# Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models

arXiv: https://arxiv.org/abs/2408.05933 · HTML: https://mlsanity.com/paper/2408.05933/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 24 of 24)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text](https://mlsanity.com/paper/2210.02928/) (2022)
- [The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)](https://mlsanity.com/paper/2402.16893/) (2024)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities](https://mlsanity.com/paper/2308.12833/) (2023)
- [A Survey on Efficient Training of Transformers](https://mlsanity.com/paper/2302.01107/) (2023)
- [Lightweight Deep Learning for Resource-Constrained Environments: A Survey](https://mlsanity.com/paper/2404.07236/) (2024)
- [Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community](https://mlsanity.com/paper/2307.09751/) (2023)
- [Octopus: On-device language model for function calling of software APIs](https://mlsanity.com/paper/2404.01549/) (2024)
- [Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey](https://mlsanity.com/paper/2002.06612/) (2020)
- [Pretrained Language Models are Symbolic Mathematics Solvers too!](https://mlsanity.com/paper/2110.03501/) (2021)
- [Visual Understanding of Complex Table Structures from Document Images](https://mlsanity.com/paper/2111.07129/) (2021)
- [Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition](https://mlsanity.com/paper/2401.12599/) (2024)
- [Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications](https://mlsanity.com/paper/2310.14103/) (2023)
- [Key Information Extraction From Documents: Evaluation And Generator](https://mlsanity.com/paper/2106.14624/) (2021)
- [Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise](https://mlsanity.com/paper/2401.01511/) (2024)
- [rollama: An R package for using generative large language models through Ollama](https://mlsanity.com/paper/2404.07654/) (2024)
- [Pre-trained language models as knowledge bases for Automotive Complaint Analysis](https://mlsanity.com/paper/2012.02558/) (2020)

## Built on by (most cited first, 4 of 4)

- [Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding](https://mlsanity.com/paper/2510.15253/) (2025)
- [Intelligent IoT Attack Detection Design via ODLLM with Feature Ranking-based Knowledge Base](https://mlsanity.com/paper/2503.21674/) (2025)
- [Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive HIL Testing](https://mlsanity.com/paper/2511.22584/) (2025)
- [Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152](https://mlsanity.com/paper/2608.16394/) (2026)

## Abstract (arXiv)

With the growing demand for offline PDF chatbots in automotive industrial production environments, optimizing the deployment of large language models (LLMs) in local, low-performance settings has become increasingly important. This study focuses on enhancing Retrieval-Augmented Generation (RAG) techniques for processing complex automotive industry documents using locally deployed Ollama models. Based on the Langchain framework, we propose a multi-dimensional optimization approach for Ollama's local RAG implementation. Our method addresses key challenges in automotive document processing, including multi-column layouts and technical specifications. We introduce improvements in PDF processing, retrieval mechanisms, and context compression, tailored to the unique characteristics of automotive industry documents. Additionally, we design custom classes supporting embedding pipelines and an agent supporting self-RAG based on LangGraph best practices. To evaluate our approach, we constructed a proprietary dataset comprising typical automotive industry documents, including technical reports and corporate regulations. We compared our optimized RAG model and self-RAG agent against a naive RAG baseline across three datasets: our automotive industry dataset, QReCC, and CoQA. Results demonstrate significant improvements in context precision, context recall, answer relevancy, and faithfulness, with particularly notable performance on the automotive industry dataset. Our optimization scheme provides an effective solution for deploying local RAG systems in the automotive sector, addressing the specific needs of PDF chatbots in industrial production environments. This research has important implications for advancing information processing and intelligent production in the automotive industry.