Chatbots with RAG: LangChain Full Walkthrough

In this video, we work through building a chatbot using Retrieval Augmented Generation (RAG) from start to finish. We use OpenAI's gpt-3.5-turbo Large Language Model (LLM) as the "engine", we implement it with LangChain's ChatOpenAI class, use OpenAI's text-embedding-ada-002 for embedding, and the Pinecone vector database as our knowledge base. 📌 Code: https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/rag-chatbot.ipynb 🌟 Build Better Agents + RAG: https://platform.aurelio.ai (use "JBMARCH2025" coupon code for $20 free credits) 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ 00:00 Chatbots with RAG 00:59 RAG Pipeline 02:35 Hallucinations in LLMs 04:08 LangChain ChatOpenAI Chatbot 09:11 Reducing LLM Hallucinations 13:37 Adding Context to Prompts 17:47 Building the Vector Database 25:14 Adding RAG to Chatbot 28:52 Testing the RAG Chatbot 32:56 Important Notes when using RAG #artificialintelligence #nlp #ai #langchain #openai #vectordb