DocuVision: PDF Q&A Without LangChain Plus a Vector DB

DocuVision is presented as a document retrieval-augmented generation approach built with Pixeltable, in which PDF documents are stored in a Docs table, automatically split into searchable text chunks with page-position metadata, and indexed using embeddings for similarity-based retrieval. Rather than assembling separate tools for PDF loading, splitting, vector storage, retrieval, and orchestration, the approach uses declarative tables and views so that document deletion and index updates follow the underlying data lineage. A sample implementation uses a sentence-transformer embedding model, retrieves the most relevant passages for questions such as regional revenue breakdowns, and sends those passages to Gemini for cited-answer generation. The description distinguishes text-based passage RAG from chart or image interpretation, noting that page images and vision processing could be added later as separate columns. It also describes deployment through Pixeltable Cloud using database, schema, and service update commands, while emphasizing a simplified workflow of declaring documents, chunking them, indexing text, and querying the resulting data.

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