Merge pull request #56 from sank8-2/dev
chore: added pre-commit-hooks and ruff formatting for commit-hooks
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zrguo
GitHub
50
README.md
50
README.md
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<a href="https://pypi.org/project/lightrag-hku/"><img src="https://img.shields.io/pypi/v/lightrag-hku.svg"></a>
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<a href="https://pepy.tech/project/lightrag-hku"><img src="https://static.pepy.tech/badge/lightrag-hku/month"></a>
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</p>
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This repository hosts the code of LightRAG. The structure of this code is based on [nano-graphrag](https://github.com/gusye1234/nano-graphrag).
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</div>
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## 🎉 News
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## 🎉 News
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- [x] [2024.10.18]🎯🎯📢📢We’ve added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
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- [x] [2024.10.17]🎯🎯📢📢We have created a [Discord channel](https://discord.gg/mvsfu2Tg)! Welcome to join for sharing and discussions! 🎉🎉
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- [x] [2024.10.16]🎯🎯📢📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
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- [x] [2024.10.15]🎯🎯📢📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
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- [x] [2024.10.16]🎯🎯📢📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
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- [x] [2024.10.15]🎯🎯📢📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
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## Install
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@@ -92,7 +92,7 @@ print(rag.query("What are the top themes in this story?", param=QueryParam(mode=
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<details>
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<summary> Using Open AI-like APIs </summary>
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LightRAG also support Open AI-like chat/embeddings APIs:
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LightRAG also supports Open AI-like chat/embeddings APIs:
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```python
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], **kwargs
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@@ -129,7 +129,7 @@ rag = LightRAG(
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<details>
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<summary> Using Hugging Face Models </summary>
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If you want to use Hugging Face models, you only need to set LightRAG as follows:
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```python
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from lightrag.llm import hf_model_complete, hf_embedding
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@@ -145,7 +145,7 @@ rag = LightRAG(
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embedding_dim=384,
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max_token_size=5000,
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func=lambda texts: hf_embedding(
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texts,
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texts,
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tokenizer=AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2"),
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embed_model=AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
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)
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@@ -157,7 +157,7 @@ rag = LightRAG(
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<details>
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<summary> Using Ollama Models </summary>
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If you want to use Ollama models, you only need to set LightRAG as follows:
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```python
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from lightrag.llm import ollama_model_complete, ollama_embedding
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@@ -171,7 +171,7 @@ rag = LightRAG(
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embedding_dim=768,
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max_token_size=8192,
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func=lambda texts: ollama_embedding(
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texts,
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texts,
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embed_model="nomic-embed-text"
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)
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),
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```
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## Evaluation
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### Dataset
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The dataset used in LightRAG can be download from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain).
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The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain).
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### Generate Query
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LightRAG uses the following prompt to generate high-level queries, with the corresponding code located in `example/generate_query.py`.
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LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `example/generate_query.py`.
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<details>
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<summary> Prompt </summary>
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```python
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Given the following description of a dataset:
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@@ -228,18 +228,18 @@ Output the results in the following structure:
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...
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```
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</details>
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### Batch Eval
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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`.
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<details>
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<summary> Prompt </summary>
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```python
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---Role---
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You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
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---Goal---
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You will evaluate two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
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You will evaluate two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
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- **Comprehensiveness**: How much detail does the answer provide to cover all aspects and details of the question?
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- **Diversity**: How varied and rich is the answer in providing different perspectives and insights on the question?
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| **Empowerment** | 36.69% | **63.31%** | 45.09% | **54.91%** | 42.81% | **57.19%** | **52.94%** | 47.06% |
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| **Overall** | 43.62% | **56.38%** | 45.98% | **54.02%** | 45.70% | **54.30%** | **51.86%** | 48.14% |
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## Reproduce
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## Reproduce
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All the code can be found in the `./reproduce` directory.
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### Step-0 Extract Unique Contexts
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@@ -311,7 +311,7 @@ First, we need to extract unique contexts in the datasets.
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<details>
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<summary> Code </summary>
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```python
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def extract_unique_contexts(input_directory, output_directory):
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@@ -370,12 +370,12 @@ For the extracted contexts, we insert them into the LightRAG system.
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<details>
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<summary> Code </summary>
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```python
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def insert_text(rag, file_path):
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with open(file_path, mode='r') as f:
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unique_contexts = json.load(f)
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retries = 0
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max_retries = 3
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while retries < max_retries:
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### Step-2 Generate Queries
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We extract tokens from both the first half and the second half of each context in the dataset, then combine them as the dataset description to generate queries.
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We extract tokens from the first and the second half of each context in the dataset, then combine them as dataset descriptions to generate queries.
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<details>
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<summary> Code </summary>
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```python
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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@@ -410,7 +410,7 @@ def get_summary(context, tot_tokens=2000):
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summary_tokens = start_tokens + end_tokens
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summary = tokenizer.convert_tokens_to_string(summary_tokens)
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return summary
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```
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</details>
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@@ -420,12 +420,12 @@ For the queries generated in Step-2, we will extract them and query LightRAG.
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<details>
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<summary> Code </summary>
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```python
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def extract_queries(file_path):
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with open(file_path, 'r') as f:
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data = f.read()
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data = data.replace('**', '')
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queries = re.findall(r'- Question \d+: (.+)', data)
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@@ -479,7 +479,7 @@ def extract_queries(file_path):
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```python
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@article{guo2024lightrag,
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title={LightRAG: Simple and Fast Retrieval-Augmented Generation},
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title={LightRAG: Simple and Fast Retrieval-Augmented Generation},
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author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang},
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year={2024},
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eprint={2410.05779},
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