Build Your Own Local PDF RAG Chatbot (Tutorial)

In this tutorial, we'll explore how to create a local RAG (Retrieval Augmented Generation) pipeline that processes and allows you to chat with your PDF file(s) using Ollama and LangChain. We will also create a Streamlit app for the UI. ✅ We'll start by loading a PDF file using the "UnstructuredPDFLoader" ✅ Then, we'll split the loaded PDF data into chunks using the "RecursiveCharacterTextSplitter" ✅ Create embeddings of the chunks using "OllamaEmbeddings" ✅ We'll then use the "from_documents" method of "Chroma" to create a new vector database, passing in the updated chunks and Ollama embeddings ✅ Finally, we'll answer questions based on the new PDF document using the "chain.invoke" method and provide a question as input The model will retrieve relevant context from the updated vector database, generate an answer based on the context and question, and return the parsed output. TIMESTAMPS: ============ 00:00:00 - Introduction 00:00:38 - Reference to previous PDF RAG tutorial 00:01:08 - Project directory structure 00:03:00 - Import required libraries 00:05:09 - PDF content overview 00:06:07 - Text chunking and overlap technique 00:07:43 - Create vector embeddings and load to vector database 00:09:01 - Build a retriever 00:21:01 - Streamlit app overview 00:27:01 - Conclusion and outro LINKS: ===== 🔗 GitHub repo: https://github.com/tonykipkemboi/olla... Follow me on socials: 𝕏 →   / tonykipkemboi   LinkedIn →   / tonykipkemboi   Join this channel to get access to perks:    / @tonykipkemboi   #ollama #langchain #streamlit #vectordatabase #pdf #nlp #machinelearning #ai #llm #RAG #retrievalaugmentedgeneration