We will set the DeepSeek API key from NVIDIA NIM microservice (Yes, I’ll show you how). NVIDIA NIM (Inference Microservices) is a set of microservices that help deploy AI models across clouds, data centers, and workstations. We will be using LangChain as our LLM framework to bind everything. We will be using SingleStore as our vector database.
Let’s Get Started (Follow Along)
Prerequisites [All are FREE]
- Free SingleStore account (free trial available)
- Free DeepSeek API key from NVIDIA NIM
First thing is to create a free SingleStore account. Login to your account and create a workspace and a database for yourself.
After creating a workspace, create a database attached to that workspace. Click on create a database as shown in the dashboard screenshot to create a database.
Cool. Now, we created our database to store our custom documents for our RAG application.
The next step is to create a Notebook. Yes, a free notebook environment. SingleStore has this cool integrated feature where you can use their Notebooks [Just like your Google collab]
Go to Data Studio and then create a new Notebook.
Give a name to your Notebook
This is where you will land.
Make sure to select your workspace and database you created from the dropdown as shown below. My workspace name is ‘pavappy-workspace-1’ and the database I created is ‘DeepSeek’. So I have selected both.
Now we are all set to code our RAG application. Start adding all the below code step-by-step into the newly created notebook (make sure to run each code snippet aswell)
Start with installing all the required libraries and dependencies.
!pip install langchain --quiet
!pip install pdf2image --quiet
!pip install pdfminer.six --quiet
!pip install singlestoredb --quiet
!pip install tiktoken --quiet
!pip install --upgrade unstructured==0.10.14 --quiet
!pip install -qU pypdf langchain_community
!pip install langchain-nvidia-ai-endpoints --quiet
!pip install langchain-deepseek-official --quiet
Import libraries
from langchain.document_loaders import PyPDFLoader
from langchain_nvidia_ai_endpoints import ChatNVIDIA
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.vectorstores import SingleStoreDB
import os
Load your custom document [I have used a publicly available pdf, you can replace and use your own)
file_path = "https://unctad.org/system/files/official-document/wesp2023_en.pdf"
loader = PyPDFLoader(file_path)
data = loader.load()
Split document into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
texts = text_splitter.split_documents(data)
Set up OpenAI embeddings (for vectorization)
os.environ["OPENAI_API_KEY"] = "Add your OpenAI API key"
embedding = OpenAIEmbeddings()
Store embeddings in SingleStore
docsearch = SingleStoreDB.from_documents(
texts,
embedding,
table_name="deepseek_rag", # Replace table name with any name
host="admin:password@host_url:3306/database_name", # Replace with your SingleStore connection
port=3306
)
In the above code, admin is constant and don’t change that. You can get your password from the access tab and host url can get as shown below. Go to your deployments tab, you should see your workspace, click on connect and then see the dropdown as below. Select ‘SQL IDE’ from there and you will see all the required details.
Next, Initialize DeepSeek through NVIDIA NIM
Get your DeepSeek-R1 API Key for free from NVIDIA NIM microservice. Get it from HERE.
client = ChatNVIDIA(
model="deepseek-ai/deepseek-r1",
api_key="Add your DeepSeek-R1 API key you received from NVIDIA NIM microservice", # Replace with your NVIDIA API key
temperature=0.7,
top_p=0.8,
max_tokens=4096
)
Create RAG chain
qa_chain = RetrievalQA.from_chain_type(
llm=client,
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={"k": 3}),
return_source_documents=True
)
Query the RAG system
query = "What India's GDP growth is projected to be?"
result = qa_chain.invoke({"query": query})
Display results
print("Answer:", result["result"])
print("\nSources:")
for doc in result["source_documents"]:
print(f"- Page {doc.metadata['page']}: {doc.page_content[:100]}...")
You should see a fine response from the model:)
You can go check your database to see how the data has been chunked and stored in the vector embeddings format.
Here is the complete code repo you can try:)
GitHub – pavanbelagatti/DeepSeek-R1-LangChain-SingleStore-Tutorial
Below is my step-by-step video tutorial.
Thank you for reading my article. Hope you tried the entire tutorial. You can also follow me & all my content through my Youtube channel. I create AI/ML/Data related videos on a weekly basis.
Build Robust RAG Systems Using DeepSeek-R1 & LangChain! was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
Pavan Belagatti | Sciencx (2025-02-06T19:50:49+00:00) Build Robust RAG Systems Using DeepSeek-R1 & LangChain!. Retrieved from https://www.scien.cx/2025/02/06/build-robust-rag-systems-using-deepseek-r1-langchain/
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