Step‑by‑Step Guide: Building a PDF‑Based RAG Knowledge Base with LangChain, Streamlit, DashScope & DeepSeek
This tutorial shows how to create a lightweight Retrieval‑Augmented Generation (RAG) system that indexes multiple PDF files, stores their embeddings in a FAISS vector database, and answers user queries through a LangChain agent powered by DashScope embeddings and the DeepSeek‑Chat model, all wrapped in a Streamlit UI.
