Day 14 — Build Your First RAG System

What We Are Building

🚀

YouTube Chat — A Complete RAG System

Today we build a real working RAG application — a chatbot that answers questions about any YouTube video. This project uses every concept from Days 1 through 13, assembled into one working pipeline in LangChain.

💡 Components used: Document Loaders, Text Splitting, Embeddings, Vector Stores, Retrievers, and Chains — all in one application.

Step 1

📥

Fetch the YouTube Transcript

Use the YouTube Transcript API to fetch the video's transcript. The raw output is a list of timestamped text segments — join them into one continuous string.

Extract the video ID from the URLUse only the ID — not the full YouTube URL

Call YouTubeTranscriptApi.get_transcript()Specify language: "en" for English

Join all segments into one stringThis becomes your raw document for the RAG pipeline

Step 2

✂️

Split the Transcript into Chunks

Use RecursiveCharacterTextSplitter with chunk_size=1000 and chunk_overlap=200 as a starting point. A 2-hour video typically produces 150–200 chunks.

💡 Smaller chunks = more precise retrieval. Larger chunks = more surrounding context. Test both for your use case.

Step 3

🗄️

Create the Vector Store

Use OpenAI Embeddings and FAISS for local development. FAISS.from_documents() embeds every chunk and stores the results in one call.

·vectorstore = FAISS.from_documents(chunks, embeddings)

·Every chunk is automatically embedded and indexed in one call

Step 4

⛓️

Build the RAG Chain

Create a retriever, design a prompt template, connect the LLM, and wire everything together. One invoke call runs the entire pipeline.

·Parallel Chain — Retrieves context AND passes question through simultaneously

·Prompt Template — Combines context and question into a structured LLM prompt

·LLM — Generates the final answer from the combined prompt

·Output Parser — Extracts the clean string response

💡 Essential prompt instruction: "Answer ONLY from the provided context. If insufficient, say I don't know." — This prevents hallucination.

✦

You have built a complete RAG system — from YouTube transcript to working chatbot. Load, Split, Embed, Store, Retrieve, Augment, Generate — the full pipeline in approximately 30 lines of LangChain code. Try it with different videos and experiment with chunk sizes and prompts to see the difference each makes.

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