From 70baa4b572c71ec309eee88ce41d36bc189a04e8 Mon Sep 17 00:00:00 2001
From: LarFii <834462287@qq.com>
Date: Tue, 15 Oct 2024 19:55:30 +0800
Subject: [PATCH] update README.md

---
 README.md | 43 +++++++++++++++++++++++++++++++++++++------
 1 file changed, 37 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 562585e4..cc282c27 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,9 @@ This repository hosts the code of LightRAG. The structure of this code is based
 ![请添加图片描述](https://i-blog.csdnimg.cn/direct/b2aaf634151b4706892693ffb43d9093.png)
 </div>
 
+## 🎉 News 
+- [x] [2024.10.15]🎯🎯📢📢LightRAG now supports Hugging Face models! 
+
 ## Install
 
 * Install from source
@@ -35,17 +38,27 @@ pip install lightrag-hku
 
 ## Quick Start
 
-* Set OpenAI API key in environment: `export OPENAI_API_KEY="sk-...".`
-* Download the demo text "A Christmas Carol by Charles Dickens" 
+* Set OpenAI API key in environment if using OpenAI models: `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:
+Use the below Python snippet to initialize LightRAG and perform queries:
 
 ```python
 from lightrag import LightRAG, QueryParam
+from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
 
-rag = LightRAG(working_dir="./dickens")
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=gpt_4o_mini_complete  # Use gpt_4o_mini_complete LLM model
+    # llm_model_func=gpt_4o_complete  # Optionally, use a stronger model
+)
 
 with open("./book.txt") as f:
     rag.insert(f.read())
@@ -62,13 +75,31 @@ print(rag.query("What are the top themes in this story?", param=QueryParam(mode=
 # Perform hybrid search
 print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))
 ```
-Batch Insert
+### Using Hugging Face Models
+If you want to use Hugging Face models, you only need to set LightRAG as follows:
 ```python
+from lightrag.llm import hf_model_complete, hf_embedding
+from transformers import AutoModel, AutoTokenizer
+
+# Initialize LightRAG with Hugging Face model
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=hf_model_complete,  # Use Hugging Face complete model for text generation
+    llm_model_name='meta-llama/Llama-3.1-8B-Instruct',  # Model name from Hugging Face
+    embedding_func=hf_embedding,  # Use Hugging Face embedding function
+    tokenizer=AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2"),
+    embed_model=AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+)
+```
+### Batch Insert
+```python
+# Batch Insert: Insert multiple texts at once
 rag.insert(["TEXT1", "TEXT2",...])
 ```
-Incremental Insert
+### Incremental Insert
 
 ```python
+# Incremental Insert: Insert new documents into an existing LightRAG instance
 rag = LightRAG(working_dir="./dickens")
 
 with open("./newText.txt") as f: