How to Build a RAG System Using DeepSeek R1?

Ever wondered how AI can fetch real-time data and give you accurate, fact-based answers instead of making things up?

That’s exactly what a Retrieval-Augmented Generation (RAG) system does! 💡

A RAG system combines a search engine with a generative AI model, allowing it to retrieve relevant information before answering your question. This makes AI responses more accurate, reliable, and up-to-date.

In this article, I’ll show you how to build your own RAG system using DeepSeek R1. And don’t worry, I’ll keep things simple, fun, and easy to follow! 🚀

What is DeepSeek R1?

Imagine an AI that doesn’t just guess answers but actually looks things up before responding. That’s DeepSeek R1!

DeepSeek R1 is an open-source AI model designed specifically for Retrieval-Augmented Generation (RAG).

Instead of relying only on pre-trained knowledge (which can get outdated fast), it retrieves information from external sources before generating a response.

This makes it perfect for:

✅ AI chatbots that need real-time, fact-based responses
✅ Smart search engines that fetch relevant results instantly
✅ AI research assistants that can summarize large documents
✅ Automated customer support that understands and responds accurately

If you want to build an AI that “thinks” before it speaks, DeepSeek R1 is the way to go!

Why Use DeepSeek R1 for a RAG System?

Okay, so why should you use DeepSeek R1 instead of a regular chatbot model?

Here’s why:

🔥 Real-time Knowledge – Instead of relying on outdated training data, RAG systems fetch fresh, relevant information every time.

🎯 More Accurate Responses – Since it looks up facts before answering, the AI is way less likely to make mistakes or “hallucinate” information.

🧠 Context-Aware Answers – DeepSeek R1 doesn’t just generate text—it understands your question and retrieves the best possible answer from its knowledge base.

⚡ Scalable and Efficient – Whether you’re building a small AI chatbot or a large-scale search engine, this system can handle it all.

Simply put, if you want a chatbot that’s smart, fact-driven, and actually helpful, DeepSeek R1 is a game changer.

What We’ll Build Today

Now for the exciting part! 🚀

In this guide, we’ll create a fully functional RAG system that can:
✅ Accept a user’s question
✅ Retrieve relevant information from a document database
✅ Use DeepSeek R1 to generate an intelligent, fact-based response

By the end, you’ll have a working AI chatbot that can search, think, and respond just like a human assistant!

Let’s dive in! 🎯

Also Learn: How to Get DeepSeek API Key?

Step 1: Install the Necessary Tools

Before we start coding, let’s set up everything we need.

Open your terminal or command prompt and run this command:

pip install torch transformers deepseek-r1 faiss-cpu

Here’s what each package does:
🔹 torch – Helps run the AI model efficiently
🔹 transformers – Handles DeepSeek R1 for text generation
🔹 deepseek-r1 – The star of our show—our AI model! ✨
🔹 faiss-cpu – Helps us quickly search and retrieve information

Once installed, we’re ready to go! 🚀

Also Learn: How to Build AI Agent Using DeepSeek?

Step 2: Load the DeepSeek R1 Model

Now, let’s get our AI model up and running:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "deepseek-ai/deepseek-r1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

This loads the DeepSeek R1 model and its tokenizer, which converts text into something the AI can understand.

Simple, right?

Step 3: Set Up a Searchable Knowledge Base

To make our AI “smart,” we need a way to store and retrieve information.

We’ll use FAISS, a powerful tool for searching large datasets.

First, let’s convert some example documents into searchable vectors:

from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Load the sentence embedding model
embed_model = SentenceTransformer("all-MiniLM-L6-v2")

# Example documents
documents = [
    "DeepSeek R1 is an advanced AI model for retrieval-augmented generation.",
    "FAISS is a library for efficient similarity search and clustering of dense vectors.",
    "Python is a powerful programming language for AI development.",
]

# Convert text into vector embeddings
doc_vectors = np.array(embed_model.encode(documents))
index = faiss.IndexFlatL2(doc_vectors.shape[1])
index.add(doc_vectors)

This allows our system to find relevant info quickly, just like Google does! 🔍

Step 4: Retrieve Relevant Information

Now, let’s write a function to search for the best matching documents when a user asks a question:

def retrieve_docs(query, top_k=2):
    query_vector = np.array(embed_model.encode([query]))
    distances, indices = index.search(query_vector, top_k)
    retrieved_docs = [documents[i] for i in indices[0]]
    return retrieved_docs

# Test it out
query = "What is FAISS?"
retrieved_info = retrieve_docs(query)
print("Retrieved Documents:", retrieved_info)

Now, our AI can search through its knowledge base and find relevant info in seconds!

Also Learn: How to Build AI Application with DeepSeek-V3?

Step 5: Generate AI Responses Using DeepSeek R1

Now comes the magic! ✨

We’ll combine retrieved documents with DeepSeek R1 to generate human-like responses:

def generate_response(query):
    retrieved_docs = retrieve_docs(query)
    input_text = "Context: " + " ".join(retrieved_docs) + "\nQuery: " + query

    inputs = tokenizer(input_text, return_tensors="pt")
    output = model.generate(**inputs, max_length=200)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return response

# Test our AI chatbot
query = "Explain FAISS."
response = generate_response(query)
print("AI Response:", response)

This makes our AI not just smart but also factually accurate!

Conclusion

And there you have it, a fully functional RAG system using DeepSeek R1!

With this setup, you now have an AI chatbot that can:
✅ Search for relevant documents
✅ Retrieve useful information
✅ Generate accurate, human-like responses

But this is just the beginning! You can improve this further by:

Expanding the knowledge base with more data
Using better embedding models for improved accuracy
Turning this into a real-time chatbot API

Want me to guide you on deploying this as a web app? Let me know in the comments!

Frequently Asked Questions (FAQs)