🔍 Multimodal Search and RAG

A comprehensive implementation of Multimodal Retrieval-Augmented Generation (RAG) and Search. This project enables semantic search and context-aware generation across multiple data modalities, utilizing vision-language encoders, vector databases, and Large Multimodal Models (LMMs).

---

🚀 Features

• Multimodal Embedding Generation: Jointly encode text and images into a shared semantic vector space.
• Vector Database Integration: Efficient similarity search for multimodal data retrieval.
• Large Multimodal Models Pipeline (large multimodal models.py): Dedicated wrapper and logic for interfacing with state-of-the-art vision-language models (e.g., GPT-4V, Gemini, or LLaVA) to reason over retrieved text and image context.
• Cross-Modal Retrieval & Generation: Fetch relevant images or text chunks based on user queries and synthesize grounded textual responses.

---

🏗️ Architecture

The system operates through structured components:

1. Retrieval Engine: Fetches top-k relevant text/image chunks matching the user query from the vector store.
2. Generation Pipeline (large multimodal models.py): Formulates prompts combining retrieved visual/textual contexts and passes them to the Large Multimodal Model for response generation.

---

🛠️ Getting Started

Prerequisites

• Python 3.8 or higher
• Git
• API Keys for your chosen provider (e.g., OpenAI, Google Gemini)

Installation

1. Clone the repository:

   git clone [https://github.com/ivyanalyst/Multimodal-Search-and-RAG.git](https://github.com/ivyanalyst/Multimodal-Search-and-RAG.git)
   cd Multimodal-Search-and-RAG

---

1. Create a virtual environment:

Bash python -m venv venv source venv/bin/activate Install dependencies:

2. Bash pip install -r requirements.txt

3. Environment Variables: Create a .env file in the root directory:

Code snippet OPENAI_API_KEY="openai_api_key" GOOGLE_API_KEY="google_api_key"

💻 Usage Running the LMM Generation Script To test or execute the multimodal model logic directly via large multimodal models.py:

Bash python "large multimodal models.py" 📁 Project Structure Plaintext Multimodal-Search-and-RAG/ │ ├── large multimodal models.py # Core logic for interfacing with LMMs ├── requirements.txt # Python dependencies ├── .env.example # Environment variables template └── README.md # Project documentation

🤝 Contributing Contributions are welcome! Feel free to open issues or submit pull requests.

📜 License Distributed under the MIT License. See LICENSE for more information.