LangChain Multi-Query Retriever for RAG - Advanced Technique for Broader Vector Space Search
Offered By: James Briggs via YouTube
Course Description
Overview
Explore an advanced Retrieval-Augmented Generation (RAG) technique called "Multi-Query" in LangChain through this 19-minute video tutorial. Learn how to broaden search scores by using an LLM to transform a single query into multiple queries, enabling a more comprehensive vector space search and diverse result set. Follow along as the instructor demonstrates the implementation using OpenAI's text-embedding-ada-002, gpt-3.5-turbo, Pinecone vector database, and the LangChain library. Discover the process of creating a LangChain MultiQueryRetriever, adding generation capabilities, utilizing Sequential Chain for RAG, customizing the Multi-Query approach, reducing hallucination, and integrating Multi-Query into a larger RAG pipeline. Gain practical insights into enhancing AI-powered information retrieval and generation systems.
Syllabus
LangChain Multi-Query
What is Multi-Query in RAG?
RAG Index Code
Creating a LangChain MultiQueryRetriever
Adding Generation to Multi-Query
RAG in LangChain using Sequential Chain
Customizing LangChain Multi Query
Reducing Multi Query Hallucination
Multi Query in a Larger RAG Pipeline
Taught by
James Briggs
Related Courses
Pinecone Vercel Starter Template and RAG - Live Code Review Part 2Pinecone via YouTube Will LLMs Kill Search? The Future of Information Retrieval
Aleksa Gordić - The AI Epiphany via YouTube RAG But Better: Rerankers with Cohere AI - Improving Retrieval Pipelines
James Briggs via YouTube Advanced RAG - Contextual Compressors and Filters - Lecture 4
Sam Witteveen via YouTube Canopy: A New RAG Framework for Easy Top-Tier Performance
James Briggs via YouTube