ragflow Agent工作流

🏠 Local Deployment

🐳 Docker Deployment

📝 Docker Deployment Prerequisites

• Recommended starting configuration: 4 CPU cores, 16 GB RAM, and 50 GB of available disk space. Actual requirements depend on the document engine, data volume, parsing tasks, and concurrency. Local models and other optional components may require additional resources.
• Docker >= 24.0.0 & Docker Compose >= v2.26.1
• gVisor: Required only when using the Self-Managed container Sandbox.

Docker deployment does not require Go on the host. Self-Managed container Sandbox requires gVisor; other Sandbox providers do not require gVisor on the RAGFlow host.

[!TIP] If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine.

🚀 Start up the server

1. If using Elasticsearch, set vm.max_map_count on the Docker host to at least 262144. This step is usually unnecessary with Infinity:

To check the value of vm.max_map_count: > >

   > sysctl vm.max_map_count
   > 

> > If you use Elasticsearch and the value is below 262144, reset it: > >

   > # In this case, we set it to 262144:
   > sudo sysctl -w vm.max_map_count=262144
   > 

> > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the > vm.max_map_count value in /etc/sysctl.conf accordingly: > >

   > vm.max_map_count=262144
   > 

2. Clone the repository:

   git clone https://github.com/infiniflow/ragflow.git
   

[!NOTE] > The v1.0.0-rc1 tag and later release tags use the Go implementation. See the Go Docker image build and platform support guide only if you need to build an image locally.

   > # Enter the Docker deployment directory.
   > cd ragflow/docker
   > # Check out the Go v1.0.0-rc1 release tag.
   > git checkout v1.0.0-rc1
   > # Start the Go services and their dependencies in the background.
   > docker compose -f docker-compose.yml up -d
   > 

In the default MySQL configuration, the Go image entrypoint runs database migrations before starting Syncer, Admin, API, and Ingestor through bin/ragflow_server.

In the RAGFlow open-source 1.0 release, DeepDoc uses CPU inference for layout analysis, OCR, and table recognition.

1. Check service status and API readiness after startup:

   docker ps
   

The command above displays dependency status. RAGFlow itself does not define a Compose healthcheck; confirm readiness through its API:

   curl -f http://localhost/api/v1/system/healthz
   

An HTTP 200 response indicates readiness. If you changed SVR_WEB_HTTP_PORT, use that port in the health-check URL. If startup fails, inspect the relevant service logs with docker logs --tail 50 <service>. 5. In your web browser, enter the IP address of your server and log in to RAGFlow.

With the default settings, you only need to enter http://IP_OF_YOUR_MACHINE (sans port number) as the default > HTTP serving port 80 can be omitted when using the default configurations. > 6. After signing in, add an LLM, embedding, and reranker on the model provider page, including the model name, service address, and API key.

⚙️ Docker Configuration and Adjustment

Go Docker deployment uses docker/.env and docker/docker-compose.yml, uses Kvrocks for cache and Checkpoint storage, and uses NATS JetStream as the message queue. Configure the image, ports, passwords, document engine, and model image source as described in the Docker configuration guide. For platform limitations and macOS requirements, see the Go Docker image build and platform support guide.

For document-engine changes, configuration updates, restarting services, and retaining or removing existing data, follow the Docker configuration guide.

🛀 **Automated and effortless RAG workflow**

• Streamlined RAG orchestration catered to both personal and large businesses.
• Configurable LLMs as well as embedding models.
• Multiple recall paired with fused re-ranking.
• Intuitive APIs for seamless integration with business.