Multimodal QA System (LLM + Vector Search)

A sophisticated question-answering system that leverages both large language models and vector search capabilities to process and respond to queries involving multiple types of data, including text, images, and structured data.

Project Goals

1. Implement efficient vector search for multiple data types
2. Develop context-aware response generation
3. Create adaptive UI for various query types
4. Build comprehensive API for third-party integration

Key Features

• Multi-modal data processing
• Hybrid search system
• Real-time response generation
• Custom knowledge base integration
• Interactive query builder

System Components

Vector Search Engine

• Efficient indexing of multiple data types
• Semantic similarity search
• Custom embedding generation

Language Model Integration

• Context-aware query processing
• Dynamic prompt generation
• Response refinement pipeline

User Interface

• Intuitive query builder
• Real-time response preview
• Result explanation system

Development Roadmap

Phase 1: Core Engine

• [ ] Vector search implementation
• [ ] LLM integration
• [ ] Basic API development

Phase 2: Advanced Features

• [ ] Multimodal processing
• [ ] Custom knowledge base
• [ ] Response optimization

Phase 3: UI & Integration

• [ ] Web interface
• [ ] API documentation
• [ ] Integration examples