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Build Your Own Local PDF RAG Chatbot (Tutorial)
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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
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:
=====
Follow me on socials:
Join this channel to get access to perks:
#ollama #langchain #streamlit #vectordatabase #pdf #nlp #machinelearning #ai #llm #RAG #retrievalaugmentedgeneration
✅ 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
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:
=====
Follow me on socials:
Join this channel to get access to perks:
#ollama #langchain #streamlit #vectordatabase #pdf #nlp #machinelearning #ai #llm #RAG #retrievalaugmentedgeneration
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