Chat With Your Documents Locally Using AnythingLLM and Ollama
Dev.to / 6/15/2026
💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage
Key Points
- The article explains how to build a private, local RAG (retrieval-augmented generation) chat system by combining AnythingLLM with Ollama so users can query their own PDFs, Word documents, code, and web pages without cloud dependency.
- The proposed architecture uses AnythingLLM as the desktop/server app with an embedded vector database and agent capabilities, while Ollama locally serves the LLM for both chat and embeddings.
- It provides step-by-step setup instructions: install Ollama (optionally via Docker), pull the default Qwen3 14B model and an embedder model (nomic-embed-text), then install AnythingLLM (desktop or Docker) and connect it to Ollama.
- Users can create isolated workspaces per project, use built-in agent skills such as web search and summarization, and run the system even on CPU-only machines.
- The piece compares local operation to cloud options like ChatGPT/GPTs, emphasizing lower ongoing costs and improved privacy because documents and processing stay on the user’s machine.
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