RAGFlow ollama 部署本地知识库

Ollama 部署本地大模型

Ollama 的优势

• 轻量级
• 本地化
• 多模型支持

安装 ollama

在 Ollama 下载安装

或者通过 brew 安转

brew install ollama --cask

部署 Deepseek-R1

ollama run deepseek-r1:32b

部署 RAGFLOW

IMPORTANT

• While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a linux/arm64 or darwin/arm64 host machine as well.
• For ARM64 platforms, please upgrade the xgboost version in pyproject.toml to 1.6.0 and ensure unixODBC is properly installed.

RAGFlow 没有提供 ARM 平台的 Docker 镜像，需要自行本地构建

构建过程参考 Build RAGFlow Docker image | RAGFlow

IMPORTANT

• While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a linux/arm64 or darwin/arm64 host machine as well.
• For ARM64 platforms, please upgrade the xgboost version in pyproject.toml to 1.6.0 and ensure unixODBC is properly installed.

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git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
uv run download_deps.py
docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
docker build -f Dockerfile -t infiniflow/ragflow:nightly .

1. 国内的话，可以先修改一下 Dockerfile 中的 ARG NEED_MIRROR 可以把 0 修改成 1 ,使用国内的软件源。

2. 修改 docker/.env 的 RAGFLOW_IMAGE 配置，值改成构建好的 镜像名 ragflow:nightly

3. 运行服务

   1 2	cd docker docker compose -f docker-compose-macos.yml up -d

4. 构建完成以后，可以访问 127.0.0.1:80 来运行 RAGFlow

配置 RAGFlow ATTACH

配置本地模型文件的时候需要注意下

RAGFlow 是通过 bridge 方式桥接的，访问宿主服务，使用 host.docker.internal:port 可以访问到宿主服务