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

Understand

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.