INGESTION
- PyMuPDF
- Text extraction
- Table parsing
- Image handling
WHAT IS THE MULTIMODAL RAG SYSTEM
PDF RAG.
A fast document-intelligence pipeline for enterprise-style Q and A across text, tables, and images inside dense PDF collections.
03about
1
PyMuPDF extraction
2
Qdrant vector DB
3
2.4x faster retrieval
A MULTIMODAL RAG
PIPELINE FOR COMPLEX
DOCUMENT SETS THAT NEED
SPEED
02tech Stack
RETRIEVAL
- Qdrant
- Embeddings
- Vector search
- Context assembly
LLM LAYER
- GPT-5
- LlamaIndex
- Enterprise Q and A
- Python orchestration
03functionalities
EXTRACT TEXT, TABLES, AND IMAGES FROM PDFS SO DIFFERENT CONTENT TYPES CAN BE SEARCHED TOGETHER
STORE EMBEDDINGS IN QDRANT AND BENCHMARK RETRIEVAL PERFORMANCE FOR COMPLEX DOCUMENT COLLECTIONS
DELIVER ENTERPRISE-STYLE QUESTION ANSWERING THROUGH A MULTIMODAL RAG STACK INSTEAD OF A GENERIC CHATBOT WRAPPER
