ragflow: A Python repository from jiaogaochao

💡 What is RAGFlow?

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.

🌟 Key Features

🍭 "Quality in, quality out"

Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
Finds "needle in a data haystack" of literally unlimited tokens.

🍱 Template-based chunking

Intelligent and explainable.
Plenty of template options to choose from.

🌱 Grounded citations with reduced hallucinations

Visualization of text chunking to allow human intervention.
Quick view of the key references and traceable citations to support grounded answers.

🍔 Compatibility with heterogeneous data sources

Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.

🛀 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.

📌 Latest Features

2024-04-16 Add an embedding model 'bce-embedding-base_v1' from BCEmbedding.
2024-04-16 Add FastEmbed, which is designed specifically for light and speedy embedding.
2024-04-11 Support Xinference for local LLM deployment.
2024-04-10 Add a new layout recognization model for analyzing Laws documentation.
2024-04-08 Support Ollama for local LLM deployment.
2024-04-07 Support Chinese UI.

🔎 System Architecture

🎬 Get Started

📝 Prerequisites

CPU >= 4 cores
RAM >= 12 GB
Docker >= 24.0.0 & Docker Compose >= v2.26.1

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

🚀 Start up the server

Ensure vm.max_map_count >= 262144 (more):
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:
```
$ cd ragflow/docker
$ chmod +x ./entrypoint.sh
$ 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

In your web browser, enter the IP address of your server and log in to RAGFlow.

In the given scenario, 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.
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 ./docs/llm_api_key_setup.md for more information.

The show is now on!

🔧 Configurations

When it comes to system configurations, you will need to manage the following files:

.env: Keeps the fundamental setups for the system, such as SVR_HTTP_PORT, MYSQL_PASSWORD, and MINIO_PASSWORD.
service_conf.yaml: Configures the back-end services.
docker-compose.yml: The system relies on docker-compose.yml to start up.

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 all system configurations require a system reboot to take effect:
$ docker-compose up -d

🛠️ Build from source

To build the Docker images from source:

$ git clone https://github.com/infiniflow/ragflow.git
$ cd ragflow/
$ docker build -t infiniflow/ragflow:v0.3.0 .
$ cd ragflow/docker
$ chmod +x ./entrypoint.sh
$ docker compose up -d

📚 Documentation

📜 Roadmap

See the RAGFlow Roadmap 2024

🏄 Community

🙌 Contributing

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.

jiaogaochao/ragflow