Update README.md

README.md

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 Retrieval-Augmented Generation (RAG) has become a key paradigm to enhance Large Language Models (LLMs) with external knowledge. While most RAG systems focus on **text corpora**, real-world information is often stored in **tables** across web pages, Wikipedia, and relational databases. Existing methods struggle to retrieve and reason across **multiple heterogeneous tables**.
 
-This repository provides the implementation of **T-RAG**, a novel table-corpora-aware RAG framework featuring:
-
-- **Hierarchical Memory Index** – organizes heterogeneous table knowledge at multiple granularities.  
-- **Multi-Stage Retrieval** – coarse-to-fine retrieval combining clustering, subgraph reasoning, and PageRank.  
-- **Graph-Aware Prompting** – injects relational priors into LLMs for structured tabular reasoning.  
-- **MultiTableQA Benchmark** – a large-scale dataset with **57,193 tables** and **23,758 questions** across various tabular tasks.
-
 For MultiTableQA, we release a comprehensive benchmark, including five different datasets covering table fact-checking, single-hop QA, and multi-hop QA: 
 | Dataset              | Link |
 |-----------------------|------|
@@ -34,91 +27,6 @@ For MultiTableQA, we release a comprehensive benchmark, including five different
 
 MultiTableQA extends the traditional single-table QA setting into a multi-table retrieval and question answering benchmark, enabling more realistic and challenging evaluations.
 
----
-
-## ✨ Sample Usage
-
-To get started with the T-RAG framework and the MultiTableQA benchmark, follow these steps.
-
-### 🚀 Installation
-
-```bash
-git clone https://github.com/jiaruzouu/T-RAG.git
-cd T-RAG
-
-conda create -n trag python=3.11.9
-conda activate trag
-
-# Install dependencies
-pip install -r requirements.txt
-```
-
-### 1. MultiTableQA Data Preparation
-
-To download and preprocess the **MultiTableQA** benchmark:
-
-```bash
-cd table2graph
-bash scripts/prepare_data.sh
-```
-
-This script will automatically fetch the source tables, apply decomposition (row/column splitting), and generate the benchmark splits.
-
-### 2. Run T-RAG Retrieval
-
-To run hierarchical index construction and multi-stage retrieval:
-
-**Stage 1 & 2: Table to Graph Construction & Coarse-grained Multi-way Retrieval**
-
-Stages 1 & 2 include:
-- Table Linearization
-- Multi-way Feature Extraction
-- Hypergraph Construction by Multi-way Clustering
-- Typical Node Selection for Efficient Table Retrieval
-- Query-Cluster Assignment
-
-To run this,
-
-```bash
-cd src
-cd table2graph
-bash scripts/table_cluster_run.sh #  or python scripts/table_cluster_run.py
-```
-
-**Stage 3: Fine-grained sub-graph Retrieval**
-Stage 3 includes:
-- Local Subgraph Construction
-- Iterative Personalized PageRank for Retrieval.
-
-To run this,
-```bash
-cd src
-cd table2graph
-python scripts/subgraph_retrieve_run.py
-```
-
-*Note: Our method supports different embedding methods such as E5, contriever, sentence-transformer, etc.**
-
-### 3. Downstream Inference with LLMs
-
-Evaluate T-RAG with an (open/closed-source) LLM of your choice (e.g., GPT-4o, Claude-3.5, Qwen):
-
-For Closed-source LLM, please first insert your key under `key.json`
-```json
-{
-    "openai": "<YOUR_OPENAI_API_KEY>",
-    "claude": "<YOUR_CLAUDE_API_KEY>"
-}
-```
-
-To run end-to-end model inference and evaluation,
-
-```bash
-cd src
-cd downstream_inference
-bash scripts/overall_run.sh
-```
-
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 # Citation