A Scalable Retrieval-Augmented Generation Pipeline for Domain-Specific Knowledge Applications

This work presents a Retrieval-Augmented Generation (RAG) pipeline that integrates document preprocessing, embedding-based retrieval, and large language model (LLM) generation into a unified framework. The pipeline begins with the ingestion of PDF documents, followed by text cleaning, sentence segmentation, and chunking to ensure compatibility with embedding model constraints. High-dimensional vector representations are generated using transformer-based embedding models and stored for downstream use. Semantic similarity search, implemented via dot product and cosine similarity, enables efficient retrieval of contextually relevant text. For scalability, the framework is designed to accommodate vector indexing methods such as Faiss. On the generation side, locally hosted LLM (Gemma-7B) is employed with optional quantization for reduced resource consumption. Retrieved context is integrated with user queries to enhance the accuracy and relevance of generated responses. This pipeline demonstrates a practical approach for building domain-specific, retrieval-augmented applications that balance efficiency, scalability, and adaptability to local compute environments.