UltraRAG, an open-source multimodal Retrieval-Augmented Generation (RAG) framework, is designed to process and leverage various types of content, including visual information, as part of its multimodal capabilities. The framework explicitly supports multimodal inputs, allowing its Retriever, Generator, and Evaluator modules to natively handle text, vision, and cross-modal inputs. This means that while UltraRAG may not directly ingest raw video files for end-to-end processing without prior extraction, it can effectively process the visual and textual components derived from video content. For instance, frames extracted from a video, along with their associated textual descriptions or audio transcripts, can be integrated into an UltraRAG pipeline. The framework's ability to integrate Multimodal Large Language Models (MLLMs) like MiniCPM-V and multimodal retrievers further underscores its capacity to work with diverse data modalities, enabling sophisticated RAG applications that combine information from both visual and textual sources.
The architecture of UltraRAG, particularly with the release of UltraRAG 2.1, has been enhanced with native multimodal support, making it suitable for research and deployment scenarios that involve mixed data types. It features comprehensive tools for managing knowledge bases and supports diverse document formats, implying flexibility in how multimodal information is prepared and fed into the system. While specific examples of direct video file ingestion are not prominently highlighted, the emphasis on "vision" and "cross-modal inputs" within its multimodal context clearly indicates its capacity to handle visual data. This is further exemplified by the integration of the VisRAG pipeline, which is a vision-based RAG approach for multi-modality documents, directly into UltraRAG.
To process video content using UltraRAG, a typical workflow would involve preprocessing the video into its constituent modalities. This often includes extracting key frames as images and transcribing the audio into text. These extracted images and text can then be vectorized and indexed in a vector database, such as Zilliz Cloud. UltraRAG's modular design would then allow for the retrieval of relevant visual and textual information based on a query, followed by generation using an LLM that can synthesize information from these different modalities. This approach enables UltraRAG to build advanced RAG systems that can understand and respond to queries that require insights from both the visual and auditory aspects of video content, facilitating use cases like video content search and analysis, or generating summaries based on visual and spoken information.