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.

This example goes over how to use LangChain to conduct embedding tasks withipex-llm optimizations on Intel GPU. This would be helpful in applications such as RAG, document QA, etc.

Note

It is recommended that only Windows users with Intel Arc A-Series GPU (except for Intel Arc A300-Series or Pro A60) run this Jupyter notebook directly. For other cases (e.g. Linux users, Intel iGPU, etc.), it is recommended to run the code with Python scripts in terminal for best experiences.

Install Prerequisites​

To benefit from IPEX-LLM on Intel GPUs, there are several prerequisite steps for tools installation and environment preparation.

If you are a Windows user, visit theInstall IPEX-LLM on Windows with Intel GPU Guide, and followInstall Prerequisites to update GPU driver (optional) and install Conda.

If you are a Linux user, visit theInstall IPEX-LLM on Linux with Intel GPU, and followInstall Prerequisites to install GPU driver, Intel® oneAPI Base Toolkit 2024.0, and Conda.

Setup​

After the prerequisites installation, you should have created a conda environment with all prerequisites installed.Start the jupyter service in this conda environment:

%pip install-qU langchain langchain-community

Install IPEX-LLM for optimizations on Intel GPU, as well assentence-transformers.

%pip install--pre--upgrade ipex-llm[xpu]--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
%pip install sentence-transformers

Note

You can also usehttps://pytorch-extension.intel.com/release-whl/stable/xpu/cn/ as the extra-indel-url.

Runtime Configuration​

For optimal performance, it is recommended to set several environment variables based on your device:

For Windows Users with Intel Core Ultra integrated GPU​

import os

os.environ["SYCL_CACHE_PERSISTENT"]="1"
os.environ["BIGDL_LLM_XMX_DISABLED"]="1"

For Windows Users with Intel Arc A-Series GPU​

import os

os.environ["SYCL_CACHE_PERSISTENT"]="1"

Note

For the first time that each model runs on Intel iGPU/Intel Arc A300-Series or Pro A60, it may take several minutes to compile.

For other GPU type, please refer tohere for Windows users, andhere for Linux users.

Basic Usage​

Settingdevice to"xpu" inmodel_kwargs when initializingIpexLLMBgeEmbeddings will put the embedding model on Intel GPU and benefit from IPEX-LLM optimizations:

from langchain_community.embeddingsimport IpexLLMBgeEmbeddings

embedding_model= IpexLLMBgeEmbeddings(
    model_name="BAAI/bge-large-en-v1.5",
    model_kwargs={"device":"xpu"},
    encode_kwargs={"normalize_embeddings":True},
)

API Reference

• IpexLLMBgeEmbeddings

sentence="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."
query="What is IPEX-LLM?"

text_embeddings= embedding_model.embed_documents([sentence, query])
print(f"text_embeddings[0][:10]:{text_embeddings[0][:10]}")
print(f"text_embeddings[1][:10]:{text_embeddings[1][:10]}")

query_embedding= embedding_model.embed_query(query)
print(f"query_embedding[:10]:{query_embedding[:10]}")

Related​

• Embedding modelconceptual guide
• Embedding modelhow-to guides