How to Build a Private RAG Chatbot with AnythingLLM

Retrieval-augmented generation lets you chat with your own documents, but most solutions send your files to the cloud. AnythingLLM is an all-in-one application that runs entirely on your machine, combining a friendly interface, a vector database, and support for local or remote models. This tutorial shows you how to build a private RAG chatbot with AnythingLLM from scratch.

Why AnythingLLM Stands Out

Most RAG stacks require stitching together an embedding model, a vector store, and a chat client. AnythingLLM bundles all of these in a single desktop installer so you focus on your documents instead of infrastructure. It also supports multi-user workspaces in the Docker edition.

Step 1: Install AnythingLLM

Download the desktop app for macOS, Windows, or Linux, and run the installer. On first launch, a setup wizard asks you to choose an LLM provider and an embedding provider. You can select a local backend such as Ollama to keep everything offline.

Step 2: Configure Your Model

Pick the chat model you want to answer questions. If you installed Ollama, point AnythingLLM at the local endpoint and choose a downloaded model. For heavier workloads, connect to a remote API or a self-hosted server running vLLM.

Step 3: Create a Workspace

Workspaces are containers for a set of documents and a chat thread. Create one per project, such as “Legal” or “Product Docs”. Each workspace keeps its own vector index, so answers stay focused and don’t mix unrelated sources.

Step 4: Upload and Embed Documents

Drag files into the workspace uploader. AnythingLLM accepts PDFs, Word files, text, and web pages. It parses each document, splits it into chunks, and stores the embeddings in the vector database. Use the file picker to control exactly which documents are active.

Step 5: Ask Questions with Citations

Switch the chat mode to query and ask a question. The system retrieves the most relevant chunks, feeds them to the model, and returns an answer with source references. Click a citation to jump to the original passage and verify the response.

Step 6: Tune Retrieval Quality

If answers miss key facts, adjust the chunk size and overlap in settings. Increasing the number of retrieved chunks gives more context but can dilute focus. Experiment on a few known questions and inspect the retrieved text before and after each change.

Step 7: Share Securely

To let teammates use the bot, deploy the Docker image on an internal server. Enable authentication, restrict network access, and back up the data directory. Because AnythingLLM can run fully locally, no document ever has to leave your infrastructure.

Conclusion

AnythingLLM turns private RAG from a complex engineering project into a guided setup. Install it, connect a model, upload your documents, and start asking questions with citations. You get accurate, sourced answers without sacrificing privacy.