🔒Building a local RAG system ensures the security of sensitive documents.
📄Chatting with offline models makes it easy to interact with personal documents.
🌐Using OpenAI GPT models offline avoids sharing information online.
💡Noric embed text is a high-performing embedding model for vector conversion.
🔀Chroma DB stores vector embeddings for efficient retrieval and querying.
Why should I build a local RAG system?
—A local RAG system ensures the security and privacy of sensitive documents, protecting them from online exposure.
How can I use offline models to chat with personal documents?
—By using OpenAI GPT models offline, you can interact with personal documents without sharing them online, ensuring privacy.
What is the advantage of using Noric embed text?
—Noric embed text is a high-performing embedding model that converts text into vector representations, making it easier for vector-based operations.
What is Chroma DB used for?
—Chroma DB is a vector database that stores vector embeddings, allowing for efficient retrieval and querying of document information.
How does building a local RAG system enhance document security?
—Building a local RAG system ensures that sensitive documents, such as medical or financial records, are not exposed to the internet, minimizing the risk of data breaches.
00:00Learn how to build a local RAG system without connecting to the internet.
00:02Reasons to build a local RAG system include protecting sensitive documents and maintaining privacy.
00:09The process involves using Noric embed text to convert text into vectors and storing the vector embeddings in Chroma DB.
00:23Benefits of using a local RAG system include offline interaction with personal documents and avoiding sharing information online.
01:14The pipeline includes ingesting PDFs, creating vector embeddings, and retrieving information from the vector database.
05:13Install the required libraries, import the necessary modules, and follow the code to build your local RAG system.
05:46Understand how ingesting PDFs, creating vector embeddings, and retrieving information from the vector database work together to provide a secure and efficient system.
07:22Building a local RAG system enhances document security and allows for offline interaction with personal documents using OpenAI GPT models.
