Day-339 of my Make Project in Langchain #coding

Day-339 of my Make Project in Langchain #coding
A full-stack Retrieval-Augmented Generation (RAG) system that ingests multiple research paper PDFs and generates structured summaries, comparative analysis, and question answering with citations.
Built using LangChain, ChromaDB, OpenAI models, and Streamlit.
✨ Features
1. Multi-PDF Ingestion
Upload any number of research papers. The system parses each PDF, extracts text, and stores metadata such as filename and page number.
2. Intelligent Chunking
PDFs are chunked using a section-aware text splitter optimized for scientific papers to retain context during retrieval.
3. Vector Database (ChromaDB)
Chunks are embedded using OpenAI embeddings or optional local embddings.
A persistent vector store enables fast retrieval even across app restarts.
4. RAG-Powered Summaries
Ask questions like:
“Give a structured summary of all uploaded papers.”
“Compare Paper A vs Paper B.”
“Summarize only the methodology sections.”
“What gaps do these papers highlight?”
The model uses retrieved chunks + PDF metadata to generate grounded responses.
5. Citations from Source PDFs
The system displays outputs that reference the original PDFs (via metadata such as source_file).
6. Simple and Fast UI
A clean Streamlit interface for:
Uploading PDFs
Building the RAG index
Entering questions
Viewing generated summaries
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Видео Day-339 of my Make Project in Langchain #coding канала Sandeep Muhal