diff --git a/README.md b/README.md
index 346a2d1b..0cff8218 100644
--- a/README.md
+++ b/README.md
@@ -9,12 +9,12 @@ This repository hosts the code of LightRAG. The structure of this code is based
* Install from source
-```
+```bash
cd LightRAG
pip install -e .
```
* Install from PyPI
-```
+```bash
pip install lightrag-hku
```
@@ -22,12 +22,12 @@ pip install lightrag-hku
* Set OpenAI API key in environment: `export OPENAI_API_KEY="sk-...".`
* Download the demo text "A Christmas Carol by Charles Dickens"
-```
+```bash
curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt
```
Use the below python snippet:
-```
+```python
from lightrag import LightRAG, QueryParam
rag = LightRAG(working_dir="./dickens")
@@ -48,12 +48,12 @@ print(rag.query("What are the top themes in this story?", param=QueryParam(mode=
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybird")))
```
Batch Insert
-```
+```python
rag.insert(["TEXT1", "TEXT2",...])
-```
+```python
Incremental Insert
-```
+```python
rag = LightRAG(working_dir="./dickens")
with open("./newText.txt") as f:
@@ -65,7 +65,7 @@ The dataset used in LightRAG can be download from [TommyChien/UltraDomain](https
### Generate Query
LightRAG uses the following prompt to generate high-level queries, with the corresponding code located in `example/generate_query.py`.
-```
+```json
Given the following description of a dataset:
{description}
@@ -91,7 +91,7 @@ Output the results in the following structure:
### Batch Eval
To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
-```
+```json
---Role---
You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
---Goal---
@@ -134,32 +134,33 @@ Output your evaluation in the following JSON format:
}}
```
### Overall Performance Table
+### Overall Performance Table
| | **Agriculture** | | **CS** | | **Legal** | | **Mix** | |
|----------------------|-------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
| | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** | NaiveRAG | **LightRAG** |
-| **Comprehensiveness** | 32.69% | 67.31% | 35.44% | 64.56% | 19.05% | 80.95% | 36.36% | 63.64% |
-| **Diversity** | 24.09% | 75.91% | 35.24% | 64.76% | 10.98% | 89.02% | 30.76% | 69.24% |
-| **Empowerment** | 31.35% | 68.65% | 35.48% | 64.52% | 17.59% | 82.41% | 40.95% | 59.05% |
-| **Overall** | 33.30% | 66.70% | 34.76% | 65.24% | 17.46% | 82.54% | 37.59% | 62.40% |
+| **Comprehensiveness** | 32.69% | **67.31%** | 35.44% | **64.56%** | 19.05% | **80.95%** | 36.36% | **63.64%** |
+| **Diversity** | 24.09% | **75.91%** | 35.24% | **64.76%** | 10.98% | **89.02%** | 30.76% | **69.24%** |
+| **Empowerment** | 31.35% | **68.65%** | 35.48% | **64.52%** | 17.59% | **82.41%** | 40.95% | **59.05%** |
+| **Overall** | 33.30% | **66.70%** | 34.76% | **65.24%** | 17.46% | **82.54%** | 37.59% | **62.40%** |
| | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** |
-| **Comprehensiveness** | 32.05% | 67.95% | 39.30% | 60.70% | 18.57% | 81.43% | 38.89% | 61.11% |
-| **Diversity** | 29.44% | 70.56% | 38.71% | 61.29% | 15.14% | 84.86% | 28.50% | 71.50% |
-| **Empowerment** | 32.51% | 67.49% | 37.52% | 62.48% | 17.80% | 82.20% | 43.96% | 56.04% |
-| **Overall** | 33.29% | 66.71% | 39.03% | 60.97% | 17.80% | 82.20% | 39.61% | 60.39% |
+| **Comprehensiveness** | 32.05% | **67.95%** | 39.30% | **60.70%** | 18.57% | **81.43%** | 38.89% | **61.11%** |
+| **Diversity** | 29.44% | **70.56%** | 38.71% | **61.29%** | 15.14% | **84.86%** | 28.50% | **71.50%** |
+| **Empowerment** | 32.51% | **67.49%** | 37.52% | **62.48%** | 17.80% | **82.20%** | 43.96% | **56.04%** |
+| **Overall** | 33.29% | **66.71%** | 39.03% | **60.97%** | 17.80% | **82.20%** | 39.61% | **60.39%** |
| | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** |
-| **Comprehensiveness** | 24.39% | 75.61% | 36.49% | 63.51% | 27.68% | 72.32% | 42.17% | 57.83% |
-| **Diversity** | 24.96% | 75.34% | 37.41% | 62.59% | 18.79% | 81.21% | 30.88% | 69.12% |
-| **Empowerment** | 24.89% | 75.11% | 34.99% | 65.01% | 26.99% | 73.01% | 45.61% |54.39% |
-| **Overall** | 23.17% | 76.83% | 35.67% | 64.33% | 27.68% | 72.32% | 42.72% | 57.28% |
+| **Comprehensiveness** | 24.39% | **75.61%** | 36.49% | **63.51%** | 27.68% | **72.32%** | 42.17% | **57.83%** |
+| **Diversity** | 24.96% | **75.34%** | 37.41% | **62.59%** | 18.79% | **81.21%** | 30.88% | **69.12%** |
+| **Empowerment** | 24.89% | **75.11%** | 34.99% | **65.01%** | 26.99% | **73.01%** | **45.61%** | **54.39%** |
+| **Overall** | 23.17% | **76.83%** | 35.67% | **64.33%** | 27.68% | **72.32%** | 42.72% | **57.28%** |
| | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** |
-| **Comprehensiveness** | 45.56% | 54.44% | 45.98% | 54.02% | 47.13% | 52.87% | 51.86% | 48.14% |
-| **Diversity** | 19.65% | 80.35% | 39.64% | 60.36% | 25.55% | 74.45% | 35.87% | 64.13% |
-| **Empowerment** | 36.69% | 63.31% | 45.09% | 54.91% | 42.81% | 57.19% | 52.94% | 47.06% |
-| **Overall** | 43.62% | 56.38% | 45.98% | 54.02% | 45.70% | 54.30% | 51.86% | 48.14% |
+| **Comprehensiveness** | 45.56% | **54.44%** | 45.98% | **54.02%** | 47.13% | **52.87%** | **51.86%** | 48.14% |
+| **Diversity** | 19.65% | **80.35%** | 39.64% | **60.36%** | 25.55% | **74.45%** | 35.87% | **64.13%** |
+| **Empowerment** | 36.69% | **63.31%** | 45.09% | **54.91%** | 42.81% | **57.19%** | **52.94%** | 47.06% |
+| **Overall** | 43.62% | **56.38%** | 45.98% | **54.02%** | 45.70% | **54.30%** | **51.86%** | 48.14% |
## Code Structure
-```
+```json
.
├── examples
│ ├── batch_eval.py
@@ -182,7 +183,7 @@ Output your evaluation in the following JSON format:
```
## Citation
-```
+```json
@article{guo2024lightrag,
title={LightRAG: Simple and Fast Retrieval-Augmented Generation},
author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang},
@@ -192,4 +193,3 @@ archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
-