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Run GraphRAG with IPEX-LLM on Intel GPU

The GraphRAG project is designed to leverage large language models (LLMs) for extracting structured and meaningful data from unstructured texts; by integrating it with ipex-llm, users can now easily utilize local LLMs running on Intel GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max).

Table of Contents

Quickstart

1. Install and Start Ollama Service on Intel GPU

Follow the steps in Run Ollama with IPEX-LLM on Intel GPU Guide to install and run Ollama on Intel GPU. Ensure that ollama serve is running correctly and can be accessed through a local URL (e.g., https://127.0.0.1:11434).

2. Prepare LLM and Embedding Model

In another terminal window, separate from where you executed ollama serve, download the LLM and embedding model using the following commands:

  • For Linux users:

    export no_proxy=localhost,127.0.0.1
# LLM
./ollama pull mistral
# Embedding model
./ollama pull nomic-embed-text

  • For Windows users:

    Please run the following command in Miniforge or Anaconda Prompt.

    set no_proxy=localhost,127.0.0.1
:: LLM
ollama pull mistral
:: Embedding model
ollama pull nomic-embed-text

Tip

Here we take mistral and nomic-embed-text as an example. You could have a try on other LLMs or embedding models in ollama.com.

3. Setup Python Environment for GraphRAG

To run the LLM and embedding model on a local machine, we utilize the graphrag-local-ollama repository:

git clone https://github.com/TheAiSingularity/graphrag-local-ollama.git
cd graphrag-local-ollama

conda create -n graphrag-local-ollama python=3.10
conda activate graphrag-local-ollama

pip install -e .

pip install ollama
pip install plotly

in which pip install ollama is for enabling restful APIs through python, and pip install plotly is for visualizing the knowledge graph.

4. Index GraphRAG

The environment is now ready for GraphRAG with local LLMs and embedding models running on Intel GPUs. Before querying GraphRAG, it is necessary to first index GraphRAG, which could be a resource-intensive operation.

Tip

Refer to here for more details in GraphRAG process explanation.

Prepare Input Corpus

Some sample documents are used here as input corpus for indexing GraphRAG, based on which LLM will create a knowledge graph.

Perpare the input corpus, and then initialize the workspace:

  • For Linux users:

    # define inputs corpus
mkdir -p ./ragtest/input
cp input/* ./ragtest/input

export no_proxy=localhost,127.0.0.1

# initialize ragtest folder
python -m graphrag.index --init --root ./ragtest

# prepare settings.yml, please make sure the initialized settings.yml in ragtest folder is replaced by settings.yml in graphrag-ollama-local folder
cp settings.yaml ./ragtest

  • For Windows users:

    Please run the following command in Miniforge or Anaconda Prompt.

    :: define inputs corpus
mkdir ragtest && cd ragtest && mkdir input && cd .. 
xcopy input\* .\ragtest\input

set no_proxy=localhost,127.0.0.1

:: initialize ragtest folder
python -m graphrag.index --init --root .\ragtest

:: prepare settings.yml, please make sure the initialized settings.yml in ragtest folder is replaced by settings.yml in graphrag-ollama-local folder
copy settings.yaml .\ragtest /y

Update settings.yml

In the settings.yml file inside the ragtest folder, add the configuration request_timeout: 1800.0 for llm. Besides, if you would like to use LLMs or embedding models other than mistral or nomic-embed-text, you are required to update the settings.yml in ragtest folder accordingly:

llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: openai_chat
  model: mistral # change it accordingly if using another LLM
  model_supports_json: true
  request_timeout: 1800.0 # add this configuration; you could also increase the request_timeout
  api_base: http://localhost:11434/v1

embeddings:
  async_mode: threaded
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding
    model: nomic_embed_text # change it accordingly if using another embedding model
    api_base: http://localhost:11434/api

Conduct GraphRAG indexing

Finally, conduct GraphRAG indexing, which may take a while:

python -m graphrag.index --root ragtest

You will got message 🚀 All workflows completed successfully. after the GraphRAG indexing is successfully finished.

(Optional) Visualize Knowledge Graph

For a clearer view of the knowledge graph, you can visualize it by specifying the path to the .graphml file in the visualize-graphml.py script, like below:

  • For Linux users:

    graph = nx.read_graphml('ragtest/output/20240715-151518/artifacts/summarized_graph.graphml')

  • For Windows users:

    graph = nx.read_graphml('ragtest\\output\\20240715-151518\\artifacts\\summarized_graph.graphml')

and run the following command to interactively visualize the knowledge graph:

python visualize-graphml.py

5. Query GraphRAG

After the GraphRAG has been successfully indexed, you could conduct query based on the knowledge graph through:

python -m graphrag.query --root ragtest --method global "What is Transformer?"

Note

Only the global query method is supported for now.

The sample output looks like:

INFO: Reading settings from ragtest\settings.yaml
creating llm client with {'api_key': 'REDACTED,len=9', 'type': "openai_chat", 'model': 'mistral', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 1.0, 'request_timeout': 1800.0, 'api_base': 'http://localhost:11434/v1', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': True, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25}

SUCCESS: Global Search Response:  The Transformer is a type of neural network architecture primarily used for sequence-to-sequence tasks, such as machine translation and text summarization [Data: Reports (1, 2, 34, 46, 64, +more)]. It was introduced in the paper 'Attention is All You Need' by Vaswani et al. The Transformer model uses self-attention mechanisms to focus on relevant parts of the input sequence when generating an output.

The Transformer architecture was designed to overcome some limitations of Recurrent Neural Networks (RNNs), such as the vanishing gradient problem and the need for long sequences to be processed sequentially [Data: Reports (1, 2)]. The Transformer model processes input sequences in parallel, making it more efficient for handling long sequences.

The Transformer model has been very successful in various natural language processing tasks, such as machine translation and text summarization [Data: Reports (1, 34, 46, 64, +more)]. It has also been applied to other domains, such as computer vision and speech recognition. The key components of the Transformer architecture include self-attention layers, position-wise feedforward networks, and multi-head attention mechanisms [Data: Reports (1, 2, 3)].

Since its initial introduction, the Transformer model has been further developed and improved upon. Variants of the Transformer architecture, such as BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa (Robustly Optimized BERT Pretraining Approach), have achieved state-of-the-art performance on a wide range of natural language processing tasks [Data: Reports (1, 2, 34, 46, 64, +more)].