How I Built a Multi-Tenant RAG Knowledge Base with Source-Cited Answers — Pipeline, Multi-Tenancy, and Lessons

Building a multi-tenant Retrieval Augmented Generation (RAG) knowledge base is a complex endeavor that goes beyond the typical RAG chatbot tutorial. While many tutorials simply embed a few paragraphs, call similarity search, and print the answer, this approach lacks the depth required for a trustworthy product that a company would rely on for their documents.

In his project, KnowBase AI, the reporter constructed a multi-tenant SaaS knowledge base where businesses can upload documents and an AI assistant answers questions grounded in their own content with source citations. This article delves into the RAG pipeline, how multi-tenancy affects the design, and the key decisions made during development.

The RAG pipeline consists of four main steps: Ingestion, Retrieval, Grounding, and Trust. Ingestion involves uploading documents, chunking them intelligently, and storing them. Retrieval finds the chunks that answer the question, while Grounding builds a prompt from the retrieved context to avoid hallucinated answers. Trust is established by showing users source citations for the AI's responses.

Chunking text is a crucial decision in this pipeline. If chunks are too large, retrieval is imprecise, while too small chunks result in the loss of context. To address this, the reporter implemented a chunking function that creates overlapping chunks, ensuring no meaning is lost in the gaps. Each chunk is associated with its source document, allowing retrieval to trace back to the information's origin.

Multi-tenancy significantly alters the design of a RAG app compared to a single-user application. In a multi-tenant SaaS, each query, chunk, and conversation must be scoped to a specific workspace, preventing any tenant from accessing another tenant's chunks, even if the embeddings match. The data model enforces this isolation at the schema level, with separate tables for Workspaces, WorkspaceMembers, KnowledgeSources, Documents, DocumentChunks, Conversations, Messages, and ApiUsage.

The provider abstraction was another critical decision. By avoiding hard-coded model usage and instead implementing a thin provider interface, the product can run on various platforms like OpenAI, Google Gemini, or Anthropic Claude by configuration. This flexibility made it easy to develop a demo mode that functions without requiring login or API keys, as the demo mode uses mock responses.