Superfast RAG with Llama 3 and Groq

Groq API provides access to Language Processing Units (LPUs) that enable incredibly fast LLM inference. The service offers several LLMs including Meta's Llama 3. In this video, we'll implement a RAG pipeline using Llama 3 70B via Groq, an open source e5 encoder, and the Pinecone vector database. 📌 Code: https://github.com/pinecone-io/examples/blob/master/integrations/groq/groq-llama-3-rag.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/ #artificialintelligence #llama3 #groq 00:00 Groq and Llama 3 for RAG 00:37 Llama 3 in Python 04:25 Initializing e5 for Embeddings 05:56 Using Pinecone for RAG 07:24 Why We Concatenate Title and Content 10:15 Testing RAG Retrieval Performance 11:28 Initialize connection to Groq API 12:24 Generating RAG Answers with Llama 3 70B 14:37 Final Points on Why Groq Matters