PRD.md

Product Title: Multi-Modal Semantic Integration for Intelligent Document Understanding (Hybrid GraphRAG)

1. Objective
To build an intelligent document understanding system that moves beyond standard "chunk and embed" RAG. The system will parse complex, unstructured documents (PDFs), map their multi-modal elements (text, tables, images) into a relational graph, and use an autonomous agent to retrieve and synthesize distributed context to answer user queries with perfect citations.

2. Target Audience

Analysts & Researchers: Need quick, accurate answers synthesized from 200-page operational reports or technical manuals without reading every page.

3. Core Features (MVP)

Intelligent Ingestion: Accept PDF uploads and parse them into a hierarchical JSON structure, preserving layout (Headers, Paragraphs, Tables, Images).

Visual Summarization: Automatically convert extracted charts/images into detailed text summaries using a lightweight Vision-Language Model (VLM).

Hybrid Memory Core: Assign a UUID to every document element. Store semantic embeddings in a local vector database, and store structural relationships (parent, child, sibling) in an in-memory graph.

Agentic Retrieval: An intelligent routing loop that uses semantic search to find a starting node, then traverses the relational graph to gather surrounding context (e.g., fetching a table and its explanatory paragraph).

Explainable UI: A chat interface that streams answers in real-time and explicitly cites the source nodes, accompanied by a visual graph map showing how the AI found the answer.

4. Success Metrics

Demonstrate multi-modal coverage (handling text + tables + images).

Correctly answer multi-hop/cross-element questions during the live demo.

Zero system crashes when uploading a new document live.