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Setup Guide for Linux with Xeon-Series CPU

  1. Installation
  2. Start the Service

Installation

Download Langchain-Chatchat

Download the Langchain-Chatchat with IPEX-LLM integrations from this link. Unzip the content into a directory, e.g. /home/arda/Langchain-Chatchat-ipex-llm.

Install Prerequisites

We recommend to use Conda to manage the running environment. For how to install Conda on your Linux distributions, please visit here.

Install Python Dependencies

1. Create a Conda Environment

Run the following commands to create a new python environment:

conda create -n ipex-llm-langchain-chatchat python=3.11
conda activate ipex-llm-langchain-chatchat

2. Install ipex-llm

pip install --pre --upgrade ipex-llm[all]
pip3 install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2

3. Install Langchain-Chatchat Dependencies

Switch to the root directory of Langchain-Chatchat you've downloaded (refer to the download section), and install the dependencies with the commands below. Note: In the example commands we assume the root directory is /home/arda/Langchain-Chatchat-ipex-llm. Remember to change it to your own path.

cd /home/arda/Langchain-Chatchat-ipex-llm
pip install -r requirements_ipex_llm.txt 
pip install -r requirements_api_ipex_llm.txt
pip install -r requirements_webui.txt
# Due to an known issue on CPU, run with Llama-2 model
# will require to install transformers==4.34.0 to generate the correct result.
pip install transformers==4.34.0

Configuration

  • In root directory of Langchain-Chatchat, run the following command to create a config: 
    python copy_config_example.py
  • Edit the file configs/model_config.py, change MODEL_ROOT_PATH to the absolute path of the parent directory where all the downloaded models (LLMs, embedding models, ranking models, etc.) are stored.
  • Edit the file configs/model_config.py, change EMBEDDING_DEVICE and LLM_DEVICE to cpu.

Download Models

Download the models and place them in the directory MODEL_ROOT_PATH (refer to details in Configuration section).

Currently, we support only the LLM/embedding models specified in the table below. You can download these models using the link provided in the table. Note: Ensure the model folder name matches the last segment of the model ID following "/", for example, for THUDM/chatglm3-6b, the model folder name should be chatglm3-6b.

Model Category download link
THUDM/chatglm3-6b Chinese LLM HF or ModelScope
meta-llama/Llama-2-7b-chat-hf English LLM HF
BAAI/bge-large-zh-v1.5 Chinese Embedding HF
BAAI/bge-large-en-v1.5 English Embedding HF

Start the Service

Run the following commands:

conda activate ipex-llm-langchain-chatchat

source ipex-llm-init -t

export no_proxy='localhost,127.0.0.1'

# We recommend to use cores in one socket for the best performance.
# You may need to change the 0-47 below accordingly.
numactl -C 0-47 -m 0 python startup.py -a

Note

The above numa configuration lead to optimal performance for Intel(R) Xeon(R) Platinum 8468.

You can find the Web UI's URL printted on the terminal logs, e.g. http://localhost:8501/.

Open a browser and navigate to the URL to use the Web UI.