Back office ops · Production

PDF query chat assistant built with Upstage AI Solar models and LangChain

The problem

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.

First attempt

Traditional PDF readers like PyPDF can lose context or return tables and structured data in a jumbled, unorganized manner.

Workflow diagram · grounded in source
1
Load PDFs via layout analysis
ai_action
“The parameters of this model allow you to split the loading of the file page by page and categorize all the HTML tags from the file, making it easier for further analysis, such as categorizing tables, figures, main text content”
2
Embed text and store in FAISS
integration
“I used solar-embedding-1-large-passage to vectorize the large text from PDF files”
3
User submits query
trigger
“A retriever interface is simply used to retrieve the information from your dataset based on the user's unstructured search query”
4
Retrieve relevant sections
ai_action
“the assistant can instantly search through the embedded documents, retrieve the most relevant sections, and present concise answers”
5
Generate answer from context
output
“I used the solar-1-mini-chat model to ask questions or interact with the PDF files, using them as the context on which the model bases its answers”
Reported outcome

The chat assistant speeds up research by instantly searching embedded documents and retrieving the most relevant sections, eliminating manual reading through each document.

Reported metrics
Research retrieval speedspeeds up the process of retrieving information from a huge pile of data
Manual document readingeliminates the need to manually read through each document
Reported stack
SolarLangChainFAISSUpstageLayoutAnalysisLoadersolar-embedding-1-large-passagesolar-1-mini-chat
Source
https://mlops.community/blog/creating-a-pdf-query-assistant-with-upstage-ai-solar-and-langchain-integration
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The chat assistant speeds up research by instantly searching embedded documents and retrieving the most relevant sections, eliminating manual reading through each document.

What tools did this team use?

Solar, LangChain, FAISS, UpstageLayoutAnalysisLoader, solar-embedding-1-large-passage, solar-1-mini-chat.

What results were reported?

Research retrieval speed: speeds up the process of retrieving information from a huge pile of data; Manual document reading: eliminates the need to manually read through each document (source-reported, not independently verified).

What failed first in this deployment?

Traditional PDF readers like PyPDF can lose context or return tables and structured data in a jumbled, unorganized manner.

How is this back office ops AI workflow structured?

Load PDFs via layout analysis → Embed text and store in FAISS → User submits query → Retrieve relevant sections → Generate answer from context.

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