Generative AI based Q&A API | Vector Embeddings, Vector DB, LLMs - Context Retrieval in RAG Pipeline скачать в хорошем качестве

Generative AI based Q&A API | Vector Embeddings, Vector DB, LLMs - Context Retrieval in RAG Pipeline

Build Your Own Generative AI Q&A API with RAG (Retrieval-Augmented Generation) In this video, I walk you through how to build a backend system for answering private organizational questions using a Generative AI-powered chatbot. You'll learn how to implement the core components of a RAG pipeline—from embedding queries to generating accurate answers using LLMs. What’s Covered: 1. Accepting a user query via API 2. Converting the query to a vector using OpenAI’s text-embedding-3-small model 3. Performing similarity search in Milvus Vector Database 4. Retrieving relevant context for the query 5. Generating a final answer using a Large Language Model (LLM) This video is perfect for developers, data scientists, and AI enthusiasts looking to understand how RAG pipelines work behind the scenes for private and secure Q&A systems. Technologies Used: 1. OpenAI Embeddings 2. Milvus Vector DB 3. LLMs (OpenAI or similar) 4. RAG Architecture Don't forget to Like, Share & Subscribe for more AI and ML content! Resources & Links: Website: https://geekashram.in Related Articles : https://geekashram.in/articles/introd... https://geekashram.in/articles/introd... https://geekashram.in/articles/workfl... https://geekashram.in/articles/data-i... Have questions? Drop them in the comments! #RAG #LLM #AI #MachineLearning #VectorEmbeddings #Milvus #NLP #RetrievalAugmentedGeneration #geekashram #generativeai #aitutorial #aitutorialforbeginners #aiinhindi #ankitsirgeekashram