The Service Assistant Project is a retrieval-augmented generation (RAG) platform designed to provide intelligent, context-aware responses to user queries. By leveraging a local LLM and a Milvus vector database, the application enables secure, document-based information retrieval. I architected the system to support flexible deployment models, allowing it to run either as a fully containerized service via Docker Compose or as a locally hosted development instance, ensuring parity between local development and production-like environments.
My role focused on integrating the RAG pipeline, implementing a role-based access control (RBAC) mechanism for user contexts, and designing a modular backend that handles document ingestion and feedback loops. The system was designed to be highly configurable, allowing for easy updates to user hierarchies and project-specific document access policies, ultimately providing a streamlined experience for managing domain-specific knowledge bases.