x̄ - > 🚀 How to Build an Interactive RAG Chatbot with Hugging Face and LangChain

🚀 How to Build an Interactive RAG Chatbot with Hugging Face and LangChain

Have you ever wished your chatbot could actually read your documents and answer questions based on them — instead of just guessing? That’s exactly what Retrieval-Augmented Generation (RAG) does.

In this tutorial, I’ll walk you through building an interactive RAG chatbot that can search your own PDFs, retrieve relevant information, and generate grounded answers.

Github file for the program https://github.com/Zekeriya-Ui/main/blob/main/Langflow_Interactive_RAG_chatbot_Langflow.ipynb

📌 What You’ll Learn

• Load and process your own documents (like PDFs)
• Split text into manageable chunks
• Convert those chunks into embeddings with Hugging Face
• Store them in a FAISS vector database for fast retrieval
• Build a chatbot that answers based on your docs
• Make it interactive so you can chat with your PDFs in real time

🛠️ Step 1: Install Dependencies

pip install langchain transformers faiss-cpu pypdf sentence-transformers umap-learn
  

These handle:

• LangChain → orchestration
• Transformers / Sentence-Transformers → embeddings + models
• FAISS → vector search
• PyPDF → reading PDFs
• UMAP → visualization (optional but fun)

📂 Step 2: Load Your Document

from langchain.document_loaders import PyPDFLoader

loader = PyPDFLoader("sample.pdf")
documents = loader.load()
print(f"Loaded {len(documents)} pages")
  

✂️ Step 3: Split Text into Chunks

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
docs = text_splitter.split_documents(documents)

print(f"Created {len(docs)} chunks")
print(docs[0].page_content[:300])
  

🧠 Step 4: Embeddings + Vector Store

from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(model_name="sentence-

transformers/all-MiniLM-L6-v2")
vectorstore = FAISS.from_documents(docs, embeddings)
  

🔗 Step 5: Build the RAG Chain

from langchain.chains import RetrievalQA
from langchain.llms import HuggingFaceHub

llm = HuggingFaceHub(repo_id="google/flan-t5-base")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever()
)
  

💬 Step 6: Ask Questions

query = "What is the main topic of this document?"
answer = qa_chain.run(query)

print("Q:", query)
print("A:", answer)
  

🔄 Step 7: Make It Interactive

while True:
    q = input("\nAsk a question (or type 'exit'): ")
    if q.lower() in ["exit", "quit"]:
        break
    print("\nA:", qa_chain.run(q))
  

🎨 Bonus: Visualize Retrieval

You can visualize embeddings in 2D with UMAP — seeing how your document’s chunks cluster together. This helps you understand what your retriever is really “seeing.”

🎯 Conclusion

And that’s it — you’ve built your own interactive RAG chatbot! This framework is powerful for:

• Research assistants
• Customer support bots
• Study guides for textbooks
• Knowledge bases for teams

👉 Watch the full step-by-step demo here: 📺 YouTube Tutorial

💡 What kind of documents would you like to chat with — research papers, legal contracts, or maybe your personal notes?