PDF RAG Chatbot Tutorial using Ollama, LangChain, & Streamlit [v2]

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

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