MQA

MQA is an interactive Multi-modal Query Answering system, powered by MUST and latest LLMs. It comprises five core components: Data Preprocessing, Vector Representation, Index Construction, Query Execution, and Answer Generation, all orchestrated by a dedicated coordinator to ensure smooth data flow from input to answer generation.

Requirements

We make use of the Anaconda package manager in order to avoid dependency/reproducibility problems.

1. Clone the repository

   git clone https://github.com/ZJU-DAILY/MQA
   

2. Install Python dependencies using conda.

   conda create --name mqa --file environment.yml
   conda activate mqa
   

   Or install them manually.

   conda create -n mqa -y python=3.11.5
   conda activate mqa
   conda install flask=2.2.5
   conda install -y -c pytorch pytorch=2.1.2 torchvision=0.16.2
   pip install openai==1.14.0
   pip install openai-clip
   

3. Compile C++ code for indexing and searching.

   cd ./indexing_and_search
   git clone https://github.com/ChunelFeng/CGraph.git
   cmake build
   make
   

4. install npm and nodejs>=18.0.

5. To make use of OpenAI's LLMs, please set up your API key first.

Quick start

1. Launch the Flask server as the backend.

   python app.py
   

2. Launch the frontend in another terminal instance.

   cd ./frontend
   npm install
   npm run dev
   

Usage

Data Preparation

To properly work with the MIT-States dataset, the following structure is required:

MQA_base_path
├─dataset
│  ├─base
│  ├─meta
│  ├─MitStates
│  │  └─images
│  │      ├─adj aluminum
│  │      ├─adj animal
│  │      ├─adj apple
│  │      ├─...

Pre-processed data

We provide the pre-processed data via Google Drive in case you don't have enough GPU resources or simply want to save time. Download them and move them to /dataset/base and /dataset/meta as shown in the directory above. These data will also be created during the use of MQA.

Citation

@manual{MQA,
  author    = {Mengzhao Wang and Haotian Wu and Xiangyu Ke and Yunjun Gao and Xiaoliang Xu and Lu Chen},
  title     = {An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models},
  url       = {https://github.com/ZJU-DAILY/MQA},
  year      = {2024}
}

@inproceedings{MUST_ICDE24,
title={{MUST}: An Effective and Scalable Framework for Multimodal Search of Target Modality},
author={Mengzhao Wang and Xiangyu Ke and Xiaoliang Xu and Lu Chen and Yunjun Gao and Pinpin Huang and Runkai Zhu},
booktitle={IEEE International Conference on Data Engineering (ICDE)},
year={2024}
}

@manual{MVG_VLDB2024,
  author    = {Mengzhao Wang and Xiangyu Ke and Lu Chen and Yunjun Gao},
  title     = {MVG Index: Empowering Multi-Vector Similarity Search in High-Dimensional Spaces},
  url       = {https://github.com/ZJU-DAILY/MVG/blob/main/MVG_technical_report.pdf},
  year      = {2024}
}

Acknowledgement

• CGraph: A cross-platform Directed Acyclic Graph framework based on pure C++ without any 3rd-party dependencies.