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.
LangChain Multi-Query Retriever for RAG - Advanced Technique for Broader Vector Space Search
James Briggs via YouTube
Overview
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
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