Hands-on RAG Tutorial using LlamaIndex, Gemini, and Pinecone Vector DB

Let's talk about building a simple RAG app using LlamaIndex (v0.10+) Pinecone, and Google's Gemini Pro model. A step-by-step tutorial if you're just getting started! -- Useful links: Google AI Studio: https://ai.google.dev Pinecone: https://www.pinecone.io LlamaIndex: https://www.llamaindex.ai 👉 Source code: https://www.gettingstarted.ai/how-to-use-gemini-pro-api-llamaindex-pinecone-index-to-build-rag-app/ (Updated for LlamaIndex v0.10+) -- Timeline: 00:00 Introduction 00:43 Basic definitions 02:18 How Retrieval Augmented Generation (RAG) works 03:55 Creating a Pinecone Index and getting an API Key 05:25 Getting a Google Gemini API Key 06:25 Creating a virtual environment 06:48 Installing LlamaIndex (and core packages) 07:41 Installing other dependencies 08:03 General application setup 10:42 Setting up environment variables 12:45 Validating configuration 14:11 Retrieving content from the Web 15:38 Explaining IngestionPipeline 16:49 Creating a LlamaIndex IngestionPipeline 17:16 Defining a Pinecone vector store 18:29 Running the IngestionPipeline (with Transformations) 19:37 Performing a similarity search 20:13 Creating a VectorStoreIndex 20:32 Creating a VectorIndexRetriever 21:04 Creating a RetrieverQueryEngine 22:05 Querying Google Gemini (Running the Pipeline) 22:47 Where to find the complete source code 23:15 Conclusion @LlamaIndex @pinecone-io @GoogleDevelopers @Google