The Multimodal Search System is a generative AI project that combines Vision model integration and Cross-modal alignment, built with Vision-language models (GPT-4V, LLaVA). The project follows a clean, modular pipeline where input handling, generation, and presentation stay separated, making it easy to test, extend, and present. It showcases modern generative AI techniques while producing a working, demo-ready application.
Traditional solutions to this problem are slow, static, and unable to generate new, context-aware content on demand. Without a generative AI approach built on Cross-modal alignment and Vision-language models (GPT-4V, LLaVA), users cannot get personalized, high-quality outputs quickly, and there is no straightforward way to refine or evaluate the results.
This project applies generative AI through Vision model integration, orchestrated with Vision-language models (GPT-4V, LLaVA) and Cross-modal alignment. The pipeline is designed for quality and control, with validation, evaluation, and a clean interface. It generates consistent, context-aware results and can be adapted to related tasks with minimal changes.
Vision-language models (GPT-4V, LLaVA)
Whisper for audio
Python 3.11+
Structured prompts and configuration
Error handling and retries
Evaluation and logging
Hugging Face Transformers
Modular pipeline around Vision model integration and Cross-modal alignment
Configurable generation and evaluation settings
Clear logging, retries, and cost tracking
Clean interface for results
Reusable components for related tasks
Evaluation of output quality
The project is layered: the input layer prepares and validates inputs through Vision model integration; the generation layer invokes the model with Vision-language models (GPT-4V, LLaVA) and Cross-modal alignment; and the output layer formats and presents results via Demo interface. Shared configuration, logging, and evaluation modules support all layers, keeping the system robust and easy to extend.
Set up the Python environment, project structure, and configuration files.
Build the input layer with Vision model integration and validate incoming data.
Implement the generation pipeline using Vision-language models (GPT-4V, LLaVA) and Cross-modal alignment.
Add the output and presentation layer via Demo interface.
Wire up end-to-end flows and add error handling and retries.
Evaluate output quality, tune prompts, and refine settings.
Package the project, document it, and prepare the demo and viva report.
Build production-style generative AI applications
Apply Evaluating multimodal output and Working with vision-language models
Design prompts and evaluation for generated content
Work with LLM and diffusion model APIs
Present and defend a complete GenAI project in viva
Expose the pipeline as a REST API for other apps
Add fine-tuning for higher-quality domain outputs
Add guardrails and content safety checks
Deploy with caching for lower latency and cost
The Multimodal Search System delivers a complete generative AI workflow — from input and generation to evaluation and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI skills.
