In this video, we'll learn about an advanced technique for RAG in LangChain called "Multi-Query". Multi-query allows us to broaden our search score by using an LLM to turn one query into multiple, allowing us to search a broader vector space and return a higher variety of results. In this example, we use OpenAI's text-embedding-ada-002, gpt-3.5-turbo, Pinecone vector database, and of course the LangChain library.

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00:00 LangChain Multi-Query
00:31 What is Multi-Query in RAG?
01:50 RAG Index Code
02:56 Creating a LangChain MultiQueryRetriever
07:16 Adding Generation to Multi-Query
08:51 RAG in LangChain using Sequential Chain
11:18 Customizing LangChain Multi Query
13:41 Reducing Multi Query Hallucination
16:56 Multi Query in a Larger RAG Pipeline

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