AI-Powered RAG Chatbot using Python, FAISS, LangChain, OpenAI and Streamlit #AI-Powered RAG Chatbot A Retrieval-Augmented Generation (RAG) chatbot built using Python, LangChain, FAISS, OpenAI, and Streamlit. This chatbot allows users to upload PDF documents and ask questions based on the document content. The system retrieves the most relevant information from the uploaded PDF and generates accurate answers using an LLM.
Upload PDF documents Extract text from PDFs Split documents into manageable chunks Generate embeddings using Sentence Transformers Store embeddings in FAISS Vector Database Retrieve relevant document sections Generate AI-powered responses using OpenAI User-friendly Streamlit interface Display retrieved document chunks for transparency #🔄 How It Works Step 1: Upload PDF
Users upload a PDF document through the Streamlit interface.
Step 2: Text Extraction
The system extracts text content from the uploaded PDF.
Step 3: Chunking
Large documents are divided into smaller chunks using LangChain's text splitter.
Step 4: Embedding Generation
Each chunk is converted into vector embeddings using Sentence Transformers.
Step 5: Vector Storage
Embeddings are stored in a FAISS vector database.
Step 6: Retrieval
When a question is asked, the system retrieves the most relevant chunks from the vector database.
Step 7: Response Generation
Retrieved chunks are provided as context to the OpenAI model, which generates an answer.