Скачать или смотреть Build Your Own Document Chatbot | RAG, FAISS, Embeddings & LangChain (2026) – Part 3

In this video, we continue building our Document Chatbot step by step using Retrieval Augmented Generation (RAG).

In Part 3, we focus on the core logic that powers a real-world chatbot:

Taking a user’s question

Performing semantic similarity search using FAISS

Retrieving the most relevant document chunks

Passing them to a Large Language Model (LLM)

Generating accurate answers strictly from your own documents

We implement this flow using LangChain, embeddings, and Streamlit, and explain every step in detail—from capturing user input to chaining retrieval and generation together.

This chatbot does not rely on internet search or generic ChatGPT knowledge. Instead, it answers questions directly from your uploaded documents (like PDFs, legal files, research papers, or internal company data).

By the end of this video, you’ll understand:

How semantic search actually works

How embeddings are used for question matching

How RAG pipelines retrieve and generate answers

How to tune parameters like temperature and max tokens

How to build a production-ready document Q&A chatbot in just ~50 lines of code

This tutorial is perfect for:

Beginners learning Generative AI

Developers building RAG applications

Students, lawyers, researchers, and professionals working with documents

Anyone who wants to create a ChatGPT-like chatbot for their own data

👉 In the next video, we’ll extend this chatbot further and explore more advanced use cases.