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:
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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:
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GitHub repo: https://github.com/tonykipkemboi/olla...

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