🏗️Create an advanced retrieval augmented generation system using W Tre and other open models
💾Build a knowledge base with a parser, text splitter, and embedding model
🧑💻Use L chain for question and answering with a custom prompt
🔎Implement a ranker to filter and sort relevant documents
🌐Utilize a vector database for storing and retrieving embeddings
What are the main components of the advanced retrieval augmented generation system?
—The main components include a knowledge base, ranker, watch language model, and a chain for question and answering.
What is the role of the knowledge base?
—The knowledge base is responsible for extracting structured data from PDF files and creating embedding vectors for text chunks.
How does the ranker work?
—The ranker performs pairwise ranking and filters out irrelevant documents, sorting the more relevant ones at the top.
What is the purpose of the watch language model?
—The watch language model is used for question and answering, providing responses based on the relevant documents.
Why is a vector database used?
—A vector database is used to store and retrieve embedding vectors for efficient similarity search and retrieval.
00:00Learn how to build an advanced retrieval augmented generation system
00:49Use open models and L chain for building the system
01:40Understand the essential architecture and main components
04:43Explore the knowledge base for extracting structured data from PDFs
07:57Create embedding vectors using an embedding model
09:23Utilize a vector database for storing and retrieving embeddings
13:29Implement a ranker to filter and sort relevant documents
14:47Use the watch language model for question and answering
