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@@ -1,4 +1,4 @@ -# **LinearRAG: Linear Graph Retrieval-Augmented Generation on Large-scale Corpora** +# **LinearRAG: Linear Graph Retrieval-Augmented Generation on Large-scale Corpora** > A relation-free graph construction method for efficient GraphRAG. It eliminates LLM token costs during graph construction, making GraphRAG faster and more efficient than ever. @@ -17,19 +17,16 @@ --- ## 🚀 **Highlights** - -- ✅ **Context-Preserving**: Relation-free graph construction, relying on lightweight entity recognition and semantic linking to achieve comprehensive contextual comprehension. +- ✅ **Context-Preserving**: Relation-free graph construction, relying on lightweight entity recognition and semantic linking to achieve comprehensive contextual comprehension. - ✅ **Complex Reasoning**: Enables deep retrieval via semantic bridging, achieving multi-hop reasoning in a single retrieval pass without requiring explicit relational graphs. - ✅ **High Scalability**: Zero LLM token consumption, faster processing speed, and linear time/space complexity. - + <p align="center"> <img src="figure/main_figure.png" width="95%" alt="Framework Overview"> </p> --- - ## 🎉 **News** - - **[2025-10-27]** We release **[LinearRAG](https://github.com/DEEP-PolyU/LinearRAG)**, a relation-free graph construction method for efficient GraphRAG. - **[2025-06-06]** We release **[GraphRAG-Bench](https://github.com/GraphRAG-Bench/GraphRAG-Benchmark.git)**, the benchmark for evaluating GraphRAG models. - **[2025-01-21]** We release the **[GraphRAG survey](https://github.com/DEEP-PolyU/Awesome-GraphRAG)**. @@ -38,7 +35,7 @@ ## 🛠️ **Usage** -### 1️⃣ Install Dependencies +### 1️⃣ Install Dependencies **Step 1: Install Python packages** @@ -53,7 +50,6 @@ python -m spacy download en_core_web_trf ``` > **Note:** For the `medical` dataset, you need to install the scientific/biomedical Spacy model: - ```bash pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_scibert-0.5.3.tar.gz ``` @@ -82,6 +78,7 @@ Make sure the embedding model is available at: model/all-mpnet-base-v2/ ``` + ### 2️⃣ Quick Start Example ```bash @@ -97,6 +94,7 @@ python run.py \ --dataset_name ${DATASET_NAME} \ --llm_model ${LLM_MODEL} \ --max_workers ${MAX_WORKERS} + --use_vectorized_retrieval # optional, use vectorized matrix-based retrieval for GPU acceleration if Strong GPU is available, otherwise use BFS iteration. ``` ## 🎯 **Performance** @@ -105,26 +103,17 @@ python run.py \ <img src="figure/generation_results.png" alt="framework" width="1000"> **Main results of end-to-end performance** - </div> <div align="center"> <img src="figure/efficiency_result.png" alt="framework" width="1000"> - - - - - - - **Efficiency and performance comparison.** - </div> + ## 📖 Citation If you find this work helpful, please consider citing us: - ```bibtex @article{zhuang2025linearrag, title={LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora}, @@ -133,9 +122,5 @@ If you find this work helpful, please consider citing us: year={2025} } ``` - -This project is licensed under the GNU General Public License v3.0 ([License](LICENSE.TXT)). - ## 📬 Contact - ✉️ Email: zhuangluyao523@gmail.com |