Add GraphRAG QuickStart (#11582)
* Add framework for graphrag quickstart * Add quickstart contents for graphrag * Small fixes and add toc * Update for graph * Small fixes
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Yuwen Hu
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@ -23,7 +23,8 @@ This section includes efficient guide to show you how to:
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- [Run llama.cpp with IPEX-LLM on Intel GPU](./llama_cpp_quickstart.md)
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- [Run Ollama with IPEX-LLM on Intel GPU](./ollama_quickstart.md)
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- [Run Llama 3 on Intel GPU using llama.cpp and ollama with IPEX-LLM](./llama3_llamacpp_ollama_quickstart.md)
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- [Run RAGFlow with IPEX_LLM on Intel GPU](./ragflow_quickstart.md)
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- [Run RAGFlow with IPEX-LLM on Intel GPU](./ragflow_quickstart.md)
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- [Run GraphRAG with IPEX-LLM on Intel GPU](./graphrag_quickstart.md)
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## Serving
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docs/mddocs/Quickstart/graphrag_quickstart.md
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docs/mddocs/Quickstart/graphrag_quickstart.md
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# Run GraphRAG with IPEX-LLM on Intel GPU
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The [GraphRAG project](https://github.com/microsoft/graphrag) is designed to leverage large language models (LLMs) for extracting structured and meaningful data from unstructured texts; by integrating it with [`ipex-llm`](https://github.com/intel-analytics/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).
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## Table of Contents
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- [Install and Start `Ollama` Service on Intel GPU](#1-install-and-start-ollama-service-on-intel-gpu)
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- [Prepare LLM and Embedding Model](#2-prepare-llm-and-embedding-model)
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- [Setup Python Environment for GraphRAG](#3-setup-python-environment-for-graphrag)
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- [Index GraphRAG](#4-index-graphrag)
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- [Query GraphRAG](#5-query-graphrag)
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## Quickstart
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### 1. Install and Start `Ollama` Service on Intel GPU
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Follow the steps in [Run Ollama with IPEX-LLM on Intel GPU Guide](./ollama_quickstart.md) 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`).
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> [!TIP]
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> 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`:
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>
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> ```bash
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> export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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> ```
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### 2. Prepare LLM and Embedding Model
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In another terminal window, separate from where you executed `ollama serve`, download the LLM and embedding model using the following commands:
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- For **Linux users**:
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```bash
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export no_proxy=localhost,127.0.0.1
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# LLM
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./ollama pull mistral
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# Embedding model
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./ollama pull nomic-embed-text
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```
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- For **Windows users**:
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Please run the following command in Miniforge or Anaconda Prompt.
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```cmd
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set no_proxy=localhost,127.0.0.1
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:: LLM
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ollama pull mistral
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:: Embedding model
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ollama pull nomic-embed-text
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```
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> [!TIP]
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> Here we take [`mistral`](https://ollama.com/library/mistral) and [`nomic-embed-text`](https://ollama.com/library/nomic-embed-text) as an example. You could have a try on other LLMs or embedding models in [`ollama.com`](https://ollama.com/search?p=1).
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### 3. Setup Python Environment for GraphRAG
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To run the LLM and embedding model on a local machine, we utilize the [`graphrag-local-ollama`](https://github.com/TheAiSingularity/graphrag-local-ollama) repository:
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```shell
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git clone https://github.com/TheAiSingularity/graphrag-local-ollama.git
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cd graphrag-local-ollama
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conda create -n graphrag-local-ollama python=3.10
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conda activate graphrag-local-ollama
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pip install -e .
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pip install ollama
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pip install plotly
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```
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in which `pip install ollama` is for enabling restful APIs through python, and `pip install plotly` is for visualizing the knowledge graph.
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### 4. Index GraphRAG
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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.
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> [!TIP]
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> Refer to [here](https://microsoft.github.io/graphrag/) for more details in GraphRAG process explanation.
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#### Prepare Input Corpus
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Some [sample documents](https://github.com/TheAiSingularity/graphrag-local-ollama/tree/main/input) are used here as input corpus for indexing GraphRAG, based on which LLM will create a knowledge graph.
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Perpare the input corpus, and then initialize the workspace:
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- For **Linux users**:
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```bash
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# define inputs corpus
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mkdir -p ./ragtest/input
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cp input/* ./ragtest/input
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export no_proxy=localhost,127.0.0.1
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# initialize ragtest folder
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python -m graphrag.index --init --root ./ragtest
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# prepare settings.yml, please make sure the initialized settings.yml in ragtest folder is replaced by settings.yml in graphrag-ollama-local folder
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cp settings.yaml ./ragtest
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```
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- For **Windows users**:
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Please run the following command in Miniforge or Anaconda Prompt.
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```cmd
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:: define inputs corpus
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mkdir ragtest && cd ragtest && mkdir input && cd ..
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xcopy input\* .\ragtest\input
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set no_proxy=localhost,127.0.0.1
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:: initialize ragtest folder
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python -m graphrag.index --init --root .\ragtest
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:: prepare settings.yml, please make sure the initialized settings.yml in ragtest folder is replaced by settings.yml in graphrag-ollama-local folder
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copy settings.yaml .\ragtest /y
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```
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#### Update `settings.yml`
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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:
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>
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> ```yml
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> llm:
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> api_key: ${GRAPHRAG_API_KEY}
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> type: openai_chat
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> model: mistral # change it accordingly if using another LLM
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> model_supports_json: true
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> request_timeout: 1800.0 # add this configuration; you could also increase the request_timeout
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> api_base: http://localhost:11434/v1
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>
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> embeddings:
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> async_mode: threaded
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> llm:
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> api_key: ${GRAPHRAG_API_KEY}
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> type: openai_embedding
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> model: nomic_embed_text # change it accordingly if using another embedding model
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> api_base: http://localhost:11434/api
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> ```
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Finally, conduct GraphRAG indexing, which may take a while:
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```shell
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python -m graphrag.index --root ragtest
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```
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You will got message `🚀 All workflows completed successfully.` after the GraphRAG indexing is successfully finished.
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#### (Optional) Visualize Knowledge Graph
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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:
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- For **Linux users**:
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```python
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graph = nx.read_graphml('ragtest/output/20240715-151518/artifacts/summarized_graph.graphml')
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```
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- For **Windows users**:
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```python
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graph = nx.read_graphml('ragtest\\output\\20240715-151518\\artifacts\\summarized_graph.graphml')
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```
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and run the following command to interactively visualize the knowledge graph:
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```shell
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python visualize-graphml.py
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```
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<table width="100%">
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<tr>
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<td><a href="https://llm-assets.readthedocs.io/en/latest/_images/knowledge_graph_1.png"><img src="https://llm-assets.readthedocs.io/en/latest/_images/knowledge_graph_1.png"/></a></td>
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<td><a href="https://llm-assets.readthedocs.io/en/latest/_images/knowledge_graph_2.png"><img src="https://llm-assets.readthedocs.io/en/latest/_images/knowledge_graph_2.png"/></a></td>
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</tr>
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</table>
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### 5. Query GraphRAG
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After the GraphRAG has been successfully indexed, you could conduct query based on the knowledge graph through:
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```shell
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python -m graphrag.query --root ragtest --method global "What is Transformer?"
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```
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> [!NOTE]
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> Only the `global` query method is supported for now.
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The sample output looks like:
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```log
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INFO: Reading settings from ragtest\settings.yaml
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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}
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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.
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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.
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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)].
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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)].
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```
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@ -33,6 +33,7 @@
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- [Finetune LLM with Axolotl on Intel GPU](./Quickstart/axolotl_quickstart.md)
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- [Run IPEX-LLM serving on Multiple Intel GPUs using DeepSpeed AutoTP and FastApi](./Quickstart/deepspeed_autotp_fastapi_quickstart.md)
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- [Run RAGFlow with IPEX-LLM on Intel GPU](./Quickstart/ragflow_quickstart.md)
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- [Run GraphRAG with IPEX-LLM on Intel GPU](./Quickstart/graphrag_quickstart.md)
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- [Key Features](./Overview/KeyFeatures/)
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- [PyTorch API](./Overview/KeyFeatures/optimize_model.md)
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- [`transformers`-style API](./Overview/KeyFeatures/hugging_face_format.md)
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