Multimodal-VideoRAG: Using BridgeTower Embeddings and Large Vision Language Models

Multimodal-VideoRAG is framework designed to facilitate multimodal information retrieval and question answering on videos by leveraging Video Retrieval-Augmented Generation (VideoRAG). It combines the power of Large Vision-Language Models (VLMs) and BridgeTower embeddings to perform video pre-processing, embedding generation, and multimodal vector database queries.

Features

• VideoRAG: Employs retrieval-augmented generation techniques for video understanding, enhancing the quality of multimodal responses.
• BridgeTower Integration: Leverages BridgeTower embeddings to fuse image and text information seamlessly.
• Multimodal Vector Database: Efficient multimodal data storage and querying, enabling robust retrieval of video-based information.
• Flexible Video Preprocessing: Supports a variety of video preprocessing tasks to convert raw video data into meaningful representations for downstream tasks.

Prerequisites

To set up Multimodal-VideoRAG, you will need:

• Python 3.8+
• PyTorch 1.10+
• Transformers Library (by Hugging Face)
• BridgeTower Model
• LanceDB for vector search
• Cv2 for video preprocessing

Installing Dependencies

1. Clone the repository:

   git clone https://github.com/Bhavik-Ardeshna/Multimodal-VideoRAG.git
   cd Multimodal-VideoRAG

2. Install the required Python packages:

   pip install -r requirements.txt

3. Install LanceDB for efficient similarity search:

   pip install lancedb

4. Install HuggingFace for video preprocessing:

   pip install transformers

Get Started

• BridgeTowerEmbeddings: Initializes the model to generate combined text-image embeddings.
• LanceDB: Establishes the vector store for video data storage and retrieval.
• MultimodalLanceDB Retriever: Retrieves relevant video segments based on similarity searches.
• VLMConversationalBot: Interacts with images from the video and generates descriptive responses to user prompts.

Video Retrieval

text = "astronauts mission"
retrieved_video_segments = retriever.invoke(text)

Conversational Bot Response

bot = VLMConversationalBot()
response = bot.converse(frame_path, "Describe what is happening in this image.")
print(f"Bot's Response: {response}")