge graph traversal, and exact geometric containment testing - translating the operational state of a user to a parameter space point and recalling all rules with hyper rectangular regions containing the point. The outputs of all the three paths are combined before constrained answer generation, basing each answer on logical, relational, and numerical evidence. Because retrieval is performed using mathematically precise symbolic procedures rather than similarity approximation, the SAT, graph, and geometric reasoning components are deterministic and fully explainable; large language model calls are still used for rule extraction and answer synthesis, so the system as a whole is hybrid rather than fully deterministic, and a faithfulness filter together with context-confined generation substantially mitigate, rather than formally guarantee, hallucination. The experimental findings show that this tri-representation strategy markedly enhances retrieval accuracy and reliability compared to embedding-based baselines in complicated operational query conditions, developing a scalable base of rule-based decision support in high stakes areas.