Robust Multi-Hop RAG for Enterprise Documents

Our solution for AMD Mini-Challenge 3 is a high-speed, autonomous Retrieval-Augmented Generation (RAG) system engineered to extract exact values and citations from unconstrained, mixed-format enterprise documents. Key Architecture & Capabilities: 1. Defensive Multi-Format Ingestion: The system features robust, zero-crash parsers for PDF, DOCX, XLSX, CSV, Python source code, and production logs. It natively isolates corrupted files, handles empty directories, safely skips encrypted documents without failing, and bypasses permission-locked files. 2. Multimodal OCR: Integrated optical character recognition (OCR) accurately transcribes pinout schematics, diagrams, and asset label images, enabling discovery of visual-only information. 3. High-Precision Hybrid Retrieval: Uses BM25 lexical token matching preserving technical identifiers, model names, ticket IDs, and revisions (e.g., TQ-40, ORR-1847, REV-C2, THERM_ALERT#). 4. Multi-Hop Reasoning & Citation Filtering: Implements automated entity chaining across disparate files (e.g., correlating production throttle logs with bug tracker resolutions) while strictly adhering to the "necessity, not relevance" citation rule. 5. High Performance: The indexing pass completes in under 1 second, and queries execute in approximately 0.25 seconds—far below the 30-second budget—with zero external web services or network dependencies.