Alibaba-NLP · zhzhengit · Apr 21, 2025 · Apr 21, 2025 · Apr 21, 2025 · Apr 21, 2025
diff --git a/IMG/logo.png b/IMG/logo.png
diff --git a/IMG/main_result.jpg b/IMG/main_result.jpg
diff --git a/IMG/method4.jpg b/IMG/method4.jpg
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diff --git a/README.md b/README.md
@@ -1,145 +1 @@
-<div align="center">
-<img src="IMG/logo.png" width="55%">
-</div>
-
-# A Self-Adaptive Planning Agent For Multimodal RAG
-
-![](https://img.shields.io/badge/version-1.0.0-blue)[![Pytorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?e&logo=PyTorch&logoColor=white)](https://pytorch.org/)[![arxiv badge](https://img.shields.io/badge/arxiv-2411.02937-red)](https://arxiv.org/abs/2411.02937)
-
-Repo for [*Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent*](https://arxiv.org/abs/2411.02937)
-
-🌏 The **Chinese Web Demo** is avaiable at [ModelScope](https://modelscope.cn/studios/iic/OmniSearch/summary?header=default&fullWidth=false) now！
-
-<img src="IMG/ask_test_2.5.gif" width="799" height=530>
-
-- We propose OmniSearch, a self-adaptive retrieval agent that plans each retrieval action in real-time according to question solution stage and current retrieval content. As far as we known, **OmniSearch is the first planning agent for multimodal RAG.**
-- We reveal that existing VQA-based mRAG benchmarks fail to reflect the feature that real-world questions require dynamic knowledge retrieval, and propose novel **Dyn-VQA dataset, which contains three types of dynamic questions.**
-- We **benchmark various mRAG methods** with leading MLLMs on Dyn-VQA, demonstrating their flaw in providing sufficient and relevant knowledge for dynamic questions.
-
-
-
-<div align="center">
-    <img src="IMG/method4.jpg" width="80%" height="auto" />
-</div>
-
-
-
-## 💡 Perfomance
-
-The performance of various MLLMs with different mRAG strategies are shown below:
-
-<div align="center">
-    <img src="IMG/main_result.jpg" width="80%" height="auto" />
-</div>
-
-More analysis experiments can be found in the paper.
-
-# 📚 Dyn-VQA Dataset
-
-The json item of Dyn-VQA dataset is organized in the following format:
-```json
-{
-    "image_url": "https://www.pcarmarket.com/static/media/uploads/galleries/photos/uploads/galleries/22387-pasewark-1986-porsche-944/.thumbnails/IMG_7102.JPG.jpg/IMG_7102.JPG-tiny-2048x0-0.5x0.jpg",
-    "question": "What is the model of car from this brand?",
-    "question_id": 'qid',
-    "answer": ["保时捷 944", "Porsche 944."]
-}
-```
-
-🔥 The Dyn-VQA **will be updated regularly.** Laset version: 202412.
-
-# 🛠 Dependencies
-
-```bash
-pip install -r requirement.txt
-```
-
-#### Details
-
-- Python = 3.11.9
-- [PyTorch](http://pytorch.org/) (>= 2.0.0)
-- pillow = 10.4.0
-- requests = 2.32.3
-- google-search-results = 2.4.2
-- serpapi = 0.1.5
-
-# 💻 Running OmniSearch
-
-- GPT-4V-based OmniSearch
-
-We have release the code of GPT-4V-based OmniSearch for English questions.
-
-Before running, please replace with your own OPENAI key and Google_search key. OPENAI key is at 11-th line of main.py 
-
-```python
-GPT_API_KEY = "your_actual_key_here"
-headers = {
-    "Authorization": f"Bearer {GPT_API_KEY}"
-}
-```
-
-Google_search key is at 10-th line of search_api.py
-
-```python
-API_KEY = "your api-key"
-```
-
-The result is saved to the path:
-
-```python
-output_path = os.path.join(meta_save_path, dataset_name, "output_from_gpt4v.jsonl")
-```
-
-Run the `main.py` file:
-
-```bash
-python main.py --test_dataset 'path/to/dataset.jsonl' --dataset_name NAME --meta_save_path 'path/to/results'
-```
-
-- Qwen-VL-based OmniSearch
-
-We have made the [training data](https://github.com/Alibaba-NLP/OmniSearch/tree/main/dataset/training_data) for Qwen-VL-based OmniSearch publicly available. This data, along with the [CogVLM dataset](https://modelscope.cn/datasets/ZhipuAI/CogVLM-SFT-311K), was used to jointly train the [Qwen-VL-Chat](https://www.modelscope.cn/models/Qwen/Qwen-VL-Chat) using the [SWIFT framework](https://github.com/modelscope/ms-swift). The training script can be executed as follows:
-
-```
-swift sft --model_type qwen-vl-chat --dataset /Data/Path/to/Training_data_1 /Data/Path/to/Training_data_2 --model_id_or_path /Model/Path/to/Qwen-VL-Chat/ --output_dir /Output/Model/Path --max_length 8192 --evaluation_strategy 'no'
-```
-
-
-
-
-# 🔍 Evaluation
-
-The evaluation script for token F1-Recall of the output answers can be used as follows:
-
-```bash
-python evaluate.py --evaluate_file_path [path to output jsonl file] --lang [language of the
- QA dateset: en/zh]
-```
-
-## 🔥 TODO
-
-- Release code for Qwen-VL-Chat based OmniSearch
-- Release the corresponding model weight
-- Create a benchmark for Dyn-VQA
-
-## 📄 Acknowledge
-
-- The repo is contributed by Xinyu Wang, Shuo Guo, Zhen Zhang and Yangning Li. 
-- This work was inspired by ReACT, SelfAsk, FleshLLMs. Sincere thanks for their efforts. 
-
-## 📝 Citation
-
-```bigquery
-@article{li2024benchmarkingmultimodalretrievalaugmented,
-      title={Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent}, 
-      author={Yangning Li and Yinghui Li and Xinyu Wang and Yong Jiang and Zhen Zhang and Xinran Zheng and Hui Wang and Hai-Tao Zheng and Pengjun Xie and Philip S. Yu and Fei Huang and Jingren Zhou},
-      year={2024},
-      eprint={2411.02937},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2411.02937}, 
-}
-```
-
-
-When citing our work, please kindly consider citing the original papers. The relevant citation information is listed here.
+# WebWalker