Case study · 05
InsightLens
Multimodal RAG for Document Exploration
InsightLens is a multimodal document exploration system. Users can upload PDFs, DOCX files, and images, ask questions about their content, and receive answers grounded in retrieved text and visual evidence with inspectable sources.
- Multimodal AI
- RAG
- Semantic Retrieval
Problem
Useful information in documents can appear in normal text, images, figures, tables, or scanned and rendered pages. A text-only assistant can miss important visual evidence.
How it works
Users upload PDFs, DOCX files, or images. InsightLens extracts available text, analyzes relevant visual content, and represents text and visual descriptions for semantic retrieval.
For each question, the system retrieves the most relevant evidence and generates an answer using that evidence. The implementation combines local embeddings and ChromaDB with OpenAI models where needed.
Grounded answers and sources
Answers are designed to stay grounded in retrieved document evidence. Source cards let users inspect the filename, page, evidence type, snippet, and retrieval score behind a response.
Current limitations
Documents are session-scoped and are cleared when the backend restarts.
Visual PDF analysis is intentionally limited to a configured number of pages.
Answer quality depends on the text and visual evidence that can be extracted and retrieved from the uploaded files.
Related areas
Multimodal AI · Retrieval-Augmented Generation · Semantic Retrieval