MultiPDF Chat Agent RAG-System

The Multi-PDF Chat Agent is a fully functional Retrieval-Augmented Generation (RAG) system built in Python and Streamlit that enables users to:

• Upload multiple PDF documents
• Ask natural language questions
• Receive answers grounded in the PDF content

It uses OpenAI's GPT-3.5-Turbo as the language model and FAISS as the vector database to implement a highly responsive and accurate question-answering system over unstructured data.

Problem it is solving

PDFs are rich sources of information but hard to query efficiently. This project allows users to interact with their documents as if talking to a smart assistant — instantly retrieving and summarizing the most relevant information.

Use cases:

1. Legal document review
2. Scientific paper Q&A
3. Business report understanding
4. Internal knowledge base search
5. RFP/documentation analysis

Architecture

How the RAG pipeline works

1. Input PDFs: Users drag and drop one or more PDF documents.
2. Text Extraction: Each PDF is parsed into raw text using PyPDF2.
3. Chunking: Text is split into manageable, overlapping segments using LangChain’s RecursiveCharacterTextSplitter.
4. Embedding: Text chunks are embedded
5. Vector Indexing: Chunks are stored in a FAISS index.
6. Semantic Search: On user query, FAISS retrieves the most similar chunks.
7. Answer Generation: The relevant context is passed to OpenAI's GPT-3.5-Turbo, which generates a grounded, accurate answer.
8. Output: The result is displayed in the Streamlit UI.