Absolutely baffled how it was easier to deploy a self-hosted librechat on my NAS than it is to do a "1-click-deploy" railway template for Librechat + VectorDB/RAG API for my client. # What We’re Trying to Achieve We are building a custom LLM-powered system using retrieval-augmented generation (RAG) to provide business-specific, context-aware responses. The goal is to integrate general domain knowledge with site-specific documentation and dynamic data so that responses are highly relevant and accurate for different operational environments. ## How the System Works: - **Base LLM**: Utilizes a base LLM equipped with broad industry knowledge. - **Site-Specific Context**: Injects relevant documents (manuals, engineering data, dynamic SCADA readings, etc.) to provide tailored responses. - **Structured Input**: Ensures structured input through dropdowns/UI constraints, so the LLM always receives the correct context. - **Data Combination**: Combines static and dynamic data before feeding it into the model to provide up-to-date, actionable insights. ## Stack and Template: I have attempted twice to use this Railway template to deploy this LibreChat instance: [Railway Template](https://railway.com/template/b5k2mn) ### System Architecture Components: 1. **Frontend**: - The user interface powered by LibreChat, providing a chat-based interaction layer. 2. **Retrieval-Augmented Generation (RAG) Backend**: - A VectorDB serves as the primary storage for embeddings, allowing efficient document retrieval. - The retrieval process is managed via Danny Avila's RAG API for seamless integration between the frontend and the vector database. 3. **Chat History Persistence**: - A MongoDB database is utilized to store and manage chat history, ensuring session continuity and query tracking. 4. **Search Indexing**: - Meilisearch is integrated for fast and flexible search capabilities, optimizing both query performance and document retrieval.