🧠 Build a Document Search with RAG | Hugging Face Transformers + Flan-T5 + NLP Tutorial

Ever wondered how to make your own AI-powered document search system? In this tutorial, we’ll build one step by step using Retrieval-Augmented Generation (RAG) — the same principle behind modern GenAI systems.

🎥 Watch the full video here: 👉 YouTube: Build a Document Search with RAG | Hugging Face Transformers + Flan-T5 + NLP Tutorial

🚀 What You’ll Learn

This hands-on tutorial shows you how to build an intelligent document search engine using Python, Hugging Face Transformers, Sentence Transformers, and Flan-T5.

We’ll cover:

Chunking documents — split large text files into smaller chunks for efficient processing

Text embeddings — convert chunks into semantic vector representations using Sentence Transformers

Semantic search — use cosine similarity to find the most relevant chunks for a user query

RAG pipeline — combine retrieved text with a language model (Flan-T5) for contextual, natural-language answers

End-to-end architecture — see how chunking, embedding, retrieval, and generation connect into a working RAG system

🧩 Code Overview

You’ll build 4 key components:

1. Chunker

Splits large text documents into smaller, overlapping segments for better recall.

def chunk_text(text, chunk_size=500, overlap=50): chunks = [] for i in range(0, len(text), chunk_size - overlap): chunks.append(text[i:i + chunk_size]) return chunks

1. Embedder

Encodes chunks into embeddings using Sentence Transformers.

from sentence_transformers import SentenceTransformer model = SentenceTransformer(‘all-MiniLM-L6-v2’) embeddings = model.encode(chunks)

1. Query Engine

Uses cosine similarity to retrieve the most relevant chunks.

from sklearn.metrics.pairwise import cosine_similarity scores = cosine_similarity(query_embedding, embeddings)

1. RAG Pipeline

Passes retrieved context to Flan-T5 to generate detailed, context-aware answers.

from transformers import pipeline rag_pipeline = pipeline(“text2text-generation”, model=“google/flan-t5-base”) result = rag_pipeline(f“Answer based on: {context}\nQuestion: {query}“)

🧱 Architecture Overview

Retrieval-Augmented Generation (RAG) combines:

Retriever → finds relevant text chunks

Generator → formulates a human-like answer

It allows LLMs to use external knowledge sources without retraining — perfect for dynamic knowledge bases and document search.

💻 Full Source Code

🔗 GitHub Repository: takneekigyanguru/document-search-rag

🧩 Ideal For

Building knowledge base Q&A systems

Searching large technical or business documents

Understanding GenAI + RAG pipelines end-to-end

Preparing for AI/ML or NLP interviews

🔖 Tags

RAG #HuggingFace #FlanT5 #DocumentSearch #Python #MachineLearning #NLP #AI #SemanticSearch #PythonTutorial

✨ Author

Takneeki Gyan Guru — simplifying AI, ML, Cloud, and DevOps concepts through practical tutorials and real-world demos. Follow me for more guides on AI + Cloud integration and GenAI architecture.