Implementing RAG Pipeline using LangChain | End-to-End Implementation(Part6)
Автор: TechSnazAI
Загружено: 2026-01-17
Просмотров: 47
Описание:
In this video, I am continuing my RAG (Retrieval-Augmented Generation) pipeline using LangChain.
You will learn how to load documents, split text into chunks, generate embeddings, store them in a vector database, and finally create a chatbot that answers questions from your data.
Topics covered:
What is RAG and why it is used
Document loading (PDF/Text)
Text chunking (RecursiveCharacterTextSplitter)
Embeddings generation
Vector store creation (Chroma/FAISS)
Retriever + LLM integration in LangChain
Final RAG chatbot output with working code
📌 Use-case: Chat with PDF, document Q&A, knowledge base chatbot
🔔 Subscribe for more videos on GenAI, LangChain, RAG, LLMs, Agents, n8n workflows.
#RAG #LangChain #GenAI #LLM #AIChatbot #VectorDatabase
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