ragflow/docs/guides/deploy_local_llm.md

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Deploy a local LLM

RAGFlow supports deploying models locally using Ollama or Xinference. If you have locally deployed models to leverage or wish to enable GPU or CUDA for inference acceleration, you can bind Ollama or Xinference into RAGFlow and use either of them as a local “server” for interacting with your local models.

RAGFlow seamlessly integrates with Ollama and Xinference, without the need for further environment configurations. You can use them to deploy two types of local models in RAGFlow: chat models and embedding models.

:::tip NOTE This user guide does not intend to cover much of the installation or configuration details of Ollama or Xinference; its focus is on configurations inside RAGFlow. For the most current information, you may need to check out the official site of Ollama or Xinference. :::

Deploy a local model using Ollama

Ollama enables you to run open-source large language models that you deployed locally. It bundles model weights, configurations, and data into a single package, defined by a Modelfile, and optimizes setup and configurations, including GPU usage.

:::note

• For information about downloading Ollama, see here.
• For information about configuring Ollama server, see here.
• For a complete list of supported models and variants, see the Ollama model library. :::

To deploy a local model, e.g., Llama3, using Ollama:

1. Check firewall settings

Ensure that your host machine’s firewall allows inbound connections on port 11434. For example:

sudo ufw allow 11434/tcp

2. Ensure Ollama is accessible

Restart system and use curl or your web browser to check if the service URL of your Ollama service at http://localhost:11434 is accessible.

Ollama is running

3. Run your local model

ollama run llama3

If your Ollama is installed through Docker, run the following instead:

   docker exec -it ollama ollama run llama3

4. Add Ollama

In RAGFlow, click on your logo on the top right of the page > Model Providers and add Ollama to RAGFlow:

5. Complete basic Ollama settings

In the popup window, complete basic settings for Ollama:

1. Because llama3 is a chat model, choose chat as the model type.
2. Ensure that the model name you enter here precisely matches the name of the local model you are running with Ollama.
3. Ensure that the base URL you enter is accessible to RAGFlow.
4. OPTIONAL: Switch on the toggle under Does it support Vision? if your model includes an image-to-text model.

:::caution NOTE

• If your Ollama and RAGFlow run on the same machine, use http://localhost:11434 as base URL.
• If your Ollama and RAGFlow run on the same machine and Ollama is in Docker, use http://host.docker.internal:11434 as base URL.
• If your Ollama runs on a different machine from RAGFlow, use http://<IP_OF_OLLAMA_MACHINE>:11434 as base URL. :::

:::danger WARNING If your Ollama runs on a different machine, you may also need to set the OLLAMA_HOST environment variable to 0.0.0.0 in ollama.service (Note that this is NOT the base URL):

Environment="OLLAMA_HOST=0.0.0.0"

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:::caution WARNING Improper base URL settings will trigger the following error:

Max retries exceeded with url: /api/chat (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0xffff98b81ff0>: Failed to establish a new connection: [Errno 111] Connection refused'))

:::

6. Update System Model Settings

Click on your logo > Model Providers > System Model Settings to update your model:

*You should now be able to find llama3 from the dropdown list under Chat model.*

If your local model is an embedding model, you should find your local model under Embedding model.

7. Update Chat Configuration

Update your chat model accordingly in Chat Configuration:

If your local model is an embedding model, update it on the configruation page of your knowledge base.

Deploy a local model using Xinference

Xorbits Inference(Xinference) enables you to unleash the full potential of cutting-edge AI models.

:::note

• For information about installing Xinference Ollama, see here.
• For a complete list of supported models, see the Builtin Models. :::

To deploy a local model, e.g., Mistral, using Xinference:

1. Check firewall settings

Ensure that your host machine’s firewall allows inbound connections on port 9997.

2. Start an Xinference instance

$ xinference-local --host 0.0.0.0 --port 9997

3. Launch your local model

Launch your local model (Mistral), ensuring that you replace ${quantization} with your chosen quantization method :

$ xinference launch -u mistral --model-name mistral-v0.1 --size-in-billions 7 --model-format pytorch --quantization ${quantization}

4. Add Xinference

In RAGFlow, click on your logo on the top right of the page > Model Providers and add Xinference to RAGFlow:

5. Complete basic Xinference settings

Enter an accessible base URL, such as http://<your-xinference-endpoint-domain>:9997/v1.

6. Update System Model Settings

Click on your logo > Model Providers > System Model Settings to update your model.

*You should now be able to find mistral from the dropdown list under Chat model.*

If your local model is an embedding model, you should find your local model under Embedding model.

7. Update Chat Configuration

Update your chat model accordingly in Chat Configuration:

If your local model is an embedding model, update it on the configruation page of your knowledge base.

Deploy a local model using IPEX-LLM

IPEX-LLM(IPEX-LLM) is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low latency

To deploy a local model, eg., Qwen2, using IPEX-LLM, follow the steps below:

1. Check firewall settings

Ensure that your host machine’s firewall allows inbound connections on port 11434. For example:

sudo ufw allow 11434/tcp

2. Install and Start Ollama serve using IPEX-LLM

2.1 Install IPEX-LLM for Ollama

IPEX-LLM’s support for ollama now is available for Linux system and Windows system.

Visit Run llama.cpp with IPEX-LLM on Intel GPU Guide, and follow the instructions in section Prerequisites to setup and section Install IPEX-LLM cpp to install the IPEX-LLM with Ollama binaries.

After the installation, you should have created a conda environment, named llm-cpp for instance, for running ollama commands with IPEX-LLM.

2.2 Initialize Ollama

Activate the llm-cpp conda environment and initialize Ollama by executing the commands below. A symbolic link to ollama will appear in your current directory.

• For Linux users:
  

  conda activate llm-cpp
  init-ollama

• For Windows users:

Please run the following command with administrator privilege in Miniforge Prompt.

  conda activate llm-cpp
  init-ollama.bat

[!NOTE] If you have installed higher version ipex-llm[cpp] and want to upgrade your ollama binary file, don’t forget to remove old binary files first and initialize again with init-ollama or init-ollama.bat.

Now you can use this executable file by standard ollama’s usage.

2.3 Run Ollama Serve

You may launch the Ollama service as below:

• For Linux users:

  export OLLAMA_NUM_GPU=999
  export no_proxy=localhost,127.0.0.1
  export ZES_ENABLE_SYSMAN=1
  source /opt/intel/oneapi/setvars.sh
  export SYCL_CACHE_PERSISTENT=1

  ./ollama serve

• For Windows users:

Please run the following command in Miniforge Prompt.

  set OLLAMA_NUM_GPU=999
  set no_proxy=localhost,127.0.0.1
  set ZES_ENABLE_SYSMAN=1
  set SYCL_CACHE_PERSISTENT=1

  ollama serve

Please set environment variable OLLAMA_NUM_GPU to 999 to make sure all layers of your model are running on Intel GPU, otherwise, some layers may run on CPU.

If your local LLM is running on Intel Arc™ A-Series Graphics with Linux OS (Kernel 6.2), it is recommended to additionaly set the following environment variable for optimal performance before executing ollama serve:

> export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
> ```


> To allow the service to accept connections from all IP addresses, use `OLLAMA_HOST=0.0.0.0 ./ollama serve` instead of just `./ollama serve`.

The console will display messages similar to the following:

![](https://llm-assets.readthedocs.io/en/latest/_images/ollama_serve.png)

### 3. Pull and Run Ollama Model

Keep the Ollama service on and open another terminal and run `./ollama pull <model_name>` in Linux (`ollama.exe pull <model_name>` in Windows) to automatically pull a model. e.g. `qwen2:latest`:

![](https://llm-assets.readthedocs.io/en/latest/_images/ollama_pull.png)

#### Run Ollama Model

- For **Linux users**:
  ```bash
  ./ollama run qwen2:latest

• For Windows users:

  ollama run qwen2:latest
  

  4. Configure RAGflow to use IPEX-LLM accelerated Ollama

The confiugraiton follows the steps in

Ollama Section 4 Add Ollama,

Section 5 Complete basic Ollama settings,

Section 6 Update System Model Settings,

Section 7 Update Chat Configuration