RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
APACHE-2.0 License
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
Try our demo at https://demo.ragflow.io.
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If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine.
Ensure vm.max_map_count
>= 262144:
To check the value of
vm.max_map_count
:$ sysctl vm.max_map_count
Reset
vm.max_map_count
to a value at least 262144 if it is not.# 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
Clone the repo:
$ git clone https://github.com/infiniflow/ragflow.git
Build the pre-built Docker images and start up the server:
Running the following commands automatically downloads the dev version RAGFlow Docker image. To download and run a specified Docker version, update
RAGFLOW_IMAGE
in docker/.env to the intended version, for exampleRAGFLOW_IMAGE=infiniflow/ragflow:v0.12.0
, before running the following commands.
$ cd ragflow/docker
$ docker compose up -d
The core image is about 9 GB in size and may take a while to load.
Check the server status after having the server up and running:
$ docker logs -f ragflow-server
The following output confirms a successful launch of the system:
____ ___ ______ ______ __
/ __ \ / | / ____// ____// /____ _ __
/ /_/ // /| | / / __ / /_ / // __ \| | /| / /
/ _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ /
/_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/
* Running on all addresses (0.0.0.0)
* Running on http://127.0.0.1:9380
* Running on http://x.x.x.x:9380
INFO:werkzeug:Press CTRL+C to quit
If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a
network abnormal
error because, at that moment, your RAGFlow may not be fully initialized.
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 port80
can be omitted when using the default configurations.
In service_conf.yaml, select the desired LLM factory in user_default_llm
and update the API_KEY
field with the corresponding API key.
See llm_api_key_setup for more information.
The show is on!
When it comes to system configurations, you will need to manage the following files:
SVR_HTTP_PORT
, MYSQL_PASSWORD
, and MINIO_PASSWORD
.You must ensure that changes to the .env file are in line with what are in the service_conf.yaml file.
The ./docker/README file provides a detailed description of the environment settings and service configurations, and you are REQUIRED to ensure that all environment settings listed in the ./docker/README file are aligned with the corresponding configurations in the service_conf.yaml file.
To update the default HTTP serving port (80), go to docker-compose.yml and change 80:80
to <YOUR_SERVING_PORT>:80
.
Updates to the above configurations require a reboot of all containers to take effect:
$ docker compose -f docker/docker-compose.yml up -d
This image is approximately 1 GB in size and relies on external LLM and embedding services.
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
pip3 install huggingface-hub nltk
python3 download_deps.py
docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
pip3 install huggingface-hub nltk
python3 download_deps.py
docker build -f Dockerfile -t infiniflow/ragflow:dev .
Install Poetry, or skip this step if it is already installed:
curl -sSL https://install.python-poetry.org | python3 -
Clone the source code and install Python dependencies:
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
docker compose -f docker/docker-compose-base.yml up -d
Add the following line to /etc/hosts
to resolve all hosts specified in docker/service_conf.yaml to 127.0.0.1
:
127.0.0.1 es01 mysql minio redis
In docker/service_conf.yaml, update mysql port to 5455
and es port to 1200
, as specified in docker/.env.
If you cannot access HuggingFace, set the HF_ENDPOINT
environment variable to use a mirror site:
export HF_ENDPOINT=https://hf-mirror.com
Launch backend service:
source .venv/bin/activate
export PYTHONPATH=$(pwd)
bash docker/launch_backend_service.sh
Install frontend dependencies:
cd web
npm install --force
Configure frontend to update proxy.target
in .umirc.ts to http://127.0.0.1:9380
:
Launch frontend service:
npm run dev
The following output confirms a successful launch of the system:
See the RAGFlow Roadmap 2024
RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our Contribution Guidelines first.