AnythingLLM: Private Document Chat with RAG (2026 Guide)

AnythingLLM is a self-hosted AI workspace built around one job: letting you chat with your own documents privately. It combines chat, document RAG, and agent capabilities in a single app you run on your own server, so your PDFs, contracts, and knowledge bases become a searchable, conversational resource - without any of that content being uploaded to a cloud AI vendor.

With roughly 54,000 GitHub stars, it has become one of the most popular ways to put a private “chat with your documents” layer on top of local or cloud models. This guide explains what it is, how RAG actually works here, how it keeps your data private, and how to set it up self-hosted.

What is AnythingLLM?

AnythingLLM is an open-source AI workspace focused on document RAG. Where a general chatbot answers from whatever the model already knows, AnythingLLM is designed to answer from what you give it - your files, notes, and internal knowledge.

Its core pieces are:

• Workspaces - isolated containers, each with its own document set. A workspace for legal contracts stays separate from one for engineering docs, so answers never cross-contaminate.
• Document management - upload PDFs, Word files, spreadsheets, markdown, and web pages, and AnythingLLM processes them into a searchable store.
• Pluggable models - use a local model through Ollama for full privacy, or connect a cloud API when you want more capability.
• A no-code agent builder - wire up simple agents that can search the web, run tools, or take actions, without writing code.

It runs in Docker on a VPS for team or always-on use, and also ships as a desktop app if you just want it on your own machine. For privacy-conscious individuals and teams, the Docker-on-a-server route is the one that unlocks remote access and shared workspaces.

If you are still comparing options, our rundown of the best open source AI chatbots in 2026 and the head-to-head on Open WebUI vs LibreChat vs AnythingLLM put it in context.

How does document RAG work?

RAG stands for retrieval-augmented generation, and it is the mechanism that lets a language model answer questions about documents it was never trained on. AnythingLLM manages the whole pipeline for you, but it helps to understand the four steps.

1. Chunking - each document is split into smaller passages, because feeding an entire 200-page PDF into a model at once is neither practical nor precise.
2. Embedding - every chunk is converted into a vector, a list of numbers that captures its meaning. Similar passages end up close together in vector space.
3. Storage - those vectors are saved in a vector database that AnythingLLM runs for you.
4. Retrieval - when you ask a question, your query is embedded too, and the system pulls the chunks whose vectors are closest to it. Those passages are handed to the model as context, and the model writes an answer grounded in your actual documents.

The payoff is that answers are grounded in your content rather than the model’s general training. The model is far less likely to invent facts, because it is working from the specific passages that were retrieved - and it can point back to the source. AnythingLLM ships with sensible defaults for the embedding model and vector store, so you get a working RAG pipeline out of the box and can swap components later if you need to.

How do you keep documents private?

This is where self-hosting matters. When you upload a file to a cloud chatbot, that document is transmitted to and processed by a third-party vendor, subject to their retention and data-use policies. With AnythingLLM, you decide where the data lives.

The strongest configuration pairs self-hosted AnythingLLM with a local model: documents are chunked and embedded on your server, the vector store sits on your disk, and generation runs through Ollama locally. In that setup no document content leaves your infrastructure at all. If you choose a cloud API for generation instead, only the retrieved snippets needed to answer a given question are sent to the provider - your full document library still stays put. For a deeper look at why this model beats routing everything through a hosted platform, see our piece on self-hosted AI privacy.

One detail worth calling out for teams: per-workspace isolation is a privacy feature, not just an organisational one. Because each workspace only retrieves from its own document set, sensitive material in one workspace cannot surface in another team’s answers.

How do you set it up?

Here is the shape of a self-hosted deployment. Exact commands vary with your host and version, so treat these as representative rather than copy-paste.

1. Provision a VPS. A small instance is fine if you will use a cloud API for generation; allocate more RAM (and ideally a GPU) if you plan to run larger local models.

2. Install Docker, then run AnythingLLM as a container:

   docker run -d -p 3001:3001 \
     -v anythingllm_storage:/app/server/storage \
     mintplexlabs/anythingllm
   

3. Pick your LLM provider. For full privacy, point it at a local Ollama instance. For more capability, add an API key for your chosen provider.

4. Choose an embedding model and vector database. AnythingLLM ships with working defaults, so you can accept these to start and tune later.

5. Create a workspace for a specific document set - one per team, project, or client.

6. Upload your documents. AnythingLLM chunks, embeds, and indexes them automatically; larger libraries take a little longer to process.

7. Start chatting. Ask questions and check that answers cite the right passages.

8. Put it behind a reverse proxy with TLS (for example Nginx or Caddy) so remote access is encrypted, and lock down authentication before exposing it to the internet.

None of these steps is exotic, but chaining them correctly - Docker, model provider, embeddings, vector store, reverse proxy, TLS, and auth - is where most DIY setups stall or ship something insecure.

Get a private document workspace, set up for you

If you want the privacy and grounding of AnythingLLM document RAG without the setup work, that is exactly what we do. We deploy AnythingLLM on a VPS you control, configure the embedding model and vector store, set up isolated workspaces, and secure everything behind TLS - so you get a private “chat with your documents” workspace that just works.

Ready to chat with your own documents privately? Our self-hosted AI installation service gets you a configured, secured workspace, and our AI feature add-ons cover RAG and document ingestion for your specific files.