RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. When integrated with LLMs, it is capable of providing truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
This quick start guide describes a general process from:
> If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).
This section provides instructions on setting up the RAGFlow server on Linux. If you are on a different operating system, no worries. Most steps are alike.
`vm.max_map_count`. This value sets the maximum number of memory map areas a process may have. Its default value is 65530. While most applications require fewer than a thousand maps, reducing this value can result in abmornal behaviors, and the system will throw out-of-memory errors when a process reaches the limitation.
RAGFlow v0.13.0 uses Elasticsearch for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
1.2. Reset `vm.max_map_count` to a value at least 262144 if it is not.
```bash
$ sudo sysctl -w vm.max_map_count=262144
```
:::caution WARNING
This change will be reset after a system reboot. If you forget to update the value the next time you start up the server, you may get a `Can't connect to ES cluster` exception.
:::
1.3. To ensure your change remains permanent, add or update the `vm.max_map_count` value in **/etc/sysctl.conf** accordingly:
This change will be reset after a system reboot. If you forget to update the value the next time you start up the server, you may get a `Can't connect to ES cluster` exception.
If the above steps do not work, consider using [this workaround](https://github.com/docker/for-mac/issues/7047#issuecomment-1791912053), which employs a container and does not require manual editing of the macOS settings.
#### If you are on Windows with Docker Desktop, then you *must* use docker-machine to set `vm.max_map_count`:
```bash
$ docker-machine ssh
$ sudo sysctl -w vm.max_map_count=262144
```
#### If you are on Windows with Docker Desktop WSL 2 backend, then use docker-desktop to set `vm.max_map_count`:
1.1. Run the following in WSL:
```bash
$ wsl -d docker-desktop -u root
$ sysctl -w vm.max_map_count=262144
```
:::caution WARNING
This change will be reset after you restart Docker. If you forget to update the value the next time you start up the server, you may get a `Can't connect to ES cluster` exception.
:::
1.2. If you do not wish to have to run those commands each time you restart Docker, you can update your `%USERPROFILE%.wslconfig` as follows to keep your change permanent and globally for all WSL distributions:
*This causes all WSL2 virtual machines to have that setting assigned when they start.*
:::note
If you are on Windows 11 or Windows 10 version 22H2, and have installed the Microsoft Store version of WSL, you can also update the **/etc/sysctl.conf** within the docker-desktop WSL distribution to keep your change permanent:
> The command below downloads the dev version Docker image for RAGFlow slim (`dev-slim`). Note that RAGFlow slim Docker images do not include embedding models or Python libraries and hence are approximately 1GB in size.
> - To download a RAGFlow slim Docker image of a specific version, update the `RAGFlow_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.13.0-slim`. After making this change, rerun the command above to initiate the download.
> - To download the dev version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlow_IMAGE` variable in **docker/.env** to `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. After making this change, rerun the command above to initiate the download.
> - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlow_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.13.0`. After making this change, rerun the command above to initiate the download.
A RAGFlow Docker image that includes embedding models and Python libraries is approximately 9GB in size and may take significantly longer time to load.
> If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anomaly` error because, at that moment, your RAGFlow may not be fully initialized.
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.
RAGFlow is a RAG engine and needs to work with an LLM to offer grounded, hallucination-free question-answering capabilities. RAGFlow supports most mainstream LLMs. For a complete list of supported models, please refer to [Supported Models](./references/supported_models.mdx).
> Each RAGFlow account is able to use **text-embedding-v2** for free, an embedding model of Tongyi-Qianwen. This is why you can see Tongyi-Qianwen in the **Added models** list. And you may need to update your Tongyi-Qianwen API key at a later point.
> Some models, such as the image-to-text model **qwen-vl-max**, are subsidiary to a specific LLM. And you may need to update your API key to access these models.
You are allowed to upload files to a knowledge base in RAGFlow and parse them into datasets. A knowledge base is virtually a collection of datasets. Question answering in RAGFlow can be based on a particular knowledge base or multiple knowledge bases. File formats that RAGFlow supports include documents (PDF, DOC, DOCX, TXT, MD), tables (CSV, XLSX, XLS), pictures (JPEG, JPG, PNG, TIF, GIF), and slides (PPT, PPTX).
To create your first knowledge base:
1. Click the **Knowledge Base** tab in the top middle of the page **>** **Create knowledge base**.
2. Input the name of your knowledge base and click **OK** to confirm your changes.
_You are taken to the **Configuration** page of your knowledge base._
3. RAGFlow offers multiple chunk templates that cater to different document layouts and file formats. Select the embedding model and chunk method (template) for your knowledge base.
Once you have selected an embedding model and used it to parse a file, you are no longer allowed to change it. The obvious reason is that we must ensure that all files in a specific knowledge base are parsed using the *same* embedding model (ensure that they are being compared in the same embedding space).
- If your file parsing gets stuck at below 1%, see [FAQ 4.3](https://ragflow.io/docs/dev/faq#43-why-does-my-document-parsing-stall-at-under-one-percent).
- If your file parsing gets stuck at near completion, see [FAQ 4.4](https://ragflow.io/docs/dev/faq#44-why-does-my-pdf-parsing-stall-near-completion-while-the-log-does-not-show-any-error)
You can add keywords to a file chunk to improve its ranking for queries containing those keywords. This action increases its keyword weight and can improve its position in search list.
Conversations in RAGFlow are based on a particular knowledge base or multiple knowledge bases. Once you have created your knowledge base and finished file parsing, you can go ahead and start an AI conversation.
1. Click the **Chat** tab in the middle top of the mage **>** **Create an assistant** to show the **Chat Configuration** dialogue *of your next dialogue*.
> RAGFlow offer the flexibility of choosing a different chat model for each dialogue, while allowing you to set the default models in **System Model Settings**.
- Name your assistant and specify your knowledge bases.
- **Empty response**:
- If you wish to *confine* RAGFlow's answers to your knowledge bases, leave a response here. Then when it doesn't retrieve an answer, it *uniformly* responds with what you set here.
- If you wish RAGFlow to *improvise* when it doesn't retrieve an answer from your knowledge bases, leave it blank, which may give rise to hallucinations.
RAGFlow also offers HTTP and Python APIs for you to integrate RAGFlow's capabilities into your applications. Read the following documents for more information:
- [Acquire a RAGFlow API key](./guides/develop/acquire_ragflow_api_key.md)
- [HTTP API reference](./references/http_api_reference.md)
- [Python API reference](./references/python_api_reference.md)
