RAG with OpenAI & Pinecone Vector Database ? скачать в хорошем качестве

RAG with OpenAI & Pinecone Vector Database ?

📹 VIDEO TITLE 📹 RAG with OpenAI & Pinecone Vector Database ? ✍️VIDEO DESCRIPTION ✍️ Welcome to this code-centric video tutorial on building a Retrieval-Augmented Generation (RAG) system using Python, LangChain, and the serverless Pinecone Vector Database, alongside OpenAI’s powerful language models. In this video, we'll demonstrate how you can combine the flexibility of LangChain, the scalability of serverless Pinecone, and the generative prowess of OpenAI to build a system that retrieves relevant context from your data and crafts intelligent responses. Whether you're working on chatbots, search tools, or AI assistants, this tutorial will give you a practical starting point to implement RAG in a serverless environment with minimal setup. Let’s dive right in! We’ll start by initializing the Pinecone client in serverless mode, connecting to a pre-existing index with embeddings. There is another video “Embeddings with OpenAI & Pinecone” where I show you how to do this. For the retrieval step, we’ll integrate the Pinecone retriever with an OpenAI language model using LangChain’s RetrievalQA chain. This chain allows us to fetch context-relevant data from the vector store and use OpenAI’s LLM, such as GPT-4, to generate accurate, coherent responses. We'll walk through the Python code line by line, demonstrating how to set up your API keys, preprocess and upload your data, and query the system with a natural language question. You’ll see how LangChain abstracts complex operations, making the pipeline seamless and intuitive. Finally, we’ll test the RAG system by asking it a real-world questions”. You'll see how the Pinecone database retrieves relevant snippets from the embedded knowledge base and how the OpenAI model uses this context to craft a detailed and precise response. We'll also discuss the advantages of using Pinecone in serverless mode, such as cost-effectiveness and scalability for dynamic workloads, and how LangChain simplifies RAG implementation. If you find this tutorial helpful, don’t forget to like and subscribe for more content on AI, Python, and cutting-edge technologies. Drop your questions or ideas in the comments below—I'd love to hear how you plan to use RAG in your projects! 🧑‍💻GITHUB URL 🧑‍💻 https://github.com/NewMachinaLLM/vide... 📽OTHER NEW MACHINA VIDEOS REFERENCED IN THIS VIDEO 📽 SDK(s) in Pinecone Vector DB -    • SDK(s) in Pinecone Vector DB   Pinecone Vector DB POD(s) vs Serverless -    • Pinecone Vector Database PODS vs Serverless   Meta Data Filters in Pinecone Vector DB -    • Meta Data Filters in Pinecone Vector Database   Namespaces in Pinecone Vector DB -    • Meta Data Filters in Pinecone Vector Database   Fetches & Queries in Pinecone Vector DB -    • Meta Data Filters in Pinecone Vector Database   Upserts & Deletes in Pinecone Vector DB -    • Meta Data Filters in Pinecone Vector Database   What is a Pineconde Index -    • What is a Pinecone Index ?   What is the Pinecone Vector DB -    • What is a Pinecone Index ?   What is LLM LangGraph ? -    • What is LLM LangGraph?   What is Llama Index ? -    • What is LLM Llama Index ?   LangChain HelloWorld with Open GPT 3.5 -    • LangChain HelloWorld with Open GPT 3.5   🔠KEYWORDS 🔠 #AI #LLM #LargeLanguageModel #Pinecone #PineconeVectorDatabase #Embeddings #EmbeddingLLM #OpenAI #LangChain #Python #textEmbeddingAda002