Handling document preprocessing for multimodal RAG (Retrieval-Augmented Generation) involves preparing diverse data types—like text, images, audio, and structured data—for efficient retrieval and generation. The goal is to transform raw documents into a format that allows models to understand and cross-reference information across modalities. This typically includes extraction, normalization, segmentation, and indexing. For example, a PDF containing text and charts requires extracting text (via OCR or parsers) and images (via binary extraction), then normalizing both into consistent formats (e.g., text tokens and image embeddings). Each modality is processed separately but linked through metadata to maintain context, such as associating a chart’s caption with its corresponding image embedding.
Specific preprocessing steps vary by data type. For text, this might involve tokenization, removing formatting artifacts, and splitting documents into chunks using rules like sentence boundaries or fixed token windows. Images require resizing, normalization (e.g., pixel scaling), and feature extraction using vision models like CLIP or ResNet to generate embeddings. Audio or video files might be transcribed to text (using ASR tools like Whisper) and segmented into clips with timestamps. Structured data, such as tables, need parsing into machine-readable formats (e.g., JSON) and alignment with textual descriptions. For instance, in a medical report, a table of lab results could be parsed into key-value pairs and linked to the surrounding analysis text. Metadata (e.g., document IDs, section headers) is critical to preserve relationships between chunks and their original context.
After preprocessing, data is indexed for retrieval. Multimodal RAG systems often use vector databases to store embeddings from different modalities, enabling cross-modal similarity searches. For example, a user query about “charts showing sales growth” might retrieve both relevant text snippets and image embeddings. To ensure coherence, alignment techniques like joint embeddings (e.g., training a model to map text and images into a shared space) or cross-encoder reranking can improve retrieval accuracy. Challenges include handling large files (e.g., splitting videos into manageable clips) and maintaining low latency. Tools like Apache Tika for text extraction, PyTorch for vision models, and FAISS for vector indexing are commonly used. The key is balancing granularity (small enough chunks for precision) with context preservation (large enough to retain meaning), while ensuring efficient cross-modal linking.