## 💡 What is RagFlow? [RagFlow](http://demo.ragflow.io) is a knowledge management platform built on custom-build document understanding engine and LLM, with reasoned and well-founded answers to your question. Clone this repository, you can deploy your own knowledge management platform to empower your business with AI.

## 🌟 Key Features - 🍭**Custom-build document understanding engine.** Our deep learning engine is made according to the needs of analyzing and searching various type of documents in different domain. - For documents from different domain for different purpose, the engine applies different analyzing and search strategy. - Easily intervene and manipulate the data proccessing procedure when things goes beyond expectation. - Multi-media document understanding is supported using OCR and multi-modal LLM. - 🍭**State-of-the-art table structure and layout recognition.** Precisely extract and understand the document including table content. See [README.](./deepdoc/README.md) - For PDF files, layout and table structures including row, column and span of them are recognized. - Put the table accrossing the pages together. - Reconstruct the table structure components into html table. - **Querying database dumped data are supported.** After uploading tables from any database, you can search any data records just by asking. - You can now query a database using natural language instead of using SQL. - The record number uploaded is not limited. - **Reasoned and well-founded answers.** The cited document part in LLM's answer is provided and pointed out in the original document. - The answers are based on retrieved result for which we apply vector-keyword hybrids search and re-rank. - The part of document cited in the answer is presented in the most expressive way. - For PDF file, the cited parts in document can be located in the original PDF. ## 🤺RagFlow vs. other RAG applications | Feature | RagFlow | Langchain-Chatchat | Assistants API | QAnythig | LangChain | |---------|:---------:|:----------------:|:-----------:|:-----------:|:-----------:| | **Well-Founded Answer** | :white_check_mark: | :x: | :x: | :x: | :x: | | **Trackable Chunking** | :white_check_mark: | :x: | :x: | :x: | :x: | | **Chunking Method** | Rich Variety | Naive | Naive | | Naive | Naive | | **Table Structure Recognition** | :white_check_mark: | :x: | | :x: | :x: | :x: | | **Structured Data Lookup** | :white_check_mark: | :x: | :x: | :x: | :x: | :x: | | **Programming Approach** | API-oriented | API-oriented | API-oriented | API-oriented | Python Code-oriented | | **RAG Engine** | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: | | **Prompt IDE** | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: | | **Supported LLMs** | Rich Variety | Rich Variety | OpenAI-only | QwenLLM | Rich Variety | | **Local Deployment** | :white_check_mark: | :white_check_mark: | :x: | :x: | :x: | | **Ecosystem Strategy** | Open Source | Open Source | Close Source | Open Source | Open Source | ## 🔎 System Architecture

## 🎬 Get Started ### 📝 Prerequisites - CPU >= 2 cores - RAM >= 8 GB - Docker - `vm.max_map_count` > 65535 > To check the value of `vm.max_map_count`: > > ```bash > $ sysctl vm.max_map_count > ``` > > Reset `vm.max_map_count` to a value greater than 65535 if it is not. In this case, we set it to 262144: > > ```bash > $ 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 following line in **/etc/sysctl.conf** accordingly: > > ```bash > vm.max_map_count=262144 > ``` ### Start up the RagFlow server 1. Clone the repo ```bash $ git clone https://github.com/infiniflow/ragflow.git ``` 2. > - In [service_conf.yaml](./docker/service_conf.yaml), configuration of *LLM* in **user_default_llm** is strongly recommended. > In **user_default_llm** of [service_conf.yaml](./docker/service_conf.yaml), you need to specify LLM factory and your own _API_KEY_. > If you do not have _API_KEY_ at the moment, you can specify it in Settings the next time you log in to the system. > - RagFlow supports the flowing LLM factory, with more coming in the pipeline: > [OpenAI](https://platform.openai.com/login?launch), [Tongyi-Qianwen](https://dashscope.console.aliyun.com/model), > [ZHIPU-AI](https://open.bigmodel.cn/), [Moonshot](https://platform.moonshot.cn/docs/docs) ```bash $ cd ragflow/docker $ docker compose up -d ``` ### OR ```bash $ git clone https://github.com/infiniflow/ragflow.git $ cd ragflow/ $ docker build -t infiniflow/ragflow:v1.0 . $ cd ragflow/docker $ docker compose up -d ``` > The core image is about 15 GB in size and may take a while to load. Check the server status after pulling all images and having Docker up and running: ```bash $ docker logs -f ragflow-server ``` *The following output confirms the successful launch of the system:* ```bash ____ ______ __ / __ \ ____ _ ____ _ / ____// /____ _ __ / /_/ // __ `// __ `// /_ / // __ \| | /| / / / _, _// /_/ // /_/ // __/ / // /_/ /| |/ |/ / /_/ |_| \__,_/ \__, //_/ /_/ \____/ |__/|__/ /____/ * Running on all addresses (0.0.0.0) * Running on http://127.0.0.1:9380 * Running on http://172.22.0.5:9380 INFO:werkzeug:Press CTRL+C to quit ``` In your browser, enter the IP address of your server. ## 🔧 Configurations > The default serving port is 80, if you want to change that, refer to the [docker-compose.yml](./docker-compose.yaml) and change the left part of `80:80`, say `66:80`. If you need to change the default setting of the system when you deploy it. There several ways to configure it. Please refer to this [README](./docker/README.md) to manually update the configuration. Updates to system configurations require a system reboot to take effect *docker-compose up -d* again. > If you want to change the basic setups, like port, password .etc., please refer to [.env](./docker/.env) before starting up the system. > If you change anything in [.env](./docker/.env), please check [service_conf.yaml](./docker/service_conf.yaml) which is a configuration of the back-end service and should be consistent with [.env](./docker/.env). ## 📜 Roadmap See the [RagFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162) ## 🏄 Community - [Discord](https://discord.gg/uqQ4YMDf) - [Twitter](https://twitter.com/infiniflowai) - GitHub Discussions ## 🙌 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](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md) first.