Multi Modal RAG Based LLM For Information Retrieval

This innovative system leverages a Retrieval-Augmented Generation (RAG) framework combined with a multi-modal large language model (LLM) to provide accurate and context-aware answers from vast unstructured data sources. The solution integrates cutting-edge techniques for:

1.Multi-Modal Input Handling: Supports text, images, and structured data for comprehensive information retrieval.
2.Efficient Query Resolution: Employs a pipeline with Weaviate, HuggingFace models, and custom agents like Tree of Thought and Context Agents to process complex queries seamlessly.
3.Dynamic Re-Ranking: Implements cross-encoders to re-rank retrieved information, ensuring the most relevant results are prioritized.
4.Conversational Context Management: Enables conversational responses by maintaining context and performing advanced calculations dynamically.
5.Real-World Applications: Successfully deployed for diverse use cases, including condition monitoring systems, research support, and technical documentation exploration.