LangChain Multi-Query Retriever for RAG

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. 📌 Code: https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/10-langchain-multi-query.ipynb 🌲 Subscribe for Latest Articles and Videos: https://www.pinecone.io/newsletter-signup/ 👋🏼 AI Consulting: https://aurelio.ai 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ 👉 Thumbnail credit @LaCarnevali 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 #artificialintelligence #nlp #ai #openai #chatbot #langchain #vectordb