DeepResearch handles multiple data types by first processing each format into a structured representation, then combining them for analysis. For text, it uses natural language processing (NLP) techniques like tokenization and embeddings to extract semantic meaning. For example, unstructured text from PDFs or raw documents is parsed using tools like PyPDF2 or spaCy to isolate sections, tables, or key phrases. Images are processed with computer vision models (e.g., ResNet for feature extraction or OCR libraries like Tesseract) to convert visual data into text or numerical features. PDFs are treated as hybrid documents: text is extracted alongside embedded images or diagrams, which are handled separately. This ensures raw data is transformed into a format that machine learning models can interpret.
Integration occurs through a unified embedding space or metadata tagging. For instance, text from a PDF and its associated images might be linked via document structure (e.g., captions referencing figures) or temporal context. Multimodal models like CLIP or custom transformer architectures align text and image embeddings, enabling cross-referencing. If a research query involves "climate change trends," DeepResearch might correlate text from scientific papers with graphs extracted from PDFs and satellite images, using embeddings to measure relevance. Metadata (e.g., document source, date) further enriches context, allowing the system to prioritize recent or authoritative sources.
Storage and retrieval are optimized for mixed data. Text is indexed in vector databases (e.g., FAISS) for semantic search, while images and PDFs are stored in compressed formats with metadata pointers. When a user submits a query, the system retrieves relevant embeddings from all data types, ranks them by similarity, and combines results. For example, a search for "urban planning" could return text snippets, zoning law PDFs, and city layout images, all weighted by their relevance scores. This approach ensures scalability and flexibility, as new data types (e.g., video) can be added by extending the processing pipeline without overhauling the core architecture.