The PDF Extraction Nightmare
Standard RAG pipelines use tools like PyPDF2 or pdfminer to extract text. This completely destroys the spatial structure of tables and ignores embedded charts entirely. If a user asks a question about a bar chart on page 4, a standard text-RAG pipeline is blind.
Multimodal Ingestion
We upgraded our pipeline to be multimodal. We convert each PDF page into a high-resolution image. We then pass the image through a Vision-Language Model (VLM) like GPT-4o or Claude 3.5 Sonnet.
We prompt the VLM: "Generate a comprehensive markdown description of this page. Explicitly transcribe all tables into markdown format. Summarize the data trends shown in any charts or graphs."
We then embed these rich, structured markdown summaries into our vector database instead of raw extracted text. Our agents can now accurately answer questions like "What was the Q3 revenue trend shown in the bar chart?" because the VLM translated the visual chart into semantic text during the ingestion phase.