back_office_ops · workflow
PDF query chat assistant built with Upstage AI Solar models and LangChain
PDFs with complex layouts containing tables and images are difficult for traditional parsers, which lose context or return jumbled, unorganized data; manually sifting through large piles of research papers is slow and tedious.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Load PDFs via layout analysis
The UpstageLayoutAnalysisLoader parses PDF files page by page, categorizing all HTML tags including tables, figures, and main text content.
Tools used
SolarLangChainFAISSUpstageLayoutAnalysisLoadersolar-embedding-1-large-passagesolar-1-mini-chat
Outcome
The chat assistant speeds up research by instantly searching embedded documents and retrieving the most relevant sections, eliminating manual reading through each document.
What failed first
Traditional PDF readers like PyPDF can lose context or return tables and structured data in a jumbled, unorganized manner.
Results
Volumeeliminates the need to manually read through each document
Grounding & classification
Source type: technical build writeup
22 fields verified against source quotes.
conversational aidata extractiondocument aiknowledge searchragknowledge basesource backedtools describedworkflow describededucationemployee productivitytime savedtechnical build writeupback office opsdocument to recordrag answering