Retrieval-Augmented Generation (RAG) Pipeline for Document Querying

Overview

Developed a Retrieval-Augmented Generation (RAG) pipeline that enables users to query and interact with documents in natural language. The system processes PDF files by chunking them, generating embeddings, and storing these embeddings in an OpenSearch Vector Database for high-precision semantic retrieval.

Tech Stack

Key Features

• Provides contextual and accurate answers by grounding responses in documents.
• Handles large unstructured datasets efficiently with vector similarity search.
• Improves information retrieval speed and user experience compared to traditional search/chatbots.
• Scalable and adaptable for use cases like research, customer support, and knowledge management.

Integration

Integrated Ollama with Gemma3:1B LLM to deliver context-aware, document-grounded responses, improving accuracy and relevance in real-time interactions.