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Title:How to chat with your PDFs using local Large Language Models [Ollama RAG]
Duration:23:00
Viewed:41,261
Published:08-04-2024
Source:Youtube

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'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: ============ 0:00 - Introduction 0:07 - Why you need to use local RAG 0:52 - Local PDF RAG pipeline flowchart 5:49 - Ingesting PDF file for RAG pipeline 8:46 - Creating vector embeddings from PDF and store in ChromaDB 14:07 - Chatting with PDF using Ollama RAG 20:03 - Summary of the RAG project 22:33 - Conclusion and outro LINKS: ===== 🔗 GitHub repo: https://github.com/tonykipkemboi/ollama_pdf_rag/tree/main Follow me on socials: 𝕏 → https://twitter.com/tonykipkemboi LinkedIn → https://www.linkedin.com/in/tonykipkemboi/ #ollama #langchain #vectordatabase #pdf #nlp #machinelearning #ai #llm #RAG



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