DeepResearch handles multiple data types (text, images, PDFs) by implementing modular processing pipelines tailored to each data type, followed by unified storage and retrieval systems. Each pipeline extracts structured information while preserving context, enabling cross-data analysis. For example, text is parsed for semantic meaning, images are analyzed for visual features, and PDFs are decomposed into text, images, and layout metadata. This approach ensures that diverse data types are processed efficiently while maintaining interoperability for downstream tasks.
Text processing involves natural language processing (NLP) techniques like tokenization, entity recognition, and embedding generation. For instance, a research paper’s text might be split into sections, with key terms tagged and converted into vector representations. Images are handled using computer vision models to detect objects, extract features, or generate captions. A diagram in a PDF could be converted to an image, analyzed for visual patterns, and linked to its textual description. PDFs are parsed using tools like PyMuPDF or PDFMiner to separate text, tables, and images, while retaining structural details like headers or footnotes. Metadata (e.g., author, publication date) is also extracted for context.
After processing, all data types are stored in a unified format, such as JSON documents with standardized fields for text embeddings, image features, and metadata. This allows developers to query across data types using a single interface. For example, a user could search for “climate change trends” and retrieve relevant text snippets, charts from PDFs, and satellite images. To optimize performance, DeepResearch might use databases like Elasticsearch for text and FAISS for vector similarity, ensuring fast retrieval. By decoupling data processing from storage/retrieval, the system remains scalable and adaptable to new data types or analysis requirements.