DataArcTech · Mi221e · Aug 6, 2025 · Jul 30, 2025 · Aug 2, 2025 · Aug 2, 2025
diff --git a/.gitignore b/.gitignore
@@ -215,3 +215,6 @@ results/
 # .env files
 .env
 .env.local
+
+# development files
+**dev**
diff --git a/README.md b/README.md
@@ -28,7 +28,7 @@ A factory for building advanced RAG (Retrieval-Augmented Generation) pipelines,
 - GraphRAG architectures 
 - Multi-modal RAG systems
 
-## Features
+## 🌟Features
 
 <div>
   <img src="./assets/knowledge_base_screenshot.png" alt="Example Knowledge Base Screenshot of RAG-Factory" width="800">
@@ -46,11 +46,16 @@ pip install -e .
 ```
 
 ## Usage
+```bash
+bash run.sh naive_rag/graph_rag/mm_rag
+```
+or
 
 ```bash
 python main.py --config examples/graphrag/config.yaml
 ```
 
+
 ## Examples
 
 See the `examples/` directory for sample configurations and usage.
@@ -59,17 +64,25 @@ See the `examples/` directory for sample configurations and usage.
 
 ### ✅ Implemented Features
 - Vector RAG (基于Qdrant实现)
-- Graph RAG (支持知识图检索)
+- Graph RAG (基于Neo4j实现)
+- Multi-modal RAG (基于Neo4j实现文本和图像向量存储与检索)
 - Lightweight SQLite Cache (轻量级缓存方案)
 
 ### 🚧 Planned Features
-- Multi-modal RAG (多模态检索增强生成)
 - ReAct QueryEngine (交互式查询引擎)
 - Query Engineering:
   - Query Rewriting (查询重写)
   - Sub-Questions (子问题分解)
 - Agentic RAG (智能工具选择优化性能)
 
+## 🙏 Acknowledgements
+This project draws inspiration from and gratefully acknowledges the contributions of the following open-source project:
+- [llama-index](https://github.com/run-llama/llama_index)
+- [llama-factory](https://github.com/hiyouga/LLaMA-Factory)
+- [Qdrant](https://github.com/qdrant/qdrant)
+- [Neo4j](https://github.com/neo4j/neo4j)
+
+
 ## ⭐ Star History
 
 <a href="https://star-history.com/#DataArcTech/RAG-Factory&Date">

diff --git a/data/multimodal_test_samples/documents.json b/data/multimodal_test_samples/documents.json
diff --git a/data/multimodal_test_samples/images/1*2wrx2joD1PaAsC27pshn3A.png b/data/multimodal_test_samples/images/1*2wrx2joD1PaAsC27pshn3A.png
diff --git a/data/multimodal_test_samples/images/1*6pALyl4xZ2 G9LnGlMR18g.png b/data/multimodal_test_samples/images/1*6pALyl4xZ2 G9LnGlMR18g.png
diff --git a/data/multimodal_test_samples/images/1*93ZK9-74dYv4eXY-Oe bkA.png b/data/multimodal_test_samples/images/1*93ZK9-74dYv4eXY-Oe bkA.png
diff --git a/data/multimodal_test_samples/images/1*CTF2gfNwx4v7V-0qM4s6uw.png b/data/multimodal_test_samples/images/1*CTF2gfNwx4v7V-0qM4s6uw.png
diff --git a/data/multimodal_test_samples/images/1*DbWfNKMRWJcomb9N5QIhOQ.png b/data/multimodal_test_samples/images/1*DbWfNKMRWJcomb9N5QIhOQ.png
diff --git a/data/multimodal_test_samples/images/1*GFJ9 TnLk2oDnVGtXAwARw.png b/data/multimodal_test_samples/images/1*GFJ9 TnLk2oDnVGtXAwARw.png
diff --git a/data/multimodal_test_samples/images/1*H7FLmJdZvGarmGGs81SIRA.png b/data/multimodal_test_samples/images/1*H7FLmJdZvGarmGGs81SIRA.png
diff --git a/data/multimodal_test_samples/images/1*Jp-QxrEj IYlOga84KTyBw.png b/data/multimodal_test_samples/images/1*Jp-QxrEj IYlOga84KTyBw.png
diff --git a/data/multimodal_test_samples/images/1*LM6CuPZiogendK21 UHwPA.png b/data/multimodal_test_samples/images/1*LM6CuPZiogendK21 UHwPA.png
diff --git a/data/multimodal_test_samples/images/1*OB43zXlHa4fcnL3n44JxcQ.png b/data/multimodal_test_samples/images/1*OB43zXlHa4fcnL3n44JxcQ.png
diff --git a/data/multimodal_test_samples/images/1*OCjG5oY6DyOnLuo1 N4OlA.png b/data/multimodal_test_samples/images/1*OCjG5oY6DyOnLuo1 N4OlA.png
diff --git a/data/multimodal_test_samples/images/1*PHsfndcMjOMoAdUAx8IJrw.png b/data/multimodal_test_samples/images/1*PHsfndcMjOMoAdUAx8IJrw.png
diff --git a/data/multimodal_test_samples/images/1*RYCIqV1Gfp18VkXfY411Xg.png b/data/multimodal_test_samples/images/1*RYCIqV1Gfp18VkXfY411Xg.png
diff --git a/data/multimodal_test_samples/images/1*VVWecqvibBb2vVXuyKU5rw.png b/data/multimodal_test_samples/images/1*VVWecqvibBb2vVXuyKU5rw.png
diff --git a/data/multimodal_test_samples/images/1*b8pT6KIERk3ZPd-729TmXA.png b/data/multimodal_test_samples/images/1*b8pT6KIERk3ZPd-729TmXA.png
diff --git a/data/multimodal_test_samples/images/1*chHcyzoxurmTuzVsYgrdLA.png b/data/multimodal_test_samples/images/1*chHcyzoxurmTuzVsYgrdLA.png
diff --git a/data/multimodal_test_samples/images/1*k9jUdnEB8sR5g0fZzqSiNA.png b/data/multimodal_test_samples/images/1*k9jUdnEB8sR5g0fZzqSiNA.png
diff --git a/data/multimodal_test_samples/images/1*mEipkYmePMYvX4t5tU8u2w.png b/data/multimodal_test_samples/images/1*mEipkYmePMYvX4t5tU8u2w.png
diff --git a/data/multimodal_test_samples/images/1*oY5JoNIikQZj3RsXyMrb3w.png b/data/multimodal_test_samples/images/1*oY5JoNIikQZj3RsXyMrb3w.png
diff --git a/data/multimodal_test_samples/images/1*yscMghSQVAQvQeHV6pZ4PQ.png b/data/multimodal_test_samples/images/1*yscMghSQVAQvQeHV6pZ4PQ.png
diff --git a/data/multimodal_test_samples/images_metadata.json b/data/multimodal_test_samples/images_metadata.json
@@ -0,0 +1,102 @@
+[
+    {
+        "image_url": "https://cdn-images-1.medium.com/max/800/1*yscMghSQVAQvQeHV6pZ4PQ.png",
+        "caption": "Knowledge graph schema representing microservice architecture and their tasks. Image by\u00a0author.",
+        "file_name": "1*yscMghSQVAQvQeHV6pZ4PQ"
+    },
+    {
+        "image_url": "https://cdn-images-1.medium.com/max/800/1*LM6CuPZiogendK21_UHwPA.png",
+        "caption": "Node properties of a Microservice and Task nodes. Image by\u00a0author.",
+        "file_name": "1*LM6CuPZiogendK21 UHwPA"
+    },
+    {
+        "image_url": "https://cdn-images-1.medium.com/max/800/1*6pALyl4xZ2_G9LnGlMR18g.png",
+        "caption": "Combining multiple data sources into a knowledge graph. Image by\u00a0author.",
+        "file_name": "1*6pALyl4xZ2 G9LnGlMR18g"
+    },
+    {
+        "image_url": "https://cdn-images-1.medium.com/max/800/1*k9jUdnEB8sR5g0fZzqSiNA.png",
+        "caption": "Subset of the DevOps graph. Image by\u00a0author.",
+        "file_name": "1*k9jUdnEB8sR5g0fZzqSiNA"
+    },
+    {
+        "image_url": "https://cdn-images-1.medium.com/max/800/1*H7FLmJdZvGarmGGs81SIRA.png",
+        "caption": "Vector similarity search in a RAG application. Image by\u00a0author.",
+        "file_name": "1*H7FLmJdZvGarmGGs81SIRA"
+    },
+    {
+        "image_url": "https://cdn-images-1.medium.com/max/800/1*VVWecqvibBb2vVXuyKU5rw.png",
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+        "file_name": "1*chHcyzoxurmTuzVsYgrdLA"
+    },
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+    {
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+        "caption": "The goal of information extraction pipeline is to extract structured information from unstructured text. Image by the\u00a0author.",
+        "file_name": "1*RYCIqV1Gfp18VkXfY411Xg"
+    },
+    {
+        "image_url": "https://cdn-images-1.medium.com/max/800/1*93ZK9-74dYv4eXY-Oe_bkA.png",
+        "caption": "Multiple steps of information extraction pipeline. Image by\u00a0author.",
+        "file_name": "1*93ZK9-74dYv4eXY-Oe bkA"
+    },
+    {
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+]