Multimodal Document RAG Platform
A multimodal RAG system for PDF parsing, vector retrieval, and document-grounded chat, built as one integrated upload-to-answer experience.
A guided preview that walks visitors through document selection, retrieval inspection, and grounded answering for multimodal RAG workflows.
Why this local version exists
This local sandbox focuses on the strongest product signal: visible retrieval evidence. It is a better portfolio demo than exposing an unrestricted upload endpoint.
Explore the retrieval workflow
This sandbox walks through the product flow that matters most in a document RAG system: choosing a document, inspecting retrieved chunks, and watching the grounded answer stay tied to explicit evidence.
Sample document library
Grounded question
PDF + chartsParse + chunk
Normalize mixed-layout PDF content into searchable text blocks.
Retrieve context
Expose the exact chunks that will ground the final answer.
Generate answer
Compose a cited response from retrieved evidence instead of hallucinated recall.
Retrieval inspector
Retrieved chunks
Grounded answer
Response with citations
Choose a sample document, keep the question visible, and run the preview to watch retrieval and answer generation unfold step by step.
Sandbox telemetry
Product flow checkpoints
Activity log
The sandbox log will show how the document moves from upload to retrieval-backed answer generation.
What to try
Pick a sample document with a different structure and switch the question prompt.
Run the preview and inspect which chunks the system retrieves first.
Compare the retrieved evidence with the final cited answer.
What this demo proves
You understand document pipelines beyond simple chat over text embeddings.
You can expose retrieval quality in a user-facing interface instead of hiding it.
You know how to present multimodal RAG as a product workflow, not just a backend stack.
Surface
Document upload, retrieval inspector, grounded QA
Interaction
Chunk visibility before final answer
Best signal
Document AI product design with deployable architecture