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How to self-host a chat-with-your-documents engine with Ragflow

Start RAGFlow with Docker Compose, connect your models and ask questions over your own files.

Short answer: Clone the repo with git clone https://github.com/infiniflow/ragflow.git and enter the folder with cd ragflow/docker; then check out the release tag with git checkout v1.0.0-rc3; start the services with docker compose -f docker-compose.yml up -d. All steps.

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Credentials. You paste model provider API keys into RAGFlow, so restrict access to the server and change default passwords in docker/.env before exposing it.

Who it is forA small team that wants a private knowledge assistant over policies, manuals and reports.

The problemHosted RAG tools mean uploading internal documents to someone else, and building your own pipeline takes weeks.

The outcomeA RAGFlow web UI on your own server where you upload documents and get answers with citations.

What you need first

  • Docker 24.0.0 or later and Docker Compose v2.26.1 or later
  • A starting machine of 4 CPU cores, 16 GB RAM and 50 GB free disk
  • An LLM, an embedding model and a reranker, added with their service address and API key after sign-in
  • If you use Elasticsearch, vm.max_map_count set to at least 262144

Steps

  1. Clone the repo with git clone https://github.com/infiniflow/ragflow.git and enter the folder with cd ragflow/docker
  2. Check out the release tag with git checkout v1.0.0-rc3
  3. Start the services with docker compose -f docker-compose.yml up -d
  4. Confirm it is ready with curl -f http://localhost/api/v1/system/healthz, and check logs with docker logs --tail 50 ragflow-cpu if it is not
  5. Open the server IP in a browser, sign in, and add an LLM, embedding model and reranker on the model provider page

Prompt for your agent

Deploy infiniflow/ragflow on this server with Docker Compose at tag v1.0.0-rc3, wait until the healthz endpoint returns 200, and tell me the URL to open. List what I still need to configure on the model provider page.

Caveat. In the 1.0 open-source release DeepDoc uses CPU inference for layout, OCR and tables, so parsing large document sets takes time.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for infiniflow/ragflow and "Self-host a chat-with-your-documents engine"

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